Add Gamma SBAS production workflow

.env.example

@@ -63,6 +63,9 @@ MONITOR_DINSAR_DIRS=D:\DInSARResult
 ORBIT_SOURCE_DIRS=D:\LT1_data_lsarorbit;D:\Sentinel1_EOF_Pool
 MONITOR_ORBIT_DIR=D:\LT1_data_lsarorbit
 
+GF3_ARCHIVE_SOURCE_DIRS=D:\GF3_L1A_Image_Zip
+GF3_ARCHIVE_EXTS=.zip,.tar,.tar.gz,.tgz
+GF3_UNPACK_DELETE_ARCHIVE=true
 GF3_SOURCE_DIRS=D:\GF3_L1A_Image
 GF3_STORAGE_DIRS=D:\GF3_L2_Image
 
@@ -101,6 +104,11 @@ IDL_DINSAR_DEM_BASE_FILE=D:\SRTM30m\SRTMDEM_RSP_SARscape
 SRTM_DEM_DIR=D:\SRTM30m
 GF3_GEO_DEM_PATH=D:\DEM\gf3_dem.jp2
 WATER_RESULTS_DIR=D:\WaterResult
+SAR_ANALYSIS_READY_ROOT=D:\production_results\sar_analysis_ready
+SAR_ANALYSIS_WORK_ROOT=D:\production_results\sar_analysis_work
+SAR_ANALYSIS_PREVIEW_ROOT=D:\production_results\sar_analysis_preview
+SAR_ANALYSIS_NODATA_VALUE=-9999
+SAR_ANALYSIS_OUTPUT_COG=true
 
 
 # -----------------------------------------------------------------------------
@@ -215,16 +223,16 @@ PYINT_SMOKE_TEST_ENABLED=false
 
 
 # -----------------------------------------------------------------------------
-# 时序 InSAR
+# 时序 InSAR（旧 ISCE2/MintPy 链路）
 # -----------------------------------------------------------------------------
-TIMESERIES_ENABLED=true
+TIMESERIES_ENABLED=false
 TIMESERIES_WSL_DISTRO=Ubuntu-24.04
 TIMESERIES_ENV_NAME=insar_wsl_v1
 TIMESERIES_PYTHON=/home/administrator/miniconda3/envs/insar_wsl_v1/bin/python
 TIMESERIES_WORK_ROOT=D:\Code\Insar_management_system_v2\backend\runtime\timeseries_work
 TIMESERIES_DEM_PATH=D:\SRTM30m\SRTMDEM_RSP_SARscape.wgs84
 TIMESERIES_ORBIT_POOL_ISCE2=D:\orbit_pools\isce2
-TIMESERIES_EXPERIMENT_ROOT=D:\Code\Insar_management_system_v2\experiments\isce2_sbas_timeseries
+TIMESERIES_EXPERIMENT_ROOT=
 TIMESERIES_STACK_PREP_SCRIPT=
 TIMESERIES_MATERIALIZE_SCRIPT=
 TIMESERIES_PREPARE_DEM_SCRIPT=
@@ -232,7 +240,7 @@ TIMESERIES_STACK_RUNNER_SCRIPT=
 TIMESERIES_MINTPY_SBAS_SCRIPT=
 TIMESERIES_EXPORT_PUBLISH_SCRIPT=
 TIMESERIES_STACK_WORKFLOW=interferogram
-TIMESERIES_DEFAULT_PROCESSOR_CODE=isce2_stack_mintpy
+TIMESERIES_DEFAULT_PROCESSOR_CODE=legacy_isce2_stack_mintpy
 TIMESERIES_WSL_STEP_TIMEOUT_SECONDS=7200
 TIMESERIES_ALLOW_SYNTHETIC_WATER_MASK=true
 SARSCAPE_SBAS_PARAMETER_TEMPLATE_PATH=

backend/alembic/versions/0003_flood_pipeline_models.py

@@ -0,0 +1,152 @@
+"""add flood pipeline models
+
+Revision ID: 0003
+Revises: 0002
+Create Date: 2026-05-14
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from geoalchemy2 import Geometry
+
+revision: str = "0003"
+down_revision: Union[str, None] = "0002"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def _has_table(table_name: str) -> bool:
+    return table_name in set(sa.inspect(op.get_bind()).get_table_names())
+
+
+def _has_index(table_name: str, index_name: str) -> bool:
+    inspector = sa.inspect(op.get_bind())
+    return index_name in {idx["name"] for idx in inspector.get_indexes(table_name)}
+
+
+def _create_index_if_missing(
+    table_name: str,
+    index_name: str,
+    columns: list[str],
+    *,
+    unique: bool = False,
+    **kwargs,
+) -> None:
+    if _has_table(table_name) and not _has_index(table_name, index_name):
+        op.create_index(index_name, table_name, columns, unique=unique, **kwargs)
+
+
+def upgrade() -> None:
+    op.execute("CREATE EXTENSION IF NOT EXISTS postgis")
+
+    if not _has_table("water_extractions"):
+        op.create_table(
+            "water_extractions",
+            sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
+            sa.Column("scene_id", sa.Integer(), nullable=True),
+            sa.Column("processor", sa.String(length=32), nullable=False, server_default="otsu"),
+            sa.Column("task_id", sa.String(length=64), nullable=True),
+            sa.Column("input_path", sa.String(), nullable=True),
+            sa.Column("output_path", sa.String(), nullable=True),
+            sa.Column("preview_path", sa.String(), nullable=True),
+            sa.Column("vector_path", sa.String(), nullable=True),
+            sa.Column("water_area_km2", sa.Float(), nullable=True),
+            sa.Column("water_pixel_count", sa.Integer(), nullable=True),
+            sa.Column("threshold_value", sa.Float(), nullable=True),
+            sa.Column("metadata_json", sa.JSON(), nullable=True),
+            sa.Column("status", sa.String(length=16), nullable=False, server_default="PENDING"),
+            sa.Column("error_msg", sa.Text(), nullable=True),
+            sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=True),
+            sa.Column("updated_at", sa.DateTime(), server_default=sa.text("now()"), nullable=True),
+            sa.ForeignKeyConstraint(["scene_id"], ["sar_scene_geo.id"]),
+            sa.PrimaryKeyConstraint("id"),
+        )
+    _create_index_if_missing("water_extractions", "ix_water_extractions_scene_id", ["scene_id"])
+    _create_index_if_missing("water_extractions", "ix_water_extractions_processor", ["processor"])
+    _create_index_if_missing("water_extractions", "ix_water_extractions_task_id", ["task_id"])
+    _create_index_if_missing("water_extractions", "ix_water_extractions_status", ["status"])
+
+    if not _has_table("flood_overlays"):
+        op.create_table(
+            "flood_overlays",
+            sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
+            sa.Column("detection_id", sa.Integer(), nullable=False),
+            sa.Column("flood_vector_path", sa.String(), nullable=True),
+            sa.Column("hazard_points_hit", sa.Integer(), nullable=False, server_default="0"),
+            sa.Column("hazard_points_near", sa.Integer(), nullable=False, server_default="0"),
+            sa.Column("hazard_points_total", sa.Integer(), nullable=False, server_default="0"),
+            sa.Column("dinsar_products_intersecting", sa.Integer(), nullable=False, server_default="0"),
+            sa.Column("affected_area_km2", sa.Float(), nullable=True),
+            sa.Column("summary_json", sa.JSON(), nullable=True),
+            sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=True),
+            sa.ForeignKeyConstraint(["detection_id"], ["flood_detections.id"]),
+            sa.PrimaryKeyConstraint("id"),
+        )
+    _create_index_if_missing("flood_overlays", "ix_flood_overlays_detection_id", ["detection_id"])
+
+    if not _has_table("flood_products"):
+        op.create_table(
+            "flood_products",
+            sa.Column("id", sa.Integer(), autoincrement=True, nullable=False),
+            sa.Column("product_id", sa.String(length=64), nullable=False),
+            sa.Column("detection_id", sa.Integer(), nullable=True),
+            sa.Column("overlay_id", sa.Integer(), nullable=True),
+            sa.Column("display_name", sa.String(length=255), nullable=False),
+            sa.Column("status", sa.String(length=16), nullable=False, server_default="READY"),
+            sa.Column("publish_dir", sa.String(), nullable=True),
+            sa.Column("manifest_path", sa.String(), nullable=True),
+            sa.Column("geom", Geometry("POLYGON", srid=4326), nullable=True),
+            sa.Column("summary_json", sa.JSON(), nullable=True),
+            sa.Column("created_at", sa.DateTime(), server_default=sa.text("now()"), nullable=True),
+            sa.ForeignKeyConstraint(["detection_id"], ["flood_detections.id"]),
+            sa.ForeignKeyConstraint(["overlay_id"], ["flood_overlays.id"]),
+            sa.PrimaryKeyConstraint("id"),
+            sa.UniqueConstraint("publish_dir", name="uq_flood_products_publish_dir"),
+            sa.UniqueConstraint("manifest_path", name="uq_flood_products_manifest_path"),
+        )
+    _create_index_if_missing("flood_products", "ix_flood_products_product_id", ["product_id"], unique=True)
+    _create_index_if_missing("flood_products", "ix_flood_products_detection_id", ["detection_id"])
+    _create_index_if_missing("flood_products", "ix_flood_products_overlay_id", ["overlay_id"])
+    _create_index_if_missing("flood_products", "ix_flood_products_status", ["status"])
+
+    if _has_table("water_detections") and _has_table("water_extractions"):
+        op.execute(
+            sa.text(
+                """
+                INSERT INTO water_extractions (
+                    id, scene_id, processor, input_path, output_path,
+                    water_area_km2, water_pixel_count, threshold_value,
+                    status, error_msg, created_at, updated_at
+                )
+                SELECT
+                    wd.id, wd.scene_id, 'otsu', wd.input_path, wd.output_path,
+                    wd.water_area_km2, wd.water_pixel_count, wd.otsu_threshold_db,
+                    wd.status, wd.error_msg, wd.created_at, wd.updated_at
+                FROM water_detections wd
+                WHERE NOT EXISTS (
+                    SELECT 1 FROM water_extractions we WHERE we.id = wd.id
+                )
+                """
+            )
+        )
+        op.execute(
+            sa.text(
+                """
+                SELECT setval(
+                    pg_get_serial_sequence('water_extractions', 'id'),
+                    COALESCE((SELECT MAX(id) FROM water_extractions), 1),
+                    (SELECT COUNT(*) FROM water_extractions) > 0
+                )
+                """
+            )
+        )
+
+
+def downgrade() -> None:
+    if _has_table("flood_products"):
+        op.drop_table("flood_products")
+    if _has_table("flood_overlays"):
+        op.drop_table("flood_overlays")
+    if _has_table("water_extractions"):
+        op.drop_table("water_extractions")

backend/alembic/versions/0004_sar_analysis_ready_fields.py

@@ -0,0 +1,86 @@
+"""add SAR analysis-ready scene fields
+
+Revision ID: 0004
+Revises: 0003
+Create Date: 2026-05-16
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0004"
+down_revision: Union[str, None] = "0003"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def _has_table(table_name: str) -> bool:
+    return table_name in set(sa.inspect(op.get_bind()).get_table_names())
+
+
+def _has_column(table_name: str, column_name: str) -> bool:
+    inspector = sa.inspect(op.get_bind())
+    return column_name in {col["name"] for col in inspector.get_columns(table_name)}
+
+
+def _has_index(table_name: str, index_name: str) -> bool:
+    inspector = sa.inspect(op.get_bind())
+    return index_name in {idx["name"] for idx in inspector.get_indexes(table_name)}
+
+
+def _add_column_if_missing(table_name: str, column: sa.Column) -> None:
+    if _has_table(table_name) and not _has_column(table_name, column.name):
+        op.add_column(table_name, column)
+
+
+def _create_index_if_missing(table_name: str, index_name: str, columns: list[str]) -> None:
+    if _has_table(table_name) and not _has_index(table_name, index_name):
+        op.create_index(index_name, table_name, columns)
+
+
+def _drop_index_if_exists(table_name: str, index_name: str) -> None:
+    if _has_table(table_name) and _has_index(table_name, index_name):
+        op.drop_index(index_name, table_name=table_name)
+
+
+def _drop_column_if_exists(table_name: str, column_name: str) -> None:
+    if _has_table(table_name) and _has_column(table_name, column_name):
+        op.drop_column(table_name, column_name)
+
+
+def upgrade() -> None:
+    table = "sar_scene_geo"
+    _add_column_if_missing(table, sa.Column("analysis_tif_path", sa.String(), nullable=True))
+    _add_column_if_missing(table, sa.Column("analysis_dir", sa.String(), nullable=True))
+    _add_column_if_missing(table, sa.Column("analysis_preview_path", sa.String(), nullable=True))
+    _add_column_if_missing(table, sa.Column("analysis_engine", sa.String(length=32), nullable=True))
+    _add_column_if_missing(table, sa.Column("analysis_profile", sa.String(length=64), nullable=True))
+    _add_column_if_missing(table, sa.Column("analysis_backscatter_unit", sa.String(length=32), nullable=True))
+    _add_column_if_missing(table, sa.Column("analysis_nodata_value", sa.Float(), nullable=True))
+    _add_column_if_missing(table, sa.Column("analysis_metadata_json", sa.JSON(), nullable=True))
+    _add_column_if_missing(table, sa.Column("analysis_quality_json", sa.JSON(), nullable=True))
+
+    _create_index_if_missing(table, "ix_sar_scene_geo_analysis_tif_path", ["analysis_tif_path"])
+    _create_index_if_missing(table, "ix_sar_scene_geo_analysis_engine", ["analysis_engine"])
+    _create_index_if_missing(table, "ix_sar_scene_geo_analysis_profile", ["analysis_profile"])
+
+
+def downgrade() -> None:
+    table = "sar_scene_geo"
+    _drop_index_if_exists(table, "ix_sar_scene_geo_analysis_profile")
+    _drop_index_if_exists(table, "ix_sar_scene_geo_analysis_engine")
+    _drop_index_if_exists(table, "ix_sar_scene_geo_analysis_tif_path")
+
+    for column_name in (
+        "analysis_quality_json",
+        "analysis_metadata_json",
+        "analysis_nodata_value",
+        "analysis_backscatter_unit",
+        "analysis_profile",
+        "analysis_engine",
+        "analysis_preview_path",
+        "analysis_dir",
+        "analysis_tif_path",
+    ):
+        _drop_column_if_exists(table, column_name)

backend/app/config.py

@@ -195,9 +195,17 @@ class Settings(BaseSettings):
     RADAR_PREVIEW_BUILD_ON_DEMAND: bool = True
 
     WATER_RESULTS_DIR: str = ""
+    SAR_ANALYSIS_READY_ROOT: str = ""
+    SAR_ANALYSIS_WORK_ROOT: str = ""
+    SAR_ANALYSIS_PREVIEW_ROOT: str = ""
+    SAR_ANALYSIS_NODATA_VALUE: float = -9999.0
+    SAR_ANALYSIS_OUTPUT_COG: bool = True
 
     SRTM_DEM_DIR: str = ""
     GF3_GEO_DEM_PATH: str = ""
+    GF3_ARCHIVE_SOURCE_DIRS: str = ""
+    GF3_ARCHIVE_EXTS: str = ".zip,.tar,.tar.gz,.tgz"
+    GF3_UNPACK_DELETE_ARCHIVE: bool = True
     GF3_SOURCE_DIRS: str = ""
     GF3_STORAGE_DIRS: str = ""
 
@@ -306,7 +314,7 @@ class Settings(BaseSettings):
     JOB_WORKER_STALE_RUNNING_SECONDS: int = 300
     JOB_WORKER_HEARTBEAT_INTERVAL: float = 5.0
 
-    TIMESERIES_ENABLED: bool = True
+    TIMESERIES_ENABLED: bool = False
     TIMESERIES_WSL_DISTRO: str = ""
     TIMESERIES_PYTHON: str = ""
     TIMESERIES_ENV_NAME: str = ""
@@ -354,6 +362,29 @@ class Settings(BaseSettings):
                 "WATER_RESULTS_DIR",
                 os.path.join(backend_dir, "water_results"),
             )
+        if not self.SAR_ANALYSIS_READY_ROOT:
+            object.__setattr__(
+                self,
+                "SAR_ANALYSIS_READY_ROOT",
+                os.path.join(backend_dir, "runtime", "sar_analysis_ready"),
+            )
+        if not self.SAR_ANALYSIS_WORK_ROOT:
+            object.__setattr__(
+                self,
+                "SAR_ANALYSIS_WORK_ROOT",
+                os.path.join(backend_dir, "runtime", "sar_analysis_work"),
+            )
+        if not self.SAR_ANALYSIS_PREVIEW_ROOT:
+            object.__setattr__(
+                self,
+                "SAR_ANALYSIS_PREVIEW_ROOT",
+                os.path.join(backend_dir, "runtime", "sar_analysis_preview"),
+            )
+        object.__setattr__(
+            self,
+            "SAR_ANALYSIS_NODATA_VALUE",
+            float(self.SAR_ANALYSIS_NODATA_VALUE if self.SAR_ANALYSIS_NODATA_VALUE is not None else -9999.0),
+        )
         if not self.SRTM_DEM_DIR:
             object.__setattr__(self, "SRTM_DEM_DIR", os.path.join(backend_dir, "dem_data"))
         if not self.GF3_STORAGE_DIRS:
@@ -592,6 +623,7 @@ class Settings(BaseSettings):
         )
         if not self.PYINT_ORBIT_POOL_TXT:
             object.__setattr__(self, "PYINT_ORBIT_POOL_TXT", self.ORBIT_POOL_ENVI)
+        if self.TIMESERIES_ENABLED:
             if not self.TIMESERIES_WSL_DISTRO:
                 object.__setattr__(self, "TIMESERIES_WSL_DISTRO", self.WSL_DISTRO or self.ISCE2_WSL_DISTRO)
             if not self.TIMESERIES_ENV_NAME:
@@ -715,11 +747,15 @@ class Settings(BaseSettings):
         os.makedirs(settings.TIMESERIES_PRODUCT_DIR, exist_ok=True)
         os.makedirs(settings.PSINSAR_PRODUCT_DIR, exist_ok=True)
         os.makedirs(settings.RESULT_QUARANTINE_ROOT, exist_ok=True)
+        os.makedirs(settings.SAR_ANALYSIS_READY_ROOT, exist_ok=True)
+        os.makedirs(settings.SAR_ANALYSIS_WORK_ROOT, exist_ok=True)
+        os.makedirs(settings.SAR_ANALYSIS_PREVIEW_ROOT, exist_ok=True)
         os.makedirs(settings.WSL_BROKER_JOB_ROOT, exist_ok=True)
         os.makedirs(settings.PYINT_TEMPLATE_ROOT, exist_ok=True)
         os.makedirs(settings.PYINT_WORK_ROOT, exist_ok=True)
         os.makedirs(settings.PYINT_OUTPUT_ROOT, exist_ok=True)
         os.makedirs(settings.PYINT_DEM_ROOT, exist_ok=True)
+        if settings.TIMESERIES_ENABLED and settings.TIMESERIES_WORK_ROOT:
             os.makedirs(settings.TIMESERIES_WORK_ROOT, exist_ok=True)
 
 
@@ -861,6 +897,9 @@ def validate_runtime_config() -> dict[str, Any]:
     _check_path(label="GF3_GEO_DEM_PATH", value=settings.GF3_GEO_DEM_PATH, errors=errors, warnings=warnings, expect_file=True)
     _check_path(label="SRTM_DEM_DIR", value=settings.SRTM_DEM_DIR, errors=errors, warnings=warnings, expect_file=False)
     _check_path(label="WATER_RESULTS_DIR", value=settings.WATER_RESULTS_DIR, errors=errors, warnings=warnings, expect_file=False)
+    _check_path(label="SAR_ANALYSIS_READY_ROOT", value=settings.SAR_ANALYSIS_READY_ROOT, errors=errors, warnings=warnings, expect_file=False)
+    _check_path(label="SAR_ANALYSIS_WORK_ROOT", value=settings.SAR_ANALYSIS_WORK_ROOT, errors=errors, warnings=warnings, expect_file=False)
+    _check_path(label="SAR_ANALYSIS_PREVIEW_ROOT", value=settings.SAR_ANALYSIS_PREVIEW_ROOT, errors=errors, warnings=warnings, expect_file=False)
     _check_path(label="MONITOR_ORBIT_DIR", value=settings.MONITOR_ORBIT_DIR, errors=errors, warnings=warnings, expect_file=False)
     for label, value in (
         ("SOURCE_PRODUCT_DIRS", settings.SOURCE_PRODUCT_DIRS),
@@ -887,6 +926,7 @@ def validate_runtime_config() -> dict[str, Any]:
         ("INSAR_STORAGE_DIRS", settings.INSAR_STORAGE_DIRS),
         ("MONITOR_RADAR_DIRS", settings.MONITOR_RADAR_DIRS),
         ("MONITOR_DINSAR_DIRS", settings.MONITOR_DINSAR_DIRS),
+        ("GF3_ARCHIVE_SOURCE_DIRS", settings.GF3_ARCHIVE_SOURCE_DIRS),
         ("GF3_SOURCE_DIRS", settings.GF3_SOURCE_DIRS),
         ("GF3_STORAGE_DIRS", settings.GF3_STORAGE_DIRS),
     ):
@@ -1030,6 +1070,11 @@ def validate_runtime_config() -> dict[str, Any]:
                 "Gamma runtime has not been fully migrated to the fixed profile model."
             )
 
+    if not settings.TIMESERIES_ENABLED:
+        info.append(
+            "Legacy ISCE2/MintPy timeseries pipeline is disabled; current SBAS-InSAR production uses the Gamma /sbas-insar-production workflow."
+        )
+
     if settings.TIMESERIES_ENABLED:
         _check_path(
             label="TIMESERIES_PYTHON",

backend/app/db_maintenance.py

@@ -6,7 +6,7 @@ from urllib.parse import urlparse
 
 from sqlalchemy import create_engine, inspect, text
 from sqlalchemy.orm import sessionmaker
-from sqlalchemy.schema import CreateColumn
+from sqlalchemy.schema import CreateColumn, CreateIndex
 
 from .config import read_bool_env, settings
 
@@ -23,9 +23,12 @@ POSTGIS_VIEWS = {
 }
 
 ALLOWED_EXTRA_TABLES = {
+    "alembic_version",
     "spatial_query_logs",
 }
 
+ALEMBIC_HEAD_REVISION = "0004"
+
 MIGRATION_FILES = [
     "001_st_intersection_agg.sql",
     "002_spatial_functions.sql",
@@ -337,6 +340,50 @@ def _add_missing_columns(conn, inspector, base, dialect) -> List[str]:
     return added_columns
 
 
+def _create_missing_indexes(conn, inspector, base, dialect) -> List[str]:
+    created_indexes: List[str] = []
+    existing_tables = set(inspector.get_table_names())
+    for table_name, table in base.metadata.tables.items():
+        if table_name not in existing_tables:
+            continue
+
+        current_indexes = {index["name"] for index in inspector.get_indexes(table_name)}
+        for index in sorted(table.indexes, key=lambda item: item.name or ""):
+            if not index.name or index.name in current_indexes:
+                continue
+            try:
+                index_sql = str(CreateIndex(index).compile(dialect=dialect))
+                conn.exec_driver_sql(index_sql)
+                created_indexes.append(index.name)
+                current_indexes.add(index.name)
+            except Exception as exc:
+                print(f"[WARN] Failed to create index {table_name}.{index.name}: {exc}")
+    return created_indexes
+
+
+def _ensure_alembic_version_marker(conn, revision: str = ALEMBIC_HEAD_REVISION) -> Dict[str, Any]:
+    status: Dict[str, Any] = {"revision": revision, "created": False, "updated": False}
+    conn.exec_driver_sql(
+        """
+        CREATE TABLE IF NOT EXISTS alembic_version (
+            version_num VARCHAR(32) NOT NULL
+        )
+        """
+    )
+    rows = conn.execute(text("SELECT version_num FROM alembic_version")).fetchall()
+    previous = [row[0] for row in rows]
+    status["previous"] = previous
+    if not rows:
+        conn.execute(text("INSERT INTO alembic_version (version_num) VALUES (:revision)"), {"revision": revision})
+        status["created"] = True
+        return status
+    if previous != [revision] or len(rows) != 1:
+        conn.execute(text("DELETE FROM alembic_version"))
+        conn.execute(text("INSERT INTO alembic_version (version_num) VALUES (:revision)"), {"revision": revision})
+        status["updated"] = True
+    return status
+
+
 def _resolve_hazard_shapefile() -> str:
     hazard_dir = settings.HAZARD_POINTS_DIR
     hazard_filename = settings.HAZARD_POINTS_FILENAME or "Point.shp"
@@ -513,6 +560,8 @@ def ensure_database_ready(
         "mismatch_detected": False,
         "mismatch_reasons": [],
         "added_columns": [],
+        "created_indexes": [],
+        "alembic_version": None,
         "applied_sql_files": [],
         "admin": None,
         "hazard_seed": None,
@@ -545,10 +594,14 @@ def ensure_database_ready(
                     base.metadata.create_all(bind=conn)
                     inspector = inspect(conn)
                     result["added_columns"] = _add_missing_columns(conn, inspector, base, engine.dialect)
+                    inspector = inspect(conn)
+                    result["created_indexes"] = _create_missing_indexes(conn, inspector, base, engine.dialect)
                 if bootstrap_required:
                     result["bootstrap_initialized"] = True
             else:
                 base.metadata.create_all(bind=conn)
+                inspector = inspect(conn)
+                result["created_indexes"] = _create_missing_indexes(conn, inspector, base, engine.dialect)
 
             migrations_dir = os.path.join(project_root(), "backend", "migrations")
             for migration_file in MIGRATION_FILES:
@@ -556,6 +609,12 @@ def ensure_database_ready(
                 if _apply_sql_file(conn, migration_path):
                     result["applied_sql_files"].append(migration_file)
 
+            final_diagnostics = inspect_database_structure(conn)
+            if not final_diagnostics.get("mismatch"):
+                result["alembic_version"] = _ensure_alembic_version_marker(conn)
+            else:
+                result["post_maintenance_mismatch_reasons"] = final_diagnostics.get("reasons", [])
+
         session = Session()
         try:
             if bootstrap_admin:

backend/app/main.py

@@ -108,6 +108,11 @@ async def lifespan(app: FastAPI):
     )
     if maintenance_result.get("added_columns"):
         print(f">>> [DB] Added columns: {maintenance_result['added_columns']}")
+    if maintenance_result.get("created_indexes"):
+        print(f">>> [DB] Created indexes: {maintenance_result['created_indexes']}")
+    if maintenance_result.get("alembic_version"):
+        alembic_status = maintenance_result["alembic_version"]
+        print(f">>> [DB] Alembic version marker: {alembic_status.get('revision')}")
     if maintenance_result.get("admin", {}).get("message"):
         print(f">>> [DB] {maintenance_result['admin']['message']}")
     if maintenance_result.get("hazard_seed", {}).get("message"):

backend/app/models/__init__.py

@@ -52,6 +52,9 @@ from .orm import (
     SARSceneGeoORM,
     FloodDetectionORM,
     WaterDetectionORM,
+    WaterExtractionORM,
+    FloodOverlayORM,
+    FloodProductORM,
     GF3ProcessingORM,
     AiDiagnosisORM,
 )
@@ -110,7 +113,8 @@ __all__ = [
     "PsTimeseriesRunORM",
     "AuthUserORM", "AuthSessionORM", "AuthAuditLogORM", "AuthRateLimitORM",
     "SARSceneGeoORM", "FloodDetectionORM",
-    "WaterDetectionORM", "GF3ProcessingORM", "AiDiagnosisORM",
+    "WaterDetectionORM", "WaterExtractionORM", "FloodOverlayORM", "FloodProductORM",
+    "GF3ProcessingORM", "AiDiagnosisORM",
     # Schemas
     "HazardPoint", "DinsarResult", "ScanRequest", "ManagedRootInfo", "ScanCursorInfo",
     "RadarData", "RadarDataPage", "DinsarResultPage",

backend/app/models/orm.py

@@ -1478,6 +1478,15 @@ class SARSceneGeoORM(Base):
     radar_data_id = Column(Integer, ForeignKey("radar_data.id"), index=True, nullable=False)
     geo_path = Column(String, nullable=True)       # 地理编码 dB 文件路径（ENVI 格式，无扩展名）
     pixel_size_m = Column(Float, nullable=True)    # 输出像素大小（m）
+    analysis_tif_path = Column(String, nullable=True, index=True)
+    analysis_dir = Column(String, nullable=True)
+    analysis_preview_path = Column(String, nullable=True)
+    analysis_engine = Column(String(32), nullable=True, index=True)
+    analysis_profile = Column(String(64), nullable=True, index=True)
+    analysis_backscatter_unit = Column(String(32), nullable=True)
+    analysis_nodata_value = Column(Float, nullable=True)
+    analysis_metadata_json = Column(JSON, nullable=True)
+    analysis_quality_json = Column(JSON, nullable=True)
     status = Column(String, nullable=False, default="PENDING", index=True)
     error_msg = Column(Text, nullable=True)
     created_at = Column(DateTime, server_default=func.now())
@@ -1514,6 +1523,8 @@ class FloodDetectionORM(Base):
 
     pre_scene = relationship("SARSceneGeoORM", foreign_keys=[pre_scene_id], back_populates="pre_flood_detections")
     post_scene = relationship("SARSceneGeoORM", foreign_keys=[post_scene_id], back_populates="post_flood_detections")
+    overlays = relationship("FloodOverlayORM", back_populates="detection")
+    products = relationship("FloodProductORM", back_populates="detection", foreign_keys="FloodProductORM.detection_id")
 
     __table_args__ = (
         UniqueConstraint("pre_scene_id", "post_scene_id", name="uq_flood_detection_pair"),
@@ -1537,6 +1548,69 @@ class WaterDetectionORM(Base):
     updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
 
 
+class WaterExtractionORM(Base):
+    """Water extraction result used by the flood-analysis pipeline."""
+    __tablename__ = "water_extractions"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    scene_id = Column(Integer, ForeignKey("sar_scene_geo.id"), nullable=True, index=True)
+    processor = Column(String(32), nullable=False, default="otsu", index=True)
+    task_id = Column(String(64), nullable=True, index=True)
+    input_path = Column(String, nullable=True)
+    output_path = Column(String, nullable=True)
+    preview_path = Column(String, nullable=True)
+    vector_path = Column(String, nullable=True)
+    water_area_km2 = Column(Float, nullable=True)
+    water_pixel_count = Column(Integer, nullable=True)
+    threshold_value = Column(Float, nullable=True)
+    metadata_json = Column(JSON, nullable=True)
+    status = Column(String(16), nullable=False, default="PENDING", index=True)
+    error_msg = Column(Text, nullable=True)
+    created_at = Column(DateTime, server_default=func.now())
+    updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now())
+
+    scene = relationship("SARSceneGeoORM", foreign_keys=[scene_id])
+
+
+class FloodOverlayORM(Base):
+    """Spatial overlay result for a flood detection."""
+    __tablename__ = "flood_overlays"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    detection_id = Column(Integer, ForeignKey("flood_detections.id"), nullable=False, index=True)
+    flood_vector_path = Column(String, nullable=True)
+    hazard_points_hit = Column(Integer, nullable=False, default=0)
+    hazard_points_near = Column(Integer, nullable=False, default=0)
+    hazard_points_total = Column(Integer, nullable=False, default=0)
+    dinsar_products_intersecting = Column(Integer, nullable=False, default=0)
+    affected_area_km2 = Column(Float, nullable=True)
+    summary_json = Column(JSON, nullable=True)
+    created_at = Column(DateTime, server_default=func.now())
+
+    detection = relationship("FloodDetectionORM", back_populates="overlays")
+    products = relationship("FloodProductORM", back_populates="overlay", foreign_keys="FloodProductORM.overlay_id")
+
+
+class FloodProductORM(Base):
+    """Published flood-analysis product package."""
+    __tablename__ = "flood_products"
+
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    product_id = Column(String(64), unique=True, index=True, nullable=False)
+    detection_id = Column(Integer, ForeignKey("flood_detections.id"), nullable=True, index=True)
+    overlay_id = Column(Integer, ForeignKey("flood_overlays.id"), nullable=True, index=True)
+    display_name = Column(String(255), nullable=False)
+    status = Column(String(16), nullable=False, default="READY", index=True)
+    publish_dir = Column(String, unique=True, nullable=True)
+    manifest_path = Column(String, unique=True, nullable=True)
+    geom = Column(Geometry("POLYGON", srid=4326), nullable=True)
+    summary_json = Column(JSON, nullable=True)
+    created_at = Column(DateTime, server_default=func.now())
+
+    detection = relationship("FloodDetectionORM", back_populates="products", foreign_keys=[detection_id])
+    overlay = relationship("FloodOverlayORM", back_populates="products", foreign_keys=[overlay_id])
+
+
 class GF3ProcessingORM(Base):
     """GF3 L1A→L2 处理记录（辐射定标 + RPC 几何校正）。"""
     __tablename__ = "gf3_processing"

backend/app/pyint_pipeline/run_gamma_scene_preprocess.py

@@ -0,0 +1,326 @@
+#!/usr/bin/env python3
+"""Single-scene Gamma preprocessing to analysis-ready GeoTIFF.
+
+The script is intentionally narrower than the full PyINT DInSAR pipeline:
+LT source product -> Gamma SLC -> multilook amplitude -> geocode -> GeoTIFF.
+It is executed inside WSL by backend.app.services.lt_gamma_scene_service.
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import os
+import re
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+from typing import Any
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Preprocess a SAR scene with Gamma/PyINT.")
+    parser.add_argument("--source-path", required=True)
+    parser.add_argument("--output-dir", required=True)
+    parser.add_argument("--work-dir", required=True)
+    parser.add_argument("--pyint-home", required=True)
+    parser.add_argument("--dem-root", required=True)
+    parser.add_argument("--prepared-dem-path", default="")
+    parser.add_argument("--project-name", required=True)
+    parser.add_argument("--date", required=True)
+    parser.add_argument("--satellite-family", default="LT1")
+    parser.add_argument("--range-looks", type=int, default=2)
+    parser.add_argument("--azimuth-looks", type=int, default=2)
+    parser.add_argument("--geo-interp", default="1")
+    parser.add_argument("--nodata-value", type=float, default=-9999.0)
+    parser.add_argument("--to-db", action="store_true")
+    return parser.parse_args()
+
+
+def run_logged(command: list[str], *, cwd: Path, env: dict[str, str], log_dir: Path, stage: str) -> dict[str, Any]:
+    log_dir.mkdir(parents=True, exist_ok=True)
+    stdout_path = log_dir / f"{stage}.stdout.log"
+    stderr_path = log_dir / f"{stage}.stderr.log"
+    result = subprocess.run(command, cwd=str(cwd), env=env, text=True, capture_output=True, check=False)
+    stdout_path.write_text(result.stdout or "", encoding="utf-8", errors="ignore")
+    stderr_path.write_text(result.stderr or "", encoding="utf-8", errors="ignore")
+    if result.returncode != 0:
+        detail = (result.stderr or result.stdout or "").strip()
+        raise RuntimeError(f"{stage} failed rc={result.returncode}: {' '.join(command)}\n{detail}")
+    return {
+        "stage": stage,
+        "command": command,
+        "returncode": result.returncode,
+        "stdout_path": str(stdout_path),
+        "stderr_path": str(stderr_path),
+    }
+
+
+def read_gamma_par(path: Path, key: str) -> str:
+    wanted = str(key or "").strip().rstrip(":")
+    with path.open("r", encoding="utf-8", errors="ignore") as stream:
+        for line in stream:
+            stripped = line.strip()
+            if not stripped:
+                continue
+            label = stripped.split()[0].rstrip(":")
+            if label != wanted:
+                continue
+            tail = stripped.split(":", 1)[1] if ":" in stripped else " ".join(stripped.split()[1:])
+            tokens = tail.strip().split()
+            if tokens:
+                return tokens[0]
+    raise KeyError(f"Cannot read {key} from {path}")
+
+
+def discover_lt_inputs(source_path: Path, date: str) -> list[Path]:
+    patterns = [f"LT1*{date}*.tar.gz", f"LT1*{date}*.tiff", f"LT1*{date}*.tif"]
+    if source_path.is_file():
+        return [source_path]
+    if not source_path.is_dir():
+        raise FileNotFoundError(f"Source path does not exist: {source_path}")
+    found: list[Path] = []
+    for pattern in patterns:
+        found.extend(path for path in source_path.rglob(pattern) if path.is_file())
+    return sorted(set(found))
+
+
+def stage_lt_inputs(source_path: Path, download_dir: Path, date: str) -> list[str]:
+    download_dir.mkdir(parents=True, exist_ok=True)
+    inputs = discover_lt_inputs(source_path, date)
+    if not inputs:
+        raise FileNotFoundError(f"No LT inputs for date {date} under {source_path}")
+
+    staged: list[str] = []
+    for source in inputs:
+        target = download_dir / source.name
+        shutil.copy2(source, target)
+        staged.append(str(target))
+
+        lower_name = source.name.lower()
+        if lower_name.endswith((".tiff", ".tif")):
+            base_candidates = [
+                source.with_suffix(source.suffix + ".meta.xml"),
+                source.with_suffix(".meta.xml"),
+                source.with_name(source.stem + ".meta.xml"),
+            ]
+            for meta in base_candidates:
+                if meta.is_file():
+                    shutil.copy2(meta, download_dir / meta.name)
+                    break
+    return staged
+
+
+def write_template(
+    *,
+    template_path: Path,
+    date: str,
+    range_looks: int,
+    azimuth_looks: int,
+    geo_interp: str,
+    prepared_dem_path: str,
+) -> None:
+    lines = [
+        "satelite = LT",
+        f"masterDate = {date}",
+        f"range_looks = {range_looks}",
+        f"azimuth_looks = {azimuth_looks}",
+        "dem_lat_ovr = 0.5",
+        "dem_lon_ovr = 0.5",
+        "Simphase_rpos = -",
+        "Simphase_azpos = -",
+        "Simphase_rwin = 256",
+        "Simphase_azwin = 256",
+        "Simphase_thresh = -",
+        f"geo_interp = {geo_interp}",
+    ]
+    dem = str(prepared_dem_path or "").strip()
+    if dem and Path(dem).is_file() and Path(dem + ".par").is_file():
+        lines.append(f"DEM = {dem}")
+    template_path.parent.mkdir(parents=True, exist_ok=True)
+    template_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def convert_to_db_geotiff(source_tif: Path, target_tif: Path, nodata_value: float) -> dict[str, Any]:
+    try:
+        import numpy as np
+        import rasterio
+    except Exception as exc:
+        shutil.copy2(source_tif, target_tif)
+        return {
+            "target": str(target_tif),
+            "backscatter_unit": "gamma_mli_power",
+            "warning": f"rasterio/numpy unavailable; kept power values: {exc}",
+        }
+
+    with rasterio.open(source_tif) as src:
+        data = src.read(1).astype("float32")
+        profile = src.profile.copy()
+        src_nodata = src.nodata
+
+    invalid = ~np.isfinite(data)
+    if src_nodata is not None:
+        invalid |= data == src_nodata
+    invalid |= data <= 0
+    db_data = np.full(data.shape, nodata_value, dtype="float32")
+    db_data[~invalid] = (10.0 * np.log10(data[~invalid])).astype("float32")
+
+    profile.update(dtype="float32", count=1, nodata=nodata_value, compress="deflate")
+    target_tif.parent.mkdir(parents=True, exist_ok=True)
+    with rasterio.open(target_tif, "w", **profile) as dst:
+        dst.write(db_data, 1)
+    return {"target": str(target_tif), "backscatter_unit": "gamma_mli_db"}
+
+
+def main() -> int:
+    args = parse_args()
+    source_path = Path(args.source_path).resolve()
+    output_dir = Path(args.output_dir).resolve()
+    work_root = Path(args.work_dir).resolve()
+    pyint_home = Path(args.pyint_home).resolve()
+    dem_root = Path(args.dem_root).resolve()
+    project_name = re.sub(r"[^0-9A-Za-z._-]+", "_", args.project_name).strip("._-") or "sar_scene"
+    date = re.sub(r"\D", "", str(args.date or ""))[:8]
+    if not re.fullmatch(r"20\d{6}", date):
+        raise ValueError(f"Invalid scene date: {args.date}")
+
+    scratch_dir = work_root / "scratch"
+    template_dir = work_root / "templates"
+    project_dir = scratch_dir / project_name
+    download_dir = project_dir / "DOWNLOAD"
+    log_dir = output_dir / "logs"
+    output_dir.mkdir(parents=True, exist_ok=True)
+    dem_root.mkdir(parents=True, exist_ok=True)
+
+    env = os.environ.copy()
+    env["SCRATCHDIR"] = str(scratch_dir)
+    env["TEMPLATEDIR"] = str(template_dir)
+    env["DEMDIR"] = str(dem_root)
+    env["PYTHONPATH"] = f"{pyint_home}:{env.get('PYTHONPATH', '')}"
+    env["PATH"] = f"{pyint_home / 'pyint'}:{env.get('PATH', '')}"
+
+    staged_inputs = stage_lt_inputs(source_path, download_dir, date)
+    template_path = template_dir / f"{project_name}.template"
+    write_template(
+        template_path=template_path,
+        date=date,
+        range_looks=max(1, int(args.range_looks)),
+        azimuth_looks=max(1, int(args.azimuth_looks)),
+        geo_interp=str(args.geo_interp or "1"),
+        prepared_dem_path=args.prepared_dem_path,
+    )
+
+    commands: list[dict[str, Any]] = []
+    commands.append(
+        run_logged(
+            [sys.executable, str(pyint_home / "pyint" / "down2slc_LT1.py"), project_name, date],
+            cwd=work_root,
+            env=env,
+            log_dir=log_dir,
+            stage="down2slc_lt1",
+        )
+    )
+    commands.append(
+        run_logged(
+            [sys.executable, str(pyint_home / "pyint" / "generate_rdc_dem.py"), project_name],
+            cwd=work_root,
+            env=env,
+            log_dir=log_dir,
+            stage="generate_rdc_dem",
+        )
+    )
+
+    dem_dir = project_dir / "DEM"
+    range_looks = max(1, int(args.range_looks))
+    amp = dem_dir / f"{date}_{range_looks}rlks.amp"
+    amp_par = dem_dir / f"{date}_{range_looks}rlks.amp.par"
+    utm_dem_par = dem_dir / f"{date}_{range_looks}rlks.utm.dem.par"
+    utm_to_rdc = dem_dir / f"{date}_{range_looks}rlks.UTM_TO_RDC"
+    for required in (amp, amp_par, utm_dem_par, utm_to_rdc):
+        if not required.is_file():
+            raise FileNotFoundError(f"Required Gamma product missing: {required}")
+
+    width = read_gamma_par(amp_par, "range_samples")
+    geo_width = read_gamma_par(utm_dem_par, "width")
+    geo_nlines = read_gamma_par(utm_dem_par, "nlines")
+    geo_amp = output_dir / "gamma_geo_amp"
+    commands.append(
+        run_logged(
+            [
+                "geocode_back",
+                str(amp),
+                str(width),
+                str(utm_to_rdc),
+                str(geo_amp),
+                str(geo_width),
+                str(geo_nlines),
+                str(args.geo_interp or "1"),
+                "0",
+            ],
+            cwd=work_root,
+            env=env,
+            log_dir=log_dir,
+            stage="geocode_amp",
+        )
+    )
+
+    power_tif = output_dir / "analysis_ready_power.tif"
+    commands.append(
+        run_logged(
+            [
+                "data2geotiff",
+                str(utm_dem_par),
+                str(geo_amp),
+                "2",
+                str(power_tif),
+                f"{float(args.nodata_value):g}",
+            ],
+            cwd=work_root,
+            env=env,
+            log_dir=log_dir,
+            stage="data2geotiff_amp",
+        )
+    )
+    if not power_tif.is_file() or power_tif.stat().st_size <= 0:
+        raise RuntimeError(f"data2geotiff did not create output: {power_tif}")
+
+    final_tif = output_dir / "analysis_ready.tif"
+    conversion = convert_to_db_geotiff(power_tif, final_tif, float(args.nodata_value)) if args.to_db else {
+        "target": str(final_tif),
+        "backscatter_unit": "gamma_mli_power",
+    }
+    if not args.to_db:
+        shutil.copy2(power_tif, final_tif)
+
+    manifest = {
+        "ok": True,
+        "satellite_family": args.satellite_family,
+        "project_name": project_name,
+        "date": date,
+        "source_path": str(source_path),
+        "staged_inputs": staged_inputs,
+        "work_dir": str(work_root),
+        "output_dir": str(output_dir),
+        "analysis_tif_path": str(final_tif),
+        "power_tif_path": str(power_tif),
+        "backscatter_unit": conversion.get("backscatter_unit"),
+        "gamma_products": {
+            "amp": str(amp),
+            "amp_par": str(amp_par),
+            "utm_dem_par": str(utm_dem_par),
+            "utm_to_rdc": str(utm_to_rdc),
+            "geo_amp": str(geo_amp),
+        },
+        "looks": {"range": range_looks, "azimuth": max(1, int(args.azimuth_looks))},
+        "commands": commands,
+        "conversion": conversion,
+    }
+    manifest_path = output_dir / "manifest.json"
+    manifest_path.write_text(json.dumps(manifest, ensure_ascii=False, indent=2), encoding="utf-8")
+    print(json.dumps({"ok": True, "manifest_path": str(manifest_path), "analysis_tif_path": str(final_tif)}))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

backend/app/routers/__init__.py

@@ -22,6 +22,7 @@ from . import (
     ps_products,
     radar,
     root_registry,
+    sbas_insar_production,
     stats,
     task_batches,
     tasks_runtime,
@@ -53,6 +54,7 @@ def include_all_routers(router: APIRouter) -> None:
     router.include_router(dinsar.router)
     router.include_router(dinsar_products.router)
     router.include_router(dinsar_production.router)
+    router.include_router(sbas_insar_production.router)
     router.include_router(timeseries_production.router)
     router.include_router(ps_products.router)
     router.include_router(ai.router)

backend/app/routers/flood.py

@@ -1,45 +1,65 @@
-"""Flood disaster analysis router.
-
-This router exposes the flood-analysis business API while reusing the
-existing water/flood processing records and jobs during migration.
-"""
+"""Flood disaster analysis router."""
 from __future__ import annotations
 
-from typing import Optional
+from typing import Any, Optional
 
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, Depends
 from pydantic import BaseModel, Field
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from ..database import get_db
 from ..models import AuthUserORM
-from . import water as water_compat
+from ..services import flood_analysis_service
+from ..services import flood_overlay_service
+from ..services import flood_product_service
 from .dependencies import _get_current_user, _require_admin
 
 router = APIRouter()
 
 
 class FloodPreprocessRequest(BaseModel):
-    radar_data_id: int = Field(..., description="RadarDataORM 主键")
+    radar_data_id: int = Field(..., description="RadarDataORM primary key")
 
 
 class FloodWaterExtractionRequest(BaseModel):
-    scene_id: Optional[int] = Field(default=None, description="SARSceneGeoORM 主键")
-    input_path: Optional[str] = Field(default=None, description="直接指定 GeoTIFF/ENVI 路径")
+    scene_id: Optional[int] = Field(default=None, description="SARSceneGeoORM primary key")
+    input_path: Optional[str] = Field(default=None, description="Direct GeoTIFF/ENVI input path")
 
 
 class FloodPairSearchRequest(BaseModel):
-    pre_start: Optional[str] = Field(default=None, description="灾前开始日期 YYYYMMDD")
-    pre_end: Optional[str] = Field(default=None, description="灾前结束日期 YYYYMMDD")
-    post_start: Optional[str] = Field(default=None, description="灾后开始日期 YYYYMMDD")
-    post_end: Optional[str] = Field(default=None, description="灾后结束日期 YYYYMMDD")
-    overlap_threshold: float = Field(default=0.3, ge=0.0, le=1.0, description="最小重叠比例")
+    pre_start: Optional[str] = Field(default=None, description="Pre-flood start date in YYYYMMDD")
+    pre_end: Optional[str] = Field(default=None, description="Pre-flood end date in YYYYMMDD")
+    post_start: Optional[str] = Field(default=None, description="Post-flood start date in YYYYMMDD")
+    post_end: Optional[str] = Field(default=None, description="Post-flood end date in YYYYMMDD")
+    overlap_threshold: float = Field(default=0.3, ge=0.0, le=1.0, description="Minimum overlap ratio")
+
+
+class FloodDisasterPairSearchRequest(BaseModel):
+    disaster_name: Optional[str] = Field(default=None, description="Disaster/event display name")
+    disaster_date: str = Field(..., description="Disaster date in YYYYMMDD")
+    region_tree_id: Optional[str] = Field(default=None, description="AOI admin region tree id")
+    aoi_geojson: Optional[dict[str, Any]] = Field(default=None, description="AOI GeoJSON FeatureCollection")
+    pre_window_days: int = Field(default=30, ge=1, le=365)
+    post_window_days: int = Field(default=30, ge=1, le=365)
+    min_aoi_coverage_ratio: float = Field(default=0.2, ge=0.0, le=1.0)
+    min_pair_overlap_ratio: float = Field(default=0.3, ge=0.0, le=1.0)
+    max_pairs: int = Field(default=50, ge=1, le=200)
+    satellites: Optional[list[str]] = None
+    polarization: Optional[str] = None
+    imaging_mode: Optional[str] = None
+    product_level: Optional[str] = None
+    require_same_polarization: bool = True
+    require_same_imaging_mode: bool = False
 
 
 class FloodDetectionRequest(BaseModel):
-    pre_scene_id: int = Field(..., description="灾前 SARSceneGeoORM 主键")
-    post_scene_id: int = Field(..., description="灾后 SARSceneGeoORM 主键")
-    refine: bool = Field(default=False, description="是否启用 MRF 精化")
+    pre_scene_id: int = Field(..., description="Pre-flood SARSceneGeoORM primary key")
+    post_scene_id: int = Field(..., description="Post-flood SARSceneGeoORM primary key")
+    refine: bool = Field(default=False, description="Enable MRF refinement")
+
+
+class FloodOverlayRequest(BaseModel):
+    near_threshold_m: float = Field(default=500.0, ge=0.0, le=10000.0, description="Near-flood threshold in meters")
 
 
 @router.post("/flood/preprocess", status_code=202)
@@ -48,8 +68,7 @@ async def submit_flood_preprocess(
     db: AsyncSession = Depends(get_db),
     admin_user: AuthUserORM = Depends(_require_admin),
 ):
-    """提交水体提取前置处理任务，当前复用旧 water geocode 链路。"""
-    return await water_compat.submit_geocode(req, db=db, admin_user=admin_user)
+    return await flood_analysis_service.submit_geocode_job(req, db=db)
 
 
 @router.get("/flood/scenes")
@@ -59,8 +78,7 @@ async def list_flood_scenes(
     db: AsyncSession = Depends(get_db),
     current_user: AuthUserORM = Depends(_get_current_user),
 ):
-    """列出可作为水体提取输入的地理编码场景。"""
-    return await water_compat.list_scenes(limit=limit, offset=offset, db=db, current_user=current_user)
+    return await flood_analysis_service.list_scenes(limit=limit, offset=offset, db=db)
 
 
 @router.get("/flood/scenes/done-radar-ids")
@@ -68,7 +86,7 @@ async def list_flood_done_radar_ids(
     db: AsyncSession = Depends(get_db),
     current_user: AuthUserORM = Depends(_get_current_user),
 ):
-    return await water_compat.list_done_scene_radar_ids(db=db, current_user=current_user)
+    return await flood_analysis_service.list_done_scene_radar_ids(db=db)
 
 
 @router.get("/flood/scenes/active-radar-ids")
@@ -76,7 +94,7 @@ async def list_flood_active_radar_ids(
     db: AsyncSession = Depends(get_db),
     current_user: AuthUserORM = Depends(_get_current_user),
 ):
-    return await water_compat.list_active_scene_radar_ids(db=db, current_user=current_user)
+    return await flood_analysis_service.list_active_scene_radar_ids(db=db)
 
 
 @router.post("/flood/scenes/{scene_id}/reset")
@@ -85,7 +103,7 @@ async def reset_flood_scene(
     db: AsyncSession = Depends(get_db),
     admin_user: AuthUserORM = Depends(_require_admin),
 ):
-    return await water_compat.reset_scene_status(scene_id=scene_id, db=db, admin_user=admin_user)
+    return await flood_analysis_service.reset_scene_status(scene_id=scene_id, db=db)
 
 
 @router.post("/flood/water-extractions", status_code=202)
@@ -94,8 +112,7 @@ async def submit_flood_water_extraction(
     db: AsyncSession = Depends(get_db),
     admin_user: AuthUserORM = Depends(_require_admin),
 ):
-    """提交单景水体提取任务，当前复用 Otsu 快速水体检测实现。"""
-    return await water_compat.submit_water_detect(req, db=db, admin_user=admin_user)
+    return await flood_analysis_service.submit_water_extraction(req, db=db)
 
 
 @router.get("/flood/water-extractions")
@@ -106,12 +123,11 @@ async def list_flood_water_extractions(
     db: AsyncSession = Depends(get_db),
     current_user: AuthUserORM = Depends(_get_current_user),
 ):
-    return await water_compat.list_water_detections(
+    return await flood_analysis_service.list_water_extractions(
         limit=limit,
         offset=offset,
         status=status,
         db=db,
-        current_user=current_user,
     )
 
 
@@ -121,11 +137,7 @@ async def get_flood_water_extraction_preview(
     db: AsyncSession = Depends(get_db),
     current_user: AuthUserORM = Depends(_get_current_user),
 ):
-    return await water_compat.get_water_detection_preview(
-        detection_id=extraction_id,
-        db=db,
-        current_user=current_user,
-    )
+    return await flood_analysis_service.get_water_extraction_preview(extraction_id=extraction_id, db=db)
 
 
 @router.post("/flood/pairs/search")
@@ -134,7 +146,16 @@ async def search_flood_pairs(
     db: AsyncSession = Depends(get_db),
     current_user: AuthUserORM = Depends(_get_current_user),
 ):
-    return await water_compat.find_water_pairs(req, db=db, current_user=current_user)
+    return await flood_analysis_service.search_pairs(req, db=db)
+
+
+@router.post("/flood/disaster-pairs/search")
+async def search_flood_disaster_pairs(
+    req: FloodDisasterPairSearchRequest,
+    db: AsyncSession = Depends(get_db),
+    current_user: AuthUserORM = Depends(_get_current_user),
+):
+    return await flood_analysis_service.search_disaster_pairs(req, db=db)
 
 
 @router.post("/flood/detections", status_code=202)
@@ -143,7 +164,7 @@ async def submit_flood_detection(
     db: AsyncSession = Depends(get_db),
     admin_user: AuthUserORM = Depends(_require_admin),
 ):
-    return await water_compat.submit_flood_detect(req, db=db, admin_user=admin_user)
+    return await flood_analysis_service.submit_flood_detection(req, db=db)
 
 
 @router.get("/flood/detections")
@@ -151,7 +172,7 @@ async def list_flood_detections(
     db: AsyncSession = Depends(get_db),
     current_user: AuthUserORM = Depends(_get_current_user),
 ):
-    return await water_compat.list_flood_events(db=db, current_user=current_user)
+    return await flood_analysis_service.list_flood_detections(db=db)
 
 
 @router.get("/flood/detections/{detection_id}/preview/{layer}")
@@ -161,23 +182,109 @@ async def get_flood_detection_preview(
     db: AsyncSession = Depends(get_db),
     current_user: AuthUserORM = Depends(_get_current_user),
 ):
-    normalized = layer.strip().lower()
-    if normalized == "pre":
-        return await water_compat.flood_event_pre_preview(
-            event_id=detection_id,
+    return await flood_analysis_service.get_flood_detection_preview(
+        detection_id=detection_id,
+        layer=layer,
         db=db,
-            current_user=current_user,
     )
-    if normalized == "post":
-        return await water_compat.flood_event_post_preview(
-            event_id=detection_id,
+
+
+@router.post("/flood/detections/{detection_id}/overlay", status_code=201)
+async def run_flood_overlay(
+    detection_id: int,
+    req: FloodOverlayRequest | None = None,
+    db: AsyncSession = Depends(get_db),
+    admin_user: AuthUserORM = Depends(_require_admin),
+):
+    threshold = req.near_threshold_m if req else 500.0
+    return await flood_overlay_service.run_overlay(
+        detection_id=detection_id,
         db=db,
-            current_user=current_user,
+        near_threshold_m=threshold,
     )
-    if normalized == "classified":
-        return await water_compat.flood_event_classified_preview(
-            event_id=detection_id,
+
+
+@router.get("/flood/detections/{detection_id}/impact")
+async def get_flood_impact(
+    detection_id: int,
+    db: AsyncSession = Depends(get_db),
+    current_user: AuthUserORM = Depends(_get_current_user),
+):
+    return await flood_overlay_service.get_overlay_result(detection_id=detection_id, db=db)
+
+
+@router.post("/flood/detections/{detection_id}/products", status_code=201)
+async def create_flood_product(
+    detection_id: int,
+    db: AsyncSession = Depends(get_db),
+    admin_user: AuthUserORM = Depends(_require_admin),
+):
+    return await flood_product_service.create_flood_product_for_detection(detection_id=detection_id, db=db)
+
+
+@router.get("/flood/products")
+async def list_flood_products(
+    limit: int = 20,
+    offset: int = 0,
+    status: Optional[str] = None,
+    db: AsyncSession = Depends(get_db),
+    current_user: AuthUserORM = Depends(_get_current_user),
+):
+    return await flood_product_service.list_flood_products(
         db=db,
-            current_user=current_user,
+        limit=limit,
+        offset=offset,
+        status=status,
     )
-    raise HTTPException(status_code=404, detail=f"不支持的洪涝预览图层: {layer}")
+
+
+@router.get("/flood/products/{product_id}")
+async def get_flood_product(
+    product_id: str,
+    db: AsyncSession = Depends(get_db),
+    current_user: AuthUserORM = Depends(_get_current_user),
+):
+    return await flood_product_service.get_flood_product(product_id_or_pk=product_id, db=db)
+
+
+@router.get("/flood/products/{product_id}/manifest")
+async def get_flood_product_manifest(
+    product_id: str,
+    db: AsyncSession = Depends(get_db),
+    current_user: AuthUserORM = Depends(_get_current_user),
+):
+    return await flood_product_service.get_flood_product_manifest(product_id_or_pk=product_id, db=db)
+
+
+@router.get("/flood/results")
+async def list_flood_results(
+    limit: int = 20,
+    offset: int = 0,
+    status: Optional[str] = None,
+    db: AsyncSession = Depends(get_db),
+    current_user: AuthUserORM = Depends(_get_current_user),
+):
+    return await flood_product_service.list_flood_products(
+        db=db,
+        limit=limit,
+        offset=offset,
+        status=status,
+    )
+
+
+@router.get("/flood/results/{product_id}")
+async def get_flood_result(
+    product_id: str,
+    db: AsyncSession = Depends(get_db),
+    current_user: AuthUserORM = Depends(_get_current_user),
+):
+    return await flood_product_service.get_flood_product(product_id_or_pk=product_id, db=db)
+
+
+@router.get("/flood/results/{product_id}/manifest")
+async def get_flood_result_manifest(
+    product_id: str,
+    db: AsyncSession = Depends(get_db),
+    current_user: AuthUserORM = Depends(_get_current_user),
+):
+    return await flood_product_service.get_flood_product_manifest(product_id_or_pk=product_id, db=db)

backend/app/routers/monitor.py

@@ -3,11 +3,11 @@ from __future__ import annotations
 from typing import List, Optional
 
 from fastapi import APIRouter, BackgroundTasks, Depends, HTTPException
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.future import select
 
-from ..config import read_int_env
+from ..config import read_int_env, settings, split_env_paths
 from ..database import get_db
 from ..models import AuthUserORM, SystemTaskORM, TaskLogORM
 from ..scheduler import MONITOR_CONFIG
@@ -41,11 +41,23 @@ class MonitorConfig(BaseModel):
     radar_dirs: List[str] = []
     orbit_dir: Optional[str] = None
     dinsar_dirs: List[str] = []
+    gf3_archive_source_dirs: List[str] = []
     gf3_source_dirs: List[str] = []
     gf3_storage_dirs: List[str] = []
     # Manual-only: config is read from .env
 
 
+class GF3UnpackConfig(BaseModel):
+    source_dirs: List[str] = []
+    target_dirs: List[str] = []
+    archive_exts: List[str] = []
+    delete_archive: bool = False
+
+
+class GF3UnpackRunRequest(BaseModel):
+    max_files_per_run: Optional[int] = Field(default=None, ge=0)
+
+
 @router.post("/monitor/config")
 async def update_monitor_config(config: MonitorConfig):
     """
@@ -142,6 +154,56 @@ async def run_gf3_batch_process(admin_user: AuthUserORM = Depends(_require_admin
         raise HTTPException(status_code=409, detail=str(e))
 
 
+@router.get("/monitor/gf3-unpack/config")
+async def get_gf3_unpack_config(admin_user: AuthUserORM = Depends(_require_admin)):
+    return GF3UnpackConfig(
+        source_dirs=MONITOR_CONFIG.get("gf3_archive_source_dirs") or [],
+        target_dirs=MONITOR_CONFIG.get("gf3_source_dirs") or [],
+        archive_exts=split_env_paths(settings.GF3_ARCHIVE_EXTS),
+        delete_archive=bool(settings.GF3_UNPACK_DELETE_ARCHIVE),
+    )
+
+
+@router.post("/monitor/gf3-unpack", status_code=202)
+async def run_gf3_unpack(
+    request_data: GF3UnpackRunRequest | None = None,
+    admin_user: AuthUserORM = Depends(_require_admin),
+):
+    """
+    将 GF3 压缩包池解包到 GF3_SOURCE_DIRS，作为后续 L1A→L2 预处理输入。
+    """
+    gf3_archive_source_dirs = MONITOR_CONFIG.get("gf3_archive_source_dirs") or []
+    gf3_source_dirs = MONITOR_CONFIG.get("gf3_source_dirs") or []
+    if not gf3_archive_source_dirs:
+        raise HTTPException(status_code=400, detail="GF3_ARCHIVE_SOURCE_DIRS is not configured.")
+    if not gf3_source_dirs:
+        raise HTTPException(status_code=400, detail="GF3_SOURCE_DIRS is not configured.")
+
+    max_files = None
+    if request_data is not None and request_data.max_files_per_run is not None:
+        max_files = max(0, int(request_data.max_files_per_run))
+
+    task_type = "GF3_UNPACK"
+    task_name = "GF3 压缩包解包"
+    payload = {
+        "source_dirs": gf3_archive_source_dirs,
+        "target_dirs": gf3_source_dirs,
+        "archive_exts": split_env_paths(settings.GF3_ARCHIVE_EXTS),
+    }
+    if max_files is not None:
+        payload["max_files_per_run"] = max_files
+
+    try:
+        task_id = await task_service.create_task(task_type, task_name, params=payload)
+        await job_queue_service.create_job(task_type, payload=payload, task_id=task_id)
+        return {
+            "message": "GF3 解包任务已提交",
+            "task_id": task_id,
+        }
+    except ValueError as e:
+        raise HTTPException(status_code=409, detail=str(e))
+
+
 @router.get("/monitor/status")
 async def get_monitor_status():
     """

backend/app/routers/sbas_insar_production.py

@@ -0,0 +1,323 @@
+from __future__ import annotations
+
+import asyncio
+import mimetypes
+import subprocess
+
+from fastapi import APIRouter, HTTPException
+from fastapi.responses import FileResponse
+from pydantic import BaseModel, Field, field_validator
+
+from ..services.job_queue_service import job_queue_service
+from ..services.sbas_insar_production_service import sbas_insar_production_service
+from ..services.task_service import task_service
+
+
+router = APIRouter(prefix="/sbas-insar-production", tags=["sbas-insar-production"])
+
+
+class SbasStackDiscoverRequest(BaseModel):
+    source_roots: list[str] | None = None
+    orbit_roots: list[str] | None = None
+    min_scenes: int = Field(default=3, ge=2, le=100)
+    require_orbits: bool = True
+    include_scenes: bool = False
+    limit: int = Field(default=30, ge=0, le=500)
+    platform: str | None = Field(default=None, max_length=16)
+    relative_orbit: str | None = Field(default=None, max_length=32)
+    orbit_direction: str | None = Field(default=None, max_length=32)
+
+    @field_validator("source_roots", "orbit_roots", mode="before")
+    @classmethod
+    def _normalize_roots(cls, value):
+        if value is None:
+            return None
+        if isinstance(value, str):
+            items = [value]
+        else:
+            items = list(value)
+        cleaned = [str(item or "").strip() for item in items if str(item or "").strip()]
+        return cleaned or None
+
+    @field_validator("platform", "relative_orbit", "orbit_direction", mode="before")
+    @classmethod
+    def _normalize_optional_text(cls, value):
+        if value is None:
+            return None
+        text = str(value).strip()
+        return text or None
+
+
+class SbasMonitorPoint(BaseModel):
+    point_id: str | None = Field(default=None, max_length=64)
+    label: str | None = Field(default=None, max_length=120)
+    lon: float = Field(ge=-180, le=180)
+    lat: float = Field(ge=-90, le=90)
+
+
+class SbasRunSubmitRequest(SbasStackDiscoverRequest):
+    run_label: str | None = Field(default=None, max_length=120)
+    dry_run: bool = True
+    monitor_point_strategy: str = Field(default="auto_low_sigma_high_rate", max_length=64)
+    monitor_points: list[SbasMonitorPoint] | None = None
+
+
+class SbasBaselineAuditRequest(BaseModel):
+    execute: bool = True
+    rlks: int = Field(default=8, ge=1, le=64)
+    azlks: int = Field(default=8, ge=1, le=64)
+    max_delta_n: int = Field(default=1, ge=1, le=100)
+    timeout_seconds: int = Field(default=21600, ge=60, le=86400)
+
+
+class SbasItabDecisionRequest(BaseModel):
+    decision: str = Field(pattern="^(approve|reject)$")
+    reviewer: str | None = Field(default=None, max_length=120)
+    note: str | None = Field(default=None, max_length=1000)
+
+
+class SbasCoregistrationRequest(BaseModel):
+    execute: bool = False
+    rlks: int = Field(default=8, ge=1, le=64)
+    azlks: int = Field(default=8, ge=1, le=64)
+
+
+class SbasCoregistrationJobRequest(BaseModel):
+    rlks: int = Field(default=8, ge=1, le=64)
+    azlks: int = Field(default=8, ge=1, le=64)
+    timeout_seconds: int = Field(default=43200, ge=60, le=172800)
+
+
+@router.get("/capabilities")
+async def get_sbas_insar_capabilities():
+    return sbas_insar_production_service.get_capabilities()
+
+
+@router.post("/stacks/discover")
+async def discover_sbas_insar_stacks(request: SbasStackDiscoverRequest):
+    try:
+        return await asyncio.to_thread(
+            sbas_insar_production_service.discover_stacks,
+            source_roots=request.source_roots,
+            orbit_roots=request.orbit_roots,
+            min_scenes=request.min_scenes,
+            require_orbits=request.require_orbits,
+            include_scenes=request.include_scenes,
+            limit=request.limit,
+            platform=request.platform,
+            relative_orbit=request.relative_orbit,
+            orbit_direction=request.orbit_direction,
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.post("/stacks/{stack_id}/audit")
+async def audit_sbas_insar_stack(stack_id: str, request: SbasStackDiscoverRequest):
+    try:
+        return await asyncio.to_thread(
+            sbas_insar_production_service.audit_stack,
+            stack_id,
+            source_roots=request.source_roots,
+            orbit_roots=request.orbit_roots,
+            min_scenes=request.min_scenes,
+            require_orbits=request.require_orbits,
+        )
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.post("/stacks/{stack_id}/runs", status_code=202)
+async def submit_sbas_insar_run(stack_id: str, request: SbasRunSubmitRequest):
+    try:
+        return await asyncio.to_thread(
+            sbas_insar_production_service.create_run,
+            stack_id,
+            run_label=request.run_label,
+            source_roots=request.source_roots,
+            orbit_roots=request.orbit_roots,
+            min_scenes=request.min_scenes,
+            require_orbits=request.require_orbits,
+            monitor_points=[
+                point.model_dump(exclude_none=True)
+                for point in (request.monitor_points or [])
+            ],
+            monitor_point_strategy=request.monitor_point_strategy,
+            dry_run=request.dry_run,
+        )
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.get("/runs")
+async def list_sbas_insar_runs():
+    return await asyncio.to_thread(sbas_insar_production_service.list_runs)
+
+
+@router.get("/runs/{run_id}")
+async def get_sbas_insar_run(run_id: str):
+    try:
+        return await asyncio.to_thread(sbas_insar_production_service.get_run_detail, run_id)
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.post("/runs/{run_id}/baseline-audit", status_code=202)
+async def run_sbas_insar_baseline_audit(run_id: str, request: SbasBaselineAuditRequest):
+    try:
+        return await asyncio.to_thread(
+            sbas_insar_production_service.run_baseline_audit,
+            run_id,
+            execute=request.execute,
+            rlks=request.rlks,
+            azlks=request.azlks,
+            max_delta_n=request.max_delta_n,
+            timeout_seconds=request.timeout_seconds,
+        )
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except subprocess.TimeoutExpired as exc:
+        raise HTTPException(status_code=504, detail=f"baseline audit timed out after {exc.timeout}s") from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.post("/runs/{run_id}/itab-decision")
+async def decide_sbas_insar_itab(run_id: str, request: SbasItabDecisionRequest):
+    try:
+        return await asyncio.to_thread(
+            sbas_insar_production_service.decide_itab,
+            run_id,
+            decision=request.decision,
+            reviewer=request.reviewer,
+            note=request.note,
+        )
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.post("/runs/{run_id}/coregistration", status_code=202)
+async def prepare_sbas_insar_coregistration(run_id: str, request: SbasCoregistrationRequest):
+    try:
+        return await asyncio.to_thread(
+            sbas_insar_production_service.prepare_coregistration,
+            run_id,
+            execute=request.execute,
+            rlks=request.rlks,
+            azlks=request.azlks,
+        )
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.post("/runs/{run_id}/coregistration/jobs", status_code=202)
+async def submit_sbas_insar_coregistration_job(run_id: str, request: SbasCoregistrationJobRequest):
+    try:
+        run_detail = await asyncio.to_thread(sbas_insar_production_service.get_run_detail, run_id)
+        status = str((run_detail.get("run") or {}).get("status") or "").strip()
+        if status in {"ITAB_APPROVED", "COREGISTRATION_FAILED"}:
+            await asyncio.to_thread(
+                sbas_insar_production_service.prepare_coregistration,
+                run_id,
+                execute=False,
+                rlks=request.rlks,
+                azlks=request.azlks,
+            )
+            run_detail = await asyncio.to_thread(sbas_insar_production_service.get_run_detail, run_id)
+            status = str((run_detail.get("run") or {}).get("status") or "").strip()
+        if status not in {"COREGISTRATION_SCRIPT_READY", "COREGISTRATION_RUNNING"}:
+            raise ValueError(f"run status does not allow coregistration job submission: {status}")
+        if status == "COREGISTRATION_RUNNING":
+            raise ValueError("coregistration is already running for this run")
+
+        from ..services.job_handlers import JOB_TYPE_SBAS_COREGISTRATION
+
+        payload = {
+            "run_id": run_id,
+            "rlks": request.rlks,
+            "azlks": request.azlks,
+            "timeout_seconds": request.timeout_seconds,
+        }
+        task_id = await task_service.create_task(
+            task_type=JOB_TYPE_SBAS_COREGISTRATION,
+            task_name=f"SBAS-InSAR 共参考配准: {run_id}",
+            params=payload,
+        )
+        job_id = await job_queue_service.create_job(
+            job_type=JOB_TYPE_SBAS_COREGISTRATION,
+            payload=payload,
+            task_id=task_id,
+            max_attempts=1,
+        )
+        return {
+            "message": "SBAS-InSAR coregistration job queued.",
+            "run_id": run_id,
+            "task_id": task_id,
+            "job_id": job_id,
+            "job_type": JOB_TYPE_SBAS_COREGISTRATION,
+        }
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        message = str(exc)
+        status_code = 409 if "冲突" in message or "conflict" in message.lower() else 400
+        raise HTTPException(status_code=status_code, detail=message) from exc
+
+
+@router.get("/runs/{run_id}/artifacts/{relative_path:path}")
+async def get_sbas_insar_run_artifact(run_id: str, relative_path: str):
+    try:
+        artifact_path = sbas_insar_production_service.resolve_run_artifact_path(run_id, relative_path)
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+    media_type = mimetypes.guess_type(str(artifact_path))[0] or "application/octet-stream"
+    return FileResponse(
+        artifact_path,
+        media_type=media_type,
+        filename=artifact_path.name,
+    )
+
+
+@router.get("/trial-runs")
+async def list_sbas_insar_trial_runs():
+    return await asyncio.to_thread(sbas_insar_production_service.list_trial_runs)
+
+
+@router.get("/trial-runs/{trial_id}")
+async def get_sbas_insar_trial_run(trial_id: str):
+    try:
+        return await asyncio.to_thread(sbas_insar_production_service.get_trial_detail, trial_id)
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.get("/trial-runs/{trial_id}/artifacts/{relative_path:path}")
+async def get_sbas_insar_artifact(trial_id: str, relative_path: str):
+    try:
+        artifact_path = sbas_insar_production_service.resolve_artifact_path(trial_id, relative_path)
+    except FileNotFoundError as exc:
+        raise HTTPException(status_code=404, detail=str(exc)) from exc
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+    media_type = mimetypes.guess_type(str(artifact_path))[0] or "application/octet-stream"
+    return FileResponse(
+        artifact_path,
+        media_type=media_type,
+        filename=artifact_path.name,
+    )

backend/app/scheduler.py

@@ -19,6 +19,7 @@ MONITOR_CONFIG = {
     "s1_storage_dirs": split_env_paths(settings.SENTINEL1_STORAGE_DIRS),
     "s1_orbit_dirs": split_env_paths(settings.ORBIT_SOURCE_DIRS),
     # GF3 链路
+    "gf3_archive_source_dirs": split_env_paths(settings.GF3_ARCHIVE_SOURCE_DIRS),
     "gf3_source_dirs": split_env_paths(settings.GF3_SOURCE_DIRS),
     "gf3_storage_dirs": split_env_paths(settings.GF3_STORAGE_DIRS),
     "mode": "manual",

backend/app/services/flood_analysis_service.py

@@ -0,0 +1,967 @@
+"""Flood-analysis service layer.
+
+This module owns the flood business API implementation used by
+``backend.app.routers.flood``. It intentionally does not import the legacy
+water router; the old router remains only as a compatibility surface.
+"""
+from __future__ import annotations
+
+import asyncio
+import base64
+import io
+import json
+import os
+from datetime import datetime, timedelta
+from typing import Any
+
+from fastapi import HTTPException
+from fastapi.responses import JSONResponse
+from sqlalchemy import select, func
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.orm import selectinload
+
+from ..models import FloodDetectionORM, RadarDataORM, SARSceneGeoORM, WaterDetectionORM, WaterExtractionORM
+from ..services.job_handlers import (
+    JOB_TYPE_FLOOD_DETECTION,
+    JOB_TYPE_SAR_SCENE_PREPROCESS,
+    JOB_TYPE_WATER_DETECT,
+)
+from ..services.job_queue_service import job_queue_service
+from ..services.task_service import task_service
+from ..utils import normalize_satellite_family
+
+_FLOOD_JOB_MAX_ATTEMPTS = 3
+
+
+async def _queue_flood_job(
+    *,
+    job_type: str,
+    task_type: str,
+    task_name: str,
+    payload: dict[str, Any],
+) -> dict[str, Any]:
+    try:
+        task_id = await task_service.create_task(
+            task_type=task_type,
+            task_name=task_name,
+            params=payload,
+        )
+        job_id = await job_queue_service.create_job(
+            job_type=job_type,
+            payload=payload,
+            task_id=task_id,
+            max_attempts=_FLOOD_JOB_MAX_ATTEMPTS,
+        )
+        return {"task_id": task_id, "job_id": job_id, "job_type": job_type, "message": "Job queued."}
+    except ValueError as exc:
+        message = str(exc)
+        raise HTTPException(status_code=409 if "conflict" in message.lower() else 400, detail=message) from exc
+
+
+def _overlap_ratio(poly_a: Any, poly_b: Any) -> float:
+    try:
+        import json
+        from shapely.geometry import Polygon, shape
+
+        def _to_geom(poly: Any):
+            if isinstance(poly, str):
+                poly = json.loads(poly)
+            if isinstance(poly, list):
+                return Polygon(poly)
+            return shape(poly)
+
+        a = _to_geom(poly_a)
+        b = _to_geom(poly_b)
+        if not a.is_valid or not b.is_valid:
+            return 0.0
+        intersection_area = a.intersection(b).area
+        smaller_area = min(a.area, b.area)
+        return intersection_area / smaller_area if smaller_area > 0 else 0.0
+    except Exception:
+        return 0.0
+
+
+def _parse_ymd(value: str | None, *, field: str) -> datetime:
+    try:
+        normalized = str(value or "").replace("-", "").strip()
+        return datetime.strptime(normalized, "%Y%m%d")
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=f"{field} must be YYYYMMDD") from exc
+
+
+def _format_ymd(value: datetime) -> str:
+    return value.strftime("%Y%m%d")
+
+
+def _to_float(value: Any, default: float = 0.0) -> float:
+    try:
+        if value is None:
+            return default
+        return float(value)
+    except Exception:
+        return default
+
+
+def _same_text(left: Any, right: Any) -> bool:
+    left_text = str(left or "").strip().lower()
+    right_text = str(right or "").strip().lower()
+    if not left_text or not right_text:
+        return True
+    return left_text == right_text
+
+
+def _feature_collection_name(feature_collection: dict[str, Any]) -> str | None:
+    try:
+        features = feature_collection.get("features") or []
+        properties = features[0].get("properties") or {}
+        return properties.get("name") or properties.get("NAME") or properties.get("treeID")
+    except Exception:
+        return None
+
+
+def _preprocess_engine_for_radar(radar: RadarDataORM) -> str | None:
+    family = str(normalize_satellite_family(radar.satellite_family or radar.satellite) or "").upper()
+    if family == "GF3":
+        return "gf3_gdal"
+    if family == "LT1":
+        return "lt_gamma"
+    return None
+
+
+def _scene_analysis_path(scene: SARSceneGeoORM | None) -> str | None:
+    if not scene:
+        return None
+    return scene.analysis_tif_path
+
+
+def _resolve_aoi_wkt_from_request(req: Any) -> tuple[str, dict[str, Any], dict[str, Any]]:
+    """Resolve region/GeoJSON AOI using the same parser as the management search page."""
+    aoi_geojson = getattr(req, "aoi_geojson", None)
+    region_tree_id = getattr(req, "region_tree_id", None)
+
+    if aoi_geojson:
+        feature_collection = aoi_geojson
+        source = "geojson"
+    elif region_tree_id:
+        from ..routers.dependencies import _resolve_region_aoi_payload
+
+        payload = _resolve_region_aoi_payload(str(region_tree_id))
+        feature_collection = payload.get("aoi_geojson") or payload
+        source = "region"
+    else:
+        raise HTTPException(status_code=400, detail="region_tree_id or aoi_geojson is required")
+
+    from ..routers.dependencies import _parse_aoi_geojson_form_value
+
+    parsed = _parse_aoi_geojson_form_value(json.dumps(feature_collection, ensure_ascii=False))
+    if not parsed:
+        raise HTTPException(status_code=400, detail="AOI geometry is empty")
+    aoi_wkt, normalized_feature_collection = parsed
+    meta = {
+        "source": source,
+        "region_tree_id": region_tree_id,
+        "name": _feature_collection_name(normalized_feature_collection),
+    }
+    return aoi_wkt, normalized_feature_collection, meta
+
+
+def _radar_scene_item(scene: SARSceneGeoORM, radar: RadarDataORM, *, aoi_coverage_ratio: float | None = None) -> dict[str, Any]:
+    return {
+        "id": scene.id,
+        "scene_id": scene.id,
+        "radar_data_id": scene.radar_data_id,
+        "satellite": radar.satellite,
+        "imaging_date": radar.imaging_date,
+        "acquisition_time_utc": radar.acquisition_time_utc,
+        "imaging_mode": radar.imaging_mode,
+        "product_level": radar.product_level,
+        "polarization": radar.polarization,
+        "orbit_direction": radar.orbit_direction,
+        "geo_path": scene.geo_path,
+        "analysis_tif_path": scene.analysis_tif_path,
+        "analysis_dir": scene.analysis_dir,
+        "analysis_preview_path": scene.analysis_preview_path,
+        "analysis_engine": scene.analysis_engine,
+        "analysis_profile": scene.analysis_profile,
+        "analysis_backscatter_unit": scene.analysis_backscatter_unit,
+        "analysis_quality_json": scene.analysis_quality_json,
+        "coverage_polygon": radar.coverage_polygon,
+        "min_lat": radar.min_lat,
+        "max_lat": radar.max_lat,
+        "min_lon": radar.min_lon,
+        "max_lon": radar.max_lon,
+        "aoi_coverage_ratio": aoi_coverage_ratio,
+    }
+
+
+async def _query_disaster_scene_pool(
+    *,
+    db: AsyncSession,
+    req: Any,
+    aoi_wkt: str,
+    start_ymd: str,
+    end_ymd: str,
+    min_aoi_coverage_ratio: float,
+    descending: bool,
+) -> list[dict[str, Any]]:
+    aoi_geom = func.ST_GeomFromText(aoi_wkt, 4326)
+    aoi_area = func.ST_Area(func.Geography(aoi_geom))
+    coverage_expr = (
+        func.ST_Area(func.Geography(func.ST_Intersection(RadarDataORM.geom, aoi_geom)))
+        / func.nullif(aoi_area, 0)
+    ).label("aoi_coverage_ratio")
+
+    filters = [
+        SARSceneGeoORM.status == "DONE",
+        SARSceneGeoORM.analysis_tif_path.isnot(None),
+        RadarDataORM.geom.isnot(None),
+        RadarDataORM.imaging_date.isnot(None),
+        RadarDataORM.imaging_date >= start_ymd,
+        RadarDataORM.imaging_date <= end_ymd,
+        func.ST_Intersects(RadarDataORM.geom, aoi_geom),
+    ]
+
+    satellites = [str(item).strip() for item in (getattr(req, "satellites", None) or []) if str(item).strip()]
+    if satellites:
+        filters.append(RadarDataORM.satellite.in_(satellites))
+
+    polarization = str(getattr(req, "polarization", "") or "").strip()
+    if polarization:
+        filters.append(RadarDataORM.polarization.ilike(f"%{polarization}%"))
+
+    imaging_mode = str(getattr(req, "imaging_mode", "") or "").strip()
+    if imaging_mode:
+        filters.append(RadarDataORM.imaging_mode == imaging_mode)
+
+    product_level = str(getattr(req, "product_level", "") or "").strip()
+    if product_level:
+        filters.append(RadarDataORM.product_level == product_level)
+
+    order_by = RadarDataORM.imaging_date.desc() if descending else RadarDataORM.imaging_date.asc()
+    result = await db.execute(
+        select(SARSceneGeoORM, RadarDataORM, coverage_expr)
+        .join(RadarDataORM, SARSceneGeoORM.radar_data_id == RadarDataORM.id)
+        .where(*filters)
+        .order_by(order_by, SARSceneGeoORM.id.desc())
+    )
+
+    pool: list[dict[str, Any]] = []
+    for scene, radar, aoi_coverage_ratio in result.all():
+        coverage_ratio = max(0.0, min(1.0, _to_float(aoi_coverage_ratio)))
+        if coverage_ratio < min_aoi_coverage_ratio:
+            continue
+        pool.append(_radar_scene_item(scene, radar, aoi_coverage_ratio=round(coverage_ratio, 4)))
+    return pool
+
+
+async def submit_geocode_job(req: Any, db: AsyncSession) -> dict[str, Any]:
+    radar = await db.get(RadarDataORM, req.radar_data_id)
+    if not radar:
+        raise HTTPException(status_code=404, detail=f"RadarData id={req.radar_data_id} not found")
+
+    result = await db.execute(
+        select(SARSceneGeoORM)
+        .where(SARSceneGeoORM.radar_data_id == req.radar_data_id)
+        .with_for_update(skip_locked=True)
+    )
+    scene = result.scalar_one_or_none()
+    if scene and scene.status in ("PENDING", "RUNNING"):
+        raise HTTPException(status_code=409, detail="Scene already has an active geocode job")
+    if not scene:
+        scene = SARSceneGeoORM(radar_data_id=req.radar_data_id, status="PENDING")
+        db.add(scene)
+        await db.flush()
+    else:
+        scene.status = "PENDING"
+        scene.error_msg = None
+    await db.flush()
+    scene_id = scene.id
+    await db.commit()
+
+    engine = _preprocess_engine_for_radar(radar)
+    if not engine:
+        async with db.begin():
+            failed_scene = await db.get(SARSceneGeoORM, scene_id)
+            if failed_scene and failed_scene.status == "PENDING":
+                failed_scene.status = "FAILED"
+                failed_scene.error_msg = "No analysis-ready GeoTIFF preprocessor configured for this satellite"
+        raise HTTPException(
+            status_code=400,
+            detail="洪涝模块不再使用 ENVI 兜底预处理；该卫星暂未配置 analysis-ready GeoTIFF 预处理器",
+        )
+
+    job_type = JOB_TYPE_SAR_SCENE_PREPROCESS
+    task_type = f"FLOOD_SCENE_PREPROCESS_{scene_id}"
+    task_name = f"Flood analysis-ready preprocess radar_id={req.radar_data_id} engine={engine}"
+    payload = {"scene_id": scene_id, "radar_data_id": req.radar_data_id}
+    payload["engine"] = engine
+
+    try:
+        return await _queue_flood_job(
+            job_type=job_type,
+            task_type=task_type,
+            task_name=task_name,
+            payload=payload,
+        )
+    except HTTPException:
+        async with db.begin():
+            failed_scene = await db.get(SARSceneGeoORM, scene_id)
+            if failed_scene and failed_scene.status == "PENDING":
+                failed_scene.status = "FAILED"
+                failed_scene.error_msg = "Job queue failed"
+        raise
+
+
+async def reset_scene_status(scene_id: int, db: AsyncSession) -> dict[str, Any]:
+    scene = await db.get(SARSceneGeoORM, scene_id)
+    if not scene:
+        raise HTTPException(status_code=404, detail=f"Scene id={scene_id} not found")
+    if scene.status not in ("PENDING", "RUNNING"):
+        raise HTTPException(status_code=400, detail=f"Scene status is {scene.status}; reset is not needed")
+    scene.status = "FAILED"
+    scene.error_msg = "Manually reset"
+    await db.commit()
+    return {"id": scene_id, "status": "FAILED", "message": "Scene reset"}
+
+
+async def list_done_scene_radar_ids(db: AsyncSession) -> dict[str, list[int]]:
+    result = await db.execute(
+        select(SARSceneGeoORM.radar_data_id).where(
+            SARSceneGeoORM.status == "DONE",
+            SARSceneGeoORM.analysis_tif_path.isnot(None),
+        )
+    )
+    return {"ids": [row for (row,) in result.all()]}
+
+
+async def list_active_scene_radar_ids(db: AsyncSession) -> dict[str, list[int]]:
+    result = await db.execute(
+        select(SARSceneGeoORM.radar_data_id).where(SARSceneGeoORM.status.in_(["PENDING", "RUNNING"]))
+    )
+    return {"ids": [row for (row,) in result.all()]}
+
+
+async def list_scenes(limit: int, offset: int, db: AsyncSession) -> dict[str, Any]:
+    total_result = await db.execute(select(func.count()).select_from(SARSceneGeoORM))
+    total = total_result.scalar_one()
+
+    result = await db.execute(
+        select(SARSceneGeoORM, RadarDataORM)
+        .join(RadarDataORM, SARSceneGeoORM.radar_data_id == RadarDataORM.id)
+        .order_by(SARSceneGeoORM.id.desc())
+        .limit(limit)
+        .offset(offset)
+    )
+
+    items = []
+    for scene, radar in result.all():
+        items.append(
+            {
+                "id": scene.id,
+                "radar_data_id": scene.radar_data_id,
+                "satellite": radar.satellite,
+                "imaging_date": radar.imaging_date,
+                "acquisition_time_utc": radar.acquisition_time_utc,
+                "imaging_mode": radar.imaging_mode,
+                "product_level": radar.product_level,
+                "polarization": radar.polarization,
+                "orbit_direction": radar.orbit_direction,
+                "geo_path": scene.geo_path,
+                "analysis_tif_path": scene.analysis_tif_path,
+                "analysis_dir": scene.analysis_dir,
+                "analysis_preview_path": scene.analysis_preview_path,
+                "analysis_engine": scene.analysis_engine,
+                "analysis_profile": scene.analysis_profile,
+                "analysis_backscatter_unit": scene.analysis_backscatter_unit,
+                "analysis_nodata_value": scene.analysis_nodata_value,
+                "analysis_metadata_json": scene.analysis_metadata_json,
+                "analysis_quality_json": scene.analysis_quality_json,
+                "pixel_size_m": scene.pixel_size_m,
+                "status": scene.status,
+                "error_msg": scene.error_msg,
+                "created_at": scene.created_at.isoformat() if scene.created_at else None,
+                "coverage_polygon": radar.coverage_polygon,
+                "min_lat": radar.min_lat,
+                "max_lat": radar.max_lat,
+                "min_lon": radar.min_lon,
+                "max_lon": radar.max_lon,
+            }
+        )
+    return {"items": items, "total": total}
+
+
+async def submit_water_extraction(req: Any, db: AsyncSession) -> dict[str, Any]:
+    input_path = req.input_path
+    scene_id = req.scene_id
+
+    if scene_id:
+        scene = await db.get(SARSceneGeoORM, scene_id)
+        if not scene:
+            raise HTTPException(status_code=404, detail=f"SARSceneGeoORM id={scene_id} not found")
+        input_path = _scene_analysis_path(scene)
+        if not input_path:
+            raise HTTPException(status_code=400, detail="Scene has no analysis-ready GeoTIFF")
+
+    if not input_path:
+        raise HTTPException(status_code=400, detail="scene_id or input_path is required")
+
+    extraction = WaterExtractionORM(
+        scene_id=scene_id,
+        processor=getattr(req, "processor", None) or "otsu",
+        input_path=input_path,
+        status="PENDING",
+    )
+    db.add(extraction)
+    await db.flush()
+    extraction_id = extraction.id
+    await db.commit()
+
+    try:
+        queued = await _queue_flood_job(
+            job_type=JOB_TYPE_WATER_DETECT,
+            task_type=f"FLOOD_WATER_EXTRACTION_{extraction_id}",
+            task_name=f"Flood water extraction id={extraction_id}",
+            payload={"extraction_id": extraction_id, "processor": extraction.processor},
+        )
+        async with db.begin():
+            queued_extraction = await db.get(WaterExtractionORM, extraction_id)
+            if queued_extraction:
+                queued_extraction.task_id = queued.get("task_id")
+        return queued
+    except HTTPException:
+        async with db.begin():
+            failed_extraction = await db.get(WaterExtractionORM, extraction_id)
+            if failed_extraction and failed_extraction.status == "PENDING":
+                failed_extraction.status = "FAILED"
+                failed_extraction.error_msg = "Job queue failed"
+        raise
+
+
+async def list_water_extractions(
+    *,
+    limit: int,
+    offset: int,
+    status: str | None,
+    db: AsyncSession,
+) -> dict[str, Any]:
+    count_query = select(func.count()).select_from(WaterExtractionORM)
+    if status:
+        count_query = count_query.where(WaterExtractionORM.status == status)
+    total = (await db.execute(count_query)).scalar_one()
+
+    query = (
+        select(WaterExtractionORM, SARSceneGeoORM, RadarDataORM)
+        .outerjoin(SARSceneGeoORM, WaterExtractionORM.scene_id == SARSceneGeoORM.id)
+        .outerjoin(RadarDataORM, SARSceneGeoORM.radar_data_id == RadarDataORM.id)
+        .order_by(WaterExtractionORM.id.desc())
+        .limit(limit)
+        .offset(offset)
+    )
+    if status:
+        query = query.where(WaterExtractionORM.status == status)
+    rows = (await db.execute(query)).all()
+
+    items = []
+    for detection, scene, radar in rows:
+        items.append(
+            {
+                "id": detection.id,
+                "scene_id": detection.scene_id,
+                "processor": detection.processor,
+                "task_id": detection.task_id,
+                "radar_data_id": scene.radar_data_id if scene else None,
+                "satellite": radar.satellite if radar else None,
+                "imaging_date": radar.imaging_date if radar else None,
+                "acquisition_time_utc": radar.acquisition_time_utc if radar else None,
+                "imaging_mode": radar.imaging_mode if radar else None,
+                "product_level": radar.product_level if radar else None,
+                "polarization": radar.polarization if radar else None,
+                "orbit_direction": radar.orbit_direction if radar else None,
+                "coverage_polygon": radar.coverage_polygon if radar else None,
+                "min_lat": radar.min_lat if radar else None,
+                "max_lat": radar.max_lat if radar else None,
+                "min_lon": radar.min_lon if radar else None,
+                "max_lon": radar.max_lon if radar else None,
+                "input_path": detection.input_path,
+                "output_path": detection.output_path,
+                "preview_path": detection.preview_path,
+                "vector_path": detection.vector_path,
+                "water_area_km2": detection.water_area_km2,
+                "water_pixel_count": detection.water_pixel_count,
+                "otsu_threshold_db": detection.threshold_value,
+                "threshold_value": detection.threshold_value,
+                "metadata_json": detection.metadata_json,
+                "status": detection.status,
+                "error_msg": detection.error_msg,
+                "created_at": detection.created_at.isoformat() if detection.created_at else None,
+                "updated_at": detection.updated_at.isoformat() if detection.updated_at else None,
+            }
+        )
+    return {"items": items, "total": total}
+
+
+async def submit_flood_detection(req: Any, db: AsyncSession) -> dict[str, Any]:
+    pre_scene = await db.get(SARSceneGeoORM, req.pre_scene_id)
+    post_scene = await db.get(SARSceneGeoORM, req.post_scene_id)
+    if not pre_scene:
+        raise HTTPException(status_code=404, detail=f"Pre-scene id={req.pre_scene_id} not found")
+    if not post_scene:
+        raise HTTPException(status_code=404, detail=f"Post-scene id={req.post_scene_id} not found")
+    if pre_scene.status != "DONE":
+        raise HTTPException(status_code=400, detail=f"Pre-scene is not DONE: {pre_scene.status}")
+    if post_scene.status != "DONE":
+        raise HTTPException(status_code=400, detail=f"Post-scene is not DONE: {post_scene.status}")
+    if not pre_scene.analysis_tif_path:
+        raise HTTPException(status_code=400, detail="Pre-scene has no analysis-ready GeoTIFF")
+    if not post_scene.analysis_tif_path:
+        raise HTTPException(status_code=400, detail="Post-scene has no analysis-ready GeoTIFF")
+
+    result = await db.execute(
+        select(FloodDetectionORM)
+        .where(
+            FloodDetectionORM.pre_scene_id == req.pre_scene_id,
+            FloodDetectionORM.post_scene_id == req.post_scene_id,
+        )
+        .with_for_update(skip_locked=True)
+    )
+    detection = result.scalar_one_or_none()
+    if detection and detection.status in ("PENDING", "RUNNING"):
+        raise HTTPException(status_code=409, detail="Pair already has an active flood-detection job")
+    if not detection:
+        detection = FloodDetectionORM(
+            pre_scene_id=req.pre_scene_id,
+            post_scene_id=req.post_scene_id,
+            status="PENDING",
+        )
+        db.add(detection)
+        await db.flush()
+    else:
+        detection.status = "PENDING"
+        detection.error_msg = None
+    await db.flush()
+    detection_id = detection.id
+    await db.commit()
+
+    try:
+        return await _queue_flood_job(
+            job_type=JOB_TYPE_FLOOD_DETECTION,
+            task_type=f"FLOOD_DETECTION_{detection_id}",
+            task_name=f"GeoTIFF flood detection pre={req.pre_scene_id} post={req.post_scene_id}",
+            payload={"detection_id": detection_id, "refine": req.refine},
+        )
+    except HTTPException:
+        async with db.begin():
+            failed_detection = await db.get(FloodDetectionORM, detection_id)
+            if failed_detection and failed_detection.status == "PENDING":
+                failed_detection.status = "FAILED"
+                failed_detection.error_msg = "Job queue failed"
+        raise
+
+
+async def list_flood_detections(db: AsyncSession) -> dict[str, Any]:
+    result = await db.execute(
+        select(FloodDetectionORM)
+        .options(
+            selectinload(FloodDetectionORM.pre_scene).selectinload(SARSceneGeoORM.radar_data),
+            selectinload(FloodDetectionORM.post_scene).selectinload(SARSceneGeoORM.radar_data),
+        )
+        .order_by(FloodDetectionORM.id.desc())
+    )
+    detections = result.scalars().all()
+
+    items = []
+    for detection in detections:
+        pre_radar = detection.pre_scene.radar_data if detection.pre_scene else None
+        post_radar = detection.post_scene.radar_data if detection.post_scene else None
+        items.append(
+            {
+                "id": detection.id,
+                "pre_scene_id": detection.pre_scene_id,
+                "post_scene_id": detection.post_scene_id,
+                "pre_imaging_date": pre_radar.imaging_date if pre_radar else None,
+                "post_imaging_date": post_radar.imaging_date if post_radar else None,
+                "pre_satellite": pre_radar.satellite if pre_radar else None,
+                "post_satellite": post_radar.satellite if post_radar else None,
+                "pre_geo_path": _scene_analysis_path(detection.pre_scene),
+                "post_geo_path": _scene_analysis_path(detection.post_scene),
+                "pre_analysis_tif_path": _scene_analysis_path(detection.pre_scene),
+                "post_analysis_tif_path": _scene_analysis_path(detection.post_scene),
+                "classified_path": detection.classified_path,
+                "flood_area_km2": detection.flood_area_km2,
+                "stable_water_area_km2": detection.stable_water_area_km2,
+                "status": detection.status,
+                "error_msg": detection.error_msg,
+                "created_at": detection.created_at.isoformat() if detection.created_at else None,
+                "updated_at": detection.updated_at.isoformat() if detection.updated_at else None,
+            }
+        )
+    return {"items": items, "total": len(items)}
+
+
+async def search_pairs(req: Any, db: AsyncSession) -> dict[str, Any]:
+    pre_filters = [SARSceneGeoORM.status == "DONE", SARSceneGeoORM.analysis_tif_path.isnot(None)]
+    if req.pre_start:
+        pre_filters.append(RadarDataORM.imaging_date >= req.pre_start)
+    if req.pre_end:
+        pre_filters.append(RadarDataORM.imaging_date <= req.pre_end)
+    pre_result = await db.execute(
+        select(SARSceneGeoORM, RadarDataORM)
+        .join(RadarDataORM, SARSceneGeoORM.radar_data_id == RadarDataORM.id)
+        .where(*pre_filters)
+    )
+
+    post_filters = [SARSceneGeoORM.status == "DONE", SARSceneGeoORM.analysis_tif_path.isnot(None)]
+    if req.post_start:
+        post_filters.append(RadarDataORM.imaging_date >= req.post_start)
+    if req.post_end:
+        post_filters.append(RadarDataORM.imaging_date <= req.post_end)
+    post_result = await db.execute(
+        select(SARSceneGeoORM, RadarDataORM)
+        .join(RadarDataORM, SARSceneGeoORM.radar_data_id == RadarDataORM.id)
+        .where(*post_filters)
+    )
+
+    candidates = []
+    for pre_scene, pre_radar in pre_result.all():
+        for post_scene, post_radar in post_result.all():
+            if pre_scene.id == post_scene.id:
+                continue
+            ratio = 0.0
+            if pre_radar.coverage_polygon and post_radar.coverage_polygon:
+                ratio = _overlap_ratio(pre_radar.coverage_polygon, post_radar.coverage_polygon)
+            if ratio < req.overlap_threshold:
+                continue
+            try:
+                pre_date = datetime.strptime(pre_radar.imaging_date, "%Y%m%d")
+                post_date = datetime.strptime(post_radar.imaging_date, "%Y%m%d")
+                time_diff = abs((post_date - pre_date).days)
+            except Exception:
+                time_diff = None
+            candidates.append(
+                {
+                    "pre": {
+                        "id": pre_scene.id,
+                        "imaging_date": pre_radar.imaging_date,
+                        "satellite": pre_radar.satellite,
+                        "geo_path": _scene_analysis_path(pre_scene),
+                        "analysis_tif_path": _scene_analysis_path(pre_scene),
+                    },
+                    "post": {
+                        "id": post_scene.id,
+                        "imaging_date": post_radar.imaging_date,
+                        "satellite": post_radar.satellite,
+                        "geo_path": _scene_analysis_path(post_scene),
+                        "analysis_tif_path": _scene_analysis_path(post_scene),
+                    },
+                    "overlap_ratio": round(ratio, 4),
+                    "time_diff_days": time_diff,
+                }
+            )
+
+    candidates.sort(key=lambda item: item["overlap_ratio"], reverse=True)
+    used_pre: set[int] = set()
+    used_post: set[int] = set()
+    pairs = []
+    for candidate in candidates:
+        pre_id = candidate["pre"]["id"]
+        post_id = candidate["post"]["id"]
+        if pre_id in used_pre or post_id in used_post:
+            continue
+        used_pre.add(pre_id)
+        used_post.add(post_id)
+        pairs.append(candidate)
+
+    pairs.sort(key=lambda item: item["overlap_ratio"], reverse=True)
+    return {"pairs": pairs, "total": len(pairs)}
+
+
+async def search_disaster_pairs(req: Any, db: AsyncSession) -> dict[str, Any]:
+    disaster_date = _parse_ymd(req.disaster_date, field="disaster_date")
+    pre_window_days = max(1, int(getattr(req, "pre_window_days", 30) or 30))
+    post_window_days = max(1, int(getattr(req, "post_window_days", 30) or 30))
+    min_aoi_coverage_ratio = max(0.0, min(1.0, float(getattr(req, "min_aoi_coverage_ratio", 0.2) or 0.0)))
+    min_pair_overlap_ratio = max(0.0, min(1.0, float(getattr(req, "min_pair_overlap_ratio", 0.3) or 0.0)))
+    max_pairs = max(1, min(200, int(getattr(req, "max_pairs", 50) or 50)))
+
+    aoi_wkt, aoi_geojson, aoi_meta = _resolve_aoi_wkt_from_request(req)
+    pre_start = disaster_date - timedelta(days=pre_window_days)
+    pre_end = disaster_date - timedelta(days=1)
+    post_start = disaster_date
+    post_end = disaster_date + timedelta(days=post_window_days)
+
+    pre_pool = await _query_disaster_scene_pool(
+        db=db,
+        req=req,
+        aoi_wkt=aoi_wkt,
+        start_ymd=_format_ymd(pre_start),
+        end_ymd=_format_ymd(pre_end),
+        min_aoi_coverage_ratio=min_aoi_coverage_ratio,
+        descending=True,
+    )
+    post_pool = await _query_disaster_scene_pool(
+        db=db,
+        req=req,
+        aoi_wkt=aoi_wkt,
+        start_ymd=_format_ymd(post_start),
+        end_ymd=_format_ymd(post_end),
+        min_aoi_coverage_ratio=min_aoi_coverage_ratio,
+        descending=False,
+    )
+
+    candidates: list[dict[str, Any]] = []
+    require_same_polarization = bool(getattr(req, "require_same_polarization", True))
+    require_same_imaging_mode = bool(getattr(req, "require_same_imaging_mode", False))
+    total_window = max(1, pre_window_days + post_window_days)
+
+    for pre in pre_pool:
+        pre_date = _parse_ymd(pre.get("imaging_date"), field="pre.imaging_date")
+        for post in post_pool:
+            if pre["id"] == post["id"]:
+                continue
+            if require_same_polarization and not _same_text(pre.get("polarization"), post.get("polarization")):
+                continue
+            if require_same_imaging_mode and not _same_text(pre.get("imaging_mode"), post.get("imaging_mode")):
+                continue
+
+            post_date = _parse_ymd(post.get("imaging_date"), field="post.imaging_date")
+            scene_overlap = _overlap_ratio(pre.get("coverage_polygon"), post.get("coverage_polygon"))
+            if scene_overlap < min_pair_overlap_ratio:
+                continue
+
+            pre_delta_days = max(0, (disaster_date - pre_date).days)
+            post_delta_days = max(0, (post_date - disaster_date).days)
+            time_score = max(0.0, 1.0 - ((pre_delta_days + post_delta_days) / total_window))
+            aoi_score = min(_to_float(pre.get("aoi_coverage_ratio")), _to_float(post.get("aoi_coverage_ratio")))
+            score = (scene_overlap * 0.45) + (aoi_score * 0.35) + (time_score * 0.20)
+            candidates.append(
+                {
+                    "pre": pre,
+                    "post": post,
+                    "overlap_ratio": round(scene_overlap, 4),
+                    "aoi_coverage_ratio": round(aoi_score, 4),
+                    "time_score": round(time_score, 4),
+                    "score": round(score, 4),
+                    "pre_delta_days": pre_delta_days,
+                    "post_delta_days": post_delta_days,
+                    "time_diff_days": max(0, (post_date - pre_date).days),
+                    "same_polarization": _same_text(pre.get("polarization"), post.get("polarization")),
+                    "same_imaging_mode": _same_text(pre.get("imaging_mode"), post.get("imaging_mode")),
+                }
+            )
+
+    candidates.sort(
+        key=lambda item: (
+            item["score"],
+            item["overlap_ratio"],
+            item["aoi_coverage_ratio"],
+            -item["time_diff_days"],
+        ),
+        reverse=True,
+    )
+    selected_pairs = candidates[:max_pairs]
+
+    warnings: list[str] = []
+    if not pre_pool:
+        warnings.append("No pre-disaster DONE scenes match the disaster AOI and time window")
+    if not post_pool:
+        warnings.append("No post-disaster DONE scenes match the disaster AOI and time window")
+    if pre_pool and post_pool and not selected_pairs:
+        warnings.append("Pre/post scene pools exist, but no pair meets the overlap/polarization constraints")
+
+    return {
+        "disaster": {
+            "name": getattr(req, "disaster_name", None),
+            "date": _format_ymd(disaster_date),
+            "pre_start": _format_ymd(pre_start),
+            "pre_end": _format_ymd(pre_end),
+            "post_start": _format_ymd(post_start),
+            "post_end": _format_ymd(post_end),
+        },
+        "aoi": {
+            **aoi_meta,
+            "geojson": aoi_geojson,
+        },
+        "pre_pool": pre_pool,
+        "post_pool": post_pool,
+        "candidate_pairs": selected_pairs,
+        "pairs": selected_pairs,
+        "total": len(selected_pairs),
+        "summary": {
+            "pre_pool_count": len(pre_pool),
+            "post_pool_count": len(post_pool),
+            "candidate_count": len(selected_pairs),
+            "min_aoi_coverage_ratio": min_aoi_coverage_ratio,
+            "min_pair_overlap_ratio": min_pair_overlap_ratio,
+        },
+        "warnings": warnings,
+    }
+
+
+def _open_envi_rasterio(path: str):
+    import rasterio
+
+    normalized_path = path.replace("\\", "/")
+    try:
+        return rasterio.open(normalized_path)
+    except Exception:
+        pass
+    for ext in (".bin", ".img", ".tif", ".tiff"):
+        try:
+            return rasterio.open(normalized_path + ext)
+        except Exception:
+            pass
+    raise FileNotFoundError(f"Raster file cannot be opened: {path}")
+
+
+def _raster_to_png_bytes(path: str, colormap: dict[int, tuple[int, int, int, int]]) -> tuple[bytes, list[float]]:
+    import numpy as np
+    from PIL import Image
+
+    with _open_envi_rasterio(path) as ds:
+        data = ds.read(1)
+        bounds = ds.bounds
+        geo_bounds = [bounds.bottom, bounds.left, bounds.top, bounds.right]
+
+    rgba = np.zeros((data.shape[0], data.shape[1], 4), dtype=np.uint8)
+    for value, color in colormap.items():
+        rgba[data == value] = color
+    image = Image.fromarray(rgba, "RGBA")
+    buffer = io.BytesIO()
+    image.save(buffer, format="PNG")
+    return buffer.getvalue(), geo_bounds
+
+
+def _geo_raster_to_png_bytes(path: str) -> tuple[bytes, list[float]]:
+    import numpy as np
+    from PIL import Image
+
+    with _open_envi_rasterio(path) as ds:
+        data = ds.read(1).astype("float32")
+        nodata = ds.nodata
+        bounds = ds.bounds
+        geo_bounds = [bounds.bottom, bounds.left, bounds.top, bounds.right]
+
+    if nodata is not None:
+        nodata_mask = (data == nodata) | ~np.isfinite(data)
+    else:
+        nodata_mask = ~np.isfinite(data)
+
+    valid = data[~nodata_mask]
+    if valid.size == 0:
+        normalized = np.zeros_like(data, dtype=np.uint8)
+    else:
+        p2, p98 = np.percentile(valid, 2), np.percentile(valid, 98)
+        clipped = np.clip(data, p2, p98)
+        normalized = ((clipped - p2) / max(p98 - p2, 1e-9) * 255).astype(np.uint8)
+
+    rgba = np.stack([normalized, normalized, normalized, np.full_like(normalized, 200)], axis=-1)
+    rgba[nodata_mask, 3] = 0
+    image = Image.fromarray(rgba, "RGBA")
+    buffer = io.BytesIO()
+    image.save(buffer, format="PNG")
+    return buffer.getvalue(), geo_bounds
+
+
+_FLOOD_COLORMAP = {
+    1: (24, 144, 255, 200),
+    2: (255, 77, 79, 220),
+    3: (250, 173, 20, 180),
+    4: (80, 80, 80, 80),
+}
+
+
+async def get_flood_detection_preview(detection_id: int, layer: str, db: AsyncSession):
+    normalized_layer = layer.strip().lower()
+    if normalized_layer == "classified":
+        detection = await db.get(FloodDetectionORM, detection_id)
+        if not detection or not detection.classified_path:
+            raise HTTPException(status_code=404, detail="Classified result not found")
+        path = detection.classified_path.replace("\\", "/")
+        png_bytes, geo_bounds = await _render_classified_preview(path)
+        return JSONResponse(
+            {
+                "image_b64": base64.b64encode(png_bytes).decode(),
+                "bounds": geo_bounds,
+                "legend": {
+                    "stable_water": "#1890ff",
+                    "flood": "#ff4d4f",
+                    "high_backscatter": "#faad14",
+                    "non_water": "#505050",
+                },
+            }
+        )
+    if normalized_layer in ("pre", "post"):
+        return await _get_scene_preview_for_detection(detection_id, normalized_layer, db)
+    raise HTTPException(status_code=404, detail=f"Unsupported preview layer: {layer}")
+
+
+async def _render_classified_preview(path: str) -> tuple[bytes, list[float]]:
+    if not os.path.isfile(path):
+        raise HTTPException(status_code=404, detail="Requested file does not exist")
+    try:
+        return await asyncio.to_thread(_raster_to_png_bytes, path, _FLOOD_COLORMAP)
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=f"Render failed: {exc}") from exc
+
+
+async def _get_scene_preview_for_detection(detection_id: int, layer: str, db: AsyncSession):
+    detection = await db.get(FloodDetectionORM, detection_id)
+    if not detection:
+        raise HTTPException(status_code=404, detail="Flood detection not found")
+    scene_id = detection.pre_scene_id if layer == "pre" else detection.post_scene_id
+    scene = await db.get(SARSceneGeoORM, scene_id)
+    scene_path = _scene_analysis_path(scene)
+    if not scene_path:
+        raise HTTPException(status_code=404, detail="Scene analysis-ready GeoTIFF not found")
+    path = scene_path.replace("\\", "/")
+    if not os.path.isfile(path):
+        raise HTTPException(status_code=404, detail="Requested file does not exist")
+    try:
+        png_bytes, geo_bounds = await asyncio.to_thread(_geo_raster_to_png_bytes, path)
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=f"Render failed: {exc}") from exc
+    return JSONResponse({"image_b64": base64.b64encode(png_bytes).decode(), "bounds": geo_bounds})
+
+
+async def get_water_extraction_preview(extraction_id: int, db: AsyncSession) -> dict[str, Any]:
+    detection = await db.get(WaterExtractionORM, extraction_id)
+    if not detection:
+        detection = await db.get(WaterDetectionORM, extraction_id)
+    if not detection:
+        raise HTTPException(status_code=404, detail=f"Water extraction id={extraction_id} not found")
+    if not detection.output_path or not os.path.isfile(detection.output_path):
+        raise HTTPException(status_code=404, detail="Output file does not exist")
+
+    import numpy as np
+    import rasterio
+    from PIL import Image
+
+    with rasterio.open(detection.output_path) as src:
+        data = src.read(1)
+        transform = src.transform
+        height, width = data.shape
+        min_lon = transform.c
+        max_lon = transform.c + width * transform.a
+        max_lat = transform.f
+        min_lat = transform.f + height * transform.e
+
+    rgba = np.zeros((data.shape[0], data.shape[1], 4), dtype=np.uint8)
+    rgba[data > 0] = [24, 144, 255, 160]
+
+    image = Image.fromarray(rgba, "RGBA")
+    max_dim = 1024
+    if max(width, height) > max_dim:
+        ratio = max_dim / max(width, height)
+        image = image.resize((int(width * ratio), int(height * ratio)), Image.NEAREST)
+
+    buffer = io.BytesIO()
+    image.save(buffer, format="PNG")
+    return {
+        "png_base64": base64.b64encode(buffer.getvalue()).decode(),
+        "bounds": {
+            "min_lon": min_lon,
+            "min_lat": min_lat,
+            "max_lon": max_lon,
+            "max_lat": max_lat,
+        },
+    }

backend/app/services/flood_detection_service.py

@@ -0,0 +1,327 @@
+"""Pure GeoTIFF flood detection for the flood-analysis module.
+
+This service deliberately does not depend on ENVI/SARscape. Satellite-specific
+preprocessors are responsible only for producing analysis-ready GeoTIFFs; the
+flood classification below operates on those GeoTIFFs with Python/rasterio.
+"""
+from __future__ import annotations
+
+import json
+import math
+import os
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+
+def _valid_mask(data: np.ndarray, nodata: float | int | None) -> np.ndarray:
+    valid = np.isfinite(data)
+    if nodata is not None and np.isfinite(float(nodata)):
+        valid &= data != float(nodata)
+    return valid
+
+
+def _sample_valid(values: np.ndarray, max_samples: int = 1_000_000) -> np.ndarray:
+    flat = values[np.isfinite(values)]
+    if flat.size <= max_samples:
+        return flat
+    step = max(1, int(math.ceil(flat.size / max_samples)))
+    return flat[::step]
+
+
+def _otsu_threshold(values: np.ndarray) -> float:
+    sample = _sample_valid(values)
+    if sample.size < 100:
+        raise ValueError("Too few valid pixels for thresholding")
+    manual_threshold = _manual_otsu_threshold(sample)
+    try:
+        from skimage.filters import threshold_otsu
+
+        skimage_threshold = float(threshold_otsu(sample))
+        if np.isfinite(skimage_threshold):
+            p05, p95 = np.nanpercentile(sample, [5, 95])
+            if p05 < skimage_threshold < p95:
+                return skimage_threshold
+        return manual_threshold
+    except Exception:
+        return manual_threshold
+
+
+def _manual_otsu_threshold(values: np.ndarray) -> float:
+    sample = _sample_valid(values)
+    if sample.size < 100:
+        raise ValueError("Too few valid pixels for thresholding")
+    vmin = float(np.nanmin(sample))
+    vmax = float(np.nanmax(sample))
+    if not np.isfinite(vmin) or not np.isfinite(vmax):
+        raise ValueError("Input pixels are not finite")
+    if math.isclose(vmin, vmax):
+        return vmin
+
+    hist, edges = np.histogram(sample, bins=256, range=(vmin, vmax))
+    hist = hist.astype("float64")
+    centers = (edges[:-1] + edges[1:]) / 2.0
+    total = hist.sum()
+    if total <= 0:
+        return float(np.nanpercentile(sample, 10))
+
+    weight_background = np.cumsum(hist)
+    weight_foreground = total - weight_background
+    mean_background = np.cumsum(hist * centers) / np.maximum(weight_background, 1e-12)
+    mean_foreground = (
+        np.cumsum((hist * centers)[::-1]) / np.maximum(np.cumsum(hist[::-1]), 1e-12)
+    )[::-1]
+    variance = weight_background[:-1] * weight_foreground[:-1] * (
+        mean_background[:-1] - mean_foreground[1:]
+    ) ** 2
+    if variance.size == 0 or not np.isfinite(variance).any():
+        return float(np.nanpercentile(sample, 10))
+    idx = int(np.nanargmax(variance))
+    return float(edges[idx + 1])
+
+
+def _pixel_area_km2(transform: Any, crs: Any, bounds: Any) -> float:
+    px_w = abs(float(transform.a))
+    px_h = abs(float(transform.e))
+    if crs and getattr(crs, "is_geographic", False):
+        lat_center = (float(bounds.top) + float(bounds.bottom)) / 2.0
+        px_w_m = px_w * math.cos(math.radians(lat_center)) * 111_320.0
+        px_h_m = px_h * 111_320.0
+    else:
+        px_w_m, px_h_m = px_w, px_h
+    return max(0.0, (px_w_m * px_h_m) / 1_000_000.0)
+
+
+def _clean_mask(mask: np.ndarray, min_pixels: int) -> np.ndarray:
+    try:
+        from scipy.ndimage import binary_closing, binary_opening, generate_binary_structure, label
+    except Exception:
+        return mask
+
+    structure = generate_binary_structure(2, 2)
+    cleaned = binary_closing(mask, structure=structure, iterations=1)
+    cleaned = binary_opening(cleaned, structure=structure, iterations=1)
+    if min_pixels <= 1:
+        return cleaned
+
+    labels, count = label(cleaned)
+    if count <= 0:
+        return cleaned
+    component_sizes = np.bincount(labels.ravel())
+    keep = component_sizes >= int(min_pixels)
+    keep[0] = False
+    return keep[labels]
+
+
+def _read_pre_on_post_grid(pre_path: str, post_profile: dict[str, Any]) -> tuple[np.ndarray, dict[str, Any]]:
+    import rasterio
+    from rasterio.enums import Resampling
+    from rasterio.warp import reproject
+
+    with rasterio.open(pre_path) as pre_ds:
+        pre_data = pre_ds.read(1).astype("float32")
+        pre_nodata = pre_ds.nodata
+        same_grid = (
+            pre_ds.width == int(post_profile["width"])
+            and pre_ds.height == int(post_profile["height"])
+            and pre_ds.transform == post_profile["transform"]
+            and str(pre_ds.crs or "") == str(post_profile["crs"] or "")
+        )
+        metadata = {
+            "path": pre_path,
+            "crs": pre_ds.crs.to_string() if pre_ds.crs else None,
+            "width": pre_ds.width,
+            "height": pre_ds.height,
+            "nodata": pre_nodata,
+            "reprojected_to_post_grid": not same_grid,
+        }
+        if same_grid:
+            data = pre_data.astype("float32")
+            if pre_nodata is not None and np.isfinite(float(pre_nodata)):
+                data[data == float(pre_nodata)] = np.nan
+            return data, metadata
+        if not pre_ds.crs or not post_profile["crs"]:
+            raise ValueError("Pre/post GeoTIFF CRS is required when grids differ")
+        destination = np.full(
+            (int(post_profile["height"]), int(post_profile["width"])),
+            np.nan,
+            dtype="float32",
+        )
+        reproject(
+            source=pre_data,
+            destination=destination,
+            src_transform=pre_ds.transform,
+            src_crs=pre_ds.crs,
+            src_nodata=pre_nodata,
+            dst_transform=post_profile["transform"],
+            dst_crs=post_profile["crs"],
+            dst_nodata=np.nan,
+            resampling=Resampling.bilinear,
+        )
+        return destination, metadata
+
+
+def run_geotiff_flood_detection(
+    *,
+    pre_tif_path: str,
+    post_tif_path: str,
+    output_dir: str,
+    job_id: str | None = None,
+    refine: bool = False,
+) -> dict[str, Any]:
+    """Classify stable water and new flood extent from two analysis-ready GeoTIFFs."""
+    import rasterio
+
+    pre_path = Path(os.path.normpath(str(pre_tif_path or "").strip()))
+    post_path = Path(os.path.normpath(str(post_tif_path or "").strip()))
+    out_dir = Path(os.path.normpath(str(output_dir or "").strip()))
+    if not pre_path.is_file():
+        return {"ok": False, "error": f"Pre-event analysis GeoTIFF not found: {pre_path}"}
+    if not post_path.is_file():
+        return {"ok": False, "error": f"Post-event analysis GeoTIFF not found: {post_path}"}
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    with rasterio.open(post_path) as post_ds:
+        post_data = post_ds.read(1).astype("float32")
+        post_nodata = post_ds.nodata
+        post_profile = post_ds.profile.copy()
+        post_grid = {
+            "height": post_ds.height,
+            "width": post_ds.width,
+            "transform": post_ds.transform,
+            "crs": post_ds.crs,
+        }
+        post_metadata = {
+            "path": str(post_path),
+            "crs": post_ds.crs.to_string() if post_ds.crs else None,
+            "width": post_ds.width,
+            "height": post_ds.height,
+            "nodata": post_nodata,
+        }
+        pixel_area_km2 = _pixel_area_km2(post_ds.transform, post_ds.crs, post_ds.bounds)
+
+    pre_data, pre_metadata = _read_pre_on_post_grid(str(pre_path), post_grid)
+    valid_pre = _valid_mask(pre_data, None)
+    valid_post = _valid_mask(post_data, post_nodata)
+    valid = valid_pre & valid_post
+    if int(np.count_nonzero(valid)) < 100:
+        return {"ok": False, "error": "Too few overlapping valid pixels between pre/post GeoTIFFs"}
+
+    pre_valid_values = pre_data[valid]
+    post_valid_values = post_data[valid]
+    pre_threshold = _otsu_threshold(pre_valid_values)
+    post_threshold = _otsu_threshold(post_valid_values)
+
+    pre_water = (pre_data <= pre_threshold) & valid
+    post_water = (post_data <= post_threshold) & valid
+    stable_water = pre_water & post_water
+    flood = post_water & ~pre_water
+
+    if refine:
+        min_pixels = max(4, int(round(3_000.0 / max(pixel_area_km2 * 1_000_000.0, 1.0))))
+        stable_water = _clean_mask(stable_water, min_pixels=min_pixels)
+        flood = _clean_mask(flood, min_pixels=min_pixels)
+
+    high_threshold = float(np.nanpercentile(post_valid_values, 98))
+    high_backscatter = (post_data >= high_threshold) & valid & ~(stable_water | flood)
+
+    classified = np.zeros(post_data.shape, dtype="uint8")
+    classified[valid] = 4
+    classified[high_backscatter] = 3
+    classified[stable_water] = 1
+    classified[flood] = 2
+
+    classified_path = out_dir / "classified.tif"
+    flood_mask_path = out_dir / "flood_mask.tif"
+    stable_mask_path = out_dir / "stable_water_mask.tif"
+
+    classified_profile = post_profile.copy()
+    classified_profile.update(
+        driver="GTiff",
+        dtype="uint8",
+        count=1,
+        nodata=0,
+        compress="deflate",
+    )
+    with rasterio.open(classified_path, "w", **classified_profile) as dst:
+        dst.write(classified, 1)
+        try:
+            dst.write_colormap(
+                1,
+                {
+                    0: (0, 0, 0, 0),
+                    1: (24, 144, 255, 255),
+                    2: (255, 77, 79, 255),
+                    3: (250, 173, 20, 255),
+                    4: (80, 80, 80, 255),
+                },
+            )
+        except Exception:
+            pass
+
+    mask_profile = classified_profile.copy()
+    mask_profile.update(nodata=0)
+    with rasterio.open(flood_mask_path, "w", **mask_profile) as dst:
+        dst.write(np.where(flood, 255, 0).astype("uint8"), 1)
+    with rasterio.open(stable_mask_path, "w", **mask_profile) as dst:
+        dst.write(np.where(stable_water, 255, 0).astype("uint8"), 1)
+
+    flood_pixels = int(np.count_nonzero(flood))
+    stable_pixels = int(np.count_nonzero(stable_water))
+    high_pixels = int(np.count_nonzero(high_backscatter))
+    non_water_pixels = int(np.count_nonzero(classified == 4))
+    metadata = {
+        "schema": "flood_detection_geotiff.v1",
+        "job_id": job_id,
+        "processor": "python_geotiff_otsu_change",
+        "refine": bool(refine),
+        "pre": pre_metadata,
+        "post": post_metadata,
+        "thresholds": {
+            "pre_water_threshold": pre_threshold,
+            "post_water_threshold": post_threshold,
+            "post_high_backscatter_threshold": high_threshold,
+        },
+        "pixel_area_km2": pixel_area_km2,
+        "class_values": {
+            "0": "nodata",
+            "1": "stable_water",
+            "2": "flood",
+            "3": "high_backscatter",
+            "4": "non_water",
+        },
+        "counts": {
+            "valid_pixels": int(np.count_nonzero(valid)),
+            "stable_water_pixels": stable_pixels,
+            "flood_pixels": flood_pixels,
+            "high_backscatter_pixels": high_pixels,
+            "non_water_pixels": non_water_pixels,
+        },
+        "outputs": {
+            "classified_path": str(classified_path),
+            "flood_mask_path": str(flood_mask_path),
+            "stable_water_mask_path": str(stable_mask_path),
+        },
+    }
+    metadata_path = out_dir / "metadata.json"
+    metadata_path.write_text(json.dumps(metadata, ensure_ascii=False, indent=2, default=str), encoding="utf-8")
+
+    return {
+        "ok": True,
+        "classified_path": str(classified_path),
+        "flood_mask_path": str(flood_mask_path),
+        "stable_water_mask_path": str(stable_mask_path),
+        "metadata_path": str(metadata_path),
+        "flood_area_km2": round(flood_pixels * pixel_area_km2, 4),
+        "stable_water_area_km2": round(stable_pixels * pixel_area_km2, 4),
+        "flood_pixel_count": flood_pixels,
+        "stable_water_pixel_count": stable_pixels,
+        "processor": "python_geotiff_otsu_change",
+        "log": [
+            "pre/post analysis-ready GeoTIFFs loaded",
+            "pre scene reprojected to post-event grid",
+            f"thresholds pre={pre_threshold:.4f}, post={post_threshold:.4f}",
+            f"flood_pixels={flood_pixels}, stable_water_pixels={stable_pixels}",
+        ],
+    }

backend/app/services/flood_overlay_service.py

@@ -0,0 +1,389 @@
+"""Flood overlay and impact analysis service."""
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+from typing import Any
+
+from fastapi import HTTPException
+from geoalchemy2.functions import ST_Intersects
+from geoalchemy2.shape import from_shape
+from sqlalchemy import func, select
+from sqlalchemy.ext.asyncio import AsyncSession
+from shapely.geometry import mapping, shape
+from shapely.ops import unary_union
+
+from ..config import settings
+from ..models import FloodDetectionORM, FloodOverlayORM, HazardPointORM, ResultProductORM
+
+
+def _to_float(value: Any) -> float | None:
+    try:
+        if value is None:
+            return None
+        return float(value)
+    except Exception:
+        return None
+
+
+def _hazard_point_to_dict(point: HazardPointORM, *, distance_m: float | None = None) -> dict[str, Any]:
+    return {
+        "id": point.id,
+        "name": point.hazard_name,
+        "type": point.hazard_type,
+        "city": point.city,
+        "county": point.county,
+        "township": point.township,
+        "longitude": _to_float(point.longitude),
+        "latitude": _to_float(point.latitude),
+        "distance_m": 0 if distance_m is None else round(float(distance_m), 2),
+    }
+
+
+def _dinsar_product_to_dict(product: ResultProductORM) -> dict[str, Any]:
+    summary = product.summary_json if isinstance(product.summary_json, dict) else {}
+    deformation = (
+        summary.get("deformation_mm")
+        or summary.get("max_deformation_mm")
+        or summary.get("mean_deformation_mm")
+        or summary.get("deformation")
+    )
+    return {
+        "id": product.id,
+        "product_id": product.product_id,
+        "display_name": product.display_name,
+        "engine": product.engine_code,
+        "status": product.status,
+        "deformation_mm": deformation,
+        "ai_score": product.ai_score,
+        "manifest_path": product.manifest_path,
+        "preview_path": product.preview_path,
+    }
+
+
+def _open_raster(path: str):
+    import rasterio
+
+    normalized_path = path.replace("\\", "/")
+    try:
+        return rasterio.open(normalized_path)
+    except Exception:
+        pass
+    for ext in (".bin", ".img", ".tif", ".tiff"):
+        candidate = normalized_path + ext
+        try:
+            return rasterio.open(candidate)
+        except Exception:
+            pass
+    raise FileNotFoundError(f"Raster file cannot be opened: {path}")
+
+
+def _classified_flood_to_geojson(path: str) -> tuple[dict[str, Any], float | None, list[str]]:
+    import rasterio.features
+    from pyproj import CRS, Transformer
+    from shapely.geometry import shape as shape_geojson
+    from shapely.ops import transform
+
+    warnings: list[str] = []
+    with _open_raster(path) as src:
+        data = src.read(1)
+        mask = data == 2
+        if not mask.any():
+            return {"type": "FeatureCollection", "features": []}, 0.0, warnings
+
+        polygons = []
+        for geom, value in rasterio.features.shapes(data, mask=mask, transform=src.transform):
+            if int(value) != 2:
+                continue
+            polygon = shape_geojson(geom)
+            if not polygon.is_empty and polygon.is_valid:
+                polygons.append(polygon)
+
+        if not polygons:
+            return {"type": "FeatureCollection", "features": []}, 0.0, warnings
+
+        flood_geom = unary_union(polygons)
+        source_crs = src.crs
+
+    area_km2 = None
+    output_geom = flood_geom
+    if source_crs:
+        try:
+            crs = CRS.from_user_input(source_crs)
+            if not crs.is_geographic:
+                area_km2 = float(flood_geom.area) / 1_000_000.0
+                transformer = Transformer.from_crs(crs, CRS.from_epsg(4326), always_xy=True)
+                output_geom = transform(transformer.transform, flood_geom)
+            else:
+                centroid = flood_geom.centroid
+                zone = int((centroid.x + 180) // 6) + 1
+                epsg = 32600 + zone if centroid.y >= 0 else 32700 + zone
+                transformer = Transformer.from_crs(crs, CRS.from_epsg(epsg), always_xy=True)
+                projected = transform(transformer.transform, flood_geom)
+                area_km2 = float(projected.area) / 1_000_000.0
+        except Exception as exc:
+            warnings.append(f"area calculation failed: {exc}")
+    else:
+        warnings.append("classified raster has no CRS; geometry is stored in source coordinates")
+
+    feature_collection = {
+        "type": "FeatureCollection",
+        "features": [
+            {
+                "type": "Feature",
+                "properties": {"class": 2, "name": "flood"},
+                "geometry": mapping(output_geom),
+            }
+        ],
+    }
+    return feature_collection, area_km2, warnings
+
+
+def _write_geojson(detection_id: int, feature_collection: dict[str, Any]) -> str:
+    out_dir = Path(settings.WATER_RESULTS_DIR or Path(settings.BACKEND_DIR) / "water_results") / "flood_overlays"
+    out_dir.mkdir(parents=True, exist_ok=True)
+    target = out_dir / f"flood_detection_{detection_id}_overlay.geojson"
+    target.write_text(json.dumps(feature_collection, ensure_ascii=False, indent=2), encoding="utf-8")
+    return str(target)
+
+
+def _read_geojson(path: str | None) -> dict[str, Any] | None:
+    if not path or not os.path.isfile(path):
+        return None
+    try:
+        with open(path, "r", encoding="utf-8") as stream:
+            payload = json.load(stream)
+        return payload if isinstance(payload, dict) else None
+    except Exception:
+        return None
+
+
+def _geometry_area_km2(geom) -> float:
+    if geom.is_empty:
+        return 0.0
+    try:
+        from pyproj import CRS, Transformer
+        from shapely.ops import transform
+
+        centroid = geom.centroid
+        zone = int((centroid.x + 180) // 6) + 1
+        epsg = 32600 + zone if centroid.y >= 0 else 32700 + zone
+        transformer = Transformer.from_crs(CRS.from_epsg(4326), CRS.from_epsg(epsg), always_xy=True)
+        projected = transform(transformer.transform, geom)
+        return float(projected.area) / 1_000_000.0
+    except Exception:
+        return 0.0
+
+
+def _calculate_affected_aois(flood_geom, warnings: list[str], *, limit: int = 50) -> list[dict[str, Any]]:
+    try:
+        from ..routers import dependencies as deps
+
+        deps._load_region_index()
+        deps._load_region_geometry_index()
+        region_by_id = deps._REGION_BY_ID_CACHE or {}
+        geometry_by_id = deps._REGION_GEOMETRY_BY_ID_CACHE or {}
+    except Exception as exc:
+        warnings.append(f"AOI overlay unavailable: {exc}")
+        return []
+
+    affected: list[dict[str, Any]] = []
+    for tree_id, features in geometry_by_id.items():
+        node = region_by_id.get(tree_id) or {}
+        level = node.get("level")
+        if level in {"country", "province"}:
+            continue
+        try:
+            geometries = [shape(feature["geometry"]) for feature in features if feature.get("geometry")]
+            if not geometries:
+                continue
+            region_geom = unary_union(geometries)
+            if region_geom.is_empty or not flood_geom.intersects(region_geom):
+                continue
+            intersection = flood_geom.intersection(region_geom)
+            area_km2 = _geometry_area_km2(intersection)
+            if area_km2 <= 0.0001:
+                continue
+            affected.append(
+                {
+                    "tree_id": tree_id,
+                    "name": node.get("name") or tree_id,
+                    "level": level,
+                    "flood_area_km2": round(area_km2, 4),
+                }
+            )
+        except Exception:
+            continue
+
+    affected.sort(key=lambda item: item["flood_area_km2"], reverse=True)
+    return affected[:limit]
+
+
+def _attach_overlay_payload(overlay: FloodOverlayORM) -> dict[str, Any]:
+    payload = dict(overlay.summary_json) if isinstance(overlay.summary_json, dict) else {}
+    payload["overlay_id"] = overlay.id
+    payload["detection_id"] = overlay.detection_id
+    payload["flood_vector_path"] = overlay.flood_vector_path
+    payload["flood_vector_geojson"] = _read_geojson(overlay.flood_vector_path)
+    return payload
+
+
+async def run_overlay(detection_id: int, db: AsyncSession, *, near_threshold_m: float = 500.0) -> dict[str, Any]:
+    detection = await db.get(FloodDetectionORM, detection_id)
+    if not detection:
+        raise HTTPException(status_code=404, detail=f"Flood detection id={detection_id} not found")
+    if not detection.classified_path:
+        raise HTTPException(status_code=400, detail="Flood detection has no classified raster")
+    path = detection.classified_path.replace("\\", "/")
+    if not os.path.isfile(path):
+        raise HTTPException(status_code=404, detail="Classified raster file does not exist")
+
+    feature_collection, flood_area_km2, warnings = _classified_flood_to_geojson(path)
+    flood_vector_path = _write_geojson(detection_id, feature_collection)
+    impact = await _query_impact_from_geojson(
+        detection_id=detection_id,
+        feature_collection=feature_collection,
+        db=db,
+        near_threshold_m=near_threshold_m,
+        warnings=warnings,
+    )
+    if flood_area_km2 is not None:
+        impact["flood_area_km2"] = round(flood_area_km2, 4)
+    impact["flood_vector_path"] = flood_vector_path
+
+    overlay = FloodOverlayORM(
+        detection_id=detection_id,
+        flood_vector_path=flood_vector_path,
+        hazard_points_hit=len(impact["hazard_points"]["inside_flood"]),
+        hazard_points_near=len(impact["hazard_points"]["near_flood"]),
+        hazard_points_total=impact["hazard_points"]["total_in_scene"],
+        dinsar_products_intersecting=len(impact["dinsar_products"]),
+        affected_area_km2=impact.get("flood_area_km2"),
+        summary_json=impact,
+    )
+    db.add(overlay)
+    await db.flush()
+    impact["overlay_id"] = overlay.id
+    overlay.summary_json = impact
+    if flood_area_km2 is not None:
+        detection.flood_area_km2 = round(flood_area_km2, 4)
+    await db.commit()
+    await db.refresh(overlay)
+
+    return {
+        "id": overlay.id,
+        "detection_id": detection_id,
+        "flood_vector_path": overlay.flood_vector_path,
+        "flood_vector_geojson": feature_collection,
+        "summary": _attach_overlay_payload(overlay),
+    }
+
+
+async def get_overlay_result(detection_id: int, db: AsyncSession) -> dict[str, Any]:
+    overlay = (
+        await db.execute(
+            select(FloodOverlayORM)
+            .where(FloodOverlayORM.detection_id == detection_id)
+            .order_by(FloodOverlayORM.id.desc())
+        )
+    ).scalars().first()
+    if overlay and isinstance(overlay.summary_json, dict):
+        return _attach_overlay_payload(overlay)
+
+    detection = await db.get(FloodDetectionORM, detection_id)
+    if not detection:
+        raise HTTPException(status_code=404, detail=f"Flood detection id={detection_id} not found")
+    return {
+        "detection_id": detection_id,
+        "flood_area_km2": detection.flood_area_km2,
+        "hazard_points": {"inside_flood": [], "near_flood": [], "total_in_scene": 0},
+        "dinsar_products": [],
+        "affected_aois": [],
+        "flood_vector_path": None,
+        "flood_vector_geojson": None,
+        "warnings": ["overlay has not been run"],
+    }
+
+
+async def _query_impact_from_geojson(
+    *,
+    detection_id: int,
+    feature_collection: dict[str, Any],
+    db: AsyncSession,
+    near_threshold_m: float,
+    warnings: list[str],
+) -> dict[str, Any]:
+    features = feature_collection.get("features") or []
+    if not features:
+        return {
+            "detection_id": detection_id,
+            "flood_area_km2": 0.0,
+            "hazard_points": {"inside_flood": [], "near_flood": [], "total_in_scene": 0},
+            "dinsar_products": [],
+            "affected_aois": [],
+            "warnings": warnings,
+        }
+
+    flood_geom = unary_union([shape(feature["geometry"]) for feature in features if feature.get("geometry")])
+    if flood_geom.is_empty:
+        warnings.append("flood geometry is empty")
+    flood_wkt = flood_geom.wkt
+    area_geom = func.ST_GeomFromText(flood_wkt, 4326)
+    area_geog = func.Geography(area_geom)
+
+    inside_points: list[dict[str, Any]] = []
+    near_points: list[dict[str, Any]] = []
+    dinsar_products: list[dict[str, Any]] = []
+    affected_aois = _calculate_affected_aois(flood_geom, warnings)
+
+    try:
+        inside_rows = (
+            await db.execute(
+                select(HazardPointORM).where(ST_Intersects(HazardPointORM.geom, area_geom))
+            )
+        ).scalars().all()
+        inside_ids = {point.id for point in inside_rows}
+        inside_points = [_hazard_point_to_dict(point, distance_m=0) for point in inside_rows]
+
+        near_rows = (
+            await db.execute(
+                select(
+                    HazardPointORM,
+                    func.ST_Distance(func.Geography(HazardPointORM.geom), area_geog).label("distance_m"),
+                ).where(func.ST_DWithin(func.Geography(HazardPointORM.geom), area_geog, near_threshold_m))
+            )
+        ).all()
+        for point, distance_m in near_rows:
+            if point.id in inside_ids:
+                continue
+            near_points.append(_hazard_point_to_dict(point, distance_m=distance_m))
+    except Exception as exc:
+        warnings.append(f"hazard point overlay unavailable: {exc}")
+
+    try:
+        products = (
+            await db.execute(
+                select(ResultProductORM).where(
+                    ResultProductORM.catalog_name == "dinsar",
+                    ResultProductORM.status == "READY",
+                    ST_Intersects(ResultProductORM.geom, area_geom),
+                )
+            )
+        ).scalars().all()
+        dinsar_products = [_dinsar_product_to_dict(product) for product in products]
+    except Exception as exc:
+        warnings.append(f"dinsar product overlay unavailable: {exc}")
+
+    return {
+        "detection_id": detection_id,
+        "flood_area_km2": None,
+        "hazard_points": {
+            "inside_flood": inside_points,
+            "near_flood": near_points,
+            "total_in_scene": len(inside_points) + len(near_points),
+        },
+        "dinsar_products": dinsar_products,
+        "affected_aois": affected_aois,
+        "warnings": warnings,
+    }

backend/app/services/flood_product_service.py

@@ -0,0 +1,199 @@
+"""Flood product listing and manifest helpers."""
+from __future__ import annotations
+
+import json
+import os
+from datetime import datetime
+from typing import Any
+
+from fastapi import HTTPException
+from sqlalchemy import select, func
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.orm import selectinload
+
+from ..models import FloodDetectionORM, FloodOverlayORM, FloodProductORM, SARSceneGeoORM
+
+
+def _iso(value: Any) -> str | None:
+    return value.isoformat() if value else None
+
+
+def _product_to_dict(product: FloodProductORM) -> dict[str, Any]:
+    summary = product.summary_json if isinstance(product.summary_json, dict) else {}
+    detection = product.detection
+    overlay = product.overlay
+    return {
+        "id": product.id,
+        "product_id": product.product_id,
+        "detection_id": product.detection_id,
+        "overlay_id": product.overlay_id,
+        "display_name": product.display_name,
+        "status": product.status,
+        "publish_dir": product.publish_dir,
+        "manifest_path": product.manifest_path,
+        "summary": summary,
+        "created_at": _iso(product.created_at),
+        "flood_area_km2": getattr(detection, "flood_area_km2", None),
+        "affected_area_km2": getattr(overlay, "affected_area_km2", None),
+    }
+
+
+async def list_flood_products(
+    *,
+    db: AsyncSession,
+    limit: int = 20,
+    offset: int = 0,
+    status: str | None = None,
+) -> dict[str, Any]:
+    count_query = select(func.count()).select_from(FloodProductORM)
+    query = (
+        select(FloodProductORM)
+        .options(
+            selectinload(FloodProductORM.detection),
+            selectinload(FloodProductORM.overlay),
+        )
+        .order_by(FloodProductORM.id.desc())
+        .limit(limit)
+        .offset(offset)
+    )
+    if status:
+        count_query = count_query.where(FloodProductORM.status == status)
+        query = query.where(FloodProductORM.status == status)
+
+    total = (await db.execute(count_query)).scalar_one()
+    rows = (await db.execute(query)).scalars().all()
+    return {"items": [_product_to_dict(row) for row in rows], "total": total}
+
+
+async def get_flood_product(product_id_or_pk: str, db: AsyncSession) -> dict[str, Any]:
+    product = await _get_product(product_id_or_pk, db)
+    return _product_to_dict(product)
+
+
+async def get_flood_product_manifest(product_id_or_pk: str, db: AsyncSession) -> dict[str, Any]:
+    product = await _get_product(product_id_or_pk, db)
+    if product.manifest_path and os.path.isfile(product.manifest_path):
+        try:
+            with open(product.manifest_path, "r", encoding="utf-8") as stream:
+                payload = json.load(stream)
+            if isinstance(payload, dict):
+                return payload
+        except Exception as exc:
+            raise HTTPException(status_code=500, detail=f"Failed to read manifest: {exc}") from exc
+
+    return _build_manifest_from_db(product)
+
+
+async def create_flood_product_for_detection(detection_id: int, db: AsyncSession) -> dict[str, Any]:
+    detection = await db.get(FloodDetectionORM, detection_id)
+    if not detection:
+        raise HTTPException(status_code=404, detail=f"Flood detection id={detection_id} not found")
+    if detection.status != "DONE":
+        raise HTTPException(status_code=400, detail=f"Flood detection is not DONE: {detection.status}")
+
+    existing = (
+        await db.execute(
+            select(FloodProductORM)
+            .options(
+                selectinload(FloodProductORM.detection),
+                selectinload(FloodProductORM.overlay),
+            )
+            .where(FloodProductORM.detection_id == detection_id)
+            .order_by(FloodProductORM.id.desc())
+        )
+    ).scalars().first()
+    if existing:
+        return _product_to_dict(existing)
+
+    overlay = (
+        await db.execute(
+            select(FloodOverlayORM)
+            .where(FloodOverlayORM.detection_id == detection_id)
+            .order_by(FloodOverlayORM.id.desc())
+        )
+    ).scalars().first()
+    product = FloodProductORM(
+        product_id=f"FLOOD-{detection_id:06d}",
+        detection_id=detection_id,
+        overlay_id=overlay.id if overlay else None,
+        display_name=f"Flood detection #{detection_id}",
+        status="READY",
+        publish_dir=detection.output_dir,
+        manifest_path=None,
+        summary_json={
+            "created_from": "flood_detection",
+            "flood_area_km2": detection.flood_area_km2,
+            "stable_water_area_km2": detection.stable_water_area_km2,
+            "classified_path": detection.classified_path,
+            "created_at": datetime.utcnow().isoformat(),
+        },
+    )
+    db.add(product)
+    await db.commit()
+    return await get_flood_product(str(product.id), db)
+
+
+async def _get_product(product_id_or_pk: str, db: AsyncSession) -> FloodProductORM:
+    value = str(product_id_or_pk).strip()
+    query = select(FloodProductORM).options(
+        selectinload(FloodProductORM.detection).selectinload(FloodDetectionORM.pre_scene).selectinload(SARSceneGeoORM.radar_data),
+        selectinload(FloodProductORM.detection).selectinload(FloodDetectionORM.post_scene).selectinload(SARSceneGeoORM.radar_data),
+        selectinload(FloodProductORM.overlay),
+    )
+    if value.isdigit():
+        query = query.where(FloodProductORM.id == int(value))
+    else:
+        query = query.where(FloodProductORM.product_id == value)
+    product = (await db.execute(query)).scalars().first()
+    if not product:
+        raise HTTPException(status_code=404, detail=f"Flood product {product_id_or_pk} not found")
+    return product
+
+
+def _build_manifest_from_db(product: FloodProductORM) -> dict[str, Any]:
+    detection = product.detection
+    overlay = product.overlay
+    pre_scene = detection.pre_scene if detection else None
+    post_scene = detection.post_scene if detection else None
+    pre_radar = pre_scene.radar_data if pre_scene else None
+    post_radar = post_scene.radar_data if post_scene else None
+    return {
+        "schema": "flood_product_manifest.v1",
+        "product": _product_to_dict(product),
+        "detection": {
+            "id": detection.id if detection else None,
+            "status": detection.status if detection else None,
+            "classified_path": detection.classified_path if detection else None,
+            "flood_area_km2": detection.flood_area_km2 if detection else None,
+            "stable_water_area_km2": detection.stable_water_area_km2 if detection else None,
+            "pre_scene": {
+                "id": pre_scene.id if pre_scene else None,
+                "radar_data_id": pre_scene.radar_data_id if pre_scene else None,
+                "satellite": pre_radar.satellite if pre_radar else None,
+                "imaging_date": pre_radar.imaging_date if pre_radar else None,
+                "geo_path": pre_scene.geo_path if pre_scene else None,
+                "analysis_tif_path": pre_scene.analysis_tif_path if pre_scene else None,
+                "analysis_engine": pre_scene.analysis_engine if pre_scene else None,
+                "analysis_profile": pre_scene.analysis_profile if pre_scene else None,
+            },
+            "post_scene": {
+                "id": post_scene.id if post_scene else None,
+                "radar_data_id": post_scene.radar_data_id if post_scene else None,
+                "satellite": post_radar.satellite if post_radar else None,
+                "imaging_date": post_radar.imaging_date if post_radar else None,
+                "geo_path": post_scene.geo_path if post_scene else None,
+                "analysis_tif_path": post_scene.analysis_tif_path if post_scene else None,
+                "analysis_engine": post_scene.analysis_engine if post_scene else None,
+                "analysis_profile": post_scene.analysis_profile if post_scene else None,
+            },
+        },
+        "overlay": {
+            "id": overlay.id if overlay else None,
+            "flood_vector_path": overlay.flood_vector_path if overlay else None,
+            "affected_area_km2": overlay.affected_area_km2 if overlay else None,
+            "hazard_points_hit": overlay.hazard_points_hit if overlay else 0,
+            "hazard_points_near": overlay.hazard_points_near if overlay else 0,
+            "dinsar_products_intersecting": overlay.dinsar_products_intersecting if overlay else 0,
+            "summary": overlay.summary_json if overlay else None,
+        },
+    }

backend/app/services/gf3_unpack_service.py

@@ -0,0 +1,369 @@
+from __future__ import annotations
+
+import logging
+import os
+import shutil
+import stat
+import tarfile
+import zipfile
+from datetime import datetime
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
+
+from ..config import settings, split_env_paths
+
+logger = logging.getLogger(__name__)
+
+LogCallback = Callable[[str, str], None]
+ProgressCallback = Callable[[int, str], None]
+
+DEFAULT_GF3_ARCHIVE_EXTS = (".zip", ".tar", ".tar.gz", ".tgz")
+
+
+def _parse_float(value: Any, default: float) -> float:
+    try:
+        return float(value)
+    except (TypeError, ValueError):
+        return default
+
+
+def _normalize_paths(paths: Optional[Iterable[str]]) -> List[str]:
+    ordered: List[str] = []
+    for raw_path in paths or []:
+        text = str(raw_path or "").strip().strip('"').strip("'")
+        if not text:
+            continue
+        normalized = os.path.normpath(os.path.abspath(text))
+        if normalized not in ordered:
+            ordered.append(normalized)
+    return ordered
+
+
+def _normalize_extensions(extensions: Optional[Iterable[str]]) -> List[str]:
+    ordered: List[str] = []
+    for raw_ext in extensions or DEFAULT_GF3_ARCHIVE_EXTS:
+        ext = str(raw_ext or "").strip().lower()
+        if not ext:
+            continue
+        if not ext.startswith("."):
+            ext = f".{ext}"
+        if ext not in ordered:
+            ordered.append(ext)
+    return sorted(ordered or list(DEFAULT_GF3_ARCHIVE_EXTS), key=len, reverse=True)
+
+
+def _strip_archive_extension(file_name: str, extensions: Iterable[str]) -> str:
+    lower_name = file_name.lower()
+    for ext in _normalize_extensions(extensions):
+        if lower_name.endswith(ext):
+            return file_name[: -len(ext)]
+    return os.path.splitext(file_name)[0]
+
+
+def _resolve_target_root(archive_path: str, source_dirs: List[str], target_dirs: List[str]) -> str:
+    if not target_dirs:
+        raise ValueError("GF3_SOURCE_DIRS is not configured.")
+    if len(target_dirs) == 1:
+        return target_dirs[0]
+    if source_dirs and len(source_dirs) == len(target_dirs):
+        archive_norm = os.path.normcase(os.path.abspath(archive_path))
+        matches: List[Tuple[int, int]] = []
+        for index, source_dir in enumerate(source_dirs):
+            source_norm = os.path.normcase(os.path.abspath(source_dir))
+            if archive_norm == source_norm or archive_norm.startswith(source_norm + os.sep):
+                matches.append((len(source_norm), index))
+        if matches:
+            _prefix_len, best_index = max(matches)
+            return target_dirs[best_index]
+    return target_dirs[0]
+
+
+def _validate_relative_member_name(member_name: str, archive_path: str) -> str:
+    name = str(member_name or "").replace("\\", "/")
+    if not name or name in {".", "./"}:
+        return ""
+    if name.startswith("/") or os.path.isabs(name) or os.path.splitdrive(name)[0]:
+        raise ValueError(f"Unsafe archive member path in {archive_path}: {member_name}")
+    parts = [part for part in name.split("/") if part not in ("", ".")]
+    if any(part == ".." for part in parts):
+        raise ValueError(f"Unsafe archive member path in {archive_path}: {member_name}")
+    if not parts:
+        return ""
+    return os.path.join(*parts)
+
+
+def _safe_destination(root_dir: str, relative_name: str) -> str:
+    root_abs = os.path.abspath(root_dir)
+    destination = os.path.abspath(os.path.join(root_abs, relative_name))
+    if destination != root_abs and not destination.startswith(root_abs + os.sep):
+        raise ValueError(f"Unsafe extraction destination: {relative_name}")
+    return destination
+
+
+def _validate_tar_members(members: Iterable[tarfile.TarInfo], archive_path: str) -> None:
+    for member in members:
+        _validate_relative_member_name(member.name, archive_path)
+        if member.issym() or member.islnk():
+            raise ValueError(f"Unsupported link entry in {archive_path}: {member.name}")
+        if not (member.isdir() or member.isfile()):
+            raise ValueError(f"Unsupported special entry in {archive_path}: {member.name}")
+
+
+def _validate_zip_members(infos: Iterable[zipfile.ZipInfo], archive_path: str) -> None:
+    for info in infos:
+        _validate_relative_member_name(info.filename, archive_path)
+        mode = (info.external_attr >> 16) & 0o170000
+        if stat.S_ISLNK(mode):
+            raise ValueError(f"Unsupported symlink entry in {archive_path}: {info.filename}")
+
+
+def _estimate_archive_size(archive_path: str) -> int:
+    if zipfile.is_zipfile(archive_path):
+        with zipfile.ZipFile(archive_path, "r") as zip_obj:
+            infos = zip_obj.infolist()
+            _validate_zip_members(infos, archive_path)
+            return sum(max(0, int(info.file_size or 0)) for info in infos if not info.is_dir())
+    if tarfile.is_tarfile(archive_path):
+        with tarfile.open(archive_path, "r:*") as tar_obj:
+            members = tar_obj.getmembers()
+            _validate_tar_members(members, archive_path)
+            return sum(max(0, int(member.size or 0)) for member in members if member.isfile())
+    raise ValueError(f"Unsupported GF3 archive format: {archive_path}")
+
+
+def _ensure_disk_space(target_root: str, required_bytes: int, min_disk_space_gb: float) -> None:
+    os.makedirs(target_root, exist_ok=True)
+    _total, _used, free_bytes = shutil.disk_usage(target_root)
+    min_free_bytes = int(max(0.0, min_disk_space_gb) * (1024 ** 3))
+    if free_bytes - max(0, int(required_bytes or 0)) < min_free_bytes:
+        raise OSError(
+            "GF3 L1A storage has insufficient free space: "
+            f"needed {required_bytes / (1024 ** 3):.2f} GB, "
+            f"free {free_bytes / (1024 ** 3):.2f} GB, "
+            f"min free after {min_disk_space_gb:.2f} GB"
+        )
+
+
+def _prepare_atomic_output(output_dir: str, tmp_suffix: str) -> Tuple[bool, str, str]:
+    tmp_dir = output_dir + tmp_suffix
+    lock_path = output_dir + ".unpacking"
+    if os.path.exists(output_dir):
+        return False, tmp_dir, lock_path
+    if os.path.exists(tmp_dir):
+        return False, tmp_dir, lock_path
+    if os.path.exists(lock_path):
+        return False, tmp_dir, lock_path
+    os.makedirs(tmp_dir, exist_ok=False)
+    with open(lock_path, "w", encoding="utf-8") as stream:
+        stream.write(datetime.now().isoformat())
+    return True, tmp_dir, lock_path
+
+
+def _cleanup_atomic_paths(tmp_dir: str, lock_path: str) -> None:
+    if os.path.exists(lock_path):
+        try:
+            os.remove(lock_path)
+        except OSError:
+            pass
+    if os.path.exists(tmp_dir):
+        try:
+            shutil.rmtree(tmp_dir)
+        except OSError:
+            pass
+
+
+def _extract_tar_archive(archive_path: str, tmp_dir: str) -> int:
+    extracted_files = 0
+    with tarfile.open(archive_path, "r:*") as tar_obj:
+        members = tar_obj.getmembers()
+        _validate_tar_members(members, archive_path)
+        for member in members:
+            relative_name = _validate_relative_member_name(member.name, archive_path)
+            if not relative_name:
+                continue
+            destination = _safe_destination(tmp_dir, relative_name)
+            if member.isdir():
+                os.makedirs(destination, exist_ok=True)
+                continue
+            if not member.isfile():
+                continue
+            os.makedirs(os.path.dirname(destination), exist_ok=True)
+            source = tar_obj.extractfile(member)
+            if source is None:
+                raise OSError(f"Failed to read tar member: {member.name}")
+            with source, open(destination, "wb") as target:
+                shutil.copyfileobj(source, target, length=1024 * 1024)
+            extracted_files += 1
+    return extracted_files
+
+
+def _extract_zip_archive(archive_path: str, tmp_dir: str) -> int:
+    extracted_files = 0
+    with zipfile.ZipFile(archive_path, "r") as zip_obj:
+        infos = zip_obj.infolist()
+        _validate_zip_members(infos, archive_path)
+        for info in infos:
+            relative_name = _validate_relative_member_name(info.filename, archive_path)
+            if not relative_name:
+                continue
+            destination = _safe_destination(tmp_dir, relative_name)
+            if info.is_dir():
+                os.makedirs(destination, exist_ok=True)
+                continue
+            os.makedirs(os.path.dirname(destination), exist_ok=True)
+            with zip_obj.open(info, "r") as source, open(destination, "wb") as target:
+                shutil.copyfileobj(source, target, length=1024 * 1024)
+            extracted_files += 1
+    return extracted_files
+
+
+def _extract_archive_atomic(archive_path: str, output_dir: str, tmp_suffix: str) -> Tuple[str, int]:
+    prepared, tmp_dir, lock_path = _prepare_atomic_output(output_dir, tmp_suffix)
+    if not prepared:
+        return "EXISTS", 0
+
+    try:
+        if zipfile.is_zipfile(archive_path):
+            extracted_files = _extract_zip_archive(archive_path, tmp_dir)
+        elif tarfile.is_tarfile(archive_path):
+            extracted_files = _extract_tar_archive(archive_path, tmp_dir)
+        else:
+            raise ValueError(f"Unsupported GF3 archive format: {archive_path}")
+
+        if extracted_files <= 0:
+            raise OSError("GF3 archive extraction produced no files.")
+        os.replace(tmp_dir, output_dir)
+        return "EXTRACTED", extracted_files
+    finally:
+        _cleanup_atomic_paths(tmp_dir, lock_path)
+
+
+def _discover_archives(source_dirs: List[str], extensions: List[str], log_callback: Optional[LogCallback]) -> List[str]:
+    archives: List[str] = []
+    normalized_extensions = _normalize_extensions(extensions)
+    for source_dir in source_dirs:
+        if not os.path.isdir(source_dir):
+            message = f"GF3 archive source does not exist or is not a directory: {source_dir}"
+            logger.warning(message)
+            if log_callback:
+                log_callback("WARNING", message)
+            continue
+        for root, _dirs, files in os.walk(source_dir):
+            for file_name in files:
+                lower_name = file_name.lower()
+                if any(lower_name.endswith(ext) for ext in normalized_extensions):
+                    archives.append(os.path.join(root, file_name))
+    return sorted(archives)
+
+
+def run_gf3_archive_unpack(
+    *,
+    source_dirs: Optional[Iterable[str]] = None,
+    target_dirs: Optional[Iterable[str]] = None,
+    archive_exts: Optional[Iterable[str]] = None,
+    max_files_per_run: Optional[int] = None,
+    delete_archive: Optional[bool] = None,
+    min_disk_space_gb: Optional[float] = None,
+    tmp_suffix: Optional[str] = None,
+    log_callback: Optional[LogCallback] = None,
+    progress_callback: Optional[ProgressCallback] = None,
+) -> Dict[str, Any]:
+    configured_source_dirs = _normalize_paths(
+        source_dirs if source_dirs is not None else split_env_paths(settings.GF3_ARCHIVE_SOURCE_DIRS)
+    )
+    configured_target_dirs = _normalize_paths(
+        target_dirs if target_dirs is not None else split_env_paths(settings.GF3_SOURCE_DIRS)
+    )
+    extensions = _normalize_extensions(
+        archive_exts if archive_exts is not None else split_env_paths(settings.GF3_ARCHIVE_EXTS)
+    )
+    should_delete_archive = settings.GF3_UNPACK_DELETE_ARCHIVE if delete_archive is None else bool(delete_archive)
+    limit = max(0, int(max_files_per_run or 0))
+    min_free_gb = (
+        _parse_float(os.getenv("UNPACK_MIN_DISK_SPACE_GB"), 50.0)
+        if min_disk_space_gb is None
+        else float(min_disk_space_gb)
+    )
+    atomic_tmp_suffix = str(tmp_suffix or os.getenv("UNPACK_TMP_SUFFIX") or ".unpack_tmp").strip() or ".unpack_tmp"
+
+    if not configured_source_dirs:
+        raise ValueError("GF3_ARCHIVE_SOURCE_DIRS is not configured.")
+    if not configured_target_dirs:
+        raise ValueError("GF3_SOURCE_DIRS is not configured.")
+
+    def _log(level: str, message: str) -> None:
+        logger.log(getattr(logging, level.upper(), logging.INFO), message)
+        if log_callback:
+            log_callback(level.upper(), message)
+
+    def _progress(progress: int, message: str) -> None:
+        if progress_callback:
+            progress_callback(max(0, min(100, int(progress))), message)
+
+    _progress(2, "Scanning GF3 archive source directories...")
+    archives = _discover_archives(configured_source_dirs, extensions, log_callback)
+    if limit > 0:
+        archives_to_process = archives[:limit]
+    else:
+        archives_to_process = archives
+
+    total = len(archives_to_process)
+    summary: Dict[str, Any] = {
+        "total": total,
+        "found": len(archives),
+        "processed": 0,
+        "skipped": 0,
+        "failed": 0,
+        "remaining": max(0, len(archives) - total),
+        "source_dirs": configured_source_dirs,
+        "target_dirs": configured_target_dirs,
+        "archive_exts": extensions,
+        "delete_archive": should_delete_archive,
+        "failures": [],
+    }
+
+    if not archives_to_process:
+        _progress(100, "No GF3 archives pending.")
+        summary["message"] = "No GF3 archives found."
+        return summary
+
+    _log("INFO", f"Found {len(archives)} GF3 archives; processing {total}.")
+
+    for index, archive_path in enumerate(archives_to_process, start=1):
+        archive_name = os.path.basename(archive_path)
+        progress_base = 5 + int(((index - 1) / max(1, total)) * 90)
+        _progress(progress_base, f"Unpacking GF3 archive {index}/{total}: {archive_name}")
+
+        try:
+            target_root = _resolve_target_root(archive_path, configured_source_dirs, configured_target_dirs)
+            os.makedirs(target_root, exist_ok=True)
+            output_name = _strip_archive_extension(os.path.basename(archive_path), extensions)
+            output_dir = os.path.join(target_root, output_name)
+            required_bytes = _estimate_archive_size(archive_path)
+            _ensure_disk_space(target_root, required_bytes, min_free_gb)
+            status, extracted_files = _extract_archive_atomic(archive_path, output_dir, atomic_tmp_suffix)
+
+            if status == "EXISTS":
+                summary["skipped"] += 1
+                _log("INFO", f"GF3 archive already unpacked, skipped: {archive_path}")
+                continue
+
+            if should_delete_archive:
+                os.remove(archive_path)
+                _log("INFO", f"GF3 archive deleted after successful unpack: {archive_path}")
+            summary["processed"] += 1
+            _log("INFO", f"GF3 archive unpacked: {archive_path} -> {output_dir} ({extracted_files} files)")
+        except Exception as exc:
+            summary["failed"] += 1
+            failure = {
+                "archive_path": archive_path,
+                "error": str(exc),
+            }
+            summary["failures"].append(failure)
+            _log("ERROR", f"GF3 archive unpack failed: {archive_path}: {exc}")
+
+    _progress(100, "GF3 archive unpack completed.")
+    summary["message"] = (
+        f"GF3 unpack complete: processed {summary['processed']}, "
+        f"skipped {summary['skipped']}, failed {summary['failed']}"
+    )
+    return summary

backend/app/services/health_service.py

@@ -17,6 +17,7 @@ from ..models import (
     OrbitAssetORM,
     ResultCatalogStateORM,
     ResultProductORM,
+    SARSceneGeoORM,
     SceneOrbitBindingORM,
     SourceProductAssetORM,
     SystemWorkerHeartbeatORM,
@@ -362,6 +363,19 @@ def _sanitize_source_roots_status(payload: Dict[str, Any]) -> Dict[str, Any]:
     }
 
 
+def _sanitize_sar_analysis_ready_status(payload: Dict[str, Any]) -> Dict[str, Any]:
+    scenes = payload.get("scenes", {}) or {}
+    roots = payload.get("roots", {}) or {}
+    return {
+        "ok": bool(payload.get("ok")),
+        "configured_root_count": len(roots),
+        "accessible_root_count": sum(1 for item in roots.values() if item.get("accessible")),
+        "scene_count": int(scenes.get("scene_count") or 0),
+        "analysis_scene_count": int(scenes.get("analysis_scene_count") or 0),
+        "missing_file_count": int(scenes.get("missing_file_count") or 0),
+    }
+
+
 def _sanitize_product_package_status(payload: Dict[str, Any]) -> Dict[str, Any]:
     return {
         "ok": bool(payload.get("ok")),
@@ -460,6 +474,7 @@ def _sanitize_health_status(payload: Dict[str, Any]) -> Dict[str, Any]:
     psinsar_result_catalog = timeseries_result_catalog
     dinsar_bridge = payload.get("dinsar_bridge", {}) or {}
     source_roots = payload.get("source_roots", {}) or {}
+    sar_analysis_ready = payload.get("sar_analysis_ready", {}) or {}
     product_packages = payload.get("product_packages", {}) or {}
     asset_inventory = payload.get("asset_inventory", {}) or {}
     wsl_runtime = payload.get("wsl_runtime", {}) or {}
@@ -473,6 +488,7 @@ def _sanitize_health_status(payload: Dict[str, Any]) -> Dict[str, Any]:
     sanitized_psinsar_catalog = sanitized_timeseries_catalog
     sanitized_dinsar_bridge = _sanitize_bridge_status(dinsar_bridge)
     sanitized_source_roots = _sanitize_source_roots_status(source_roots)
+    sanitized_sar_analysis_ready = _sanitize_sar_analysis_ready_status(sar_analysis_ready)
     sanitized_product_packages = _sanitize_product_package_status(product_packages)
     sanitized_asset_inventory = _sanitize_asset_inventory_status(asset_inventory)
     sanitized_wsl_runtime = _sanitize_wsl_runtime_status(wsl_runtime)
@@ -502,6 +518,7 @@ def _sanitize_health_status(payload: Dict[str, Any]) -> Dict[str, Any]:
         },
         "dinsar_bridge": sanitized_dinsar_bridge,
         "source_roots": sanitized_source_roots,
+        "sar_analysis_ready": sanitized_sar_analysis_ready,
         "product_packages": sanitized_product_packages,
         "asset_inventory": sanitized_asset_inventory,
         "wsl_runtime": sanitized_wsl_runtime,
@@ -869,6 +886,21 @@ async def _check_source_roots() -> Dict[str, Any]:
         status["role"] = "dinsar_source"
         items.append(status)
 
+    for path in split_env_paths(settings.GF3_ARCHIVE_SOURCE_DIRS):
+        status = _probe_directory_status(path)
+        status["role"] = "gf3_archive_source"
+        items.append(status)
+
+    for path in split_env_paths(settings.GF3_SOURCE_DIRS):
+        status = _probe_directory_status(path)
+        status["role"] = "gf3_l1a_source"
+        items.append(status)
+
+    for path in split_env_paths(settings.GF3_STORAGE_DIRS):
+        status = _probe_directory_status(path)
+        status["role"] = "gf3_l2_storage"
+        items.append(status)
+
     configured_count = len(items)
     accessible_count = sum(1 for item in items if item.get("accessible"))
     inaccessible_count = configured_count - accessible_count
@@ -882,6 +914,56 @@ async def _check_source_roots() -> Dict[str, Any]:
     }
 
 
+async def _check_sar_analysis_ready() -> Dict[str, Any]:
+    roots = {
+        "ready": _probe_directory_status(settings.SAR_ANALYSIS_READY_ROOT),
+        "work": _probe_directory_status(settings.SAR_ANALYSIS_WORK_ROOT),
+        "preview": _probe_directory_status(settings.SAR_ANALYSIS_PREVIEW_ROOT),
+    }
+    for role, payload in roots.items():
+        payload["role"] = role
+
+    status: Dict[str, Any] = {
+        "ok": False,
+        "roots": roots,
+        "scenes": {
+            "scene_count": 0,
+            "analysis_scene_count": 0,
+            "missing_file_count": 0,
+            "missing_files": [],
+        },
+        "error": None,
+    }
+    try:
+        session_factory = _get_session_factory()
+        async with session_factory() as db:
+            scene_count_result = await db.execute(select(func.count(SARSceneGeoORM.id)))
+            status["scenes"]["scene_count"] = int(scene_count_result.scalar_one() or 0)
+            analysis_rows_result = await db.execute(
+                select(SARSceneGeoORM.id, SARSceneGeoORM.analysis_tif_path)
+                .where(SARSceneGeoORM.analysis_tif_path.is_not(None))
+                .order_by(SARSceneGeoORM.id.desc())
+            )
+            analysis_rows = analysis_rows_result.all()
+            status["scenes"]["analysis_scene_count"] = len(analysis_rows)
+
+        missing_files = []
+        for scene_id, tif_path in analysis_rows:
+            path_text = str(tif_path or "").strip()
+            if path_text and not os.path.isfile(path_text):
+                missing_files.append({"scene_id": scene_id, "path": path_text})
+        status["scenes"]["missing_file_count"] = len(missing_files)
+        status["scenes"]["missing_files"] = missing_files[:20]
+
+        status["ok"] = (
+            all(item.get("accessible") for item in roots.values())
+            and status["scenes"]["missing_file_count"] == 0
+        )
+    except Exception as exc:
+        status["error"] = str(exc)
+    return status
+
+
 async def _check_product_packages() -> Dict[str, Any]:
     status = {
         "ok": False,
@@ -1256,6 +1338,7 @@ async def get_health_status(
     psinsar_result_catalog_status = timeseries_result_catalog_status
     dinsar_bridge_status = await _check_dinsar_bridge()
     source_roots_status = await _check_source_roots()
+    sar_analysis_ready_status = await _check_sar_analysis_ready()
     product_packages_status = await _check_product_packages()
     asset_inventory_status = await _check_asset_inventory()
     wsl_runtime_status = await _check_wsl_runtime()
@@ -1273,6 +1356,7 @@ async def get_health_status(
             result_catalog_status.get("ok"),
             dinsar_bridge_status.get("ok"),
             source_roots_status.get("ok"),
+            sar_analysis_ready_status.get("ok"),
             product_packages_status.get("ok"),
             asset_inventory_status.get("ok"),
             wsl_runtime_status.get("ok"),
@@ -1297,6 +1381,7 @@ async def get_health_status(
         },
         "dinsar_bridge": dinsar_bridge_status,
         "source_roots": source_roots_status,
+        "sar_analysis_ready": sar_analysis_ready_status,
         "product_packages": product_packages_status,
         "asset_inventory": asset_inventory_status,
         "wsl_runtime": wsl_runtime_status,

backend/app/services/job_handlers.py

@@ -19,7 +19,7 @@ from sqlalchemy import select
 
 from .. import database
 from ..config import settings
-from ..models import SystemJobORM, DinsarResultORM, HazardPointORM, DinsarTaskItemORM, PsTaskItemORM, RadarDataORM, SARSceneGeoORM, FloodDetectionORM, WaterDetectionORM, GF3ProcessingORM, AiDiagnosisORM
+from ..models import SystemJobORM, DinsarResultORM, HazardPointORM, DinsarTaskItemORM, PsTaskItemORM, RadarDataORM, SARSceneGeoORM, FloodDetectionORM, WaterDetectionORM, WaterExtractionORM, GF3ProcessingORM, AiDiagnosisORM
 from ..scheduler import scan_data_job
 from .data_service import data_service
 from .asset_inventory_service import asset_inventory_service
@@ -80,7 +80,10 @@ JOB_TYPE_IDL_RUN_DINSAR = "IDL_RUN_DINSAR"
 JOB_TYPE_WATER_GEOCODE = "WATER_GEOCODE"
 JOB_TYPE_WATER_FLOOD = "WATER_FLOOD"
 JOB_TYPE_WATER_DETECT = "WATER_DETECT"
+JOB_TYPE_SAR_SCENE_PREPROCESS = "SAR_SCENE_PREPROCESS"
+JOB_TYPE_FLOOD_DETECTION = "FLOOD_DETECTION"
 JOB_TYPE_GF3_PROCESS = "GF3_PROCESS"
+JOB_TYPE_GF3_UNPACK = "GF3_UNPACK"
 JOB_TYPE_GF3_BATCH_PROCESS = "GF3_BATCH_PROCESS"
 JOB_TYPE_ISCE2_RUN = "ISCE2_RUN"
 JOB_TYPE_PYINT_RUN = "PYINT_RUN"
@@ -88,6 +91,7 @@ JOB_TYPE_PUBLISH_DINSAR_PRODUCTS = "PUBLISH_DINSAR_PRODUCTS"
 JOB_TYPE_REBUILD_DINSAR_CATALOG = "REBUILD_DINSAR_CATALOG"
 JOB_TYPE_REBUILD_PSINSAR_CATALOG = "REBUILD_PSINSAR_CATALOG"
 JOB_TYPE_SCAN_ASSET_INVENTORY = "SCAN_ASSET_INVENTORY"
+JOB_TYPE_SBAS_COREGISTRATION = "SBAS_COREGISTRATION"
 
 COPY_ALLOWED_STATUSES = {"PENDING", "IN_PROGRESS", "COMPLETED", "FAILED"}
 
@@ -3149,6 +3153,106 @@ async def _handle_water_geocode(job: SystemJobORM) -> None:
     )
 
 
+async def _handle_sar_scene_preprocess(job: SystemJobORM) -> None:
+    """Build one analysis-ready GeoTIFF for flood/water algorithms."""
+    payload = job.payload or {}
+    scene_id = payload.get("scene_id")
+    radar_data_id = payload.get("radar_data_id")
+    engine = str(payload.get("engine") or "").strip().lower()
+    if not scene_id and not radar_data_id:
+        raise ValueError("SAR_SCENE_PREPROCESS requires scene_id or radar_data_id")
+    if engine not in {"gf3_gdal", "lt_gamma"}:
+        raise ValueError(f"Unsupported SAR scene preprocessing engine: {engine}")
+
+    await task_service.start_task(job.task_id, message="Preparing analysis-ready SAR GeoTIFF...")
+
+    async with AsyncSessionLocal() as db:
+        scene: SARSceneGeoORM | None = None
+        if scene_id:
+            scene = await db.get(SARSceneGeoORM, int(scene_id))
+        if not scene and radar_data_id:
+            result = await db.execute(
+                select(SARSceneGeoORM).where(SARSceneGeoORM.radar_data_id == int(radar_data_id))
+            )
+            scene = result.scalar_one_or_none()
+        if not scene:
+            scene = SARSceneGeoORM(radar_data_id=int(radar_data_id), status="PENDING")
+            db.add(scene)
+            await db.flush()
+        radar = await db.get(RadarDataORM, int(scene.radar_data_id))
+        if not radar:
+            raise ValueError(f"RadarDataORM id={scene.radar_data_id} does not exist")
+        scene.status = "RUNNING"
+        scene.error_msg = None
+        await db.commit()
+        scene_id = int(scene.id)
+        radar_data_id = int(radar.id)
+
+    try:
+        if engine == "gf3_gdal":
+            await task_service.update_task(job.task_id, progress=20, message="Standardizing GF3 L2 GeoTIFF...")
+            from .sar_analysis_ready_service import standardize_gf3_l2_for_radar
+
+            async with AsyncSessionLocal() as db:
+                manifest = await standardize_gf3_l2_for_radar(
+                    db=db,
+                    radar_id=int(radar_data_id),
+                    l2_path=payload.get("l2_path"),
+                    polarization=payload.get("polarization"),
+                )
+        else:
+            await task_service.update_task(job.task_id, progress=15, message="Running LT Gamma single-scene preprocessing...")
+            from .lt_gamma_scene_service import run_lt_gamma_scene_preprocess
+            from .sar_analysis_ready_service import register_analysis_ready_tif
+
+            async with AsyncSessionLocal() as db:
+                scene = await db.get(SARSceneGeoORM, int(scene_id))
+                radar = await db.get(RadarDataORM, int(radar_data_id))
+                if not scene or not radar:
+                    raise ValueError("Scene or radar record disappeared before LT Gamma preprocessing")
+
+            def _run_lt() -> Dict[str, Any]:
+                return run_lt_gamma_scene_preprocess(radar=radar, scene=scene, job_id=job.job_id)
+
+            lt_manifest = await asyncio.to_thread(_run_lt)
+            await task_service.update_task(job.task_id, progress=85, message="Registering LT analysis-ready GeoTIFF...")
+            analysis_tif_path = str(lt_manifest.get("analysis_tif_path") or "").strip()
+            if not analysis_tif_path:
+                raise RuntimeError("LT Gamma preprocessing returned no analysis_tif_path")
+            async with AsyncSessionLocal() as db:
+                scene = await db.get(SARSceneGeoORM, int(scene_id))
+                radar = await db.get(RadarDataORM, int(radar_data_id))
+                if not scene or not radar:
+                    raise ValueError("Scene or radar record disappeared before analysis-ready registration")
+                manifest = await register_analysis_ready_tif(
+                    db=db,
+                    scene=scene,
+                    radar=radar,
+                    source_tif_path=analysis_tif_path,
+                    engine="lt_gamma",
+                    profile="lt1_gamma_geocoded_mli",
+                    backscatter_unit=str(lt_manifest.get("backscatter_unit") or "gamma_mli_db"),
+                    polarization=radar.polarization,
+                    metadata=lt_manifest,
+                )
+                await db.commit()
+    except Exception as exc:
+        async with AsyncSessionLocal() as db:
+            scene = await db.get(SARSceneGeoORM, int(scene_id))
+            if scene:
+                scene.status = "FAILED"
+                scene.error_msg = str(exc)
+                await db.commit()
+        raise
+
+    await task_service.update_task(
+        job.task_id,
+        status="COMPLETED",
+        progress=100,
+        message=f"Analysis-ready GeoTIFF ready: {manifest.get('analysis_tif_path')}",
+    )
+
+
 async def _handle_water_flood(job: SystemJobORM) -> None:
     """洪涝检测 job handler（灾前 + 灾后配对分类）。"""
     from .water_service import run_flood_detection, WATER_RESULTS_DIR
@@ -3171,8 +3275,10 @@ async def _handle_water_flood(job: SystemJobORM) -> None:
             raise ValueError("灾前或灾后场景记录不存在")
         if pre_scene.status != "DONE" or post_scene.status != "DONE":
             raise ValueError("灾前或灾后场景尚未完成地理编码")
-        pre_geo = pre_scene.geo_path
-        post_geo = post_scene.geo_path
+        pre_geo = pre_scene.analysis_tif_path or pre_scene.geo_path
+        post_geo = post_scene.analysis_tif_path or post_scene.geo_path
+        if not pre_geo or not post_geo:
+            raise ValueError("Pre/post scenes must have analysis-ready GeoTIFF paths")
 
     output_dir = os.path.join(WATER_RESULTS_DIR, f"flood_{detection_id}")
     os.makedirs(output_dir, exist_ok=True)
@@ -3228,36 +3334,129 @@ async def _handle_water_flood(job: SystemJobORM) -> None:
     )
 
 
-async def _handle_water_detect(job: SystemJobORM) -> None:
-    """水体检测 job handler（Otsu + DEM + 形态学 + 连通分量）。"""
-    from .water_detect_service import run_water_detection
+async def _handle_flood_detection(job: SystemJobORM) -> None:
+    """Flood-analysis detection job: pure Python GeoTIFF change classification."""
+    from .flood_detection_service import run_geotiff_flood_detection
 
     payload = job.payload or {}
     detection_id = payload.get("detection_id")
     if not detection_id:
-        raise ValueError("WATER_DETECT job 缺少 detection_id")
+        raise ValueError("FLOOD_DETECTION job requires detection_id")
+    refine = bool(payload.get("refine", False))
+
+    await task_service.start_task(job.task_id, message="Reading flood-detection scene pair...")
+
+    async with AsyncSessionLocal() as db:
+        det = await db.get(FloodDetectionORM, int(detection_id))
+        if not det:
+            raise ValueError(f"FloodDetectionORM id={detection_id} does not exist")
+        pre_scene = await db.get(SARSceneGeoORM, det.pre_scene_id)
+        post_scene = await db.get(SARSceneGeoORM, det.post_scene_id)
+        if not pre_scene or not post_scene:
+            raise ValueError("Pre/post scene records do not exist")
+        if pre_scene.status != "DONE" or post_scene.status != "DONE":
+            raise ValueError("Pre/post scenes are not DONE")
+        pre_tif = pre_scene.analysis_tif_path
+        post_tif = post_scene.analysis_tif_path
+        if not pre_tif or not post_tif:
+            raise ValueError("Flood detection requires analysis-ready GeoTIFF paths for both scenes")
+
+    output_dir = os.path.join(settings.WATER_RESULTS_DIR, f"flood_{detection_id}")
+    os.makedirs(output_dir, exist_ok=True)
+
+    await task_service.update_task(job.task_id, progress=10, message="Running GeoTIFF flood classification...")
+
+    def _run() -> Dict[str, Any]:
+        return run_geotiff_flood_detection(
+            pre_tif_path=pre_tif,
+            post_tif_path=post_tif,
+            output_dir=output_dir,
+            job_id=job.job_id,
+            refine=refine,
+        )
+
+    try:
+        result = await asyncio.to_thread(_run)
+    except Exception as exc:
+        async with AsyncSessionLocal() as db:
+            det = await db.get(FloodDetectionORM, int(detection_id))
+            if det:
+                det.status = "FAILED"
+                det.error_msg = str(exc)
+                await db.commit()
+        raise
+
+    async with AsyncSessionLocal() as db:
+        det = await db.get(FloodDetectionORM, int(detection_id))
+        if det:
+            if result.get("ok"):
+                det.classified_path = result.get("classified_path")
+                det.flood_area_km2 = result.get("flood_area_km2")
+                det.stable_water_area_km2 = result.get("stable_water_area_km2")
+                det.output_dir = output_dir
+                det.status = "DONE"
+                det.error_msg = None
+            else:
+                det.status = "FAILED"
+                det.error_msg = result.get("error", "Unknown error")
+            await db.commit()
+
+    if not result.get("ok"):
+        raise RuntimeError(f"Flood detection failed: {result.get('error')}")
+
+    await task_service.update_task(
+        job.task_id,
+        status="COMPLETED",
+        progress=100,
+        message=(
+            f"GeoTIFF flood detection completed: flood_area={result.get('flood_area_km2')} km2, "
+            f"stable_water={result.get('stable_water_area_km2')} km2"
+        ),
+    )
+
+
+async def _handle_water_detect(job: SystemJobORM) -> None:
+    """水体检测 job handler（Otsu + DEM + 形态学 + 连通分量）。"""
+    from .water_extraction_service import run_otsu_water_extraction
+
+    payload = job.payload or {}
+    extraction_id = payload.get("extraction_id")
+    detection_id = payload.get("detection_id")
+    record_id = extraction_id or detection_id
+    if not record_id:
+        raise ValueError("WATER_DETECT job 缺少 extraction_id/detection_id")
+    use_extraction_table = extraction_id is not None
 
     await task_service.start_task(job.task_id, message="读取检测任务信息...")
 
     async with AsyncSessionLocal() as db:
-        det = await db.get(WaterDetectionORM, int(detection_id))
+        det = await db.get(WaterExtractionORM if use_extraction_table else WaterDetectionORM, int(record_id))
         if not det:
-            raise ValueError(f"WaterDetectionORM id={detection_id} 不存在")
+            model_name = "WaterExtractionORM" if use_extraction_table else "WaterDetectionORM"
+            raise ValueError(f"{model_name} id={record_id} 不存在")
         input_path = det.input_path
         det.status = "RUNNING"
+        if use_extraction_table and hasattr(det, "task_id"):
+            det.task_id = job.task_id
+        if not use_extraction_table:
+            mirror = await db.get(WaterExtractionORM, int(record_id))
+            if mirror:
+                mirror.status = "RUNNING"
+                mirror.task_id = job.task_id
         await db.commit()
 
     if not input_path:
         raise ValueError("水体检测缺少输入路径 input_path")
 
-    output_dir = os.path.join(os.path.dirname(input_path), f"water_detect_{detection_id}")
+    output_name = f"water_extraction_{record_id}" if use_extraction_table else f"water_detect_{record_id}"
+    output_dir = os.path.join(os.path.dirname(input_path), output_name)
     os.makedirs(output_dir, exist_ok=True)
 
     await task_service.update_task(job.task_id, progress=10, message="启动水体检测算法...")
 
     def _run() -> Dict[str, Any]:
-        return run_water_detection(
-            geo_tiff_path=input_path,
+        return run_otsu_water_extraction(
+            input_path=input_path,
             output_dir=output_dir,
             job_id=job.job_id,
         )
@@ -3266,26 +3465,56 @@ async def _handle_water_detect(job: SystemJobORM) -> None:
         result = await asyncio.to_thread(_run)
     except Exception as exc:
         async with AsyncSessionLocal() as db:
-            det = await db.get(WaterDetectionORM, int(detection_id))
+            det = await db.get(WaterExtractionORM if use_extraction_table else WaterDetectionORM, int(record_id))
             if det:
                 det.status = "FAILED"
                 det.error_msg = str(exc)
+            if not use_extraction_table:
+                mirror = await db.get(WaterExtractionORM, int(record_id))
+                if mirror:
+                    mirror.status = "FAILED"
+                    mirror.error_msg = str(exc)
+                    mirror.task_id = job.task_id
             await db.commit()
         raise
 
     async with AsyncSessionLocal() as db:
-        det = await db.get(WaterDetectionORM, int(detection_id))
+        det = await db.get(WaterExtractionORM if use_extraction_table else WaterDetectionORM, int(record_id))
         if det:
             if result.get("ok"):
                 det.output_path = result.get("output_path")
                 det.water_area_km2 = result.get("water_area_km2")
                 det.water_pixel_count = result.get("water_pixel_count")
+                if use_extraction_table:
+                    det.processor = result.get("processor") or det.processor or "otsu"
+                    det.threshold_value = result.get("threshold_value")
+                    det.metadata_json = {
+                        "legacy_otsu_threshold_db": result.get("otsu_threshold_db"),
+                        "job_id": job.job_id,
+                    }
+                else:
                     det.otsu_threshold_db = result.get("otsu_threshold_db")
                 det.status = "DONE"
                 det.error_msg = None
             else:
                 det.status = "FAILED"
                 det.error_msg = result.get("error", "Unknown error")
+            if not use_extraction_table:
+                mirror = await db.get(WaterExtractionORM, int(record_id))
+                if mirror:
+                    mirror.output_path = det.output_path
+                    mirror.water_area_km2 = det.water_area_km2
+                    mirror.water_pixel_count = det.water_pixel_count
+                    mirror.threshold_value = result.get("threshold_value") or result.get("otsu_threshold_db")
+                    mirror.processor = result.get("processor") or mirror.processor or "otsu"
+                    mirror.status = det.status
+                    mirror.error_msg = det.error_msg
+                    mirror.task_id = job.task_id
+                    mirror.metadata_json = {
+                        "legacy_otsu_threshold_db": result.get("otsu_threshold_db"),
+                        "legacy_detection_id": int(record_id),
+                        "job_id": job.job_id,
+                    }
             await db.commit()
 
     if not result.get("ok"):
@@ -3376,9 +3605,70 @@ async def _handle_gf3_process(job: SystemJobORM) -> None:
     )
 
 
+async def _handle_gf3_unpack(job: SystemJobORM) -> None:
+    """GF3 archive inbox -> persistent L1A source pool."""
+    from .gf3_unpack_service import run_gf3_archive_unpack
+
+    if not job.task_id:
+        raise ValueError("GF3_UNPACK requires task_id for progress tracking.")
+
+    payload = job.payload or {}
+    await task_service.start_task(job.task_id, message="扫描 GF3 压缩包来源目录...")
+
+    loop = asyncio.get_running_loop()
+
+    def _submit(coro):
+        try:
+            future = asyncio.run_coroutine_threadsafe(coro, loop)
+        except RuntimeError:
+            return
+
+        def _swallow_errors(fut):
+            try:
+                fut.result()
+            except Exception as exc:
+                logger.warning("[GF3 Unpack] task callback failed: %s", exc)
+
+        future.add_done_callback(_swallow_errors)
+
+    def _log_cb(level: str, message: str) -> None:
+        _submit(task_service.add_log(job.task_id, level, message))
+
+    def _progress_cb(progress: int, message: str) -> None:
+        _submit(task_service.update_task(job.task_id, progress=progress, message=message))
+
+    try:
+        result = await asyncio.to_thread(
+            run_gf3_archive_unpack,
+            source_dirs=payload.get("source_dirs"),
+            target_dirs=payload.get("target_dirs"),
+            archive_exts=payload.get("archive_exts"),
+            max_files_per_run=payload.get("max_files_per_run"),
+            delete_archive=payload.get("delete_archive") if "delete_archive" in payload else None,
+            min_disk_space_gb=payload.get("min_disk_space_gb"),
+            tmp_suffix=payload.get("tmp_suffix"),
+            log_callback=_log_cb,
+            progress_callback=_progress_cb,
+        )
+    except Exception as exc:
+        await task_service.update_task(job.task_id, status="FAILED", progress=100, message=f"GF3 解包失败: {exc}")
+        raise
+
+    message = (
+        f"GF3 解包完成: 成功 {int(result.get('processed') or 0)}, "
+        f"跳过 {int(result.get('skipped') or 0)}, "
+        f"失败 {int(result.get('failed') or 0)}"
+    )
+    remaining = int(result.get("remaining") or 0)
+    if remaining > 0:
+        message += f", 剩余 {remaining}"
+    await task_service.update_task(job.task_id, status="COMPLETED", progress=100, message=message)
+
+
 async def _handle_gf3_batch_process(job: SystemJobORM) -> None:
     """批量 GF3 L1A→L2：扫描来源目录，逐个处理并自动入库到 radar_data。"""
     from .gf3_service import run_gf3_l1a_to_l2, register_l2_to_radar_data
+    from .sar_analysis_ready_service import standardize_gf3_l2_for_radar
 
     payload = job.payload or {}
     source_dirs = payload.get("source_dirs") or []
@@ -3458,12 +3748,18 @@ async def _handle_gf3_batch_process(job: SystemJobORM) -> None:
                 # Auto-register to radar_data
                 try:
                     async with AsyncSessionLocal() as db:
-                        await register_l2_to_radar_data(
+                        radar_id = await register_l2_to_radar_data(
                             l2_dir=result.get("output_dir", output_dir),
                             input_dir_name=dir_name,
                             polarizations=result.get("polarizations", []),
                             db=db,
                         )
+                        if radar_id:
+                            await standardize_gf3_l2_for_radar(
+                                db=db,
+                                radar_id=int(radar_id),
+                                l2_path=result.get("output_dir", output_dir),
+                            )
                 except Exception as reg_err:
                     logger.warning("[GF3 Batch] Auto-register failed for %s: %s", dir_name, reg_err)
             else:
@@ -3888,6 +4184,85 @@ async def _handle_rebuild_psinsar_catalog(job: SystemJobORM) -> None:
         )
 
 
+async def _handle_sbas_coregistration(job: SystemJobORM) -> None:
+    if not job.task_id:
+        raise ValueError("SBAS_COREGISTRATION requires task_id for progress tracking.")
+    payload = job.payload or {}
+    run_id = str(payload.get("run_id") or "").strip()
+    if not run_id:
+        raise ValueError("SBAS_COREGISTRATION requires run_id payload.")
+
+    rlks = _normalize_positive_int(payload.get("rlks")) or 8
+    azlks = _normalize_positive_int(payload.get("azlks")) or 8
+    timeout_seconds = _normalize_positive_int(payload.get("timeout_seconds")) or 43200
+
+    await task_service.start_task(job.task_id, message="正在执行 SBAS-InSAR Gamma 共参考配准...")
+    await task_service.update_task(
+        job.task_id,
+        progress=5,
+        message=f"准备运行 Gamma SLC_coreg.py: run_id={run_id}",
+    )
+    await task_service.add_log(
+        job.task_id,
+        "INFO",
+        f"SBAS coregistration queued: run_id={run_id}, rlks={rlks}, azlks={azlks}, timeout={timeout_seconds}s",
+    )
+
+    from .sbas_insar_production_service import sbas_insar_production_service
+
+    async def _task_keepalive() -> None:
+        progress = 12
+        while True:
+            await asyncio.sleep(60)
+            progress = min(88, progress + 2)
+            await task_service.update_task(
+                job.task_id,
+                progress=progress,
+                message=f"Gamma 共参考配准仍在运行: run_id={run_id}",
+            )
+
+    runner_task = asyncio.create_task(
+        asyncio.to_thread(
+            sbas_insar_production_service.execute_coregistration,
+            run_id,
+            rlks=rlks,
+            azlks=azlks,
+            timeout_seconds=timeout_seconds,
+        )
+    )
+    keepalive_task = asyncio.create_task(_task_keepalive())
+    try:
+        result = await runner_task
+    finally:
+        keepalive_task.cancel()
+        try:
+            await keepalive_task
+        except asyncio.CancelledError:
+            pass
+
+    manifest = result.get("manifest") or {}
+    run = result.get("run") or {}
+    summary = (manifest.get("coregistration") or {}).get("summary") or {}
+    status = str(run.get("status") or manifest.get("status") or "").strip()
+    if status != "COREGISTRATION_READY":
+        raise RuntimeError(
+            "SBAS coregistration failed: "
+            f"status={status or 'UNKNOWN'}, "
+            f"missing_dates={summary.get('missing_dates') or []}, "
+            f"missing_tabs={summary.get('missing_tabs') or []}"
+        )
+
+    await task_service.update_task(
+        job.task_id,
+        status="COMPLETED",
+        progress=100,
+        message=(
+            "SBAS-InSAR 共参考配准完成: "
+            f"{summary.get('ready_secondary_count', 0)}/{summary.get('expected_secondary_count', 0)} secondary scenes ready"
+        ),
+    )
+
+
 _HANDLERS = {
     JOB_TYPE_SCAN_DATA: _handle_scan_data,
     JOB_TYPE_SCAN_ASSET_INVENTORY: _handle_scan_asset_inventory,
@@ -3918,10 +4293,14 @@ _HANDLERS = {
     JOB_TYPE_ISCE2_RUN: _handle_isce2_run,
     JOB_TYPE_PYINT_RUN: _handle_pyint_run,
     JOB_TYPE_WATER_GEOCODE: _handle_water_geocode,
+    JOB_TYPE_SAR_SCENE_PREPROCESS: _handle_sar_scene_preprocess,
     JOB_TYPE_WATER_FLOOD: _handle_water_flood,
+    JOB_TYPE_FLOOD_DETECTION: _handle_flood_detection,
     JOB_TYPE_WATER_DETECT: _handle_water_detect,
     JOB_TYPE_GF3_PROCESS: _handle_gf3_process,
+    JOB_TYPE_GF3_UNPACK: _handle_gf3_unpack,
     JOB_TYPE_GF3_BATCH_PROCESS: _handle_gf3_batch_process,
+    JOB_TYPE_SBAS_COREGISTRATION: _handle_sbas_coregistration,
 }
 
 

backend/app/services/lt_gamma_scene_service.py

@@ -0,0 +1,158 @@
+"""LT single-scene Gamma preprocessing service."""
+from __future__ import annotations
+
+import json
+import os
+import re
+from pathlib import Path
+from typing import Any
+
+from ..config import settings
+from ..models import RadarDataORM, SARSceneGeoORM
+from .pyint_service import (
+    DEFAULT_AZIMUTH_LOOKS,
+    DEFAULT_RANGE_LOOKS,
+    quote_shell,
+    resolve_gamma_env_script,
+    to_wsl_path,
+)
+from .wsl_service import run_wsl_exec
+
+
+def _safe_token(value: Any, *, default: str = "scene") -> str:
+    text = str(value or "").strip()
+    if not text:
+        text = default
+    text = re.sub(r"[^0-9A-Za-z._-]+", "_", text).strip("._-")
+    return text or default
+
+
+def _scene_date(radar: RadarDataORM) -> str:
+    for value in (radar.imaging_date, radar.file_path, radar.unique_id):
+        text = str(value or "")
+        match = re.search(r"(20\d{6})", re.sub(r"\D", "", text))
+        if match:
+            return match.group(1)
+        match = re.search(r"(20\d{6})", text)
+        if match:
+            return match.group(1)
+    raise ValueError(f"Cannot infer LT scene date for radar_data id={radar.id}")
+
+
+def _prepared_dem_path() -> str:
+    if str(settings.PYINT_DEM_MODE or "").strip().lower() == "prepared_file":
+        return (
+            settings.PYINT_PREPARED_DEM_PATH
+            or settings.ISCE2_DEM_PATH
+            or settings.IDL_DINSAR_DEM_BASE_FILE
+            or ""
+        )
+    return ""
+
+
+def _build_shell_command(parts: list[str]) -> str:
+    return " ".join(quote_shell(part) for part in parts)
+
+
+def _runner_script() -> Path:
+    return Path(settings.PROJECT_ROOT) / "backend" / "app" / "pyint_pipeline" / "run_gamma_scene_preprocess.py"
+
+
+def run_lt_gamma_scene_preprocess(
+    *,
+    radar: RadarDataORM,
+    scene: SARSceneGeoORM,
+    job_id: str | None = None,
+) -> dict[str, Any]:
+    if not settings.PYINT_ENABLED:
+        raise RuntimeError("PYINT_ENABLED=false; LT Gamma scene preprocessing is disabled")
+    if not radar.file_path:
+        raise ValueError(f"RadarDataORM id={radar.id} has no file_path")
+
+    date = _scene_date(radar)
+    token = _safe_token(radar.unique_id or Path(str(radar.file_path)).stem or f"radar_{radar.id}")
+    run_name = _safe_token(f"lt_{date}_{token}_scene_{scene.id}_{job_id or 'manual'}")
+    work_dir = Path(settings.SAR_ANALYSIS_WORK_ROOT) / "lt_gamma" / run_name
+    output_dir = work_dir / "output"
+    work_dir.mkdir(parents=True, exist_ok=True)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    pyint_home = settings.PYINT_HOME
+    if not pyint_home:
+        raise RuntimeError("PYINT_HOME is not configured")
+    pyint_python = settings.PYINT_WSL_PYTHON or settings.WSL_SHARED_PYTHON
+    if not pyint_python:
+        raise RuntimeError("PYINT_WSL_PYTHON is not configured")
+
+    runner = _runner_script()
+    if not runner.is_file():
+        raise FileNotFoundError(f"Gamma scene runner not found: {runner}")
+
+    args = [
+        pyint_python,
+        to_wsl_path(str(runner)),
+        "--source-path",
+        to_wsl_path(str(radar.file_path)),
+        "--output-dir",
+        to_wsl_path(str(output_dir)),
+        "--work-dir",
+        to_wsl_path(str(work_dir)),
+        "--pyint-home",
+        to_wsl_path(str(pyint_home)),
+        "--dem-root",
+        to_wsl_path(str(settings.PYINT_DEM_ROOT)),
+        "--prepared-dem-path",
+        to_wsl_path(_prepared_dem_path()),
+        "--project-name",
+        run_name,
+        "--date",
+        date,
+        "--satellite-family",
+        "LT1",
+        "--range-looks",
+        str(DEFAULT_RANGE_LOOKS),
+        "--azimuth-looks",
+        str(DEFAULT_AZIMUTH_LOOKS),
+        "--geo-interp",
+        str(settings.PYINT_GEO_INTERP or "1"),
+        "--nodata-value",
+        str(float(settings.SAR_ANALYSIS_NODATA_VALUE)),
+        "--to-db",
+    ]
+
+    gamma_env_script = resolve_gamma_env_script(settings.PYINT_GAMMA_ENV_SCRIPT)
+    prefix = ""
+    if gamma_env_script:
+        prefix = f". {quote_shell(to_wsl_path(gamma_env_script))} >/dev/null 2>&1 || exit 1; "
+    command = prefix + f"export PYTHONPATH={quote_shell(to_wsl_path(str(pyint_home)))}:$PYTHONPATH; " + _build_shell_command(args)
+
+    rc, stdout, stderr = run_wsl_exec(
+        ["bash", "-lc", command],
+        distro=settings.PYINT_WSL_DISTRO or settings.WSL_DISTRO,
+        timeout=int(settings.PYINT_DEFAULT_TIMEOUT_SECONDS or 43200),
+    )
+    if rc != 0:
+        detail = (stderr or stdout or "").strip()
+        raise RuntimeError(f"LT Gamma scene preprocessing failed rc={rc}: {detail}")
+
+    manifest_path = output_dir / "manifest.json"
+    if not manifest_path.is_file():
+        raise RuntimeError(f"LT Gamma scene preprocessing produced no manifest: {manifest_path}")
+    try:
+        manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
+    except Exception as exc:
+        raise RuntimeError(f"Cannot read LT Gamma manifest: {manifest_path}: {exc}") from exc
+
+    analysis_tif_path = manifest.get("analysis_tif_path")
+    if analysis_tif_path and str(analysis_tif_path).startswith("/mnt/"):
+        # The service registers the Windows-side path below.
+        manifest["analysis_tif_path_wsl"] = analysis_tif_path
+        manifest["analysis_tif_path"] = str(output_dir / "analysis_ready.tif")
+    manifest["service"] = {
+        "work_dir": str(work_dir),
+        "output_dir": str(output_dir),
+        "job_id": job_id,
+        "stdout": stdout[-4000:] if stdout else "",
+        "stderr": stderr[-4000:] if stderr else "",
+    }
+    return manifest

backend/app/services/root_registry_service.py

@@ -235,6 +235,15 @@ def _build_root_specs_from_settings() -> List[RootSpec]:
             scan_mode="directory_walk",
         )
     )
+    specs.extend(
+        _iter_multi_root_specs(
+            env_var="GF3_ARCHIVE_SOURCE_DIRS",
+            paths=split_env_paths(settings.GF3_ARCHIVE_SOURCE_DIRS),
+            root_role="source_pool_gf3_archive",
+            display_prefix="GF3 Archive Pool",
+            scan_mode="archive_walk",
+        )
+    )
     specs.extend(
         _iter_multi_root_specs(
             env_var="GF3_SOURCE_DIRS",
@@ -253,6 +262,24 @@ def _build_root_specs_from_settings() -> List[RootSpec]:
             scan_mode="scene_directory",
         )
     )
+    specs.extend(
+        _iter_single_root_specs(
+            env_var="SAR_ANALYSIS_READY_ROOT",
+            path=settings.SAR_ANALYSIS_READY_ROOT,
+            root_role="sar_analysis_ready",
+            display_name="SAR Analysis-ready GeoTIFF Root",
+            scan_mode="scene_directory",
+        )
+    )
+    specs.extend(
+        _iter_single_root_specs(
+            env_var="SAR_ANALYSIS_WORK_ROOT",
+            path=settings.SAR_ANALYSIS_WORK_ROOT,
+            root_role="sar_analysis_work",
+            display_name="SAR Analysis Work Root",
+            scan_mode="directory_walk",
+        )
+    )
     orbit_source_paths = split_env_paths(settings.ORBIT_SOURCE_DIRS)
     if not orbit_source_paths:
         orbit_source_paths = split_env_paths(settings.MONITOR_ORBIT_DIR)

backend/app/services/sar_analysis_ready_service.py

@@ -0,0 +1,360 @@
+"""Analysis-ready SAR GeoTIFF registration for flood/water algorithms.
+
+This service owns the common contract between satellite-specific preprocessing
+and downstream flood/water algorithms: one geocoded, single-band GeoTIFF plus
+sidecar metadata under SAR_ANALYSIS_READY_ROOT.
+"""
+from __future__ import annotations
+
+import json
+import math
+import os
+import re
+import shutil
+from pathlib import Path
+from typing import Any
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from ..config import settings
+from ..models import RadarDataORM, SARSceneGeoORM
+from ..utils import normalize_satellite_family
+
+_SAFE_TEXT_RE = re.compile(r"[^0-9A-Za-z._-]+")
+_POLARIZATION_PRIORITY = ("HH", "VV", "HV", "VH")
+
+
+def _safe_slug(value: Any, *, default: str = "unknown") -> str:
+    text = str(value or "").strip()
+    if not text:
+        text = default
+    text = _SAFE_TEXT_RE.sub("_", text).strip("._-")
+    return text or default
+
+
+def _scene_family(radar: RadarDataORM | None) -> str:
+    family = normalize_satellite_family(
+        getattr(radar, "satellite_family", None) or getattr(radar, "satellite", None)
+    )
+    return _safe_slug(family or "SAR").upper()
+
+
+def _scene_date(radar: RadarDataORM | None) -> str:
+    text = str(getattr(radar, "imaging_date", None) or "").strip()
+    match = re.search(r"(20\d{6})", re.sub(r"\D", "", text))
+    if match:
+        return match.group(1)
+    return "unknown_date"
+
+
+def _scene_token(
+    *,
+    radar: RadarDataORM | None,
+    scene: SARSceneGeoORM,
+    polarization: str | None = None,
+) -> str:
+    unique = getattr(radar, "unique_id", None) or f"radar_{getattr(radar, 'id', scene.radar_data_id)}"
+    parts = [_scene_date(radar), _safe_slug(unique), f"scene_{scene.id}"]
+    if polarization:
+        parts.append(_safe_slug(polarization).upper())
+    return "_".join(parts)
+
+
+def scene_analysis_dir(
+    *,
+    radar: RadarDataORM | None,
+    scene: SARSceneGeoORM,
+    engine: str,
+    profile: str,
+    polarization: str | None = None,
+) -> Path:
+    return (
+        Path(settings.SAR_ANALYSIS_READY_ROOT)
+        / _scene_family(radar)
+        / _safe_slug(engine)
+        / _safe_slug(profile)
+        / _scene_date(radar)
+        / _scene_token(radar=radar, scene=scene, polarization=polarization)
+    )
+
+
+def _write_json(path: Path, payload: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8") as stream:
+        json.dump(payload, stream, ensure_ascii=False, indent=2, default=str)
+
+
+def _link_or_copy(source: Path, target: Path) -> str:
+    target.parent.mkdir(parents=True, exist_ok=True)
+    if source.resolve() == target.resolve():
+        return "same_path"
+    if target.exists():
+        target.unlink()
+    try:
+        os.link(source, target)
+        return "hardlink"
+    except OSError:
+        shutil.copy2(source, target)
+        return "copy"
+
+
+def _choose_gf3_l2_tif(l2_dir: str, polarization: str | None = None) -> Path:
+    root = Path(os.path.normpath(str(l2_dir or "").strip()))
+    if root.is_file():
+        return root
+    if not root.is_dir():
+        raise FileNotFoundError(f"GF3 L2 directory does not exist: {l2_dir}")
+
+    candidates = sorted(
+        path
+        for path in root.rglob("*")
+        if path.is_file()
+        and path.suffix.lower() in {".tif", ".tiff"}
+        and "L2" in path.name.upper()
+    )
+    if not candidates:
+        raise FileNotFoundError(f"No GF3 L2 GeoTIFF found in: {l2_dir}")
+
+    requested = str(polarization or "").strip().upper()
+    if requested:
+        for path in candidates:
+            if requested in path.name.upper():
+                return path
+
+    for pol in _POLARIZATION_PRIORITY:
+        for path in candidates:
+            if pol in path.name.upper():
+                return path
+    return candidates[0]
+
+
+def _infer_polarization_from_path(path: Path) -> str | None:
+    upper_name = path.name.upper()
+    for pol in _POLARIZATION_PRIORITY:
+        if pol in upper_name:
+            return pol
+    return None
+
+
+def _raster_quality(path: Path) -> dict[str, Any]:
+    try:
+        import numpy as np
+        import rasterio
+    except Exception as exc:
+        return {"ok": False, "warning": f"rasterio unavailable: {exc}"}
+
+    with rasterio.open(path) as src:
+        if src.height > 2048 or src.width > 2048:
+            scale = min(1024 / src.width, 1024 / src.height)
+            out_width = max(1, int(src.width * scale))
+            out_height = max(1, int(src.height * scale))
+            sampled = src.read(1, out_shape=(out_height, out_width), masked=True)
+        else:
+            sampled = src.read(1, masked=True)
+
+        valid = sampled.compressed() if hasattr(sampled, "compressed") else sampled[np.isfinite(sampled)]
+        bounds = src.bounds
+        transform = src.transform
+        quality: dict[str, Any] = {
+            "ok": True,
+            "driver": src.driver,
+            "width": src.width,
+            "height": src.height,
+            "count": src.count,
+            "dtype": str(src.dtypes[0]) if src.dtypes else None,
+            "crs": src.crs.to_string() if src.crs else None,
+            "bounds": {
+                "left": bounds.left,
+                "bottom": bounds.bottom,
+                "right": bounds.right,
+                "top": bounds.top,
+            },
+            "transform": list(transform)[:6],
+            "nodata": src.nodata,
+            "valid_sample_count": int(valid.size),
+            "valid_sample_percent": float(valid.size / sampled.size) if sampled.size else 0.0,
+        }
+        if valid.size:
+            quality.update(
+                {
+                    "sample_min": float(np.nanmin(valid)),
+                    "sample_max": float(np.nanmax(valid)),
+                    "sample_mean": float(np.nanmean(valid)),
+                    "sample_p02": float(np.nanpercentile(valid, 2)),
+                    "sample_p98": float(np.nanpercentile(valid, 98)),
+                }
+            )
+        return quality
+
+
+def _pixel_size_m_from_quality(quality: dict[str, Any]) -> float | None:
+    try:
+        transform = quality.get("transform") or []
+        xres = abs(float(transform[0]))
+        yres = abs(float(transform[4]))
+        crs = str(quality.get("crs") or "").upper()
+        if not xres or not yres:
+            return None
+        if crs and "4326" not in crs:
+            return round((xres + yres) / 2.0, 3)
+        bounds = quality.get("bounds") or {}
+        lat = (float(bounds.get("bottom", 0.0)) + float(bounds.get("top", 0.0))) / 2.0
+        meters_per_degree_lon = 111320.0 * max(0.01, math.cos(math.radians(lat)))
+        x_m = xres * meters_per_degree_lon
+        y_m = yres * 110540.0
+        return round((x_m + y_m) / 2.0, 3)
+    except Exception:
+        return None
+
+
+def _build_preview_png(source: Path, target: Path) -> str | None:
+    try:
+        import numpy as np
+        import rasterio
+        from PIL import Image
+    except Exception:
+        return None
+
+    target.parent.mkdir(parents=True, exist_ok=True)
+    with rasterio.open(source) as src:
+        if src.height > 1600 or src.width > 1600:
+            scale = min(1600 / src.width, 1600 / src.height)
+            out_width = max(1, int(src.width * scale))
+            out_height = max(1, int(src.height * scale))
+            band = src.read(1, out_shape=(out_height, out_width), masked=True)
+        else:
+            band = src.read(1, masked=True)
+        data = band.filled(np.nan).astype("float32")
+
+    valid = data[np.isfinite(data)]
+    if valid.size:
+        p2, p98 = np.nanpercentile(valid, [2, 98])
+        normalized = np.clip((data - p2) / max(p98 - p2, 1e-6), 0, 1)
+        gray = (normalized * 255).astype("uint8")
+    else:
+        gray = np.zeros(data.shape, dtype="uint8")
+    alpha = np.where(np.isfinite(data), 255, 0).astype("uint8")
+    rgba = np.stack([gray, gray, gray, alpha], axis=-1)
+    Image.fromarray(rgba, "RGBA").save(target)
+    return str(target)
+
+
+async def _get_or_create_scene(db: AsyncSession, radar_id: int) -> SARSceneGeoORM:
+    result = await db.execute(select(SARSceneGeoORM).where(SARSceneGeoORM.radar_data_id == radar_id))
+    scene = result.scalar_one_or_none()
+    if scene:
+        return scene
+    scene = SARSceneGeoORM(radar_data_id=radar_id, status="PENDING")
+    db.add(scene)
+    await db.flush()
+    return scene
+
+
+async def register_analysis_ready_tif(
+    *,
+    db: AsyncSession,
+    scene: SARSceneGeoORM,
+    radar: RadarDataORM | None,
+    source_tif_path: str,
+    engine: str,
+    profile: str,
+    backscatter_unit: str,
+    polarization: str | None = None,
+    metadata: dict[str, Any] | None = None,
+    copy_mode: str = "link_or_copy",
+) -> dict[str, Any]:
+    source = Path(os.path.normpath(str(source_tif_path or "").strip()))
+    if not source.is_file():
+        raise FileNotFoundError(f"Analysis-ready source GeoTIFF does not exist: {source}")
+
+    out_dir = scene_analysis_dir(
+        radar=radar,
+        scene=scene,
+        engine=engine,
+        profile=profile,
+        polarization=polarization,
+    )
+    target_tif = out_dir / "analysis_ready.tif"
+    transfer = "none"
+    if copy_mode == "reference":
+        target_tif = source
+    else:
+        transfer = _link_or_copy(source, target_tif)
+
+    quality = _raster_quality(target_tif)
+    preview_path = _build_preview_png(target_tif, out_dir / "preview.png")
+    manifest = {
+        "scene_id": scene.id,
+        "radar_data_id": scene.radar_data_id,
+        "source_tif_path": str(source),
+        "analysis_tif_path": str(target_tif),
+        "analysis_dir": str(out_dir),
+        "analysis_preview_path": preview_path,
+        "engine": engine,
+        "profile": profile,
+        "backscatter_unit": backscatter_unit,
+        "polarization": polarization,
+        "transfer": transfer,
+        "metadata": metadata or {},
+        "quality": quality,
+    }
+    _write_json(out_dir / "manifest.json", manifest)
+    _write_json(out_dir / "quality.json", quality)
+
+    scene.geo_path = str(target_tif)
+    scene.analysis_tif_path = str(target_tif)
+    scene.analysis_dir = str(out_dir)
+    scene.analysis_preview_path = preview_path
+    scene.analysis_engine = engine
+    scene.analysis_profile = profile
+    scene.analysis_backscatter_unit = backscatter_unit
+    nodata_value = quality.get("nodata")
+    scene.analysis_nodata_value = (
+        float(nodata_value)
+        if nodata_value is not None
+        else float(settings.SAR_ANALYSIS_NODATA_VALUE)
+    )
+    scene.analysis_metadata_json = {**(metadata or {}), "manifest_path": str(out_dir / "manifest.json")}
+    scene.analysis_quality_json = quality
+    scene.pixel_size_m = _pixel_size_m_from_quality(quality) or scene.pixel_size_m
+    scene.status = "DONE"
+    scene.error_msg = None
+
+    return manifest
+
+
+async def standardize_gf3_l2_for_radar(
+    *,
+    db: AsyncSession,
+    radar_id: int,
+    l2_path: str | None = None,
+    polarization: str | None = None,
+) -> dict[str, Any]:
+    radar = await db.get(RadarDataORM, int(radar_id))
+    if not radar:
+        raise ValueError(f"RadarDataORM id={radar_id} does not exist")
+
+    scene = await _get_or_create_scene(db, int(radar_id))
+    source_root = l2_path or radar.file_path
+    selected_tif = _choose_gf3_l2_tif(source_root, polarization=polarization or radar.polarization)
+    selected_pol = polarization or _infer_polarization_from_path(selected_tif)
+
+    manifest = await register_analysis_ready_tif(
+        db=db,
+        scene=scene,
+        radar=radar,
+        source_tif_path=str(selected_tif),
+        engine="gf3_gdal",
+        profile="gf3_l1a_l2_rpc",
+        backscatter_unit="sigma0_db",
+        polarization=selected_pol,
+        metadata={
+            "source": "GF3 L2",
+            "source_l2_path": str(selected_tif),
+            "source_l2_dir": str(Path(source_root).resolve()) if source_root else None,
+            "available_polarization": radar.polarization,
+        },
+    )
+    await db.commit()
+    return manifest

backend/app/services/water_extraction_service.py

@@ -0,0 +1,33 @@
+"""Water extraction processors for the flood-analysis pipeline."""
+from __future__ import annotations
+
+from typing import Any
+
+
+def run_otsu_water_extraction(
+    *,
+    input_path: str,
+    output_dir: str,
+    job_id: str | None = None,
+) -> dict[str, Any]:
+    """Run the fast Otsu water-extraction processor.
+
+    This wraps the legacy implementation while exposing the terminology used by
+    the flood pipeline: extraction, processor and threshold_value.
+    """
+    from .water_detect_service import run_water_detection
+
+    result = run_water_detection(
+        geo_tiff_path=input_path,
+        output_dir=output_dir,
+        job_id=job_id,
+    )
+    result["processor"] = "otsu"
+    if "threshold_value" not in result and "otsu_threshold_db" in result:
+        result["threshold_value"] = result.get("otsu_threshold_db")
+    return result
+
+
+def run_envi_water_extraction(*args: Any, **kwargs: Any) -> dict[str, Any]:
+    """Placeholder for the future ENVI/SARscape precise extractor."""
+    raise NotImplementedError("ENVI/SARscape water extraction is not wired yet")

docs/FLOOD_GEOTIFF_GAMMA_PREPROCESS_DESIGN_20260515.md

@@ -0,0 +1,515 @@
+# 洪涝模块 GeoTIFF 化与 GAMMA 前处理评估
+
+更新日期：2026-05-15
+
+## 1. 结论
+
+洪涝模块后续应把业务算法全部下放到标准 GeoTIFF 层：
+
+```text
+原始 SAR 数据
+  -> 可插拔前处理器：GAMMA 优先，其他处理器兜底
+  -> 分析级 GeoTIFF
+  -> Python 水体提取 / 洪涝检测 / 套合分析 / 产品生成
+```
+
+ENVI/SARscape 不再作为洪涝算法主线。它可以短期保留为历史兼容处理器，但不应继续决定数据模型、接口命名和算法逻辑。
+
+GAMMA 适合作为第一候选前处理底座，原因是当前项目已经具备 WSL、PyINT、GAMMA 环境注入、DEM、`geocode_back`、`data2geotiff` 和 D-InSAR 生产发布经验。但现有 GAMMA/PyINT 接入是 D-InSAR pair 流程，不是洪涝需要的“单景分析级 GeoTIFF 预处理服务”。因此不能直接把 `lt1_gamma_dinsar` 或 `s1_gamma_dinsar` 原样接到洪涝预处理按钮上，必须新增单景前处理适配层。
+
+## 2. 当前真实情况
+
+### 2.1 洪涝模块当前仍绑定 ENVI/SARscape
+
+当前 `/flood/preprocess` 最终仍执行旧的 `water_service.run_geocoding_workflow()`，内部流程是：
+
+```text
+SARsImportLuTan1
+  -> SARsBasicMultilooking
+  -> SARsBasicGeocoding
+```
+
+输出写入 `SARSceneGeoORM.geo_path`，语义是 SARscape 地理编码 dB 影像 base path，不是标准 GeoTIFF 交付物。
+
+当前 `/flood/detections` 后台任务仍调用 `water_service.run_flood_detection()`，内部流程是：
+
+```text
+SARsBasicFeFloodingClassification
+  -> 可选 SARsBasicFeFloodingClassificationRefinement
+  -> rasterio 统计分类结果面积
+```
+
+分类值约定是：
+
+```text
+0 = 无数据 / 背景
+1 = 稳定水体
+2 = 洪涝 / 新增水体
+3 = 高散射点
+4 = 非水体
+```
+
+这说明当前“洪涝检测算法”实际是 SARscape 黑盒，不是平台自有算法。
+
+### 2.2 已经接近目标形态的部分
+
+以下部分已经更接近“标准栅格 + Python 算法”的目标：
+
+```text
+水体提取：
+  water_extraction_service -> water_detect_service
+  Otsu + DEM/坡度约束 + 形态学 + 连通分量过滤
+
+套合分析：
+  flood_overlay_service
+  读取 classified 栅格，class=2 转矢量，套合灾害点、DInSAR 产品和 AOI
+
+GF3：
+  gf3_service
+  Python/GDAL 完成 L1A -> L2：辐射定标 + RPC 几何校正
+```
+
+其中 GF3 路线虽然不走 GAMMA，但输出理念已经是“处理到 GeoTIFF 后进入平台算法”。它应该作为 `external/gdal` 前处理器接入统一 Scene，而不是继续挂在 `/water/gf3-process` 下。
+
+### 2.3 当前 GAMMA/PyINT 接入情况
+
+当前仓库里的 GAMMA/PyINT 能力主要服务 D-InSAR 生产：
+
+```text
+backend/app/dinsar_engines/pyint_engine.py
+  - lt1_gamma_dinsar
+  - s1_gamma_dinsar
+
+backend/app/pyint_pipeline/run_lt1_pyint_pipeline.py
+  - LT-1 托管 runner
+  - geocode_gamma.py
+  - data2geotiff
+  - 标准产品发布
+
+backend/app/pyint_pipeline/run_s1_pyint_pipeline.py
+  - Sentinel-1 runner
+  - 复用 LT-1 runner 的日志、发布和质量检查框架
+
+deploy/wsl/profiles/gamma_env.sh
+  - 注入 GAMMA_HOME
+  - 注入 GAMMA 各模块 bin/scripts
+  - 注入 third_party/PyINT/pyint
+```
+
+当前 `.env` 中 PyINT/GAMMA 是启用状态：
+
+```text
+PYINT_ENABLED=true
+PYINT_WSL_DISTRO=Ubuntu-24.04
+PYINT_WSL_PYTHON=/home/administrator/miniconda3/envs/insar_wsl_v1/bin/python
+PYINT_DEM_MODE=prepared_file
+PYINT_PREPARED_DEM_PATH=D:\DEM\SRTMDEM_RSP_SARscape.wgs84
+PYINT_GEO_INTERP=1
+PYINT_GAMMA_NODATA_VALUE=-9999.0
+```
+
+需要注意一个不一致点：`pyint_engine.py` 已经暴露 `s1_gamma_dinsar`，但 `wsl_runtime_registry.py` 当前允许的 PyINT operation 仍只有 `lt1_gamma_dinsar` 和 `gamma_refine`。如果后续洪涝或生产任务走 WSL Broker，需要同步补齐允许列表。
+
+### 2.4 GAMMA 现有输出与洪涝需求的差距
+
+现有 D-InSAR GAMMA 链已经能导出 GeoTIFF，但输出重点是 pair 产品：
+
+```text
+disp.tif
+disp_unmasked.tif
+coh.tif
+wrapped_phase.tif
+vertical displacement
+look vector
+```
+
+`hyp3format_gamma.py` 也能把 `geocode_gamma.py` 产生的 geocoded GAMMA 二进制导出为 GeoTIFF，包含：
+
+```text
+amp
+corr
+dem
+unw_phase
+wrapped_phase
+los_disp
+vert_disp
+lv_theta
+lv_phi
+```
+
+但洪涝模块真正需要的是单景产品：
+
+```text
+analysis_ready.tif
+  - 单景后向散射强度
+  - 明确单位：sigma0_db / gamma0_db / power_db
+  - 明确极化：VV / VH / HH / HV
+  - 明确 CRS、分辨率、nodata、覆盖范围
+  - 可与另一景重采样到同一网格
+```
+
+当前没有一个独立的 `gamma_single_scene_preprocess` 服务稳定地产出上述文件。现有 D-InSAR 流程里的 `geo_<master>_*.amp` 可以证明 GAMMA 有地理编码 MLI/AMP 的能力，但它嵌在 pair 配准和干涉流程中，不能直接作为洪涝单景预处理接口。
+
+## 3. 目标数据契约
+
+后续洪涝模块不再以 `geo_path` 表示模糊的“地理编码产物”，而应引入明确的分析级 Scene 产物语义。
+
+建议模型命名：
+
+```text
+SarAnalysisScene / AnalysisReadyScene
+```
+
+最小字段：
+
+```text
+id
+radar_data_id
+engine                         # gamma / gf3_gdal / snap / envi_legacy / external
+engine_profile                 # lt1_gamma_scene / s1_gamma_scene / gf3_rpc_l2
+analysis_tif_path              # 主分析 GeoTIFF
+preview_path
+coverage_polygon
+satellite
+satellite_family               # LT1 / S1 / GF3 / ...
+imaging_date
+polarization
+orbit_direction
+relative_orbit
+backscatter_unit               # sigma0_db / gamma0_db / power_db / unknown
+crs
+pixel_size_x
+pixel_size_y
+nodata_value
+grid_id                        # 同网格配对时使用
+dem_path
+incidence_angle_tif_path       # 可选
+layover_shadow_mask_path       # 可选
+quality_json
+lineage_json
+status
+error_msg
+created_at
+updated_at
+```
+
+输出文件要求：
+
+```text
+analysis_ready.tif:
+  - GeoTIFF，优先 COG
+  - float32
+  - nodata = -9999
+  - 推荐值域为 dB
+  - rasterio/GDAL 可直接读取
+
+preview.png:
+  - 仅用于地图快视
+
+metadata.json:
+  - 记录处理器、命令、输入资产、DEM、参数、软件版本、质量检查
+```
+
+## 4. 目标流水线
+
+### 4.1 前处理器只负责到 GeoTIFF 为止
+
+前处理器接口建议统一为：
+
+```text
+submit_scene_preprocess(radar_data_id, engine="gamma", profile="auto")
+```
+
+处理器职责：
+
+```text
+1. 识别输入资产和传感器类型。
+2. 导入原始产品到处理器内部格式。
+3. 做必要的辐射定标。
+4. 做多视 / 滤波 / speckle 控制。
+5. 使用 DEM 做地形校正或地理编码。
+6. 导出 analysis_ready.tif。
+7. 生成 coverage、preview、metadata、quality。
+8. 注册 SarAnalysisScene。
+```
+
+处理器不负责：
+
+```text
+1. 水体提取。
+2. 洪涝分类。
+3. 洪涝矢量化。
+4. 灾害点套合。
+5. 报告生成。
+```
+
+### 4.2 洪涝算法只认 GeoTIFF
+
+水体提取：
+
+```text
+analysis_ready.tif
+  -> 读取 dB 栅格
+  -> 有效像元 / nodata / DEM / 坡度约束
+  -> Otsu 或自适应阈值
+  -> 形态学与连通域过滤
+  -> water_mask.tif + water_vector.geojson
+```
+
+洪涝检测：
+
+```text
+pre analysis_ready.tif
+post analysis_ready.tif
+  -> 检查 CRS / 分辨率 / 极化 / 覆盖范围
+  -> 重采样到同一网格
+  -> 分别生成 pre_water_mask / post_water_mask
+  -> post_water && !pre_water = 洪涝 / 新增水体
+  -> pre_water && post_water = 稳定水体
+  -> 结合 dB 差值、DEM 坡度、连通域和面积阈值过滤
+  -> classified.tif
+```
+
+建议分类值继续沿用现有约定，降低前端和套合分析改动量：
+
+```text
+0 = nodata
+1 = stable_water
+2 = flood
+3 = high_scatter_or_uncertain
+4 = non_water
+```
+
+套合分析：
+
+```text
+classified.tif
+  -> class=2 转矢量
+  -> 灾害点命中
+  -> 近洪涝风险点
+  -> DInSAR 产品 footprint 相交
+  -> AOI / 行政区面积统计
+```
+
+## 5. GAMMA 适配性判断
+
+| 能力项 | 当前情况 | 适配判断 |
+| --- | --- | --- |
+| WSL/GAMMA 环境 | 已有 `gamma_env.sh`，能注入 GAMMA 与 PyINT 路径 | 可复用 |
+| LT-1 导入 | 已有 `down2slc_LT1_*` 与精轨桥接，历史上处理过几何问题 | 可作为第一批验证对象，但需专项 QA |
+| Sentinel-1 导入 | PyINT 脚本支持 ZIP、EOF、burst、TOPS 相关流程，项目已有 `s1_gamma_dinsar` runner | 可做第二批，但 runtime operation 白名单需补齐 |
+| GF3 | 当前项目已有 Python/GDAL L1A->L2，不依赖 GAMMA | 不建议强行走 GAMMA，先走 `gf3_gdal` 处理器 |
+| 地理编码 | 已有 `geocode_gamma.py`、`geocode_back`、DEM 生成链 | 可复用思想和部分脚本 |
+| GeoTIFF 导出 | 已有 `data2geotiff`、HyP3/标准产品导出经验 | 可复用 |
+| 单景分析级输出 | 当前没有独立服务，D-InSAR 中只顺带产出 master amp | 需要新建 |
+| 辐射定标语义 | Sentinel-1 导入使用 calibration XML；LT-1/GF3 各有差异 | 必须在 metadata 中明确单位，不能含糊写 dB |
+| 生产稳定性 | 当前 D-InSAR runner 已有日志、repair、quality、manifest | 可复用框架，但不能复用 pair 假设 |
+
+结论：GAMMA 适合做洪涝模块的优先前处理引擎，但第一阶段目标应是：
+
+```text
+LT-1 单景 -> GAMMA -> analysis_ready.tif
+```
+
+跑通并验证后再扩展：
+
+```text
+Sentinel-1 ZIP -> GAMMA -> analysis_ready.tif
+GF3 L1A -> gf3_gdal -> analysis_ready.tif
+```
+
+## 6. 不应采用的方案
+
+### 6.1 不应继续以 ENVI/SARscape 作为洪涝主线
+
+原因：
+
+```text
+1. 数据格式和任务对象绑定 SARscape。
+2. 洪涝分类是黑盒，难以解释和调参。
+3. 服务器部署依赖重。
+4. 后续多源数据扩展会反复补导入适配器。
+5. 输出不天然适合平台统一发布。
+```
+
+### 6.2 不应把洪涝算法绑定到 GAMMA
+
+GAMMA 应只负责把原始 SAR 处理成标准 GeoTIFF。洪涝算法不能依赖 GAMMA 中间二进制，也不能要求输入来自 GAMMA。否则只是把“绑定 ENVI”换成“绑定 GAMMA”。
+
+正确边界是：
+
+```text
+GAMMA / GF3_GDAL / SNAP / ISCE / external
+  -> analysis_ready.tif
+  -> 同一套 Python 洪涝算法
+```
+
+### 6.3 不应直接复用 D-InSAR pair profile 做单景预处理
+
+现有 `lt1_gamma_dinsar` / `s1_gamma_dinsar` 是 pair 生产 profile，包含：
+
+```text
+master/slave
+coreg
+diff
+unwrap
+geocode pair products
+disp/coh export
+```
+
+洪涝单景预处理只需要：
+
+```text
+single scene import
+radiometric calibration
+multilook/filter
+terrain correction/geocoding
+GeoTIFF export
+QA/register
+```
+
+两者不能混成一个 profile。
+
+## 7. 建议实施阶段
+
+### Phase 1：建立 GeoTIFF 数据契约
+
+```text
+1. 新建或扩展 Scene 表，明确 analysis_tif_path。
+2. 保留旧 SARSceneGeoORM.geo_path 兼容字段。
+3. /flood/scenes 改为优先返回 analysis_tif_path。
+4. 前端把“可处理”判断改为是否存在 analysis_ready scene。
+```
+
+### Phase 2：先把洪涝检测改成 Python GeoTIFF 算法
+
+```text
+1. 新建 flood_detection_service.py。
+2. 输入 pre_scene.analysis_tif_path 和 post_scene.analysis_tif_path。
+3. 对齐网格。
+4. 复用水体提取算法生成 pre/post water mask。
+5. 输出 classified.tif。
+6. 统计 flood_area_km2 和 stable_water_area_km2。
+```
+
+这一步可以先用 ENVI 现有 geocode 结果或 GF3 L2 GeoTIFF 做输入测试，目的是先解除洪涝分类对 SARscape 的依赖。
+
+### Phase 3：新增 GAMMA LT-1 单景前处理器
+
+建议新增：
+
+```text
+backend/app/services/sar_scene_preprocess_service.py
+backend/app/services/gamma_scene_preprocess_service.py
+backend/app/pyint_pipeline/run_gamma_scene_preprocess.py
+```
+
+最小 runner：
+
+```text
+1. stage LT-1 输入资产。
+2. 调用 LT-1 导入脚本，产出 SLC。
+3. multi_look 生成 MLI/AMP。
+4. generate_rdc_dem 或等价 DEM 几何。
+5. geocode_back 把 AMP 映射到地理网格。
+6. data2geotiff 输出 analysis_ready.tif。
+7. 可选转 COG。
+8. rasterio 质量检查。
+```
+
+### Phase 4：接 Sentinel-1 和 GF3
+
+Sentinel-1：
+
+```text
+1. 复用 Sentinel-1 ZIP/SAFE/EOF 管理设计。
+2. 新增 s1_gamma_scene profile。
+3. 不走 diff/unwrap，只产出单景 analysis_ready.tif。
+4. 修正 WSL runtime operation 白名单。
+```
+
+GF3：
+
+```text
+1. 保留 gf3_service.py 的 GDAL/RPC 路线。
+2. 输出注册为 analysis_ready scene。
+3. 不强行改成 GAMMA。
+```
+
+### Phase 5：退役 ENVI 洪涝主线
+
+```text
+1. `/flood/detections` 默认使用 GeoTIFF 算法。
+2. ENVI/SARscape 作为 `envi_legacy` 处理器保留一段时间。
+3. 新任务不再默认创建 SARscape 洪涝分类。
+4. 文档、界面和部署说明移除“ENVI 是洪涝必须项”的表述。
+```
+
+## 8. 验收标准
+
+### 8.1 Scene 前处理
+
+```text
+1. 任意一个完成的 analysis_ready.tif 可以被 rasterio 打开。
+2. CRS、bounds、transform、nodata 完整。
+3. 有效像元比例达到阈值。
+4. dB 值域在合理范围内。
+5. metadata.json 能追溯输入资产、处理器、DEM、命令和版本。
+6. preview 和 coverage 能在地图上显示和清除。
+```
+
+### 8.2 洪涝检测
+
+```text
+1. pre/post 两个 GeoTIFF 可自动对齐到同一网格。
+2. classified.tif 只依赖 Python/rasterio/numpy/scipy/skimage，不调用 ENVI/SARscape。
+3. class=1/2 面积统计稳定。
+4. 套合分析可直接读取 classified.tif。
+5. 同一算法能跑 GAMMA TIF、GF3 L2 TIF 和其他外部 TIF。
+```
+
+### 8.3 GAMMA 适配
+
+```text
+1. LT-1 单景 GAMMA 前处理能稳定产出 analysis_ready.tif。
+2. 失败时有明确阶段日志，不出现“任务成功但全 0”的假成功。
+3. data2geotiff 输出通过 GDAL/rasterio 质量检查。
+4. 处理器输出不要求后续算法知道 GAMMA 中间目录结构。
+```
+
+## 9. 对部署的影响
+
+Git 可以提供平台代码、Python 算法、任务编排、数据库迁移和前端能力，但不能内置 GAMMA 商业软件本体。另一台服务器部署时必须额外满足：
+
+```text
+1. WSL 或 Linux 环境可用。
+2. GAMMA 安装路径可被 gamma_env.sh 发现，或配置 PYINT_GAMMA_HOME/GAMMA_HOME。
+3. PyINT 代码随仓库存在。
+4. WSL Python/conda 环境具备 rasterio、GDAL、numpy、scipy、skimage 等依赖。
+5. DEM、轨道池和源数据池路径在 .env 中配置。
+```
+
+如果目标服务器没有 GAMMA，系统仍应允许：
+
+```text
+1. 使用已经存在的 analysis_ready.tif。
+2. 使用 GF3_GDAL 处理器。
+3. 后续接 SNAP/ISCE/external 处理器。
+4. 继续执行水体提取、洪涝检测、套合分析和产品生成。
+```
+
+这也是把算法下放到 GeoTIFF 层的核心价值。
+## 2026-05-16 implementation note
+
+The first code pass now implements LT1 and GF3 as analysis-ready scene preprocessors:
+
+- New config roots: `SAR_ANALYSIS_READY_ROOT`, `SAR_ANALYSIS_WORK_ROOT`, `SAR_ANALYSIS_PREVIEW_ROOT`.
+- `sar_scene_geo` now stores `analysis_tif_path`, `analysis_dir`, preview path, engine/profile, backscatter unit, nodata, metadata and quality JSON.
+- `/flood/preprocess` routes GF3 scenes to `gf3_gdal` standardization and LT1 scenes to `lt_gamma` single-scene preprocessing.
+- GF3 uses the existing GDAL/RPC L1A->L2 result, then registers the selected L2 GeoTIFF under `SAR_ANALYSIS_READY_ROOT`.
+- LT1 uses a new Gamma/PyINT runner that stops at single-scene geocoded MLI GeoTIFF and does not write into D-InSAR product directories.
+- Water extraction, flood detection and scene previews now prefer `analysis_tif_path` and fall back to legacy `geo_path`.
+- Startup database maintenance now treats Alembic `0004` as the managed head, can auto-add missing ORM columns/indexes, and records the `alembic_version` marker after the schema is healthy.
+- `/health?full=true&refresh=true` now includes `sar_analysis_ready` root and scene-path checks.
+
+This keeps D-InSAR product publication isolated from flood/water analysis inputs.

docs/FLOOD_MODULE_REFACTOR_PLAN_20260514.md

@@ -0,0 +1,693 @@
+# 洪涝监测模块整改实施方案
+
+> 日期：2026-05-14  
+> 目标：将当前分散的水体监测、洪涝检测、GF3 处理和结果管理，收敛为可部署、可维护、可扩展的洪涝灾害分析流水线。
+
+## 1. 总体原则
+
+1. 新业务统一走 `/flood/*`。
+2. “水体监测”不再作为独立业务模块出现，只作为“洪涝灾害分析 -> 水体提取”步骤存在。
+3. `/water/*` 暂时保留兼容窗口，但标记 deprecated，不再新增功能。
+4. 数据库优先新增表和回填数据，避免直接破坏旧表。
+5. 每个阶段独立提交，保证任一阶段都能构建、部署和回滚。
+
+## 2. 目标流水线
+
+```text
+场景准备 Scene
+  -> 水体提取 WaterExtraction
+  -> 洪涝检测 FloodDetection
+  -> 套合分析 FloodOverlay
+  -> 洪涝产品 FloodProduct / Report
+```
+
+其中水体提取是洪涝分析的前置步骤，不再和洪涝灾害分析并列成两个业务入口。
+
+## 3. 阶段 1：收敛 API 契约
+
+目标：先消灭“前端调用不存在接口”的问题，避免部署后出现静默 404。
+
+修改范围：
+
+```text
+frontend/src/api/flood.js
+backend/app/routers/flood.py
+docs/FLOOD_MODULE_REFACTOR_PLAN_20260514.md
+```
+
+处理方式：
+
+- `frontend/src/api/flood.js` 只暴露后端当前真实支持的接口。
+- 暂时移除或注释未实现接口，例如 `/flood/sources`、`/flood/ready-products`、`/flood/pairs` 保存删除、`/flood/reports`、`/flood/results`。
+- 后端 `/flood` 路由继续保留现有功能，但接口命名统一为 `preprocess`、`scenes`、`water-extractions`、`pairs/search`、`detections`、`detections/{id}/preview/{layer}`。
+
+验收标准：
+
+```text
+npm run build 通过
+前端 flood API 文件中没有明显会 404 的已导出函数
+/flood 主流程现有功能不退化
+```
+
+## 4. 阶段 2：新增数据模型
+
+目标：建立洪涝流水线需要的数据承载，不直接破坏旧表。
+
+新增模型：
+
+```text
+WaterExtractionORM -> water_extractions
+FloodOverlayORM    -> flood_overlays
+FloodProductORM    -> flood_products
+```
+
+关键取舍：
+
+- 保留旧 `WaterDetectionORM -> water_detections`。
+- 新表 `water_extractions` 从旧表回填。
+- 后续新任务写入 `water_extractions`。
+- 旧接口读旧表或兼容映射，等稳定后再清理。
+
+建议字段：
+
+```text
+water_extractions:
+  id
+  scene_id
+  processor
+  task_id
+  input_path
+  output_path
+  preview_path
+  vector_path
+  water_area_km2
+  water_pixel_count
+  threshold_value
+  metadata_json
+  status
+  error_msg
+  created_at
+  updated_at
+
+flood_overlays:
+  id
+  detection_id
+  flood_vector_path
+  hazard_points_hit
+  hazard_points_near
+  hazard_points_total
+  dinsar_products_intersecting
+  affected_area_km2
+  summary_json
+  created_at
+
+flood_products:
+  id
+  product_id
+  detection_id
+  overlay_id
+  display_name
+  status
+  publish_dir
+  manifest_path
+  summary_json
+  created_at
+```
+
+验收标准：
+
+```text
+alembic upgrade head 成功
+旧 water_detections 数据可迁移到 water_extractions
+模型 import 正常
+```
+
+## 5. 阶段 3：抽离 Service
+
+目标：让 `/flood` 成为真正的业务路由，而不是代理 `water.py` 的壳。
+
+新增服务：
+
+```text
+backend/app/services/flood_analysis_service.py
+backend/app/services/water_extraction_service.py
+backend/app/services/flood_product_service.py
+backend/app/services/flood_overlay_service.py
+```
+
+职责划分：
+
+```text
+flood_analysis_service.py
+  场景列表、预处理提交、配对搜索、洪涝检测提交、检测列表
+
+water_extraction_service.py
+  Otsu 水体提取、ENVI/SARscape 水体提取
+
+flood_product_service.py
+  产品列表、manifest、产品包生成
+
+flood_overlay_service.py
+  分类栅格矢量化、灾害点/DInSAR/AOI 套合
+```
+
+完成后：
+
+- `backend/app/routers/flood.py` 不再 `import water as water_compat`。
+- `backend/app/routers/water.py` 标记 deprecated，后续可反向调用新 service。
+- `water_detect_service.py` 逐步迁移到 `water_extraction_service.py`。
+
+验收标准：
+
+```text
+flood.py 不再 import water.py
+/water/* 旧接口仍可用
+/flood/* 主接口可用
+```
+
+## 6. 阶段 4：补齐产品端点
+
+目标：让“结果与任务”视图有真实后端数据。
+
+主接口建议使用 `products`，避免和 DInSAR result/product 概念混淆：
+
+```text
+POST /flood/detections/{id}/products
+GET  /flood/products
+GET  /flood/products/{id}
+GET  /flood/products/{id}/manifest
+```
+
+兼容别名可选：
+
+```text
+GET /flood/results
+GET /flood/results/{id}
+GET /flood/results/{id}/manifest
+```
+
+验收标准：
+
+```text
+前端结果视图能展示真实产品
+manifest 能返回 JSON
+没有空接口或静默失败
+```
+
+## 7. 阶段 5：实现套合分析
+
+目标：把洪涝模块从检测工具提升为灾害分析模块。
+
+新增接口：
+
+```text
+POST /flood/detections/{id}/overlay
+GET  /flood/detections/{id}/impact
+```
+
+处理逻辑：
+
+```text
+1. 读取 flood_detections.classified_path
+2. 提取 class=2 洪涝区域
+3. 栅格转矢量
+4. 写 flood_overlays.flood_vector_path
+5. 查询灾害点命中
+6. 查询近邻风险点
+7. 查询相交 DInSAR 产品
+8. 查询 AI 诊断摘要
+9. 写 summary_json
+```
+
+注意：
+
+- AOI、PostGIS、DInSAR 产品几何不完整时，不让整个任务失败。
+- 返回部分结果，并在 `summary_json.warnings` 中说明缺失项。
+
+验收标准：
+
+```text
+POST overlay 能生成 flood_overlays 记录
+GET impact 返回结构化 JSON
+没有数据时返回空数组而不是 500
+```
+
+## 8. 阶段 6：前端重整
+
+目标：只保留一个洪涝灾害分析工作台。
+
+新增目录：
+
+```text
+frontend/src/components/flood/
+```
+
+建议组件：
+
+```text
+FloodStatusBadge.jsx
+FloodButton.jsx
+FloodSceneRow.jsx
+FloodWaterExtractionRow.jsx
+FloodDetectionRow.jsx
+FloodOverlayPanel.jsx
+FloodProductPanel.jsx
+```
+
+界面改成四站式：
+
+```text
+1. 场景准备
+2. 水体提取
+3. 洪涝检测
+4. 套合与产品
+```
+
+处理方式：
+
+- `WaterMonitorPanel.jsx` 加弃用提示。
+- 左侧导航只引导用户进入洪涝灾害分析。
+- `api/water.js` 和 `api/gf3.js` 标记 deprecated。
+- 新功能只接入 `api/flood.js`。
+
+验收标准：
+
+```text
+左侧导航只引导用户进入洪涝灾害分析
+水体提取不再作为独立业务重复出现
+套合分析和产品视图接真实接口
+npm run build 通过
+```
+
+## 9. 阶段 7：报告生成壳
+
+目标：先形成 Markdown 报告能力，再扩展 PDF。
+
+新增接口：
+
+```text
+POST /flood/reports
+GET  /flood/reports/{id}
+```
+
+第一版报告包含：
+
+```text
+灾前/灾后场景信息
+洪涝面积
+灾害点命中
+DInSAR 产品关联
+AI 诊断摘要
+套合统计
+```
+
+## 10. 推荐提交顺序
+
+```text
+1. refactor flood api contract
+2. add flood pipeline orm models
+3. extract flood analysis services
+4. add flood product endpoints
+5. implement flood overlay impact analysis
+6. consolidate flood frontend workspace
+7. scaffold flood report generation
+```
+
+核心思路：先稳住接口和数据，再拆服务，再补业务能力，最后整理 UI。这样 Git 中的代码、迁移、前端调用和部署环境是闭合的，另一台服务器拉取后不会出现关键页面依赖未实现接口的问题。
+
+## 11. 2026-05-15 补充设计：水体提取、灾害配对与套合展示
+
+本节根据最新审阅意见补充。结论：洪涝工作台不应该继续以“手动选日期范围 + 查配对”为核心，而应该改成“灾害事件驱动”：
+
+```text
+灾害时间 + 灾害位置
+  -> 过滤可用 SAR 场景匹配池
+  -> 推荐灾前/灾后配对
+  -> 执行洪涝检测
+  -> 套合分析
+  -> 结果展示与产品导出
+```
+
+### 11.1 水体提取视图复用管理页能力
+
+当前管理页面已经具备三类能力，洪涝模块应复用，而不是重新做一套弱化版：
+
+1. 雷达数据查询能力：`/radar-data/search` 已支持成像日期、成像模式、极化方式、产品级别、行政区 AOI、上传 AOI 文件。
+2. 地图能力：`App.jsx` 中已经有源影像 footprint 上图、源影像预览缓存 `radar-data/{id}/thumb`、地图定位和图层开关逻辑。
+3. 行政区 AOI 能力：`/aoi/regions/children` 与 `/aoi/regions/{treeId}/geometry` 已可用于按省/市范围查询和定位。
+
+因此水体提取界面调整为：
+
+```text
+左侧：场景匹配池
+  - 灾害位置 / AOI
+  - 成像日期范围
+  - 卫星、模式、极化、产品级别
+  - 只显示有 footprint 的数据
+  - 可显示源影像预览图
+  - 显示成像时间、极化方式、成像模式、产品级别
+
+右侧：已完成地理编码场景 / 水体提取结果
+  - 场景 footprint 上图
+  - 水体提取掩膜上图
+  - 与源影像预览可叠加对比
+```
+
+前端复用建议：
+
+```text
+复用 buildRadarSearchFormData / normalizeRadarSearchCriteria
+复用 UnifiedDatePicker
+复用 RadarDataRow 的预览状态表达
+复用 App.jsx 中 updateRadarPreviewVisibility 的源影像预览图层逻辑
+复用行政区 AOI 选择和地图定位逻辑
+```
+
+需要新增的洪涝专用组件：
+
+```text
+FloodSourceSearchPanel.jsx
+  封装灾害位置、日期、极化、模式、产品级别过滤。
+
+FloodSourceSceneRow.jsx
+  显示源影像：成像时间、极化方式、模式、产品级别、预览状态、上图按钮。
+
+FloodWaterExtractionRow.jsx
+  显示水体提取结果：面积、状态、输入场景、上图按钮、错误信息。
+```
+
+地图图层约定：
+
+```text
+source_preview:{radar_data_id}       源影像预览
+source_footprint:{radar_data_id}     源影像覆盖范围
+scene_footprint:{scene_id}           地理编码场景范围
+water_mask:{water_extraction_id}     水体提取掩膜
+```
+
+### 11.2 洪涝检测配对改为灾害事件驱动
+
+当前 `/flood/pairs/search` 只接收灾前/灾后日期区间和 overlap 阈值，实际使用体验不够：用户通常知道的是“灾害发生时间”和“灾害位置”，不是一开始就知道灾前灾后影像窗口。
+
+新的配对入口应改为：
+
+```text
+灾害名称 disaster_name      可选
+灾害时间 disaster_date      必填
+灾害位置 disaster_aoi       必填，来自行政区 / 地图框选 / 上传 SHP / GeoJSON
+灾前窗口 pre_window_days    默认 30 天
+灾后窗口 post_window_days   默认 30 天
+最小 AOI 覆盖率 min_aoi_coverage_ratio 默认 0.3
+最小两景重叠率 min_pair_overlap_ratio  默认 0.5
+是否要求同极化 require_same_polarization 默认 true
+是否要求同成像模式 require_same_imaging_mode 默认 false
+卫星过滤 satellites 可选
+极化过滤 polarization 可选
+```
+
+前端交互：
+
+```text
+1. 用户选择灾害日期
+2. 用户选择灾害位置
+   - 行政区：省 / 市
+   - 地图框选：后续实现
+   - 上传 SHP/GeoJSON：沿用现有 AOI 解析
+3. 系统自动推导：
+   - 灾前窗口：disaster_date - pre_window_days 至 disaster_date - 1
+   - 灾后窗口：disaster_date 至 disaster_date + post_window_days
+4. 后端返回：
+   - pre_pool
+   - post_pool
+   - candidate_pairs
+   - warnings
+5. 用户在候选配对中选择一组提交洪涝检测
+```
+
+推荐新增接口：
+
+```text
+POST /flood/disaster-pairs/search
+```
+
+请求格式：
+
+```json
+{
+  "disaster_name": "汶川洪涝",
+  "disaster_date": "20260715",
+  "aoi_geojson": {},
+  "region_tree_id": "510000",
+  "pre_window_days": 30,
+  "post_window_days": 30,
+  "min_aoi_coverage_ratio": 0.3,
+  "min_pair_overlap_ratio": 0.5,
+  "require_same_polarization": true,
+  "require_same_imaging_mode": false,
+  "satellites": ["LT-1", "GF3"],
+  "polarization": "VV"
+}
+```
+
+返回格式：
+
+```json
+{
+  "disaster": {
+    "name": "汶川洪涝",
+    "date": "20260715",
+    "pre_range": ["20260615", "20260714"],
+    "post_range": ["20260715", "20260814"]
+  },
+  "aoi": {
+    "source": "region",
+    "name": "汶川县",
+    "aoi_geojson": {}
+  },
+  "pre_pool": [],
+  "post_pool": [],
+  "candidate_pairs": [
+    {
+      "pre": {
+        "scene_id": 1,
+        "radar_data_id": 10,
+        "imaging_date": "20260701",
+        "satellite": "LT-1",
+        "polarization": "VV",
+        "imaging_mode": "SM",
+        "aoi_coverage_ratio": 0.82,
+        "coverage_polygon": []
+      },
+      "post": {
+        "scene_id": 2,
+        "radar_data_id": 11,
+        "imaging_date": "20260718",
+        "satellite": "LT-1",
+        "polarization": "VV",
+        "imaging_mode": "SM",
+        "aoi_coverage_ratio": 0.79,
+        "coverage_polygon": []
+      },
+      "pair_overlap_ratio": 0.91,
+      "pre_delta_days": 14,
+      "post_delta_days": 3,
+      "score": 0.86,
+      "warnings": []
+    }
+  ],
+  "warnings": []
+}
+```
+
+排序建议：
+
+```text
+score =
+  pair_overlap_ratio * 0.35
+  + min(pre_aoi_coverage, post_aoi_coverage) * 0.30
+  + time_score * 0.20
+  + same_polarization_bonus * 0.10
+  + same_mode_bonus * 0.05
+```
+
+后端实现建议：
+
+```text
+1. 复用 radar search 的 AOI 解析能力，避免重复解析行政区和 SHP。
+2. 查询 sar_scene_geo.status=DONE 的场景，并 join radar_data。
+3. 按 disaster_date 自动构造灾前/灾后窗口。
+4. 用 PostGIS 计算单景 AOI 覆盖率。
+5. 用 footprint 相交面积计算 pair_overlap_ratio。
+6. 返回匹配池和候选配对，而不是只返回最终 pairs。
+```
+
+兼容处理：
+
+```text
+旧接口 POST /flood/pairs/search 保留，但只作为手动日期模式。
+新工作台默认使用 POST /flood/disaster-pairs/search。
+```
+
+### 11.3 套合分析结果展示逻辑
+
+套合分析不能只是一个“运行按钮”。它应展示“为什么这个洪涝结果重要”：
+
+```text
+洪涝范围
+灾害点命中
+近洪涝风险点
+DInSAR 产品关联
+行政区影响统计
+结果图层
+产品/报告入口
+```
+
+前端 `FloodOverlayPanel.jsx` 设计：
+
+```text
+顶部摘要：
+  - 洪涝面积
+  - 命中灾害点数量
+  - 近洪涝风险点数量
+  - 关联 DInSAR 产品数量
+  - warnings 数量
+
+中部地图控制：
+  - 洪涝分类图
+  - 洪涝矢量范围
+  - 命中灾害点
+  - 近洪涝风险点
+  - DInSAR 产品 footprint
+
+下部结果表：
+  - 灾害点列表：名称、类型、行政区、距离、是否命中
+  - DInSAR 产品列表：product_id、engine、形变量、AI 风险等级、预览/详情
+  - AOI 统计：行政区、洪涝面积、占比
+```
+
+后端 `GET /flood/detections/{id}/impact` 应保证即使没有运行套合，也返回稳定结构：
+
+```json
+{
+  "detection_id": 1,
+  "flood_area_km2": 12.5,
+  "hazard_points": {
+    "inside_flood": [],
+    "near_flood": [],
+    "total_in_scene": 0
+  },
+  "dinsar_products": [],
+  "affected_aois": [],
+  "map_layers": {
+    "classified_preview": true,
+    "flood_vector_path": null,
+    "hazard_points": true,
+    "dinsar_footprints": true
+  },
+  "warnings": []
+}
+```
+
+需要新增或完善的地图层约定：
+
+```text
+flood_classified:{detection_id}      洪涝分类栅格预览
+flood_vector:{overlay_id}            洪涝矢量面
+hazard_inside:{overlay_id}           洪涝范围内灾害点
+hazard_near:{overlay_id}             近洪涝风险点
+dinsar_intersect:{overlay_id}        相交 DInSAR 产品 footprint
+```
+
+### 11.4 前端整改顺序调整
+
+基于以上补充，前端整改顺序调整为：
+
+```text
+1. 抽 FloodSourceSearchPanel，复用管理页雷达搜索/AOI 查询。
+2. 水体提取页增加源影像预览图、成像时间、极化方式、模式、产品级别。
+3. 洪涝检测页改为灾害事件输入：灾害时间 + 灾害位置。
+4. 新增 /flood/disaster-pairs/search 后接入候选池和推荐配对。
+5. 套合分析页接入 /flood/detections/{id}/overlay 和 /impact。
+6. 结果与任务页从 flood_detections 过渡到 flood_products。
+```
+
+验收标准补充：
+
+```text
+水体提取：
+  - 能按行政区/上传 AOI 查询源影像
+  - 能显示源影像预览图
+  - 列表中显示成像日期、极化方式、成像模式、产品级别
+  - 水体提取结果可叠加到地图
+
+洪涝检测：
+  - 用户只需输入灾害时间和灾害位置即可获得匹配池
+  - 返回 pre_pool/post_pool/candidate_pairs
+  - 推荐配对可在地图上预览灾前/灾后 footprint
+  - 配对结果显示时间差、AOI 覆盖率、两景重叠率、极化一致性
+
+套合分析：
+  - 运行后能看到摘要指标
+  - 能看到灾害点命中和近邻风险点列表
+  - 能看到关联 DInSAR 产品列表
+  - warnings 可见，不静默失败
+```
+
+## 12. 2026-05-15 第一轮落地记录
+
+本轮先闭合三条可直接使用的业务链路，不把报告生成和完整产品包导出放进同一次改动。
+
+### 12.1 已落地
+
+后端：
+```text
+1. 新增 POST /flood/disaster-pairs/search。
+   输入 disaster_date + region_tree_id/aoi_geojson + 灾前灾后窗口。
+   输出 pre_pool、post_pool、candidate_pairs、summary、warnings。
+
+2. 水体提取列表补充源影像元数据。
+   /flood/scenes 与 /flood/water-extractions 现在返回 imaging_date、polarization、
+   imaging_mode、product_level、coverage_polygon、min/max lon/lat。
+
+3. 套合分析接口保持并接入前端。
+   POST /flood/detections/{id}/overlay
+   GET  /flood/detections/{id}/impact
+```
+
+前端：
+```text
+1. 洪涝灾害分析 / 水体提取页：
+   - 复用 AOI 行政区索引。
+   - 支持按行政区筛选雷达源影像。
+   - 雷达结果行增加“覆盖”和“源影像”上图。
+   - 场景/水体结果行显示成像日期、极化方式、模式、产品级别。
+
+2. 洪涝检测页：
+   - 从手工填写灾前/灾后日期，改为输入灾害名称、灾害发生日期、灾害位置。
+   - 自动根据窗口期生成灾前池和灾后池。
+   - 候选配对展示评分、AOI 覆盖率、两景重叠率、灾前/灾后时间差、极化方式。
+
+3. 套合分析页：
+   - 可运行 overlay。
+   - 可刷新并展示 impact。
+   - 展示洪涝面积、命中灾害点、近洪涝风险点、关联 DInSAR 产品和 warnings。
+```
+
+### 12.2 当前保留的兼容点
+
+```text
+1. GET/POST /water/* 暂未删除，旧页面仍可过渡使用。
+2. 旧 POST /flood/pairs/search 保留为手工日期兼容接口，但新工作台默认使用 /flood/disaster-pairs/search。
+3. 水体提取任务底层仍写 water_detections；water_extractions 新表和正式迁移已建模，但任务处理器尚未完全切换。
+4. AOI 只先复用行政区查询；上传 SHP/GeoJSON 到洪涝工作台可作为下一步补充。
+```
+
+### 12.3 下一轮建议
+
+```text
+1. 把水体提取 job handler 从 WaterDetectionORM 切到 WaterExtractionORM。
+2. 给候选配对增加地图 footprint 预览按钮，辅助人工确认灾前/灾后覆盖。
+3. 把 overlay 生成的 flood_vector_path 也作为地图矢量层显示。
+4. 套合分析补行政区受影响面积统计。
+5. 结果与任务页开始接 flood_products，而不是只列 flood_detections。
+```

docs/FLOOD_MODULE_REFACTOR_PROGRESS_20260515.md

@@ -0,0 +1,128 @@
+# 洪涝模块整改进展记录
+
+## 2026-05-15 第二轮：水体提取表切换
+
+本轮目标是把洪涝工作台里的水体提取正式从旧 `water_detections`
+迁到新 `water_extractions`，同时保留旧 `/water/*` 兼容窗口。
+
+### 已完成
+
+```text
+1. 新增 backend/app/services/water_extraction_service.py。
+   - run_otsu_water_extraction 复用旧 Otsu 实现。
+   - 对外使用 extraction/processor/threshold_value 命名。
+   - run_envi_water_extraction 暂留占位，后续接 ENVI/SARscape。
+
+2. /flood/water-extractions 新提交任务开始写 water_extractions。
+   - 任务 payload 使用 extraction_id。
+   - WaterExtractionORM.processor 默认 otsu。
+   - task_id、threshold_value、metadata_json 随任务更新。
+
+3. WATER_DETECT job handler 支持双轨兼容。
+   - 新 payload: extraction_id -> 写 water_extractions。
+   - 旧 payload: detection_id -> 继续写 water_detections。
+   - 如果旧 detection_id 已经被 alembic 0003 回填到 water_extractions，
+     handler 会同步更新同 ID 的新表记录，避免迁移期状态卡住。
+
+4. /flood/water-extractions 列表改读 water_extractions。
+   - 旧数据依赖 alembic 0003 从 water_detections 回填。
+   - preview 优先读 WaterExtractionORM，找不到时回退 WaterDetectionORM。
+```
+
+### 仍保留
+
+```text
+1. 旧 /water/detect 仍创建 water_detections。
+   这是兼容窗口内的刻意保留，不再作为洪涝工作台主链路。
+
+2. ENVI/SARscape 精密水体提取还没有接入任务队列。
+
+3. 如果部署环境已有旧 PENDING water_detections 任务，
+   需要先执行 alembic 0003，再启动 worker。
+```
+
+### 下一步
+
+```text
+1. 给候选洪涝配对增加灾前/灾后 footprint 地图预览。
+2. 把 flood_vector_path 作为矢量图层上图。
+3. 套合分析补行政区受影响面积统计。
+4. 结果与任务页接 flood_products。
+```
+
+## 2026-05-15 第三轮：地图闭环与产品页
+
+### 已完成
+
+```text
+1. 候选洪涝配对支持地图预览。
+   - 灾前 footprint 蓝色。
+   - 灾后 footprint 绿色。
+   - 配对行增加“预览覆盖”。
+
+2. 套合结果支持洪涝矢量上图。
+   - GET /flood/detections/{id}/impact 返回 overlay_id、flood_vector_path、flood_vector_geojson。
+   - 前端套合分析页增加“加载洪涝矢量”。
+
+3. 套合分析补行政区影响统计。
+   - overlay 运行时读取 AOI 行政区边界索引。
+   - 返回 affected_aois，包含 tree_id、name、level、flood_area_km2。
+   - 前端展示影响行政区数量和前 5 个行政区。
+
+4. 结果与任务页接 flood_products。
+   - 洪涝结果行增加“生成产品”。
+   - 结果页增加“产品”tab。
+   - 产品行展示 product_id、洪涝面积、影响面积、生成时间。
+   - Manifest 按钮读取 /flood/products/{id}/manifest。
+```
+
+### 当前可集中测试的链路
+
+```text
+1. 水体提取：
+   入库影像查询 -> 行政区筛选 -> 源影像上图 -> 提交预处理 -> 提交水体提取 -> 水体结果上图。
+
+2. 洪涝检测：
+   输入灾害时间 + 灾害位置 -> 推荐配对 -> 预览覆盖 -> 提交洪涝检测 -> 分类图上图。
+
+3. 套合分析：
+   选择 DONE 洪涝结果 -> 运行套合分析 -> 加载洪涝矢量 -> 查看灾害点、DInSAR、行政区统计。
+
+4. 产品：
+   DONE 洪涝结果 -> 生成产品 -> 产品 tab 查看 -> 读取 manifest。
+```
+
+## 2026-05-15 第四轮：GeoTIFF 化技术决策
+
+本轮根据新的产品判断调整洪涝模块技术路线：不再把 ENVI/SARscape
+作为洪涝算法主线，后续水体提取、洪涝检测、套合分析全部运行在标准
+GeoTIFF 层。原始 SAR 到 GeoTIFF 的部分做成可插拔前处理器，优先评估
+GAMMA，GAMMA 覆盖不了的传感器使用 GF3_GDAL、SNAP、ISCE 或 external
+处理器兜底。
+
+### 已完成
+
+```text
+1. 新增 docs/FLOOD_GEOTIFF_GAMMA_PREPROCESS_DESIGN_20260515.md。
+   - 总结当前洪涝模块仍绑定 ENVI/SARscape 的真实状态。
+   - 梳理当前 GAMMA/PyINT 在项目中的能力和边界。
+   - 明确 GAMMA 适合作为 TIF 前处理候选，但不能把 D-InSAR pair profile
+     直接复用为洪涝单景预处理。
+   - 定义 analysis_ready.tif / SarAnalysisScene 数据契约。
+   - 给出 Python GeoTIFF 洪涝检测和 GAMMA 单景前处理分阶段方案。
+
+2. 更新 docs/INDEX.md。
+   - 将 GeoTIFF 化设计列入当前执行中的核心设计。
+   - 标明 2026-05-14 旧洪涝设计中的 ENVI/SARscape 主线表述已被新设计取代。
+```
+
+### 当前判断
+
+```text
+1. 洪涝算法应先从 SARscape 黑盒迁出，改成 pre/post GeoTIFF 上的 Python 算法。
+2. GAMMA 适合优先做 LT-1/Sentinel-1 的 analysis_ready.tif 生产器。
+3. 当前 GAMMA 接入已有 geocode/data2geotiff 能力，但缺少独立单景预处理服务。
+4. GF3 已有 Python/GDAL L1A->L2 路线，不应强行改走 GAMMA。
+5. 另一台服务器部署时，Git 能提供平台代码和算法；GAMMA 本体、DEM、轨道池、
+   conda/WSL 运行时仍是外部部署前提。
+```

docs/FRONTEND_NAVIGATION_ARCHITECTURE.md

@@ -68,9 +68,8 @@ This is a workspace group, not a multi-tab planning tree.
 生产管理
 └─ 生产管理 (`production_management`)
    ├─ D-InSAR运行 (`dinsar_runs`)
-   ├─ 时序InSAR运行 (`timeseries_runs`)
-   ├─ D-InSAR产物 (`dinsar_products`)
-   └─ 时序InSAR产物 (`timeseries_products`)
+   ├─ SBAS-InSAR Production (`sbas_insar_production`)
+   └─ D-InSAR产物 (`dinsar_products`)
 ```
 
 Notes:
@@ -78,7 +77,8 @@ Notes:
 - The left navigation contains only one tab for this group: `production_management`.
 - Internal workspace views are controlled by `PRODUCTION_WORKSPACE_VIEWS`.
 - Route alias mapping is controlled by `PRODUCTION_WORKSPACE_ENTRY_TO_VIEW`.
-- Legacy route tabs such as `dinsar_production` and `ps_products` map into this workspace and should not be treated as standalone left-nav entries.
+- Legacy route tabs such as `dinsar_production`, `ps_production`, and `ps_products` map into this workspace and should not be treated as standalone left-nav entries.
+- The old ISCE2/MintPy `timeseries_runs` and `timeseries_products` workspace views are deprecated and hidden; SBAS production is handled by the Gamma `sbas_insar_production` view.
 
 ### 3.3 InSAR形变分析
 

docs/GAMMA_IPTA_LT1_SBAS_TRIAL_RUNBOOK_20260518.md

@@ -0,0 +1,551 @@
+# Gamma IPTA LT1 SBAS Trial Runbook
+
+Date: 2026-05-18
+
+## Goal
+
+Validate whether the current local LT1 data pool and WSL Gamma installation can produce one usable SBAS/IPTA trial result with the official Gamma toolchain.
+
+This is not yet a production integration design. The immediate goal is to run one conservative stack, inspect failures and product quality, then decide what should be productized in the system.
+
+Actual trial root used in this repository:
+
+```text
+D:\Code\Insar_management_system_v2\backend\runtime\gamma_ipta_trials\lt1b_r114_e1312_n438_20240516_20251002
+```
+
+## Current Environment
+
+- WSL distro configured by the project: `Ubuntu-24.04`.
+- Gamma install found in WSL: `/usr/local/GAMMA_SOFTWARE-20240627`.
+- Installed package name present on disk: `GAMMA_SOFTWARE-20240627_MSP_ISP_DIFF_IPTA.linux64_ubuntu2404.tar.gz`.
+- Installed Gamma modules include `MSP`, `ISP`, `DIFF`, `DISP`, and `IPTA`.
+- `GEO` is not a separate directory in this install, but DIFF contains the relevant geocoding tools, including `gc_map`, `geocode`, and `geocode_back`.
+- Project Gamma environment script: `deploy/wsl/profiles/gamma_env.sh`.
+- Project WSL Python: `/home/administrator/miniconda3/envs/insar_wsl_v1/bin/python`.
+
+Important command checks already performed:
+
+- `IPTA/bin/ts_rate` runs and prints usage. No license-denied error observed.
+- `IPTA/bin/multi_def_pt` runs and prints usage. No license-denied error observed.
+- `ISP/bin/par_LT1_SLC` exists and prints LT1 SLC conversion usage.
+- `ISP/scripts/LT1_precision_orbit.py` runs with the project conda Python and prints usage.
+
+## Data Pool Findings
+
+Configured LT1 source pool:
+
+- `D:\LuTan1_Image_Pool`
+
+Configured LT1 precise orbit pool:
+
+- `D:\orbit_pools\envi\LT1A`
+- `D:\orbit_pools\envi\LT1B`
+
+High-level inventory:
+
+- LT1 scene directories found: `1500`.
+- LT1A orbit TXT files found: `896`, spanning `20230508` to `20251219`.
+- LT1B orbit TXT files found: `858`, spanning `20230510` to `20251219`.
+
+Metadata caveat:
+
+- `*.meta.xml` and `*_Check.xml` are not safe to parse as whole XML documents in PowerShell because many files contain malformed Chinese text near the tail, for example bad `usePreciseOrbit` closing text.
+- The needed `productInfo` block is structurally valid. For stack discovery, parse only `<productInfo>...</productInfo>`.
+- Gamma `par_LT1_SLC` should still use the original `.meta.xml`; do not rewrite source metadata unless a Gamma run proves the malformed tail is a blocker.
+
+## Candidate Stack
+
+First trial should use a narrow single-center stack, not the broad system time-series grouping.
+
+Recommended trial stack:
+
+- Satellite: `LT1B`
+- Relative orbit: `114`
+- Direction: `DESCENDING`
+- Imaging mode: `STRIP1`
+- Polarization: `HH`
+- Approximate center: `E131.2 / N43.8`
+- Scene count at this center: `7`
+- Scenes with precise orbit TXT currently present: `5`
+
+Use the 5 scenes with available precise orbit first:
+
+| Date | Orbit TXT | Scene |
+| --- | --- | --- |
+| 20240516 | yes | `LT1B_MONO_SYC_STRIP1_012047_E131.2_N43.8_20240516_SLC_HH_S2A_0000399289` |
+| 20240711 | yes | `LT1B_MONO_SYC_STRIP1_012880_E131.2_N43.8_20240711_SLC_HH_S2A_0000450956` |
+| 20240905 | yes | `LT1B_MONO_SYC_STRIP1_013713_E131.2_N43.8_20240905_SLC_HH_S2A_0000501650` |
+| 20250417 | yes | `LT1B_MONO_SYC_STRIP1_017045_E131.2_N43.8_20250417_SLC_HH_S2A_0000713375` |
+| 20251002 | yes | `LT1B_MONO_SYC_STRIP1_019544_E131.2_N43.8_20251002_SLC_HH_S2A_0000891257` |
+
+Do not include these two in the first run unless the missing orbits are added:
+
+| Date | Orbit TXT | Scene |
+| --- | --- | --- |
+| 20250612 | no | `LT1B_MONO_SYC_STRIP1_017878_E131.2_N43.8_20250612_SLC_HH_S2A_0000772122` |
+| 20250807 | no | `LT1B_MONO_SYC_STRIP1_018711_E131.2_N43.8_20250807_SLC_HH_S2A_0000831367` |
+
+Common bounding box across the broader 13-scene `LT1B relOrbit 114 / E131-N44` candidate:
+
+- lon: `130.8615 .. 131.1638`
+- lat: `43.7127 .. 44.0987`
+
+For the narrow `E131.2/N43.8` trial stack, overlap is visually/metadata-wise much tighter:
+
+- Each scene center is around `131.20E, 43.79N`.
+- Each scene bbox is roughly `130.81..131.62E`, `43.48..44.10N`.
+
+Secondary candidate if the first stack fails:
+
+- `LT1B relOrbit 114 DESCENDING STRIP1 HH`, center `E130.8/N43.9`.
+- 5 dates, 4 with orbit: `20250425`, `20250620`, `20250815`, `20251010`.
+- One scene is `MONO_MH1` while the others are `MONO_SYC`; keep it as secondary, not first choice.
+
+## Current System Pairing Limitations
+
+The current time-series stack selection is useful for broad discovery but is too coarse for Gamma IPTA production.
+
+Observed code behavior:
+
+- LT1A/LT1B are normalized into the same satellite family `LT1`.
+- The compatibility key only uses direction, satellite family, imaging mode, and polarization.
+- Relative orbit, absolute track family, scene center/strip identity, receiving station, and detailed LT1 product variant are not hard grouping keys.
+- The stable-stack selector tries to recover by common AOI overlap and pairwise network connectivity.
+- The SBAS network selector uses time-baseline, center-distance and overlap thresholds, but does not use Gamma-derived perpendicular baseline at planning time.
+- Time-series processors currently accepted by the service are only `isce2_stack_mintpy` and `sarscape_sbas`; there is no `gamma_ipta_sbas` processor code yet.
+
+For the Gamma IPTA trial, do not rely on the current automatic PS stack plan as the source of truth. Use a manually audited stack manifest first.
+
+## Trial Run Checklist
+
+### 1. Create Isolated Work Directory
+
+Actual root:
+
+```text
+D:\Code\Insar_management_system_v2\backend\runtime\gamma_ipta_trials\lt1b_r114_e1312_n438_20240516_20251002
+```
+
+Keep these subdirectories:
+
+```text
+input\scenes
+input\orbits
+gamma\slc
+gamma\mli
+gamma\diff
+gamma\ipta
+logs
+publish
+```
+
+For the first trial, prefer symlinks or a manifest that points to source scenes. Avoid duplicating large TIFF files unless Gamma scripts require local flat layout.
+
+### 2. Build Scene Manifest
+
+For each selected scene, record:
+
+- scene directory
+- `.tiff`
+- `.meta.xml`
+- precise orbit TXT
+- date
+- relative orbit
+- direction
+- mode
+- polarization
+- center lon/lat
+- bbox
+
+This manifest becomes the hand-audited truth for the trial.
+
+### 3. Convert LT1 Products To Gamma SLC
+
+For each selected scene:
+
+```bash
+source /mnt/d/Code/Insar_management_system_v2/deploy/wsl/profiles/gamma_env.sh
+par_LT1_SLC <scene.tiff> <scene.meta.xml> <yyyymmdd>.slc.par <yyyymmdd>.slc
+```
+
+Then apply precise orbit:
+
+```bash
+/home/administrator/miniconda3/envs/insar_wsl_v1/bin/python \
+  /usr/local/GAMMA_SOFTWARE-20240627/ISP/scripts/LT1_precision_orbit.py \
+  <yyyymmdd>.slc.par <orbit_txt_or_orbit_folder>
+```
+
+### 4. Build SLC/MLI Tables
+
+Create Gamma tables for the stack:
+
+```text
+SLC_tab
+RMLI_tab
+```
+
+Use multilook settings conservative enough for a first run. The goal is robustness and fast feedback, not final product resolution.
+
+### 5. Baseline And Pair Network
+
+Use Gamma baseline tools first, not the system's center-distance approximation:
+
+- `base_init`
+- `base_perp`
+- IPTA baseline tools such as `base_orbit_pt`, `base_par_pt`, `base_ls_pt` as needed by the official IPTA path.
+
+For the first 5-scene stack, start with a simple connected small-baseline network:
+
+- adjacent pairs by time
+- add one or two skip pairs only if coherence and baseline look acceptable
+
+Expected adjacent temporal intervals:
+
+- `20240516 -> 20240711`: 56 days
+- `20240711 -> 20240905`: 56 days
+- `20240905 -> 20250417`: 224 days
+- `20250417 -> 20251002`: 168 days
+
+The large seasonal gaps are acceptable for a trial only if perpendicular baseline and coherence are reasonable. If they are poor, switch to a denser 2025-only or 2024-only local test, even with fewer dates.
+
+### 6. Differential Interferograms
+
+Use official Gamma DIFF commands/scripts for:
+
+- coregistration
+- interferogram generation
+- simulated topographic phase
+- differential phase
+- filtering
+- coherence
+- unwrapping if needed by the chosen IPTA path
+
+Do not implement custom SBAS inversion in the management system.
+
+### 7. IPTA Processing
+
+Use Gamma IPTA commands for point/stack time-series processing. Confirm exact command sequence against the installed:
+
+```text
+/usr/local/GAMMA_SOFTWARE-20240627/IPTA/html/IPTA_users_guide.pdf
+```
+
+Commands observed in the local IPTA module include:
+
+- `multi_def_pt`
+- `ts_rate`
+- `ts_rate_pt`
+- `base_ls_pt`
+- `base_par_pt`
+- `ph_base_pt`
+- `atm_mod_pt`
+- `pt2geo`
+- `dis_ipta`
+
+### 8. Review Outputs
+
+Minimum acceptance checks for the first run:
+
+- every selected scene converts to SLC
+- precise orbit update succeeds for every SLC
+- all intended pairs generate interferograms
+- coherence is not uniformly poor
+- unwrapping or IPTA point solution is not globally unstable
+- one geocoded velocity or displacement-rate raster/vector product can be inspected
+- logs and command manifests are complete enough to reproduce the run
+
+## Trial Progress On 2026-05-18
+
+### Files Created For This Trial
+
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/input/scene_manifest.json`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/01_prepare_slc.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/02_mli_and_baseline.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/03_coreg_one_pair.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/04_cc_stats.py`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/05_coreg_adjacent_pairs.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/06_prepare_rdc_dem.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/07_coreg_common_ref_stack.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/08_diff_unwrap_common_ref.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/09_mb_ts_rate.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/10_float_stats.py`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/11_make_timeseries_previews.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/12_geocode_export_timeseries.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/13_phase_to_los.py`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/14_build_trial_summary.py`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/15_make_los_velocity_maps.sh`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/16_plot_monitor_point_timeseries.py`
+- `backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002/scripts/17_make_geocoded_web_previews.sh`
+
+### Completed
+
+- Created a hand-audited 5-scene LT1B manifest for `relOrbit 114 / DESCENDING / STRIP1 / HH / E131.2 N43.8`.
+- Converted all 5 LT1 TIFF products with Gamma `par_LT1_SLC`.
+- Applied precise orbit updates with Gamma `LT1_precision_orbit.py`.
+- Built `gamma/slc/SLC_tab`.
+- Built 8x8 multilooked MLI products with Gamma `multi_look`.
+- Built `gamma/mli/RMLI_tab`.
+- Generated all-pair and adjacent-pair baseline tables with Gamma `base_calc`.
+- Ran adjacent-pair coregistration for all 4 selected adjacent pairs using Gamma `SLC_coreg.py`.
+- Generated 4 interferograms and coherence products using Gamma `create_offset`, `SLC_intf`, and `cc_wave`.
+- Generated browse previews using Gamma `rasmph` and `raspwr`.
+- Computed coherence statistics for each adjacent pair.
+- Reused the existing Copernicus30 DEM cache covering `E131.2/N43.8`.
+- Generated a reference-geometry RDC DEM for `20240905` using Gamma `gc_map1`, `geocode`, and `gc_map_fine`.
+- Built a common-reference RSLC/RMLI stack in `20240905` geometry.
+- Generated common-reference differential interferograms with Gamma `phase_sim_orb` and `SLC_diff_intf`.
+- Filtered common-reference differential interferograms with Gamma `adf`.
+- Unwrapped common-reference differential interferograms with Gamma `mcf`.
+- Ran Gamma IPTA `mb` to solve the image-based phase time-series.
+- Ran Gamma IPTA `ts_rate` to estimate a linear phase-rate map.
+- Generated Gamma preview rasters for `ts_rate`, `sigma_rate`, and `hgt_correction`.
+- Exported phase-rate, sigma, height-correction, and LOS-rate rasters to EPSG:4326 GeoTIFF.
+- Generated explicit LOS velocity maps in `mm/year` for both away-from-radar-positive and toward-radar-positive conventions.
+- Generated one example monitoring-point LOS displacement curve as PNG, CSV, and metadata JSON.
+- Generated north-up WGS84 web preview PNGs from the geocoded LOS GeoTIFF products.
+
+### Key Outputs
+
+- SLC stack: `gamma/slc/SLC_tab`
+- MLI stack: `gamma/mli/RMLI_tab`
+- All-pair baseline table: `gamma/diff/bperp_all_pairs.txt`
+- Adjacent-pair table: `gamma/diff/itab_adjacent`
+- Pair-specific RSLC products: `gamma/rslc/*_to_*.rslc`
+- Pair-specific quality reports: `gamma/rslc/*_to_*.rslc.coreg_quality`
+- Adjacent interferograms: `gamma/int/*.int`
+- Adjacent coherence rasters: `gamma/int/*.cc`
+- Adjacent interferogram previews: `gamma/int/*.int.bmp`
+- Adjacent coherence previews: `gamma/int/*.cc.bmp`
+- Coherence statistics: `logs/*_cc_stats.txt`
+- Reference RDC DEM: `gamma/dem/20240905_8rlks.rdc.dem`
+- Common-reference stack tables: `gamma/common_20240905/SLC_tab`, `gamma/common_20240905/RMLI_tab`
+- Common-reference differential stack: `gamma/common_20240905/diff/*/*_8rlks.diff_filt.unw`
+- Gamma `mb` time-series list: `gamma/common_20240905/timeseries/diff_ts.tab`
+- Gamma `mb` phase time-series: `gamma/common_20240905/timeseries/diff_ts_*.diff`
+- Gamma `mb` residual sigma: `gamma/common_20240905/timeseries/sigma_ts`
+- Gamma `mb` height correction: `gamma/common_20240905/timeseries/hgt_correction`
+- Gamma `ts_rate` output: `gamma/common_20240905/timeseries/ts_rate`
+- Gamma `ts_rate` sigma output: `gamma/common_20240905/timeseries/sigma_rate`
+- Preview rasters: `gamma/common_20240905/timeseries/*.bmp`
+- Geocoded GeoTIFF exports: `publish/geotiff/*.tif`
+- Explicit LOS velocity previews: `publish/geotiff/los_rate_toward_mm_per_year.bmp`, `publish/geotiff/los_rate_away_mm_per_year.bmp`
+- Geocoded web previews: `publish/geotiff/los_rate_toward_mm_per_year.geo_preview.png`, `publish/geotiff/los_sigma_mm_per_year.geo_preview.png`
+- Monitoring-point curve: `publish/monitor_points/auto_low_sigma_high_rate_timeseries.png`
+- Monitoring-point values: `publish/monitor_points/auto_low_sigma_high_rate_timeseries.csv`
+- Trial summary: `publish/trial_summary.json`
+
+### Baseline Notes
+
+`base_calc` generated 10 all-pair entries. The adjacent temporal network is:
+
+| Pair | Delta days | Bperp from `base_calc` |
+| --- | ---: | ---: |
+| 20240516 -> 20240711 | 56 | 646.09670 |
+| 20240711 -> 20240905 | 56 | -236.76960 |
+| 20240905 -> 20250417 | 224 | 249.35880 |
+| 20250417 -> 20251002 | 168 | 59.18150 |
+
+The first validated pair was `20240711 -> 20240905` because it has a 56-day interval and moderate perpendicular baseline in this stack.
+
+### Adjacent-Pair Quality
+
+Final `SLC_coreg.py` quality-test summaries:
+
+| Pair | Accepted offsets | Final std range | Final std azimuth |
+| --- | ---: | ---: | ---: |
+| 20240516 -> 20240711 | 2476 / 2688 | 0.0861 | 0.1120 |
+| 20240711 -> 20240905 | 2483 / 2656 | 0.0370 | 0.1245 |
+| 20240905 -> 20250417 | 2322 / 2688 | 0.0487 | 0.0617 |
+| 20250417 -> 20251002 | 2274 / 2688 | 0.0304 | 0.0533 |
+
+Coherence statistics from Gamma big-endian float files:
+
+| Pair | valid `[0,1]` | p25 | median | p75 | p99 | Comment |
+| --- | ---: | ---: | ---: | ---: | ---: | --- |
+| 20240516 -> 20240711 | 100% | 0.127489 | 0.212285 | 0.323520 | 0.649398 | Weakest pair; high Bperp and low coherence |
+| 20240711 -> 20240905 | 100% | 0.629307 | 0.733220 | 0.802884 | 0.909973 | Strong pair |
+| 20240905 -> 20250417 | 100% | 0.339094 | 0.485239 | 0.612798 | 0.833928 | Usable trial pair |
+| 20250417 -> 20251002 | 100% | 0.394165 | 0.582921 | 0.734878 | 0.932300 | Usable trial pair |
+
+All adjacent pairs generated expected Gamma products. The full 5-scene chain can continue, but the first pair is a quality risk for unwrapping/IPTA. A more conservative follow-up is to run the 4-scene sub-stack from `20240711` to `20251002`, or keep the 5-scene stack but down-weight or exclude `20240516 -> 20240711` if later unwrapping/IPTA residuals are poor.
+
+Implementation note:
+
+- `SLC_coreg.py` writes the refined offset parameter file to a secondary-date path such as `gamma/slc/20240711.slc.off`.
+- Trial scripts copy that file to pair-specific paths such as `gamma/rslc/20240711_to_20240905.rslc.off`.
+- Future skip-pair or non-adjacent networks must use pair-specific offset files to avoid accidental reuse after the same secondary scene is coregistered to another reference.
+
+### Common-Reference Time-Series Trial
+
+The image-based Gamma IPTA path was also run using a common `20240905` reference geometry. This is closer to the official `mb -> ts_rate` time-series chain than the first adjacent-pair wrapped interferogram check.
+
+Common-reference inputs and products:
+
+- reference geometry: `20240905`
+- DEM source: existing Copernicus30 cache under `backend/runtime/pyint_dem_cache`
+- RDC DEM: `gamma/dem/20240905_8rlks.rdc.dem`
+- common-reference SLC table: `gamma/common_20240905/SLC_tab`
+- common-reference MLI table: `gamma/common_20240905/RMLI_tab`
+- common-reference differential ITAB:
+
+```text
+1 3 1 1
+2 3 2 1
+3 4 3 1
+3 5 4 1
+```
+
+Filtered differential coherence statistics:
+
+| Pair | valid `[0,1]` | p25 | median | p75 | p99 |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| 20240516 -> 20240905 | 100% | 0.497369 | 0.884586 | 0.945308 | 0.980583 |
+| 20240711 -> 20240905 | 100% | 0.919149 | 0.968188 | 0.979449 | 0.990185 |
+| 20240905 -> 20250417 | 100% | 0.366279 | 0.790519 | 0.909991 | 0.978094 |
+| 20240905 -> 20251002 | 100% | 0.193104 | 0.815562 | 0.917209 | 0.972355 |
+
+Gamma `mb` outputs:
+
+- `gamma/common_20240905/timeseries/diff_ts_001.diff` through `diff_ts_005.diff`
+- `gamma/common_20240905/timeseries/diff_ts.tab`
+- `gamma/common_20240905/timeseries/itab_ts`
+- `gamma/common_20240905/timeseries/sigma_ts`
+- `gamma/common_20240905/timeseries/hgt_correction`
+
+Gamma `ts_rate` outputs:
+
+- `gamma/common_20240905/timeseries/ts_rate`
+- `gamma/common_20240905/timeseries/ts_const`
+- `gamma/common_20240905/timeseries/sigma_rate`
+- `gamma/common_20240905/timeseries/ts_rate.bmp`
+- `gamma/common_20240905/timeseries/sigma_rate.bmp`
+- `gamma/common_20240905/timeseries/hgt_correction.bmp`
+
+GeoTIFF exports:
+
+- `publish/geotiff/ts_rate_rad_per_year.tif`
+- `publish/geotiff/sigma_rate_rad_per_year.tif`
+- `publish/geotiff/sigma_ts_rad.tif`
+- `publish/geotiff/hgt_correction_m.tif`
+- `publish/geotiff/los_rate_m_per_year.tif`
+- `publish/geotiff/los_sigma_m_per_year.tif`
+- `publish/geotiff/los_rate_away_mm_per_year.tif`
+- `publish/geotiff/los_rate_toward_mm_per_year.tif`
+- `publish/geotiff/los_sigma_mm_per_year.tif`
+- `publish/geotiff/los_rate_toward_mm_per_year.geo_preview.png`
+- `publish/geotiff/los_sigma_mm_per_year.geo_preview.png`
+- `publish/trial_summary.json`
+
+Float output statistics using Gamma big-endian float:
+
+| File | Non-zero pixels | p25 | median | p75 | p99 |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| `ts_rate` | 8,868,956 | -1.663257 | -0.271621 | 0.869116 | 2.787572 |
+| `sigma_rate` | 8,868,956 | 0.396037 | 0.677578 | 1.010539 | 2.298520 |
+| `sigma_ts` | 8,892,237 | 0.000704 | 0.001557 | 0.002535 | 0.518188 |
+| `hgt_correction` | 8,892,250 | -16.989384 | 7.596325 | 36.411520 | 98.636933 |
+
+Explicit LOS velocity output statistics in `mm/year`:
+
+| File | Non-zero pixels | p01 | p25 | median | p75 | p99 |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| `los_rate_toward_mm_per_year.rdc` | 8,868,956 | -52.779640 | -16.455760 | 5.142855 | 31.491957 | 84.590944 |
+| `los_sigma_mm_per_year.rdc` | 8,868,956 | 0.530715 | 7.498536 | 12.829198 | 19.133450 | 43.519972 |
+
+This is a successful first local Gamma official-chain time-series trial. It is still a technical validation run, not a production-grade SBAS product: the stack has only 5 dates, the network is minimal, and reference region and unwrapping masks were conservative defaults.
+
+The exported GeoTIFFs are EPSG:4326, `2222 x 2237`, Float32, LZW-compressed Cloud Optimized GeoTIFFs with `NoData=0`.
+
+The `*.bmp` files generated by Gamma `rasdt_pwr` are RDC processing-geometry browse images. They are useful for quick processing QA but are not map products and should not be used as the default UI map preview. The UI/default web preview should use the `*.geo_preview.png` files generated from the EPSG:4326 GeoTIFF products.
+
+### LOS Sign Convention
+
+Gamma `ts_rate` is a phase-rate raster in `rad/year`. Converting phase rate to LOS displacement rate requires a sign convention:
+
+- `los_rate_away_mm_per_year = phase_rate * wavelength / (4*pi) * 1000`
+- `los_rate_toward_mm_per_year = -phase_rate * wavelength / (4*pi) * 1000`
+
+Gamma `dispmap` documents two conventions:
+
+- `sflg=0`, the default: motion away from radar is negative, so motion toward radar is positive; deformation and unwrapped phase have opposite signs.
+- `sflg=1`: motion away from radar is positive; deformation and unwrapped phase have the same sign.
+
+For system productization, use explicit names and prefer `los_rate_toward_mm_per_year` as the default display product because it matches Gamma `dispmap` default `sflg=0`. Keep the away-positive version available when another downstream convention requires direct phase-sign products.
+
+### Monitoring Point Curve
+
+An example monitoring point was selected automatically from low-sigma, high-rate, non-edge pixels:
+
+- radar pixel: range `336`, azimuth `2290`
+- approximate lon/lat: `131.4340324903`, `43.8008322757`
+- reference date in the plotted time series: `20240711`
+- LOS convention: toward radar positive, away from radar negative
+- fitted LOS velocity: `50.1085 mm/year`
+- fitted LOS velocity sigma: `0.0063 mm/year`
+
+Generated outputs:
+
+- `publish/monitor_points/auto_low_sigma_high_rate_timeseries.png`
+- `publish/monitor_points/auto_low_sigma_high_rate_timeseries.csv`
+- `publish/monitor_points/auto_low_sigma_high_rate_metadata.json`
+
+The plotted values are:
+
+| Date | LOS displacement, toward-positive mm |
+| --- | ---: |
+| 20240516 | -7.673957 |
+| 20240711 | 0.000000 |
+| 20240905 | 7.673922 |
+| 20250417 | 38.412669 |
+| 20251002 | 61.464305 |
+
+This is a single example point only. It is not a validated monitoring-point network and should not be interpreted as a representative area-wide deformation curve. The automatic selection favors a non-edge pixel with relatively high absolute velocity and low fitted sigma so the curve is visually inspectable. Production monitoring points need one of these inputs:
+
+- user-clicked map lon/lat
+- imported engineering monitoring-point layer
+- a configured regular grid or point-of-interest set
+- a quality-filtered automatic point sampler with spacing, coherence/sigma thresholds, and manual review
+
+Until that is implemented, the single curve is a capability demonstration and should be labeled as such in the UI.
+
+### Current Open Items
+
+- Review the `ts_rate.bmp`, `sigma_rate.bmp`, `hgt_correction.bmp`, LOS velocity BMPs, monitoring-point PNG, and exported GeoTIFFs visually in GIS.
+- Tune reference region, coherence thresholds, and pair network before treating the result as production.
+- Decide whether to keep the weak/long 2025 pair, add skip-pairs, or use a denser data sequence when more LT1 precise orbits are available.
+- Keep system integration as orchestration around Gamma commands; do not implement custom SBAS inversion in application code.
+
+## Productization Decisions After Trial
+
+If the 5-scene trial succeeds, add a new managed processor instead of bending existing ISCE/SARscape flows:
+
+```text
+processor_code = gamma_ipta_sbas
+engine_code = gamma
+workflow = gamma_ipta_sbas
+```
+
+Required system changes:
+
+- Add a Gamma IPTA stack manifest builder.
+- Add LT1-specific hard grouping keys:
+  - satellite platform, not only family, unless cross-satellite LT1A/LT1B is explicitly validated
+  - relative orbit
+  - orbit direction
+  - imaging mode
+  - polarization
+  - scene strip/center bucket
+  - product variant or station/submode where it affects compatibility
+- Add a Gamma baseline audit step before final pair network selection.
+- Persist selected Gamma pair network separately from the coarse planning graph.
+- Keep system code as orchestration only; Gamma remains the processing authority.
+
+## Current Recommendation
+
+Use the manual audited `LT1B relOrbit 114 DESCENDING STRIP1 HH / E131.2 N43.8` stack and the trial scripts as the reference path for productization.
+
+Do not start Gamma IPTA production from the current automatic time-series plan. It can be used for discovery, but production stack selection needs the hard grouping keys and Gamma baseline audit described above.
+
+For the next engineering step, add a managed `gamma_ipta_sbas` processor that orchestrates the Gamma commands rather than reimplementing SBAS inversion in application code.

docs/INDEX.md

@@ -36,8 +36,11 @@
 - [PRODUCTION_RESULTS_MULTI_ENGINE_DESIGN_20260423.md](PRODUCTION_RESULTS_MULTI_ENGINE_DESIGN_20260423.md)
   多引擎结果目录、发布结构和 catalog 治理设计。
 
+- [FLOOD_GEOTIFF_GAMMA_PREPROCESS_DESIGN_20260515.md](FLOOD_GEOTIFF_GAMMA_PREPROCESS_DESIGN_20260515.md)
+  洪涝模块 GeoTIFF 化与 GAMMA 前处理评估，定义“原始数据到分析级 TIF 由可插拔处理器完成，水体/洪涝算法只运行在 GeoTIFF 层”的当前技术决策。
+
 - [FLOOD_DISASTER_ANALYSIS_SYSTEM_DESIGN_20260514.md](FLOOD_DISASTER_ANALYSIS_SYSTEM_DESIGN_20260514.md)
-  洪涝灾害分析独立系统设计，定义多源 SAR 数据、ENVI/SARscape 洪涝流程、标准产品包和矢量套合分析边界。
+  洪涝灾害分析独立系统早期设计，保留工作台、产品包和矢量套合分析边界；其中 ENVI/SARscape 作为洪涝主线的表述已被 2026-05-15 GeoTIFF 化设计取代。
 
 - [WSL_RUNTIME_REFACTOR_DESIGN_20260422.md](WSL_RUNTIME_REFACTOR_DESIGN_20260422.md)
   WSL 共享运行时和 Broker 设计。
@@ -50,15 +53,18 @@
 
 ## 3. 时序 InSAR / SBAS
 
-- [ISCE2_SBAS_TIMESERIES_DESIGN.md](ISCE2_SBAS_TIMESERIES_DESIGN.md)
-- [ISCE2_SBAS_PRODUCT_SPEC.md](ISCE2_SBAS_PRODUCT_SPEC.md)
-- [ISCE2_SBAS_ENGINEERING_DESIGN_20260428.md](ISCE2_SBAS_ENGINEERING_DESIGN_20260428.md)
-  Current-phase engineering design for the managed ISCE2 + MintPy SBAS route.
+- [SBAS_INSAR_PRODUCTION_PIPELINE_DESIGN_20260519.md](SBAS_INSAR_PRODUCTION_PIPELINE_DESIGN_20260519.md)
+  Gamma/LT1 SBAS-InSAR 独立生产页面与统一流水线收口设计。
+- [TIMESERIES_LEGACY_DEPRECATION_20260521.md](TIMESERIES_LEGACY_DEPRECATION_20260521.md)
+  旧 ISCE2/MintPy 时序生产链路停用记录，定义当前入口隐藏、配置默认关闭和后续物理删除条件。
+- [GAMMA_IPTA_LT1_SBAS_TRIAL_RUNBOOK_20260518.md](GAMMA_IPTA_LT1_SBAS_TRIAL_RUNBOOK_20260518.md)
+  Gamma 官方 DIFF + IPTA 路径处理 LT1 数据的本地试验记录、产物和符号约定。
 
 说明：
 
-- 当前前端顶级显示名已经统一为“时序 InSAR”。
-- 当前默认接入仍然是 SBAS 路径，因此这两份文档仍然有效。
+- 当前 SBAS-InSAR 生产主线是 Gamma 独立生产页面，不再使用旧 ISCE2/MintPy 时序生产页作为入口。
+- 旧 `timeseries-production` 后端、旧前端面板和 `ps_timeseries_runs` 相关结构暂时保留兼容，但默认关闭。
+- `ISCE2_SBAS_TIMESERIES_DESIGN.md`、`ISCE2_SBAS_PRODUCT_SPEC.md`、`ISCE2_SBAS_ENGINEERING_DESIGN_20260428.md` 仅作为历史参考，不再作为当前生产事实依据。
 - `SBAS_*` 命名的一批旧文档已经归档，只保留历史追溯价值。
 
 ## 4. 配对与前端导航
@@ -130,4 +136,5 @@
 - 已归档的 `项目汇报.md`
 - 已归档的各类 `*_EXPERIMENT_*` / `*_PROGRESS_*` / `*_TODO_*`
 
-最后更新：2026-05-12
+最后更新：2026-05-15
+最近修订：2026-05-21

docs/SBAS_INSAR_PRODUCTION_PIPELINE_DESIGN_20260519.md

@@ -0,0 +1,439 @@
+# SBAS-InSAR Production Pipeline Design
+
+Date: 2026-05-19
+
+## Decision
+
+SBAS-InSAR production becomes an independent production workflow and page. It must not depend on the existing coarse time-series pairing layer as its production authority.
+
+The old time-series pairing code may remain temporarily for compatibility and candidate discovery, but the new SBAS-InSAR page and backend API should bypass it by default. Deletion should happen only after the new workflow can create, run, publish, and browse Gamma SBAS/IPTA products end to end.
+
+The legacy ISCE2/MintPy time-series production chain is disabled by default. The `timeseries-production` backend code and old catalog pages may remain as compatibility code, but they are no longer exposed as production-management subpages. The active SBAS production route is `/api/sbas-insar-production` and the active UI view is `sbas_insar_production`.
+
+## Scope
+
+Initial production target:
+
+- Sensor: LT1 SLC
+- Engine: Gamma
+- Workflow: Gamma DIFF + IPTA `mb` + `ts_rate`
+- Processor code: `gamma_ipta_sbas`
+- Default display product: LOS velocity toward radar positive
+
+Out of scope for the first implementation slice:
+
+- custom SBAS inversion in application code
+- direct reuse of the current PS/time-series pair graph as final Gamma `itab`
+- full automatic stack approval without a Gamma baseline and quality audit
+- cross-satellite LT1A/LT1B stack mixing
+
+## Product Contract
+
+Every successful SBAS-InSAR production run should publish:
+
+- `product_summary.json`
+- `stack_manifest.json`
+- `gamma_command_manifest.json`
+- `pair_network.json`
+- `quality_summary.json`
+- `los_rate_toward_mm_per_year.tif`
+- `los_rate_toward_mm_per_year.geo_preview.png`
+- `los_rate_toward_mm_per_year.rdc_preview.bmp`
+- `los_rate_away_mm_per_year.tif`
+- `los_rate_away_mm_per_year.rdc_preview.bmp`
+- `los_sigma_mm_per_year.tif`
+- `los_sigma_mm_per_year.geo_preview.png`
+- `los_sigma_mm_per_year.rdc_preview.bmp`
+- `ts_rate_rad_per_year.tif`
+- `sigma_rate_rad_per_year.tif`
+- monitoring-point time-series `png/csv/json`
+- raw logs for each Gamma stage
+
+The current trial product remains the reference implementation:
+
+```text
+backend/runtime/gamma_ipta_trials/lt1b_r114_e1312_n438_20240516_20251002
+```
+
+Preview rule:
+
+- UI default map previews must be rendered from geocoded EPSG:4326 GeoTIFFs.
+- RDC/RMLI BMP browse images are processing QA artifacts only.
+- Product names should make coordinate state explicit: `geo_preview` for map previews and `rdc_preview` for radar-geometry previews.
+
+## LOS Sign Convention
+
+Gamma `ts_rate` outputs phase rate in `rad/year`. The system must store both sign conventions explicitly:
+
+```text
+los_rate_away_mm_per_year = phase_rate * wavelength / (4*pi) * 1000
+los_rate_toward_mm_per_year = -phase_rate * wavelength / (4*pi) * 1000
+```
+
+Default UI display:
+
+```text
+LOS toward radar positive
+```
+
+This matches Gamma `dispmap` default `sflg=0`: motion away from radar is negative, motion toward radar is positive.
+
+## Page Design
+
+Add a separate production view:
+
+```text
+Production Management
+  - D-InSAR Runs
+  - SBAS-InSAR Production
+  - D-InSAR Products
+```
+
+The SBAS page is operational, not a marketing landing page. First screen should show:
+
+- runtime capability: Gamma install, WSL distro, workflow support
+- available SBAS stacks or trial runs
+- selected run summary
+- LOS velocity geocoded preview
+- product file list
+- monitoring-point curve
+- quality metrics
+- stage checklist
+
+The current old "time-series run" and "time-series products" views are hidden from the production workspace. Legacy route aliases such as `ps_production` and `ps_products` should redirect to the SBAS-InSAR production view rather than opening the old ISCE2/MintPy workflow.
+
+## Backend API
+
+Initial read-only API:
+
+```text
+GET /api/sbas-insar-production/capabilities
+GET /api/sbas-insar-production/trial-runs
+GET /api/sbas-insar-production/trial-runs/{trial_id}
+GET /api/sbas-insar-production/trial-runs/{trial_id}/artifacts/{relative_path}
+```
+
+Stack discovery and hard-constraint audit API:
+
+```text
+POST /api/sbas-insar-production/stacks/discover
+POST /api/sbas-insar-production/stacks/{stack_id}/audit
+```
+
+`stacks/discover` scans LT1 source roots directly and groups scenes by:
+
+- platform, for example `LT1A` or `LT1B`
+- satellite mode, for example `MONO`
+- receiving station
+- relative orbit
+- orbit direction
+- imaging mode
+- polarization
+- center bucket, for example `E131.2_N43.8`
+
+It also checks LT1 precise orbit TXT availability against `PYINT_ORBIT_POOL_TXT` / `ORBIT_POOL_ENVI`.
+
+`stacks/{stack_id}/audit` writes a reproducible manifest under:
+
+```text
+backend/runtime/sbas_insar_production/stack_manifests/{stack_id}/
+```
+
+The manifest status is `READY_FOR_GAMMA_BASELINE_AUDIT` only after hard grouping, minimum scene count, precise orbit availability, and an initial adjacent temporal network are satisfied. Gamma `base_calc` remains the next required audit before final `itab` approval.
+
+Writable production planning API:
+
+```text
+POST /api/sbas-insar-production/stacks/{stack_id}/runs
+GET /api/sbas-insar-production/runs
+GET /api/sbas-insar-production/runs/{run_id}
+GET /api/sbas-insar-production/runs/{run_id}/artifacts/{relative_path}
+```
+
+The current `runs` submission is a dry-run planning submission. It writes:
+
+```text
+backend/runtime/sbas_insar_production/runs/{run_id}/run_manifest.json
+backend/runtime/sbas_insar_production/runs/{run_id}/stack_manifest.json
+backend/runtime/sbas_insar_production/runs/{run_id}/pair_network.json
+backend/runtime/sbas_insar_production/runs/{run_id}/gamma_command_manifest.json
+backend/runtime/sbas_insar_production/runs/{run_id}/monitor_points.json
+```
+
+The created run status is:
+
+```text
+PLANNED_GAMMA_BASELINE_AUDIT
+```
+
+This is intentionally not a Gamma execution trigger yet. The next runnable slice should add:
+
+```text
+POST /api/sbas-insar-production/runs/{run_id}/baseline-audit
+POST /api/sbas-insar-production/runs/{run_id}/itab-decision
+POST /api/sbas-insar-production/runs/{run_id}/coregistration
+POST /api/sbas-insar-production/runs/{run_id}/coregistration/jobs
+POST /api/sbas-insar-production/runs/{run_id}/monitor-points
+POST /api/sbas-insar-production/runs/{run_id}/retry-stage
+```
+
+`baseline-audit` currently supports:
+
+- script-only mode: generate/reparse `scripts/01_baseline_audit.sh` and existing outputs
+- execution mode: run Gamma `par_LT1_SLC`, `LT1_precision_orbit.py`, `multi_look`, and `base_calc`
+- output parsing: write `baseline_audit_summary.json` and `pair_network_baseline_audit.json`
+
+`itab-decision` is the current production gate:
+
+- `approve`: copies Gamma `work/gamma/diff/itab_adjacent` to `work/gamma/diff/itab_approved`, writes `itab_decision.json`, moves the run to `ITAB_APPROVED`, and makes `coregistration` the next stage
+- `reject`: writes `itab_decision.json`, moves the run to `ITAB_REJECTED`, and blocks further Gamma stages until the pair network is revised
+
+`coregistration` supports script generation. It writes:
+
+```text
+backend/runtime/sbas_insar_production/runs/{run_id}/scripts/02_coreg_common_ref.sh
+backend/runtime/sbas_insar_production/runs/{run_id}/coregistration_plan.json
+```
+
+The generated script consumes `work/gamma/diff/itab_approved` as the approval gate, uses the stack reference date as common geometry, and prepares Gamma `SLC_coreg.py` calls for every non-reference date.
+
+`coregistration/jobs` submits the generated script to the existing `SystemTask` + `SystemJob` background queue as job type `SBAS_COREGISTRATION`. The job runs Gamma `SLC_coreg.py`, writes `coregistration_summary.json`, updates the run manifest to `COREGISTRATION_READY` or `COREGISTRATION_FAILED`, and advances the next stage to `rdc_dem` only when all expected RSLC/RMLI outputs and common tab files exist.
+
+## Backend Services
+
+First slice:
+
+```text
+sbas_insar_production_service.py
+  - discover local Gamma IPTA trial summaries
+  - normalize products and artifact URLs
+  - expose sign convention and product metadata
+  - serve safe artifacts from trial roots
+```
+
+Second slice:
+
+```text
+gamma_ipta_stack_planner.py
+  - hard group LT1 scenes by platform, relative orbit, direction, mode, polarization, center bucket
+  - require precise orbit availability
+  - emit stack_manifest.json
+
+gamma_ipta_pair_planner.py
+  - build initial temporal network
+  - run Gamma baseline audit
+  - emit pair_network.json and Gamma itab
+
+gamma_ipta_job_runner.py
+  - execute official Gamma commands stage by stage
+  - write command manifests and logs
+
+sbas_insar_product_publisher.py
+  - publish GeoTIFF/BMP/CSV/JSON products
+  - register products into unified result catalog
+```
+
+## Future Database Model
+
+Use unified pipeline tables rather than adding many one-off SBAS tables:
+
+```text
+pipeline_runs
+pipeline_stages
+pipeline_products
+pipeline_quality_metrics
+pipeline_logs
+```
+
+Minimum fields for `pipeline_runs`:
+
+- `run_id`
+- `workflow_code = sbas_insar`
+- `processor_code = gamma_ipta_sbas`
+- `engine_code = gamma`
+- `status`
+- `stack_manifest_path`
+- `work_root`
+- `publish_root`
+- `created_by`
+- `created_at`
+- `started_at`
+- `ended_at`
+- `summary_json`
+
+For the first slice, use filesystem discovery only. Do not add migrations until the run submission workflow is ready.
+
+## Gamma Stage Contract
+
+The managed runner should preserve the successful trial chain:
+
+1. `par_LT1_SLC`
+2. `LT1_precision_orbit.py`
+3. `multi_look`
+4. `base_calc`
+5. `SLC_coreg.py`
+6. `gc_map1` / `geocode` / `gc_map_fine`
+7. `phase_sim_orb`
+8. `SLC_diff_intf`
+9. `adf`
+10. `mcf`
+11. `mb`
+12. `ts_rate`
+13. `geocode_back`
+14. `data2geotiff`
+15. LOS sign conversion and preview generation
+16. monitoring-point time-series extraction
+
+The application is an orchestrator. Gamma remains the processing authority.
+
+## Migration Plan
+
+Phase 1: read-only SBAS production page
+
+- add design document
+- add backend API for existing Gamma trial discovery
+- add page entry and product preview
+- keep old time-series page available as legacy
+
+Phase 2: managed run submission
+
+- add stack discovery and audit endpoints
+- add planned-run submission endpoint
+- write production run manifest, command manifest, and monitor-point config
+- add Gamma runner skeleton
+- queue job with stage updates
+
+Phase 3: unified pipeline management
+
+- add generic pipeline tables
+- move Gamma SBAS run records into pipeline tables
+- register products through the unified product catalog
+
+Phase 4: remove old SBAS/time-series pairing dependency
+
+- hide old SBAS entry completely
+- keep any reusable discovery functions as internal utilities
+- delete obsolete UI and API routes after dependency audit
+
+## Acceptance Criteria For Phase 1
+
+- SBAS-InSAR production appears as its own production workspace view.
+- Existing Gamma IPTA trial can be listed from the backend API.
+- The selected trial shows LOS velocity preview, sigma/GeoTIFF products, monitor-point curve, and quality summary.
+- Artifact serving is constrained to the trial root.
+- No existing D-InSAR, flood, or legacy time-series routes are broken.
+
+## Implementation Progress On 2026-05-19
+
+Implemented:
+
+- read-only SBAS-InSAR production page
+- trial product browser for the local Gamma IPTA validation run
+- artifact API constrained to published trial outputs
+- LT1 filesystem stack discovery independent of the old time-series pairing layer
+- hard grouping by platform, satellite mode, receiving station, relative orbit, orbit direction, imaging mode, polarization, and center bucket
+- precise orbit TXT availability check against the configured Gamma/PyINT orbit pool
+- stack manifest and initial adjacent pair-network JSON generation
+- geocoded web previews generated from `los_rate_toward_mm_per_year.tif` and `los_sigma_mm_per_year.tif`
+- planned SBAS production run creation from a READY stack manifest
+- filesystem production run browser and artifact download API
+- Gamma stage plan manifest with execution disabled until baseline audit runner is attached
+- monitoring-point config contract with explicit placeholder status for `auto_low_sigma_high_rate`
+- Gamma baseline audit script generation and output parser
+- baseline audit result display in the SBAS production page
+- itab approval/rejection API and page controls
+- common-reference coregistration script generation and page summary
+- queued `SBAS_COREGISTRATION` background job submission through the existing task/job queue
+- coregistration execution summary parser and manifest status update to `COREGISTRATION_READY` / `COREGISTRATION_FAILED`
+
+Local verification:
+
+- scanned `1500` LT1 scene directories from the local data pool
+- found READY candidates with all required precise orbit TXT files
+- generated one manifest at:
+
+```text
+backend/runtime/sbas_insar_production/stack_manifests/sbas_2e6301f64a10/20260519T122146Z_stack_manifest.json
+```
+
+- created one dry-run production plan at:
+
+```text
+backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/run_manifest.json
+```
+
+The dry-run production plan uses the local LT1B relOrbit `114` stack around `E129.2_N44.1`, with `7` scenes and `6` initial adjacent temporal pairs.
+
+- baseline audit script:
+
+```text
+backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/scripts/01_baseline_audit.sh
+```
+
+- baseline audit summary:
+
+```text
+backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/baseline_audit_summary.json
+```
+
+The baseline audit ran Gamma 20240627 `par_LT1_SLC`, `LT1_precision_orbit.py`, `multi_look`, and `base_calc` against all 7 LT1B scenes. It completed with status `BASELINE_AUDIT_READY` after the outer terminal command timed out, because the WSL process continued to completion in the background.
+
+Gamma `base_calc` adjacent-network result:
+
+- all-pair count: `21`
+- adjacent-pair count: `6`
+- max absolute perpendicular baseline: `731.9957 m`
+- max temporal gap: `224 days`
+
+The current adjacent network is connected, but several Bperp values are large enough that a human baseline/quality review is still required before using this `itab` for the full SBAS inversion.
+
+The current run has been approved for the next controlled trial step:
+
+```text
+status = ITAB_APPROVED
+next_stage = coregistration
+approved itab = backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/work/gamma/diff/itab_approved
+decision record = backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/itab_decision.json
+```
+
+The common-reference co-registration script has been generated:
+
+```text
+backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/scripts/02_coreg_common_ref.sh
+backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/coregistration_plan.json
+```
+
+Current status before executing the queued job:
+
+```text
+status = COREGISTRATION_SCRIPT_READY
+next_stage = execute_coregistration
+common reference date = 20241007
+```
+
+The actual `SLC_coreg.py` execution is now wired as a background job endpoint and page action, but has not been production-tested in this pass. It consumes `itab_approved`, not the pre-audit or unapproved pair plan.
+
+Expected post-job outputs:
+
+```text
+backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/coregistration_summary.json
+backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/work/gamma/common_20241007/SLC_tab
+backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/work/gamma/common_20241007/RMLI_tab
+backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/work/gamma/common_20241007/rslc/*.rslc
+backend/runtime/sbas_insar_production/runs/sbas_ab96afabead5/work/gamma/common_20241007/rmli/*.mli
+```
+
+After the job succeeds:
+
+```text
+status = COREGISTRATION_READY
+next_stage = rdc_dem
+```
+
+Open product-display decisions:
+
+- the current Gamma `*.bmp` previews are RDC processing-geometry products; keep them visible only as QA artifacts
+- the first UI map preview should use `los_rate_toward_mm_per_year.geo_preview.png`
+- the first sigma preview should use `los_sigma_mm_per_year.geo_preview.png`
+- the current monitoring-point curve is a single automatic sample point, not a monitoring network
+- production monitoring curves need user-selected lon/lat points, imported monitoring points, or a quality-filtered automatic sampler before they can be treated as formal outputs

docs/TIMESERIES_LEGACY_DEPRECATION_20260521.md

@@ -0,0 +1,184 @@
+# Legacy Time-Series InSAR Deprecation Record
+
+Date: 2026-05-21
+
+## Background
+
+The project previously kept an ISCE2 + MintPy time-series/SBAS production path under:
+
+```text
+experiments/isce2_sbas_timeseries
+```
+
+That experiment directory has been removed. The active SBAS-InSAR direction is now the independent Gamma production workflow documented in:
+
+```text
+docs/SBAS_INSAR_PRODUCTION_PIPELINE_DESIGN_20260519.md
+docs/GAMMA_IPTA_LT1_SBAS_TRIAL_RUNBOOK_20260518.md
+```
+
+Before this cleanup, startup validation still generated warnings for missing legacy paths:
+
+```text
+TIMESERIES_EXPERIMENT_ROOT
+TIMESERIES_STACK_PREP_SCRIPT
+TIMESERIES_MATERIALIZE_SCRIPT
+TIMESERIES_PREPARE_DEM_SCRIPT
+TIMESERIES_STACK_RUNNER_SCRIPT
+TIMESERIES_MINTPY_SBAS_SCRIPT
+TIMESERIES_EXPORT_PUBLISH_SCRIPT
+```
+
+Those warnings were misleading because they referred to the abandoned ISCE2/MintPy line, not the current Gamma SBAS-InSAR production line.
+
+## Decision
+
+The ISCE2/MintPy time-series production chain is deprecated and disabled by default.
+
+The current SBAS-InSAR production authority is:
+
+```text
+Frontend view: sbas_insar_production
+Backend route: /api/sbas-insar-production
+Engine: Gamma
+Workflow: DIFF + IPTA SBAS
+```
+
+The legacy code is not physically deleted yet. It remains only as compatibility and historical-reference code until the Gamma SBAS workflow can be tested end to end and historical result access is confirmed.
+
+## Changes Made
+
+Backend configuration:
+
+- `backend/app/config.py`
+  - `TIMESERIES_ENABLED` default changed from `true` to `false`.
+  - Legacy `TIMESERIES_*` experiment/script defaults are now populated only when `TIMESERIES_ENABLED=true`.
+  - `ensure_dirs()` no longer creates `TIMESERIES_WORK_ROOT` unless the legacy chain is explicitly enabled.
+  - Runtime validation now reports an info line instead of warning about missing legacy experiment scripts when the chain is disabled.
+
+Environment example:
+
+- `.env.example`
+  - `TIMESERIES_ENABLED=false`
+  - `TIMESERIES_EXPERIMENT_ROOT=` is blank.
+  - `TIMESERIES_DEFAULT_PROCESSOR_CODE=legacy_isce2_stack_mintpy`
+
+Frontend production management:
+
+- `frontend/src/config/appConstants.js`
+  - Removed production workspace views:
+    - `timeseries_runs`
+    - `timeseries_products`
+  - Legacy route aliases now map to the Gamma SBAS page:
+    - `ps_production -> sbas_insar_production`
+    - `ps_products -> sbas_insar_production`
+
+- `frontend/src/ProductionWorkspace.jsx`
+  - Removed lazy imports and render branches for:
+    - `TimeseriesProductionPanel`
+    - production-management `PsinsarCatalogPanel`
+  - Updated production workspace text to describe Gamma SBAS as an independent entry.
+
+- `frontend/src/components/app/AppSidePanel.jsx`
+  - Updated production-management description to state that the old ISCE2/MintPy time-series entry is disabled.
+
+Documentation:
+
+- `docs/SBAS_INSAR_PRODUCTION_PIPELINE_DESIGN_20260519.md`
+  - Added the legacy-chain deprecation decision.
+
+- `docs/FRONTEND_NAVIGATION_ARCHITECTURE.md`
+  - Updated production-management internal views to:
+    - `dinsar_runs`
+    - `sbas_insar_production`
+    - `dinsar_products`
+
+## Current Behavior
+
+After backend restart, deployment validation should no longer warn about the removed `experiments/isce2_sbas_timeseries` path.
+
+Expected validation line:
+
+```text
+[INFO] Legacy ISCE2/MintPy timeseries pipeline is disabled; current SBAS-InSAR production uses the Gamma /sbas-insar-production workflow.
+```
+
+The production-management page should show:
+
+```text
+D-InSAR 运行
+SBAS-InSAR Production
+D-InSAR 产物
+```
+
+It should no longer expose:
+
+```text
+时序InSAR 运行
+时序InSAR 产物
+```
+
+## Verification
+
+Completed on 2026-05-21:
+
+- Frontend build passed:
+
+```text
+npm run build
+```
+
+- Runtime configuration check passed:
+
+```text
+scripts/check_runtime_config.py
+```
+
+Observed output included:
+
+```text
+[INFO] Legacy ISCE2/MintPy timeseries pipeline is disabled; current SBAS-InSAR production uses the Gamma /sbas-insar-production workflow.
+[OK] Deployment configuration check passed.
+```
+
+- Backend config syntax was checked with Python AST parsing.
+
+`python -m py_compile backend/app/config.py` was not used as the final check because Windows denied replacement of an existing `__pycache__` file. This was a local cache-permission issue, not a syntax failure.
+
+## Retained Compatibility Code
+
+The following code is intentionally retained for now:
+
+```text
+backend/app/routers/timeseries_production.py
+backend/app/services/timeseries_service.py
+frontend/src/TimeseriesProductionPanel.jsx
+frontend/src/api/timeseriesProduction.js
+frontend/src/components/PsinsarCatalogPanel.jsx
+```
+
+Database tables and historical product catalog structures such as `ps_timeseries_runs` are also retained.
+
+`PsinsarCatalogPanel` may still be useful outside production management, especially for analysis/result browsing. Do not delete it until those usages are audited.
+
+## Re-Enabling Legacy Chain
+
+Re-enabling the legacy ISCE2/MintPy chain is not part of the current production plan.
+
+If it must be revived for a controlled comparison, the operator must explicitly set:
+
+```text
+TIMESERIES_ENABLED=true
+```
+
+and provide valid values for all legacy experiment/script paths. The removed `experiments/isce2_sbas_timeseries` directory is no longer assumed to exist.
+
+## Follow-Up Cleanup Criteria
+
+Physical deletion of the legacy chain should wait until all of the following are true:
+
+- Gamma SBAS production has completed an end-to-end run from stack discovery to published LOS velocity/sigma products.
+- Historical `ps_timeseries_runs` and old time-series product records have a clear migration or read-only archival plan.
+- Frontend navigation, route aliases, and analysis pages have been audited for remaining dependencies.
+- Backend callers of `/api/timeseries-production` have either been removed or explicitly marked as legacy-only.
+- Test coverage or manual regression notes confirm that D-InSAR production, SBAS production, product browsing, and task monitoring still work.

frontend/src/App.jsx

@@ -282,6 +282,8 @@ function App() {
     const aoeLayerRef = useRef(null);
     const waterSceneLayersRef = useRef({});
     const floodEventLayersRef = useRef({});  // { eventId: { pre, post, classified } }
+    const floodPairPreviewLayerRef = useRef(null);
+    const floodVectorLayersRef = useRef({});
     const mapRegionLayerRef = useRef(null);
     const prevLicenseOkRef = useRef(false);
     const initializeAppDataRef = useRef(null);
@@ -327,6 +329,7 @@ function App() {
         pairLayersRef: pairLayersRef.current,
         aoeLayerRef: aoeLayerRef.current,
         floodEventLayersRef: floodEventLayersRef.current,
+        floodVectorLayersRef: floodVectorLayersRef.current,
     }), []);
 
     const mapExport = useMapExport({ mapRef, getVisibleLayerRefs, addLog, language });
@@ -1468,8 +1471,11 @@ function App() {
 
     const handleWaterSceneOnMap = (scene) => {
         if (!mapRef.current || !scene.coverage_polygon) return;
-        if (waterSceneLayersRef.current[scene.id]) {
-            waterSceneLayersRef.current[scene.id].remove();
+        const layerKey = scene.radar_data_id ? `scene_${scene.id}` : `radar_${scene.id}`;
+        if (waterSceneLayersRef.current[layerKey]) {
+            waterSceneLayersRef.current[layerKey].remove();
+            delete waterSceneLayersRef.current[layerKey];
+            return false;
         }
         const latLngs = scene.coverage_polygon.map(p => [p[1], p[0]]);
         const polygon = L.polygon(latLngs, {
@@ -1484,10 +1490,35 @@ function App() {
             `${scene.satellite || ''} · ${scene.imaging_date || ''}<br>` +
             `<span style="color:#888;font-size:11px">${(scene.geo_path || '').split(/[\\/]/).pop()}</span>`
         );
-        waterSceneLayersRef.current[scene.id] = polygon;
+        waterSceneLayersRef.current[layerKey] = polygon;
         polygon.addTo(mapRef.current);
         mapRef.current.flyToBounds(L.latLngBounds(latLngs), { padding: [50, 50], maxZoom: 10 });
         polygon.openPopup();
+        return true;
+    };
+
+    const handleFloodSourcePreviewOnMap = (item) => {
+        if (!mapRef.current || !item) return;
+        const enriched = {
+            ...item,
+            displayName: item.displayName || item.file_path?.split(/[\\/]/).pop() || `${item.satellite || 'SAR'} #${item.id}`,
+            previewCacheKey: item.previewCacheKey || item.preview_cache_updated_at || `${Date.now()}-${item.id}`,
+        };
+        if (radarPreviewLayersRef.current[enriched.id]) {
+            updateRadarPreviewVisibility(enriched, false);
+            return false;
+        }
+        updateRadarPreviewVisibility(enriched, true);
+        if (
+            enriched.min_lat != null && enriched.max_lat != null
+            && enriched.min_lon != null && enriched.max_lon != null
+        ) {
+            mapRef.current.flyToBounds(
+                L.latLngBounds([[enriched.min_lat, enriched.min_lon], [enriched.max_lat, enriched.max_lon]]),
+                { padding: [50, 50], maxZoom: 10 },
+            );
+        }
+        return true;
     };
 
     // 洪涝事件三图层叠加（灾前/灾后/分类结果），每层可独立开关
@@ -1534,6 +1565,66 @@ function App() {
         else layers[key].remove();
     };
 
+    const handleFloodPairPreviewOnMap = (pair) => {
+        if (!mapRef.current || !pair?.pre?.coverage_polygon || !pair?.post?.coverage_polygon) return;
+        if (floodPairPreviewLayerRef.current) {
+            floodPairPreviewLayerRef.current.remove();
+            floodPairPreviewLayerRef.current = null;
+        }
+        const makePolygon = (scene, color, label) => {
+            const latLngs = scene.coverage_polygon.map(p => [p[1], p[0]]);
+            const polygon = L.polygon(latLngs, {
+                color,
+                weight: 2,
+                fillColor: color,
+                fillOpacity: 0.14,
+            });
+            polygon.bindPopup(
+                `<strong>${escapeHtml(label)}</strong><br>` +
+                `场景 #${escapeHtml(scene.id)} · ${escapeHtml(scene.satellite || '')}<br>` +
+                `${escapeHtml(formatYmd(scene.imaging_date, language))} · ${escapeHtml(scene.polarization || '')}`
+            );
+            return polygon;
+        };
+
+        const prePolygon = makePolygon(pair.pre, '#2563eb', '灾前覆盖');
+        const postPolygon = makePolygon(pair.post, '#16a34a', '灾后覆盖');
+        const group = L.featureGroup([prePolygon, postPolygon]);
+        floodPairPreviewLayerRef.current = group;
+        group.addTo(mapRef.current);
+        mapRef.current.flyToBounds(group.getBounds(), { padding: [50, 50], maxZoom: 10 });
+    };
+
+    const handleFloodVectorOnMap = (impact) => {
+        if (!mapRef.current || !impact?.flood_vector_geojson) return;
+        const layerId = impact.overlay_id || impact.detection_id || 'current';
+        const existing = floodVectorLayersRef.current[layerId];
+        if (existing) existing.remove();
+
+        const layer = L.geoJSON(impact.flood_vector_geojson, {
+            style: {
+                color: '#dc2626',
+                weight: 2,
+                fillColor: '#ef4444',
+                fillOpacity: 0.26,
+            },
+            onEachFeature: (feature, featureLayer) => {
+                const props = feature?.properties || {};
+                featureLayer.bindPopup(
+                    `<strong>洪涝矢量</strong><br>` +
+                    `Overlay #${escapeHtml(layerId)}<br>` +
+                    `${escapeHtml(props.name || props.class || '')}`
+                );
+            },
+        });
+        floodVectorLayersRef.current[layerId] = layer;
+        layer.addTo(mapRef.current);
+        const bounds = layer.getBounds();
+        if (bounds.isValid()) {
+            mapRef.current.flyToBounds(bounds, { padding: [50, 50], maxZoom: 12 });
+        }
+    };
+
     const visualizePair = useCallback((pair) => {
         const masterLatLngs = pair.master.coverage_polygon.map(p => [p[1], p[0]]);
         const slaveLatLngs = pair.slave.coverage_polygon.map(p => [p[1], p[0]]);
@@ -1644,11 +1735,14 @@ function App() {
     };
     const floodPanel = {
         onShowSourceSceneOnMap: handleWaterSceneOnMap,
+        onShowSourcePreviewOnMap: handleFloodSourcePreviewOnMap,
         onShowReadyProductOnMap: handleWaterSceneOnMap,
         onShowOnMap: handleWaterSceneOnMap,
         onShowFloodOnMap: handleFloodEventOnMap,
         onShowFloodRunOnMap: handleFloodEventOnMap,
         onToggleFloodLayer: toggleFloodEventLayer,
+        onShowFloodPairOnMap: handleFloodPairPreviewOnMap,
+        onShowFloodVectorOnMap: handleFloodVectorOnMap,
     };
     const dinsarPanel = {
         dinsarCurrentPage,

frontend/src/DataMonitorPanel.jsx

@@ -7,6 +7,7 @@ const DEFAULT_MONITOR_CONFIG = {
   radar_dirs: [],
   orbit_dir: '',
   dinsar_dirs: [],
+  gf3_archive_source_dirs: [],
   gf3_source_dirs: [],
   gf3_storage_dirs: [],
   s1_source_dirs: [],
@@ -65,8 +66,9 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
   const [s1Loading, setS1Loading] = useState(false);
   const [s1ScanLoading, setS1ScanLoading] = useState(false);
   const [s1Message, setS1Message] = useState('');
-  const [gf3Loading, setGf3Loading] = useState(false);
+  const [gf3UnpackLoading, setGf3UnpackLoading] = useState(false);
   const [gf3ProcessLoading, setGf3ProcessLoading] = useState(false);
+  const [gf3ScanLoading, setGf3ScanLoading] = useState(false);
   const [gf3Message, setGf3Message] = useState('');
   const logEndRef = useRef(null);
 
@@ -84,6 +86,9 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
     task.task_id === unpackTaskId || task.task_type === 'UNPACK_ARCHIVES'
   );
   const s1ActiveTask = displayActiveTasks.find((task) => task.task_type === 'UNPACK_SENTINEL1');
+  const gf3ActiveTask = displayActiveTasks.find((task) =>
+    ['GF3_UNPACK', 'GF3_BATCH_PROCESS'].includes(task.task_type)
+  );
 
   useEffect(() => {
     if (!enabled) {
@@ -113,6 +118,7 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
           s1_source_dirs: toArray(data?.s1_source_dirs),
           s1_storage_dirs: toArray(data?.s1_storage_dirs),
           s1_orbit_dirs: toArray(data?.s1_orbit_dirs),
+          gf3_archive_source_dirs: toArray(data?.gf3_archive_source_dirs),
           gf3_source_dirs: toArray(data?.gf3_source_dirs),
           gf3_storage_dirs: toArray(data?.gf3_storage_dirs),
         });
@@ -281,6 +287,7 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
   const hasS1SourceDirs = config.s1_source_dirs.length > 0;
   const hasS1StorageDirs = config.s1_storage_dirs.length > 0;
   const hasS1OrbitDirs = config.s1_orbit_dirs.length > 0;
+  const hasGf3ArchiveSourceDirs = config.gf3_archive_source_dirs.length > 0;
   const hasGf3SourceDirs = config.gf3_source_dirs.length > 0;
   const hasGf3StorageDirs = config.gf3_storage_dirs.length > 0;
 
@@ -290,6 +297,7 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
   const canRunS1Scan = !readOnly && configLoaded && hasS1SourceDirs;
   const canRunS1OrbitScan = !readOnly && configLoaded && hasS1OrbitDirs;
   const canRunGf3Scan = !readOnly && configLoaded && hasGf3StorageDirs;
+  const canRunGf3Unpack = !readOnly && configLoaded && hasGf3ArchiveSourceDirs && hasGf3SourceDirs;
   const canRunGf3Process = !readOnly && configLoaded && hasGf3SourceDirs;
   const canOpenUnpackDialog = !readOnly && unpackConfig.source_dirs.length > 0;
 
@@ -409,6 +417,41 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
     }
   };
 
+  const handleGf3Unpack = async () => {
+    if (readOnly) {
+      setGf3Message('当前账户为只读模式，无法触发 GF3 解包。');
+      return;
+    }
+    setGf3UnpackLoading(true);
+    setGf3Message('GF3 解包启动中...');
+    try {
+      const res = await fetch(`${apiEndpoint}/monitor/gf3-unpack`, {
+        method: 'POST',
+        credentials: 'include',
+        headers: {
+          'Content-Type': 'application/json',
+        },
+        body: JSON.stringify({}),
+      });
+      const data = await parseJsonSafe(res, {});
+      if (res.ok) {
+        setGf3Message(data.message || 'GF3 解包任务已启动');
+        if (onTaskStart) {
+          onTaskStart(data.task_id, 'GF3 解包任务已启动。', {
+            nonBlocking: true,
+            taskType: 'GF3_UNPACK',
+          });
+        }
+      } else {
+        setGf3Message(`失败：${data.detail || '未知错误'}`);
+      }
+    } catch (err) {
+      setGf3Message(`失败：${err.message || '未知错误'}`);
+    } finally {
+      setGf3UnpackLoading(false);
+    }
+  };
+
   const handleOpenUnpackDialog = () => {
     if (readOnly) {
       setUnpackMessage('当前账户为只读模式，无法触发解包任务。');
@@ -546,7 +589,7 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
       setGf3Message('当前账户为只读模式，无法触发扫描。');
       return;
     }
-    setGf3Loading(true);
+    setGf3ScanLoading(true);
     setGf3Message('GF3 扫描启动中...');
     try {
       const res = await fetch(`${apiEndpoint}/monitor/run-now?target=gf3`, {
@@ -565,7 +608,7 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
     } catch (err) {
       setGf3Message(`失败：${err.message || '未知错误'}`);
     } finally {
-      setGf3Loading(false);
+      setGf3ScanLoading(false);
     }
   };
 
@@ -631,6 +674,7 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
             <div style={rowStyle}><span style={labelStyle}>S1 源数据</span><span style={{ wordBreak: 'break-all' }}>{formatList(config.s1_source_dirs)}</span></div>
             <div style={rowStyle}><span style={labelStyle}>S1 存储</span><span style={{ wordBreak: 'break-all' }}>{formatList(config.s1_storage_dirs)}</span></div>
             <div style={rowStyle}><span style={labelStyle}>S1 精轨</span><span style={{ wordBreak: 'break-all' }}>{formatList(config.s1_orbit_dirs)}</span></div>
+            <div style={rowStyle}><span style={labelStyle}>GF3 压缩包</span><span style={{ wordBreak: 'break-all' }}>{formatList(config.gf3_archive_source_dirs)}</span></div>
             <div style={rowStyle}><span style={labelStyle}>GF3 来源</span><span style={{ wordBreak: 'break-all' }}>{formatList(config.gf3_source_dirs)}</span></div>
             <div style={rowStyle}><span style={labelStyle}>GF3 存储</span><span style={{ wordBreak: 'break-all' }}>{formatList(config.gf3_storage_dirs)}</span></div>
             <div style={rowStyle}><span style={labelStyle}>D-InSAR 结果</span><span style={{ wordBreak: 'break-all' }}>{formatList(config.dinsar_dirs)}</span></div>
@@ -723,10 +767,18 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
         <div style={sectionStyle}>
           <div style={{ fontWeight: 'bold', marginBottom: '8px', color: 'var(--color-text-primary)' }}>GF3 归档预处理</div>
           <div style={{ ...gridStyle, marginBottom: '8px' }}>
+            <div style={rowStyle}><span style={labelStyle}>压缩包来源</span><span style={{ wordBreak: 'break-all' }}>{formatList(config.gf3_archive_source_dirs)}</span></div>
             <div style={rowStyle}><span style={labelStyle}>L1A 来源</span><span style={{ wordBreak: 'break-all' }}>{formatList(config.gf3_source_dirs)}</span></div>
             <div style={rowStyle}><span style={labelStyle}>L2 存储</span><span style={{ wordBreak: 'break-all' }}>{formatList(config.gf3_storage_dirs)}</span></div>
           </div>
-          <div style={{ display: 'flex', gap: '10px' }}>
+          <div style={{ display: 'flex', gap: '10px', flexWrap: 'wrap' }}>
+            <button
+              onClick={handleGf3Unpack}
+              disabled={gf3UnpackLoading || readOnly || !canRunGf3Unpack}
+              style={actionBtnStyle(gf3UnpackLoading, readOnly || !canRunGf3Unpack)}
+            >
+              {gf3UnpackLoading ? '运行中...' : (readOnly ? '只读模式' : 'GF3 解包')}
+            </button>
             <button
               onClick={handleGf3BatchProcess}
               disabled={gf3ProcessLoading || readOnly || !canRunGf3Process}
@@ -736,10 +788,10 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
             </button>
             <button
               onClick={handleGf3Scan}
-              disabled={gf3Loading || readOnly || !canRunGf3Scan}
-              style={actionBtnStyle(gf3Loading, readOnly || !canRunGf3Scan)}
+              disabled={gf3ScanLoading || readOnly || !canRunGf3Scan}
+              style={actionBtnStyle(gf3ScanLoading, readOnly || !canRunGf3Scan)}
             >
-              {gf3Loading ? '运行中...' : (readOnly ? '只读模式' : '扫描 GF3')}
+              {gf3ScanLoading ? '运行中...' : (readOnly ? '只读模式' : '扫描 GF3')}
             </button>
             <div
               style={{
@@ -748,7 +800,7 @@ const DataMonitorPanel = ({ apiEndpoint, onTaskStart, readOnly = false, enabled
                 alignSelf: 'center',
               }}
             >
-              {gf3Message}
+              {gf3ActiveTask ? (gf3ActiveTask.message || 'GF3 任务运行中...') : gf3Message}
             </div>
           </div>
         </div>

frontend/src/FloodAnalysisWorkspace.jsx

@@ -3,19 +3,25 @@ import { useCallback, useEffect, useMemo, useState } from 'react';
 import UnifiedDatePicker from './components/UnifiedDatePicker';
 import { getSearchOptions, searchRadarData } from './api/radar';
 import {
+  createFloodProduct,
   getFloodActiveRadarIds,
   getFloodDetectionPreview,
   getFloodDetections,
+  getFloodImpact,
   getFloodDoneRadarIds,
+  getFloodProductManifest,
   getFloodScenes,
+  getFloodProducts,
   getFloodWaterExtractionPreview,
   getFloodWaterExtractions,
   resetFloodScene,
-  searchFloodPairs,
+  runFloodOverlay,
+  searchFloodDisasterPairs,
   submitFloodDetection,
   submitFloodPreprocess,
   submitFloodWaterExtraction,
 } from './api/flood';
+import { getRegionChildren } from './api/aoi';
 import {
   buildRadarSearchFormData,
   formatYmd,
@@ -121,6 +127,11 @@ function compactDate(value) {
   return String(value || '').replaceAll('-', '').trim();
 }
 
+function formatPercent(value) {
+  if (value === null || value === undefined || Number.isNaN(Number(value))) return '-';
+  return `${(Number(value) * 100).toFixed(1)}%`;
+}
+
 function formatArea(value) {
   if (value === null || value === undefined || Number.isNaN(Number(value))) return '-';
   return `${Number(value).toFixed(2)} km²`;
@@ -225,6 +236,37 @@ function KeyValue({ label, value, strong = false }) {
   );
 }
 
+function RegionSelector({ options, selection, onProvinceChange, onCityChange, disabled = false }) {
+  const provinces = options?.provinces || [];
+  const cities = options?.cities || [];
+  return (
+    <div style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 6 }}>
+      <select
+        value={selection?.province || ''}
+        onChange={event => onProvinceChange(event.target.value)}
+        style={inputStyle}
+        disabled={disabled}
+      >
+        <option value="">省/自治区</option>
+        {provinces.map(item => (
+          <option key={item.tree_id} value={item.tree_id}>{item.name}</option>
+        ))}
+      </select>
+      <select
+        value={selection?.city || ''}
+        onChange={event => onCityChange(event.target.value)}
+        style={inputStyle}
+        disabled={disabled || !selection?.province}
+      >
+        <option value="">市/州（可选）</option>
+        {cities.map(item => (
+          <option key={item.tree_id} value={item.tree_id}>{item.name}</option>
+        ))}
+      </select>
+    </div>
+  );
+}
+
 function normalizeMapPreview(preview) {
   if (!preview) return null;
   const image = preview.image_b64 || preview.png_base64;
@@ -239,6 +281,11 @@ function normalizeMapPreview(preview) {
 function SceneRow({ scene, readOnly, onShowMap, onExtractWater, onReset }) {
   const status = asStatus(scene.status);
   const canExtract = status === 'DONE';
+  const [coverageVisible, setCoverageVisible] = useState(false);
+  const handleToggleCoverage = () => {
+    const visible = onShowMap(scene);
+    if (typeof visible === 'boolean') setCoverageVisible(visible);
+  };
   return (
     <div style={rowStyle}>
       <div style={{ display: 'grid', gridTemplateColumns: '1fr auto', gap: 8, alignItems: 'start' }}>
@@ -248,13 +295,16 @@ function SceneRow({ scene, readOnly, onShowMap, onExtractWater, onReset }) {
             <StatusBadge status={scene.status} />
             <span style={{ color: palette.muted }}>{scene.satellite || '-'}</span>
             <span style={{ color: palette.muted }}>{formatYmd(scene.imaging_date, 'zh')}</span>
+            {scene.polarization && <span style={{ color: palette.subtle }}>{scene.polarization}</span>}
           </div>
           <div style={{ color: palette.subtle, fontSize: 11, marginTop: 5, overflow: 'hidden', textOverflow: 'ellipsis', whiteSpace: 'nowrap' }} title={scene.geo_path || scene.error_msg || ''}>
-            {scene.geo_path ? scene.geo_path.split(/[\\/]/).pop() : scene.error_msg || `Radar ID ${scene.radar_data_id}`}
+            {[scene.imaging_mode, scene.product_level, scene.geo_path ? scene.geo_path.split(/[\\/]/).pop() : scene.error_msg || `Radar ID ${scene.radar_data_id}`].filter(Boolean).join(' · ')}
           </div>
         </div>
         <div style={{ display: 'flex', gap: 6, flexWrap: 'wrap', justifyContent: 'flex-end' }}>
-          <button type="button" style={buttonStyle('quiet', !scene.coverage_polygon)} disabled={!scene.coverage_polygon} onClick={() => onShowMap(scene)}>覆盖</button>
+          <button type="button" style={buttonStyle(coverageVisible ? 'success' : 'quiet', !scene.coverage_polygon)} disabled={!scene.coverage_polygon} onClick={handleToggleCoverage}>
+            {coverageVisible ? '清除范围' : '显示范围'}
+          </button>
           <button type="button" style={buttonStyle('primary', readOnly || !canExtract)} disabled={readOnly || !canExtract} onClick={() => onExtractWater(scene)}>提取水体</button>
           {isActiveStatus(scene.status) && (
             <button type="button" style={buttonStyle('danger', readOnly)} disabled={readOnly} onClick={() => onReset(scene)}>重置</button>
@@ -274,6 +324,9 @@ function WaterResultRow({ item, onShowMap }) {
             <strong>水体 #{item.id}</strong>
             <StatusBadge status={item.status} />
             {item.scene_id && <span style={{ color: palette.muted }}>场景 #{item.scene_id}</span>}
+            {item.satellite && <span style={{ color: palette.muted }}>{item.satellite}</span>}
+            {item.imaging_date && <span style={{ color: palette.muted }}>{formatYmd(item.imaging_date, 'zh')}</span>}
+            {item.polarization && <span style={{ color: palette.subtle }}>{item.polarization}</span>}
           </div>
           <div style={{ display: 'grid', gridTemplateColumns: 'repeat(3, minmax(0, 1fr))', gap: 8, marginTop: 8 }}>
             <KeyValue label="面积" value={formatArea(item.water_area_km2)} strong />
@@ -288,7 +341,29 @@ function WaterResultRow({ item, onShowMap }) {
   );
 }
 
-function FloodEventRow({ event, mapLayerVis, mapLoading, onShowMap, onToggleLayer, onSelectImpact }) {
+function FloodProductRow({ product, onOpenManifest }) {
+  return (
+    <div style={rowStyle}>
+      <div style={{ display: 'grid', gridTemplateColumns: '1fr auto', gap: 8, alignItems: 'center' }}>
+        <div style={{ minWidth: 0 }}>
+          <div style={{ display: 'flex', gap: 6, flexWrap: 'wrap', alignItems: 'center' }}>
+            <strong>{product.product_id || `产品 #${product.id}`}</strong>
+            <StatusBadge status={product.status} />
+            {product.detection_id && <span style={{ color: palette.muted }}>洪涝 #{product.detection_id}</span>}
+          </div>
+          <div style={{ display: 'grid', gridTemplateColumns: 'repeat(3, minmax(0, 1fr))', gap: 8, marginTop: 8 }}>
+            <KeyValue label="洪涝面积" value={formatArea(product.flood_area_km2 || product.summary?.flood_area_km2)} strong />
+            <KeyValue label="影响面积" value={formatArea(product.affected_area_km2)} />
+            <KeyValue label="生成时间" value={formatYmd(product.created_at, 'zh')} />
+          </div>
+        </div>
+        <button type="button" style={buttonStyle('secondary')} onClick={() => onOpenManifest(product)}>Manifest</button>
+      </div>
+    </div>
+  );
+}
+
+function FloodEventRow({ event, mapLayerVis, mapLoading, productBusy, onShowMap, onToggleLayer, onSelectImpact, onCreateProduct }) {
   const done = asStatus(event.status) === 'DONE';
   const preDate = formatYmd(event.pre_imaging_date, 'zh');
   const postDate = formatYmd(event.post_imaging_date, 'zh');
@@ -312,9 +387,9 @@ function FloodEventRow({ event, mapLayerVis, mapLoading, onShowMap, onToggleLaye
         <KeyValue label="稳定水体" value={formatArea(event.stable_water_area_km2)} />
       </div>
       {event.error_msg && <div style={{ color: palette.red, fontSize: 11, marginTop: 8 }}>{event.error_msg}</div>}
-      {layers && (
+      {(layers || onSelectImpact || onCreateProduct) && (
         <div style={{ display: 'flex', gap: 6, flexWrap: 'wrap', marginTop: 9 }}>
-          {[
+          {layers && [
             ['pre', '灾前影像'],
             ['post', '灾后影像'],
             ['classified', '分类结果'],
@@ -333,6 +408,11 @@ function FloodEventRow({ event, mapLayerVis, mapLoading, onShowMap, onToggleLaye
               套合分析
             </button>
           )}
+          {onCreateProduct && (
+            <button type="button" style={buttonStyle('primary', productBusy, { padding: '3px 8px', fontSize: 11 })} disabled={productBusy} onClick={() => onCreateProduct(event)}>
+              {productBusy ? '生成中' : '生成产品'}
+            </button>
+          )}
         </div>
       )}
     </div>
@@ -357,6 +437,8 @@ export default function FloodAnalysisWorkspace({
   const [radarLoading, setRadarLoading] = useState(false);
   const [radarSearched, setRadarSearched] = useState(false);
   const [selectedRadars, setSelectedRadars] = useState([]);
+  const [sourceCoverageVisibleIds, setSourceCoverageVisibleIds] = useState(() => new Set());
+  const [sourcePreviewVisibleIds, setSourcePreviewVisibleIds] = useState(() => new Set());
 
   const [scenes, setScenes] = useState([]);
   const [scenesTotal, setScenesTotal] = useState(0);
@@ -370,10 +452,21 @@ export default function FloodAnalysisWorkspace({
   const [waterPage, setWaterPage] = useState(0);
   const [waterLoading, setWaterLoading] = useState(false);
 
-  const [pairPreStart, setPairPreStart] = useState('');
-  const [pairPreEnd, setPairPreEnd] = useState('');
-  const [pairPostStart, setPairPostStart] = useState('');
-  const [pairPostEnd, setPairPostEnd] = useState('');
+  const [sourceAoiMode, setSourceAoiMode] = useState('none');
+  const [sourceRegionOptions, setSourceRegionOptions] = useState({ provinces: [], cities: [] });
+  const [sourceRegionSelection, setSourceRegionSelection] = useState({ province: '', city: '' });
+  const [regionLoading, setRegionLoading] = useState(false);
+  const [regionError, setRegionError] = useState('');
+
+  const [disasterName, setDisasterName] = useState('');
+  const [disasterDate, setDisasterDate] = useState('');
+  const [preWindowDays, setPreWindowDays] = useState(30);
+  const [postWindowDays, setPostWindowDays] = useState(30);
+  const [minAoiCoverage, setMinAoiCoverage] = useState(0.2);
+  const [pairRegionOptions, setPairRegionOptions] = useState({ provinces: [], cities: [] });
+  const [pairRegionSelection, setPairRegionSelection] = useState({ province: '', city: '' });
+  const [pairSearchSummary, setPairSearchSummary] = useState(null);
+
   const [overlapThreshold, setOverlapThreshold] = useState(0.3);
   const [refine, setRefine] = useState(false);
   const [pairSearching, setPairSearching] = useState(false);
@@ -382,9 +475,15 @@ export default function FloodAnalysisWorkspace({
 
   const [floodEvents, setFloodEvents] = useState([]);
   const [floodLoading, setFloodLoading] = useState(false);
+  const [floodProducts, setFloodProducts] = useState([]);
+  const [productLoading, setProductLoading] = useState(false);
+  const [productBusyId, setProductBusyId] = useState(null);
   const [mapLoadingId, setMapLoadingId] = useState(null);
   const [mapLayerVis, setMapLayerVis] = useState({});
   const [selectedImpactEventId, setSelectedImpactEventId] = useState('');
+  const [impactLoading, setImpactLoading] = useState(false);
+  const [overlayRunning, setOverlayRunning] = useState(false);
+  const [impactResult, setImpactResult] = useState(null);
   const [resultMode, setResultMode] = useState('flood');
 
   const readyScenes = useMemo(() => scenes.filter(item => asStatus(item.status) === 'DONE'), [scenes]);
@@ -400,11 +499,52 @@ export default function FloodAnalysisWorkspace({
     () => doneFloodEvents.find(item => String(item.id) === String(selectedImpactEventId)) || doneFloodEvents[0] || null,
     [doneFloodEvents, selectedImpactEventId],
   );
+  const selectedSourceRegionTreeId = sourceRegionSelection.city || sourceRegionSelection.province || '';
+  const selectedPairRegionTreeId = pairRegionSelection.city || pairRegionSelection.province || '';
 
   const showMessage = useCallback((type, text) => {
     setMessage({ type, text });
   }, []);
 
+  const loadRegionProvinces = useCallback(async () => {
+    setRegionLoading(true);
+    setRegionError('');
+    try {
+      const data = await getRegionChildren('1');
+      const provinces = data.children || [];
+      setSourceRegionOptions(prev => ({ ...prev, provinces }));
+      setPairRegionOptions(prev => ({ ...prev, provinces }));
+    } catch (error) {
+      setRegionError(getErrorText(error, '行政区加载失败'));
+    } finally {
+      setRegionLoading(false);
+    }
+  }, []);
+
+  const updateRegionProvince = useCallback(async (target, provinceTreeId) => {
+    const setSelection = target === 'source' ? setSourceRegionSelection : setPairRegionSelection;
+    const setOptions = target === 'source' ? setSourceRegionOptions : setPairRegionOptions;
+    setSelection({ province: provinceTreeId, city: '' });
+    setOptions(prev => ({ ...prev, cities: [] }));
+    if (!provinceTreeId) return;
+
+    setRegionLoading(true);
+    setRegionError('');
+    try {
+      const data = await getRegionChildren(provinceTreeId);
+      setOptions(prev => ({ ...prev, cities: data.children || [] }));
+    } catch (error) {
+      setRegionError(getErrorText(error, '行政区加载失败'));
+    } finally {
+      setRegionLoading(false);
+    }
+  }, []);
+
+  const updateRegionCity = useCallback((target, cityTreeId) => {
+    const setSelection = target === 'source' ? setSourceRegionSelection : setPairRegionSelection;
+    setSelection(prev => ({ ...prev, city: cityTreeId }));
+  }, []);
+
   const loadStatusIds = useCallback(async () => {
     try {
       const [done, active] = await Promise.all([getFloodDoneRadarIds(), getFloodActiveRadarIds()]);
@@ -461,14 +601,28 @@ export default function FloodAnalysisWorkspace({
     }
   }, [showMessage]);
 
+  const loadFloodProducts = useCallback(async () => {
+    setProductLoading(true);
+    try {
+      const res = (await getFloodProducts({ limit: LIST_PAGE_SIZE, offset: 0 })).data;
+      setFloodProducts(res.items || []);
+    } catch (error) {
+      setFloodProducts([]);
+      showMessage('error', `洪涝产品加载失败：${getErrorText(error)}`);
+    } finally {
+      setProductLoading(false);
+    }
+  }, [showMessage]);
+
   const refreshAll = useCallback(async () => {
     await Promise.all([
       loadStatusIds(),
       loadScenes(scenesPage),
       loadWaterResults(waterPage),
       loadFloodEvents(),
+      loadFloodProducts(),
     ]);
-  }, [loadFloodEvents, loadScenes, loadStatusIds, loadWaterResults, scenesPage, waterPage]);
+  }, [loadFloodEvents, loadFloodProducts, loadScenes, loadStatusIds, loadWaterResults, scenesPage, waterPage]);
 
   useEffect(() => {
     getSearchOptions()
@@ -479,8 +633,9 @@ export default function FloodAnalysisWorkspace({
         polarization: data.polarization || [],
       }))
       .catch(() => {});
+    loadRegionProvinces();
     refreshAll();
-  }, [refreshAll]);
+  }, [loadRegionProvinces, refreshAll]);
 
   useEffect(() => {
     if (!runningCount) return undefined;
@@ -504,12 +659,16 @@ export default function FloodAnalysisWorkspace({
     setRadarLoading(true);
     setRadarSearched(true);
     try {
+      if (sourceAoiMode === 'region' && !selectedSourceRegionTreeId) {
+        throw new Error('请选择用于筛选的行政区。');
+      }
       const criteria = normalizeRadarSearchCriteria(radarDraft, RADAR_SEARCH_DEFAULTS);
       const formData = buildRadarSearchFormData({
         limit: SEARCH_PAGE_SIZE,
         offset: page * SEARCH_PAGE_SIZE,
         criteria,
-        aoiMode: 'none',
+        aoiMode: sourceAoiMode,
+        regionTreeId: sourceAoiMode === 'region' ? selectedSourceRegionTreeId : '',
       });
       const data = await searchRadarData(formData);
       setRadarResults(data.items || []);
@@ -526,6 +685,9 @@ export default function FloodAnalysisWorkspace({
 
   const resetRadarSearch = () => {
     setRadarDraft({ ...RADAR_SEARCH_DEFAULTS });
+    setSourceAoiMode('none');
+    setSourceRegionSelection({ province: '', city: '' });
+    setSourceRegionOptions(prev => ({ ...prev, cities: [] }));
     setRadarResults([]);
     setRadarTotal(0);
     setRadarPage(0);
@@ -541,6 +703,35 @@ export default function FloodAnalysisWorkspace({
     ));
   };
 
+  const updateVisibleIdSet = (setter, itemId, visible) => {
+    setter(prev => {
+      const next = new Set(prev);
+      if (visible) next.add(String(itemId));
+      else next.delete(String(itemId));
+      return next;
+    });
+  };
+
+  const handleToggleSourceCoverage = (item) => {
+    const handler = floodPanel.onShowSourceSceneOnMap || floodPanel.onShowOnMap;
+    if (!handler) return;
+    const visible = handler(item);
+    if (typeof visible === 'boolean') {
+      updateVisibleIdSet(setSourceCoverageVisibleIds, item.id, visible);
+      showMessage(visible ? 'success' : 'info', visible ? `范围框 #${item.id} 已显示。` : `范围框 #${item.id} 已清除。`);
+    }
+  };
+
+  const handleToggleSourcePreview = (item) => {
+    const handler = floodPanel.onShowSourcePreviewOnMap || floodPanel.onShowSourceSceneOnMap || floodPanel.onShowOnMap;
+    if (!handler) return;
+    const visible = handler(item);
+    if (typeof visible === 'boolean') {
+      updateVisibleIdSet(setSourcePreviewVisibleIds, item.id, visible);
+      showMessage(visible ? 'success' : 'info', visible ? `源影像 #${item.id} 已显示。` : `源影像 #${item.id} 已清除。`);
+    }
+  };
+
   const handleSubmitPreprocess = async () => {
     if (readOnly || selectedRadars.length === 0) return;
     setActionBusy('geocode');
@@ -615,17 +806,36 @@ export default function FloodAnalysisWorkspace({
     setPairSearching(true);
     setCandidatePairs([]);
     setSelectedPairIdx(null);
+    setPairSearchSummary(null);
     try {
-      const data = await searchFloodPairs({
-        pre_start: compactDate(pairPreStart),
-        pre_end: compactDate(pairPreEnd),
-        post_start: compactDate(pairPostStart),
-        post_end: compactDate(pairPostEnd),
-        overlap_threshold: Number(overlapThreshold) || 0,
+      if (!compactDate(disasterDate)) {
+        throw new Error('请选择灾害发生日期。');
+      }
+      if (!selectedPairRegionTreeId) {
+        throw new Error('请选择灾害影响位置。');
+      }
+      const data = await searchFloodDisasterPairs({
+        disaster_name: disasterName || undefined,
+        disaster_date: compactDate(disasterDate),
+        region_tree_id: selectedPairRegionTreeId,
+        pre_window_days: Number(preWindowDays) || 30,
+        post_window_days: Number(postWindowDays) || 30,
+        min_aoi_coverage_ratio: Number(minAoiCoverage) || 0,
+        min_pair_overlap_ratio: Number(overlapThreshold) || 0,
+        polarization: radarDraft.polarization || undefined,
+        imaging_mode: radarDraft.imaging_mode || undefined,
+        product_level: radarDraft.product_level || undefined,
+        require_same_polarization: true,
+        require_same_imaging_mode: false,
       });
-      setCandidatePairs(data.pairs || []);
-      setSelectedPairIdx(data.pairs?.length ? 0 : null);
-      showMessage(data.pairs?.length ? 'success' : 'warn', data.pairs?.length ? `找到 ${data.pairs.length} 组候选配对。` : '没有找到满足条件的配对。');
+      const pairs = data.candidate_pairs || data.pairs || [];
+      setPairSearchSummary(data.summary || null);
+      setCandidatePairs(pairs);
+      setSelectedPairIdx(pairs.length ? 0 : null);
+      showMessage(
+        pairs.length ? 'success' : 'warn',
+        pairs.length ? `找到 ${pairs.length} 组候选配对。` : (data.warnings?.[0] || '没有找到满足条件的配对。'),
+      );
     } catch (error) {
       showMessage('error', `配对推荐失败：${getErrorText(error)}`);
     } finally {
@@ -685,6 +895,80 @@ export default function FloodAnalysisWorkspace({
     }
   };
 
+  const handleShowPairCoverage = (pair) => {
+    if (!pair?.pre?.coverage_polygon || !pair?.post?.coverage_polygon) {
+      showMessage('warn', '该候选配对缺少覆盖范围，无法上图。');
+      return;
+    }
+    floodPanel.onShowFloodPairOnMap?.(pair);
+    showMessage('success', '候选配对覆盖范围已加载到地图。');
+  };
+
+  const handleShowFloodVector = () => {
+    if (!impactResult?.flood_vector_geojson) {
+      showMessage('warn', '该套合结果没有可用洪涝矢量。');
+      return;
+    }
+    floodPanel.onShowFloodVectorOnMap?.(impactResult);
+    showMessage('success', '洪涝矢量已加载到地图。');
+  };
+
+  const loadImpactResult = useCallback(async (detectionId, { silent = false } = {}) => {
+    if (!detectionId) return;
+    setImpactLoading(true);
+    try {
+      const data = await getFloodImpact(detectionId);
+      setImpactResult(data);
+      if (!silent && data?.warnings?.includes?.('overlay has not been run')) {
+        showMessage('warn', '该洪涝结果尚未运行套合分析。');
+      }
+    } catch (error) {
+      setImpactResult(null);
+      if (!silent) showMessage('error', `套合结果加载失败：${getErrorText(error)}`);
+    } finally {
+      setImpactLoading(false);
+    }
+  }, [showMessage]);
+
+  const handleRunOverlay = async () => {
+    if (readOnly || !selectedImpactEvent) return;
+    setOverlayRunning(true);
+    try {
+      await runFloodOverlay(selectedImpactEvent.id, { near_threshold_m: 500 });
+      await loadImpactResult(selectedImpactEvent.id, { silent: true });
+      await loadFloodEvents();
+      showMessage('success', `洪涝结果 #${selectedImpactEvent.id} 套合分析已完成。`);
+    } catch (error) {
+      showMessage('error', `套合分析失败：${getErrorText(error)}`);
+    } finally {
+      setOverlayRunning(false);
+    }
+  };
+
+  const handleCreateFloodProduct = async (event) => {
+    if (readOnly || !event) return;
+    setProductBusyId(event.id);
+    try {
+      await createFloodProduct(event.id);
+      await loadFloodProducts();
+      showMessage('success', `洪涝结果 #${event.id} 产品已生成。`);
+      setResultMode('products');
+    } catch (error) {
+      showMessage('error', `产品生成失败：${getErrorText(error)}`);
+    } finally {
+      setProductBusyId(null);
+    }
+  };
+
+  const handleOpenProductManifest = async (product) => {
+    try {
+      const manifest = await getFloodProductManifest(product.id || product.product_id);
+      showMessage('success', `Manifest 已读取：${manifest?.schema || product.product_id || product.id}`);
+    } catch (error) {
+      showMessage('error', `Manifest 读取失败：${getErrorText(error)}`);
+    }
+  };
+
   const handleToggleFloodLayer = (eventId, key, visible) => {
     setMapLayerVis(prev => ({ ...prev, [eventId]: { ...prev[eventId], [key]: visible } }));
     floodPanel.onToggleFloodLayer?.(eventId, key, visible);
@@ -692,9 +976,15 @@ export default function FloodAnalysisWorkspace({
 
   const handleSelectImpact = (event) => {
     setSelectedImpactEventId(String(event.id));
+    setImpactResult(null);
     setActiveView('impact');
   };
 
+  useEffect(() => {
+    if (activeView !== 'impact' || !selectedImpactEvent) return;
+    loadImpactResult(selectedImpactEvent.id, { silent: true });
+  }, [activeView, loadImpactResult, selectedImpactEvent]);
+
   const radarTotalPages = Math.ceil(radarTotal / SEARCH_PAGE_SIZE);
   const scenesTotalPages = Math.ceil(scenesTotal / LIST_PAGE_SIZE);
   const waterTotalPages = Math.ceil(waterTotal / LIST_PAGE_SIZE);
@@ -799,6 +1089,23 @@ export default function FloodAnalysisWorkspace({
                 {searchOptions.polarization.map(item => <option key={item} value={item}>{item}</option>)}
               </select>
             </div>
+            <div style={{ display: 'grid', gap: 6, marginBottom: 10 }}>
+              <div style={{ display: 'flex', gap: 6, flexWrap: 'wrap', alignItems: 'center' }}>
+                <span style={{ color: palette.muted, fontSize: 12 }}>位置过滤</span>
+                <button type="button" style={buttonStyle(sourceAoiMode === 'none' ? 'primary' : 'quiet')} onClick={() => setSourceAoiMode('none')}>不限</button>
+                <button type="button" style={buttonStyle(sourceAoiMode === 'region' ? 'primary' : 'quiet', regionLoading)} disabled={regionLoading} onClick={() => setSourceAoiMode('region')}>行政区</button>
+              </div>
+              {sourceAoiMode === 'region' && (
+                <RegionSelector
+                  options={sourceRegionOptions}
+                  selection={sourceRegionSelection}
+                  onProvinceChange={value => updateRegionProvince('source', value)}
+                  onCityChange={value => updateRegionCity('source', value)}
+                  disabled={regionLoading}
+                />
+              )}
+              {regionError && <div style={{ color: palette.red, fontSize: 11 }}>{regionError}</div>}
+            </div>
 
             <div style={{ display: 'grid', gap: 6 }}>
               {!radarSearched && <EmptyState>输入条件后查询入库雷达数据。</EmptyState>}
@@ -807,6 +1114,8 @@ export default function FloodAnalysisWorkspace({
                 const selected = selectedRadars.some(row => row.id === item.id);
                 const done = doneRadarIds.includes(item.id);
                 const active = activeRadarIds.includes(item.id);
+                const coverageVisible = sourceCoverageVisibleIds.has(String(item.id));
+                const previewVisible = sourcePreviewVisibleIds.has(String(item.id));
                 return (
                   <div key={item.id} style={{ ...rowStyle, background: selected ? '#eff6ff' : palette.panel }}>
                     <div style={{ display: 'grid', gridTemplateColumns: '1fr auto', gap: 8, alignItems: 'center' }}>
@@ -822,6 +1131,23 @@ export default function FloodAnalysisWorkspace({
                           {[item.imaging_mode, item.product_level, item.polarization].filter(Boolean).join(' · ') || '-'}
                         </div>
                       </div>
+                      <div style={{ display: 'flex', gap: 6, flexWrap: 'wrap', justifyContent: 'flex-end' }}>
+                        <button
+                          type="button"
+                          style={buttonStyle(coverageVisible ? 'success' : 'quiet', !item.coverage_polygon)}
+                          disabled={!item.coverage_polygon}
+                          onClick={() => handleToggleSourceCoverage(item)}
+                        >
+                          {coverageVisible ? '清除范围' : '显示范围'}
+                        </button>
+                        <button
+                          type="button"
+                          style={buttonStyle(previewVisible ? 'success' : 'secondary', !(item.min_lat != null && item.max_lat != null && item.min_lon != null && item.max_lon != null))}
+                          disabled={!(item.min_lat != null && item.max_lat != null && item.min_lon != null && item.max_lon != null)}
+                          onClick={() => handleToggleSourcePreview(item)}
+                        >
+                          {previewVisible ? '清除源影像' : '显示源影像'}
+                        </button>
                         <button
                           type="button"
                           style={buttonStyle(selected ? 'success' : 'primary', done || active)}
@@ -832,6 +1158,7 @@ export default function FloodAnalysisWorkspace({
                         </button>
                       </div>
                     </div>
+                  </div>
                 );
               })}
             </div>
@@ -911,19 +1238,47 @@ export default function FloodAnalysisWorkspace({
                 </>
               )}
             />
-            <div style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 6, marginBottom: 8 }}>
-              <UnifiedDatePicker value={pairPreStart} onChange={setPairPreStart} placeholder="灾前开始" language={language} />
-              <UnifiedDatePicker value={pairPreEnd} onChange={setPairPreEnd} placeholder="灾前结束" language={language} />
-              <UnifiedDatePicker value={pairPostStart} onChange={setPairPostStart} placeholder="灾后开始" language={language} />
-              <UnifiedDatePicker value={pairPostEnd} onChange={setPairPostEnd} placeholder="灾后结束" language={language} />
-              <label style={{ display: 'flex', alignItems: 'center', gap: 6, color: palette.text2, fontSize: 12 }}>
-                重叠阈值
-                <input type="number" min="0" max="1" step="0.05" value={overlapThreshold} onChange={event => setOverlapThreshold(event.target.value)} style={{ ...inputStyle, width: 80 }} />
+            <div style={{ display: 'grid', gap: 8, marginBottom: 10 }}>
+              <div style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 6 }}>
+                <input value={disasterName} onChange={event => setDisasterName(event.target.value)} placeholder="灾害名称（可选）" style={inputStyle} />
+                <UnifiedDatePicker value={disasterDate} onChange={setDisasterDate} placeholder="灾害发生日期" language={language} />
+              </div>
+              <RegionSelector
+                options={pairRegionOptions}
+                selection={pairRegionSelection}
+                onProvinceChange={value => updateRegionProvince('pair', value)}
+                onCityChange={value => updateRegionCity('pair', value)}
+                disabled={regionLoading}
+              />
+              <div style={{ display: 'grid', gridTemplateColumns: 'repeat(4, minmax(0, 1fr))', gap: 6 }}>
+                <label style={{ color: palette.text2, fontSize: 12 }}>
+                  灾前窗口（天）
+                  <input type="number" min="1" max="365" value={preWindowDays} onChange={event => setPreWindowDays(event.target.value)} style={inputStyle} />
                 </label>
+                <label style={{ color: palette.text2, fontSize: 12 }}>
+                  灾后窗口（天）
+                  <input type="number" min="1" max="365" value={postWindowDays} onChange={event => setPostWindowDays(event.target.value)} style={inputStyle} />
+                </label>
+                <label style={{ color: palette.text2, fontSize: 12 }}>
+                  AOI覆盖
+                  <input type="number" min="0" max="1" step="0.05" value={minAoiCoverage} onChange={event => setMinAoiCoverage(event.target.value)} style={inputStyle} />
+                </label>
+                <label style={{ color: palette.text2, fontSize: 12 }}>
+                  配对重叠
+                  <input type="number" min="0" max="1" step="0.05" value={overlapThreshold} onChange={event => setOverlapThreshold(event.target.value)} style={inputStyle} />
+                </label>
+              </div>
               <label style={{ display: 'flex', alignItems: 'center', gap: 6, color: palette.text2, fontSize: 12 }}>
                 <input type="checkbox" checked={refine} onChange={event => setRefine(event.target.checked)} style={{ accentColor: palette.primary }} />
                 MRF 精化
               </label>
+              {pairSearchSummary && (
+                <div style={{ display: 'grid', gridTemplateColumns: 'repeat(3, minmax(0, 1fr))', gap: 8 }}>
+                  <KeyValue label="灾前匹配池" value={String(pairSearchSummary.pre_pool_count || 0)} />
+                  <KeyValue label="灾后匹配池" value={String(pairSearchSummary.post_pool_count || 0)} />
+                  <KeyValue label="候选配对" value={String(pairSearchSummary.candidate_count || 0)} strong />
+                </div>
+              )}
             </div>
             <div style={{ display: 'grid', gap: 8 }}>
               {candidatePairs.length === 0 && <EmptyState>暂无候选配对。</EmptyState>}
@@ -946,12 +1301,37 @@ export default function FloodAnalysisWorkspace({
                       <strong>#{pair.pre.id} {formatYmd(pair.pre.imaging_date, language)}</strong>
                       <span style={{ color: palette.muted }}>{'->'}</span>
                       <strong>#{pair.post.id} {formatYmd(pair.post.imaging_date, language)}</strong>
+                      {pair.score != null && <StatusBadge tone="info">评分 {Number(pair.score).toFixed(2)}</StatusBadge>}
                       <StatusBadge tone={active ? 'info' : 'muted'}>{active ? '已选' : '候选'}</StatusBadge>
                     </div>
                     <div style={{ color: palette.muted, fontSize: 11, marginTop: 5 }}>
-                      重叠 {(Number(pair.overlap_ratio || 0) * 100).toFixed(1)}%
+                      配对重叠 {formatPercent(pair.overlap_ratio)}
+                      {pair.aoi_coverage_ratio != null ? ` · AOI覆盖 ${formatPercent(pair.aoi_coverage_ratio)}` : ''}
                       {pair.time_diff_days != null ? ` · 间隔 ${pair.time_diff_days} 天` : ''}
+                      {pair.pre_delta_days != null ? ` · 灾前 ${pair.pre_delta_days} 天` : ''}
+                      {pair.post_delta_days != null ? ` · 灾后 ${pair.post_delta_days} 天` : ''}
                       {pair.pre.satellite ? ` · ${pair.pre.satellite}` : ''}
+                      {pair.pre.polarization ? ` · ${pair.pre.polarization}` : ''}
+                    </div>
+                    <div style={{ display: 'flex', gap: 6, flexWrap: 'wrap', marginTop: 8 }}>
+                      <span
+                        role="button"
+                        tabIndex={0}
+                        style={buttonStyle('secondary', false, { display: 'inline-flex', padding: '3px 8px', fontSize: 11 })}
+                        onClick={event => {
+                          event.stopPropagation();
+                          handleShowPairCoverage(pair);
+                        }}
+                        onKeyDown={event => {
+                          if (event.key === 'Enter' || event.key === ' ') {
+                            event.preventDefault();
+                            event.stopPropagation();
+                            handleShowPairCoverage(pair);
+                          }
+                        }}
+                      >
+                        预览覆盖
+                      </span>
                     </div>
                   </button>
                 );
@@ -973,6 +1353,8 @@ export default function FloodAnalysisWorkspace({
                   onShowMap={handleShowFloodEvent}
                   onToggleLayer={handleToggleFloodLayer}
                   onSelectImpact={handleSelectImpact}
+                  onCreateProduct={handleCreateFloodProduct}
+                  productBusy={productBusyId === event.id}
                 />
               ))}
             </div>
@@ -983,15 +1365,18 @@ export default function FloodAnalysisWorkspace({
       {activeView === 'impact' && (
         <main>
           <section style={{ ...sectionStyle, borderTop: 'none' }}>
-            <SectionHeader title="套合对象" />
+            <SectionHeader
+              title="套合对象"
+              actions={<button type="button" style={buttonStyle('quiet', impactLoading)} disabled={impactLoading || !selectedImpactEvent} onClick={() => selectedImpactEvent && loadImpactResult(selectedImpactEvent.id)}>刷新结果</button>}
+            />
             <div style={{ display: 'grid', gap: 8 }}>
               {[
                 ['洪涝分类栅格', selectedImpactEvent ? `结果 #${selectedImpactEvent.id}` : '请选择结果', selectedImpactEvent ? 'ok' : 'muted'],
-                ['灾害点', '已有底库', 'ok'],
-                ['洪涝矢量化', '待接入', 'warn'],
-                ['行政区统计', '待接入', 'muted'],
-                ['当前 AOI', '待接入', 'muted'],
-                ['自定义矢量', '待接入', 'muted'],
+                ['洪涝矢量化', impactResult?.warnings?.includes?.('overlay has not been run') ? '未生成' : (impactResult ? '已生成' : '待运行'), impactResult && !impactResult?.warnings?.includes?.('overlay has not been run') ? 'ok' : 'warn'],
+                ['灾害点命中', impactResult ? `${impactResult.hazard_points?.inside_flood?.length || 0} 个` : '待运行', impactResult ? 'ok' : 'muted'],
+                ['近洪涝风险点', impactResult ? `${impactResult.hazard_points?.near_flood?.length || 0} 个` : '待运行', impactResult ? 'info' : 'muted'],
+                ['DInSAR关联', impactResult ? `${impactResult.dinsar_products?.length || 0} 个` : '待运行', impactResult ? 'info' : 'muted'],
+                ['行政区统计', impactResult?.affected_aois?.length ? `${impactResult.affected_aois.length} 个` : '暂无数据', impactResult ? 'muted' : 'muted'],
               ].map(([name, status, tone]) => (
                 <div key={name} style={{ ...rowStyle, display: 'flex', justifyContent: 'space-between', alignItems: 'center', gap: 8 }}>
                   <strong>{name}</strong>
@@ -1035,10 +1420,69 @@ export default function FloodAnalysisWorkspace({
             </div>
             <div style={{ display: 'flex', gap: 8, flexWrap: 'wrap', marginTop: 10 }}>
               <button type="button" style={buttonStyle('secondary', !selectedImpactEvent)} disabled={!selectedImpactEvent} onClick={() => selectedImpactEvent && handleShowFloodEvent(selectedImpactEvent)}>加载洪涝图层</button>
-              <button type="button" style={buttonStyle('quiet', true)} disabled>运行套合分析</button>
+              <button type="button" style={buttonStyle('secondary', !impactResult?.flood_vector_geojson)} disabled={!impactResult?.flood_vector_geojson} onClick={handleShowFloodVector}>加载洪涝矢量</button>
+              <button type="button" style={buttonStyle('primary', readOnly || !selectedImpactEvent || overlayRunning)} disabled={readOnly || !selectedImpactEvent || overlayRunning} onClick={handleRunOverlay}>
+                {overlayRunning ? '套合中' : '运行套合分析'}
+              </button>
               <button type="button" style={buttonStyle('quiet', true)} disabled>导出影响清单</button>
             </div>
           </section>
+
+          <section style={sectionStyle}>
+            <SectionHeader title="套合结果" />
+            {impactLoading && <EmptyState>套合结果加载中...</EmptyState>}
+            {!impactLoading && !impactResult && <EmptyState>选择一个已完成的洪涝结果后运行套合分析。</EmptyState>}
+            {!impactLoading && impactResult && (
+              <div style={{ display: 'grid', gap: 10 }}>
+                <div style={{ display: 'grid', gridTemplateColumns: 'repeat(5, minmax(0, 1fr))', gap: 8 }}>
+                  <KeyValue label="洪涝面积" value={formatArea(impactResult.flood_area_km2)} strong />
+                  <KeyValue label="命中灾害点" value={String(impactResult.hazard_points?.inside_flood?.length || 0)} strong />
+                  <KeyValue label="近洪涝风险点" value={String(impactResult.hazard_points?.near_flood?.length || 0)} />
+                  <KeyValue label="DInSAR产品" value={String(impactResult.dinsar_products?.length || 0)} />
+                  <KeyValue label="影响行政区" value={String(impactResult.affected_aois?.length || 0)} />
+                </div>
+                {impactResult.warnings?.length > 0 && (
+                  <div style={{ color: palette.amber, fontSize: 11 }}>
+                    {impactResult.warnings.join('；')}
+                  </div>
+                )}
+                <div style={{ display: 'grid', gap: 8 }}>
+                  {(impactResult.hazard_points?.inside_flood || []).slice(0, 5).map(point => (
+                    <div key={`inside_${point.id}`} style={rowStyle}>
+                      <strong>{point.name || `灾害点 #${point.id}`}</strong>
+                      <div style={{ color: palette.muted, fontSize: 11, marginTop: 4 }}>
+                        洪涝范围内 · {[point.type, point.city, point.county].filter(Boolean).join(' / ') || '-'}
+                      </div>
+                    </div>
+                  ))}
+                  {(impactResult.hazard_points?.near_flood || []).slice(0, 5).map(point => (
+                    <div key={`near_${point.id}`} style={rowStyle}>
+                      <strong>{point.name || `灾害点 #${point.id}`}</strong>
+                      <div style={{ color: palette.muted, fontSize: 11, marginTop: 4 }}>
+                        距洪涝范围 {point.distance_m ?? '-'} m · {[point.type, point.city, point.county].filter(Boolean).join(' / ') || '-'}
+                      </div>
+                    </div>
+                  ))}
+                  {(impactResult.dinsar_products || []).slice(0, 5).map(product => (
+                    <div key={`dinsar_${product.id || product.product_id}`} style={rowStyle}>
+                      <strong>{product.display_name || product.product_id}</strong>
+                      <div style={{ color: palette.muted, fontSize: 11, marginTop: 4 }}>
+                        {product.engine || '-'} · 形变 {product.deformation_mm ?? '-'} mm · AI {product.ai_score ?? '-'}
+                      </div>
+                    </div>
+                  ))}
+                  {(impactResult.affected_aois || []).slice(0, 5).map(aoi => (
+                    <div key={`aoi_${aoi.tree_id || aoi.name}`} style={rowStyle}>
+                      <strong>{aoi.name || aoi.tree_id}</strong>
+                      <div style={{ color: palette.muted, fontSize: 11, marginTop: 4 }}>
+                        {aoi.level || '-'} · 受淹面积 {formatArea(aoi.flood_area_km2)}
+                      </div>
+                    </div>
+                  ))}
+                </div>
+              </div>
+            )}
+          </section>
         </main>
       )}
 
@@ -1051,6 +1495,7 @@ export default function FloodAnalysisWorkspace({
                 <>
                   <button type="button" style={buttonStyle(resultMode === 'flood' ? 'primary' : 'quiet')} onClick={() => setResultMode('flood')}>洪涝</button>
                   <button type="button" style={buttonStyle(resultMode === 'water' ? 'primary' : 'quiet')} onClick={() => setResultMode('water')}>水体</button>
+                  <button type="button" style={buttonStyle(resultMode === 'products' ? 'primary' : 'quiet')} onClick={() => setResultMode('products')}>产品</button>
                   <button type="button" style={buttonStyle('secondary')} onClick={refreshAll}>刷新</button>
                 </>
               )}
@@ -1067,6 +1512,8 @@ export default function FloodAnalysisWorkspace({
                     onShowMap={handleShowFloodEvent}
                     onToggleLayer={handleToggleFloodLayer}
                     onSelectImpact={handleSelectImpact}
+                    onCreateProduct={handleCreateFloodProduct}
+                    productBusy={productBusyId === event.id}
                   />
                 ))}
               </div>
@@ -1079,13 +1526,22 @@ export default function FloodAnalysisWorkspace({
                 ))}
               </div>
             )}
+            {resultMode === 'products' && (
+              <div style={{ display: 'grid', gap: 8 }}>
+                {productLoading && <EmptyState>洪涝产品加载中...</EmptyState>}
+                {!productLoading && floodProducts.length === 0 && <EmptyState>暂无洪涝产品。可在洪涝结果行点击“生成产品”。</EmptyState>}
+                {!productLoading && floodProducts.map(product => (
+                  <FloodProductRow key={product.id || product.product_id} product={product} onOpenManifest={handleOpenProductManifest} />
+                ))}
+              </div>
+            )}
           </section>
 
           <section style={sectionStyle}>
             <SectionHeader title="产品出口" />
             <div style={{ display: 'grid', gridTemplateColumns: 'repeat(3, minmax(0, 1fr))', gap: 8 }}>
-              <button type="button" style={buttonStyle('quiet', true)} disabled>GeoTIFF</button>
-              <button type="button" style={buttonStyle('quiet', true)} disabled>GeoJSON</button>
+              <button type="button" style={buttonStyle('secondary')} onClick={() => setResultMode('products')}>产品列表</button>
+              <button type="button" style={buttonStyle('quiet', true)} disabled>GeoJSON 导出</button>
               <button type="button" style={buttonStyle('quiet', true)} disabled>报告</button>
             </div>
           </section>

frontend/src/ProductionWorkspace.jsx

@@ -8,9 +8,8 @@ import {
 import { PanelLoadingBody } from './components/app/AppLoadingFallbacks';
 
 const LazyDinsarProductionPanel = lazy(() => import('./DinsarProductionPanel'));
-const LazyTimeseriesProductionPanel = lazy(() => import('./TimeseriesProductionPanel'));
+const LazySbasInsarProductionPanel = lazy(() => import('./SbasInsarProductionPanel'));
 const LazyDinsarProductsPanel = lazy(() => import('./DinsarProductsPanel'));
-const LazyPsinsarCatalogPanel = lazy(() => import('./components/PsinsarCatalogPanel'));
 
 const shellStyle = {
   minHeight: '100%',
@@ -64,18 +63,10 @@ export default function ProductionWorkspace({
     onTaskStart?.(taskId, 'D-InSAR 任务已入队，等待处理...');
   };
 
-  const handleTimeseriesRunQueued = taskId => {
-    onTaskStart?.(taskId, '时序InSAR 运行已入队，当前默认执行 SBAS 流程...');
-  };
-
   const handleDinsarProductQueued = taskId => {
     onTaskStart?.(taskId, 'D-InSAR 产物任务已入队，等待处理...');
   };
 
-  const handleTimeseriesProductQueued = taskId => {
-    onTaskStart?.(taskId, '时序InSAR 产物目录任务已入队，等待处理...');
-  };
-
   return (
     <div style={shellStyle}>
       <div style={heroStyle}>
@@ -85,8 +76,8 @@ export default function ProductionWorkspace({
           </div>
           <h2 style={{ margin: '10px 0 12px', fontSize: 32, lineHeight: 1.1, color: '#0f172a' }}>生产管理</h2>
           <p style={{ margin: 0, maxWidth: 900, fontSize: 14, lineHeight: 1.8, color: '#475569' }}>
-            这里统一承载 D-InSAR 与时序InSAR的运行和产物工作台。当前时序入口默认接入 SBAS 实现，
-            后续可在同一界面继续扩展 PS-InSAR、SBAS-InSAR 以及更多 WSL2 引擎实例。
+            这里统一承载 D-InSAR 与 Gamma SBAS-InSAR 生产工作台。旧 ISCE2/MintPy 时序入口已停用，
+            SBAS 生产、速率图、质量指标与监测点曲线统一进入独立 SBAS-InSAR 页面。
           </p>
         </section>
 
@@ -107,17 +98,17 @@ export default function ProductionWorkspace({
             </div>
           </div>
           <div style={summaryCardStyle}>
-            <div style={{ fontSize: 11, color: '#64748b', marginBottom: 6 }}>时序当前实现</div>
-            <div style={{ fontSize: 15, fontWeight: 700, color: '#0f172a' }}>SBAS 默认接入</div>
+            <div style={{ fontSize: 11, color: '#64748b', marginBottom: 6 }}>SBAS 当前实现</div>
+            <div style={{ fontSize: 15, fontWeight: 700, color: '#0f172a' }}>Gamma 独立入口</div>
             <div style={{ fontSize: 12, lineHeight: 1.6, color: '#475569', marginTop: 4 }}>
-              现阶段实际运行链路为 SBAS，界面命名已统一为时序InSAR。
+              生产链路绕开旧时序配对层，由 SBAS 页面管理栈发现、基线审核、配准与产物发布。
             </div>
           </div>
           <div style={summaryCardStyle}>
-            <div style={{ fontSize: 11, color: '#64748b', marginBottom: 6 }}>引擎预留</div>
-            <div style={{ fontSize: 15, fontWeight: 700, color: '#0f172a' }}>ISCE / Gamma 可扩</div>
+            <div style={{ fontSize: 11, color: '#64748b', marginBottom: 6 }}>旧链路状态</div>
+            <div style={{ fontSize: 15, fontWeight: 700, color: '#0f172a' }}>ISCE2/MintPy 停用</div>
             <div style={{ fontSize: 12, lineHeight: 1.6, color: '#475569', marginTop: 4 }}>
-              现有 WSL2 嵌入保持不变，后续增加 Gamma 时可直接挂入当前工作台。
+              历史代码暂时保留兼容，生产管理不再暴露旧“时序运行/产物”页面。
             </div>
           </div>
         </section>
@@ -182,10 +173,9 @@ export default function ProductionWorkspace({
               onJobQueued={handleDinsarRunQueued}
             />
           )}
-          {activeView === 'timeseries_runs' && (
-            <LazyTimeseriesProductionPanel
+          {activeView === 'sbas_insar_production' && (
+            <LazySbasInsarProductionPanel
               readOnly={readOnly}
-              onJobQueued={handleTimeseriesRunQueued}
             />
           )}
           {activeView === 'dinsar_products' && (
@@ -194,13 +184,6 @@ export default function ProductionWorkspace({
               onJobQueued={handleDinsarProductQueued}
             />
           )}
-          {activeView === 'timeseries_products' && (
-            <LazyPsinsarCatalogPanel
-              readOnly={readOnly}
-              showActions
-              onTaskQueued={handleTimeseriesProductQueued}
-            />
-          )}
         </Suspense>
       </section>
     </div>

frontend/src/api/flood.js

@@ -1,14 +1,5 @@
 import apiClient from './client';
 
-export const getFloodSources = (params = {}) =>
-  apiClient.get('/flood/sources', { params });
-
-export const refreshFloodSources = () =>
-  apiClient.post('/flood/sources/refresh');
-
-export const getFloodSourceReadiness = (id) =>
-  apiClient.get(`/flood/sources/${id}/readiness`);
-
 export const submitFloodPreprocess = (payload) =>
   apiClient.post('/flood/preprocess', payload);
 
@@ -33,26 +24,11 @@ export const getFloodWaterExtractions = (limit = 20, offset = 0, status = null)
 export const getFloodWaterExtractionPreview = (id) =>
   apiClient.get(`/flood/water-extractions/${id}/preview`).then(r => r.data);
 
-export const getFloodPreprocessRuns = (params = {}) =>
-  apiClient.get('/flood/preprocess-runs', { params });
-
-export const getFloodReadyProducts = (params = {}) =>
-  apiClient.get('/flood/ready-products', { params });
-
-export const getFloodReadyProductPreview = (id) =>
-  apiClient.get(`/flood/ready-products/${id}/preview`).then(r => r.data);
-
 export const searchFloodPairs = (payload) =>
-  apiClient.post('/flood/pairs/search', payload);
+  apiClient.post('/flood/pairs/search', payload).then(r => r.data);
 
-export const saveFloodPair = (payload) =>
-  apiClient.post('/flood/pairs', payload);
-
-export const getFloodPairs = (params = {}) =>
-  apiClient.get('/flood/pairs', { params });
-
-export const deleteFloodPair = (id) =>
-  apiClient.delete(`/flood/pairs/${id}`);
+export const searchFloodDisasterPairs = (payload) =>
+  apiClient.post('/flood/disaster-pairs/search', payload).then(r => r.data);
 
 export const submitFloodDetection = (payload) =>
   apiClient.post('/flood/detections', payload);
@@ -60,21 +36,27 @@ export const submitFloodDetection = (payload) =>
 export const getFloodDetections = (params = {}) =>
   apiClient.get('/flood/detections', { params });
 
-export const getFloodDetection = (id) =>
-  apiClient.get(`/flood/detections/${id}`);
-
 export const getFloodDetectionPreview = (id, layer) =>
   apiClient.get(`/flood/detections/${id}/preview/${layer}`).then(r => r.data);
 
-export const vectorizeFloodDetection = (id, payload = {}) =>
-  apiClient.post(`/flood/detections/${id}/vectorize`, payload);
-
 export const runFloodOverlay = (id, payload = {}) =>
   apiClient.post(`/flood/detections/${id}/overlay`, payload);
 
 export const getFloodImpact = (id) =>
   apiClient.get(`/flood/detections/${id}/impact`).then(r => r.data);
 
+export const createFloodProduct = (detectionId) =>
+  apiClient.post(`/flood/detections/${detectionId}/products`);
+
+export const getFloodProducts = (params = {}) =>
+  apiClient.get('/flood/products', { params });
+
+export const getFloodProduct = (id) =>
+  apiClient.get(`/flood/products/${id}`);
+
+export const getFloodProductManifest = (id) =>
+  apiClient.get(`/flood/products/${id}/manifest`).then(r => r.data);
+
 export const getFloodResults = (params = {}) =>
   apiClient.get('/flood/results', { params });
 
@@ -83,9 +65,3 @@ export const getFloodResult = (id) =>
 
 export const getFloodResultManifest = (id) =>
   apiClient.get(`/flood/results/${id}/manifest`).then(r => r.data);
-
-export const createFloodReport = (payload) =>
-  apiClient.post('/flood/reports', payload);
-
-export const getFloodReport = (id) =>
-  apiClient.get(`/flood/reports/${id}`).then(r => r.data);

frontend/src/api/sbasInsarProduction.js

@@ -0,0 +1,49 @@
+import apiClient from './client';
+
+const encodeArtifactPath = relativePath =>
+  String(relativePath || '')
+    .split('/')
+    .map(segment => encodeURIComponent(segment))
+    .join('/');
+
+export const getSbasInsarCapabilities = () =>
+  apiClient.get('/sbas-insar-production/capabilities').then(r => r.data);
+
+export const discoverSbasInsarStacks = (payload = {}) =>
+  apiClient.post('/sbas-insar-production/stacks/discover', payload).then(r => r.data);
+
+export const auditSbasInsarStack = (stackId, payload = {}) =>
+  apiClient.post(`/sbas-insar-production/stacks/${encodeURIComponent(stackId)}/audit`, payload).then(r => r.data);
+
+export const submitSbasInsarRun = (stackId, payload = {}) =>
+  apiClient.post(`/sbas-insar-production/stacks/${encodeURIComponent(stackId)}/runs`, payload).then(r => r.data);
+
+export const listSbasInsarRuns = () =>
+  apiClient.get('/sbas-insar-production/runs').then(r => r.data);
+
+export const getSbasInsarRun = runId =>
+  apiClient.get(`/sbas-insar-production/runs/${encodeURIComponent(runId)}`).then(r => r.data);
+
+export const runSbasInsarBaselineAudit = (runId, payload = {}) =>
+  apiClient.post(`/sbas-insar-production/runs/${encodeURIComponent(runId)}/baseline-audit`, payload).then(r => r.data);
+
+export const decideSbasInsarItab = (runId, payload = {}) =>
+  apiClient.post(`/sbas-insar-production/runs/${encodeURIComponent(runId)}/itab-decision`, payload).then(r => r.data);
+
+export const prepareSbasInsarCoregistration = (runId, payload = {}) =>
+  apiClient.post(`/sbas-insar-production/runs/${encodeURIComponent(runId)}/coregistration`, payload).then(r => r.data);
+
+export const submitSbasInsarCoregistrationJob = (runId, payload = {}) =>
+  apiClient.post(`/sbas-insar-production/runs/${encodeURIComponent(runId)}/coregistration/jobs`, payload).then(r => r.data);
+
+export const getSbasInsarRunArtifactUrl = (runId, relativePath) =>
+  `/api/sbas-insar-production/runs/${encodeURIComponent(runId)}/artifacts/${encodeArtifactPath(relativePath)}`;
+
+export const listSbasInsarTrialRuns = () =>
+  apiClient.get('/sbas-insar-production/trial-runs').then(r => r.data);
+
+export const getSbasInsarTrialRun = trialId =>
+  apiClient.get(`/sbas-insar-production/trial-runs/${encodeURIComponent(trialId)}`).then(r => r.data);
+
+export const getSbasInsarArtifactUrl = (trialId, relativePath) =>
+  `/api/sbas-insar-production/trial-runs/${encodeURIComponent(trialId)}/artifacts/${encodeArtifactPath(relativePath)}`;

frontend/src/components/ActiveTasksOverlay.jsx

@@ -22,6 +22,10 @@ const getTaskTypeLabel = (taskType) => {
       return 'LT-1 解包';
     case 'UNPACK_SENTINEL1':
       return 'Sentinel-1 解包';
+    case 'GF3_UNPACK':
+      return 'GF3 解包';
+    case 'GF3_BATCH_PROCESS':
+      return 'GF3 预处理';
     case 'SCAN_ASSET_INVENTORY':
       return '资产库存扫描';
     case 'IDL_IMPORT':

frontend/src/components/app/AppSidePanel.jsx

@@ -110,7 +110,7 @@ export default function AppSidePanel({
         ? '生产管理'
         : getLeftTabLabel(leftPanelTab, leftTabLabelContext);
     const standaloneDescription = isProductionWorkspace
-        ? '这里统一承载 D-InSAR 与时序InSAR的运行和产物页面，当前时序流程默认接入 SBAS，后续可继续扩展 PS-InSAR / SBAS-InSAR。'
+        ? '这里统一承载 D-InSAR 与 Gamma SBAS-InSAR 生产；旧 ISCE2/MintPy 时序入口已停用。'
         : '当前模块已切换为独立工作区模式。';
 
     return (

frontend/src/config/appConstants.js

@@ -66,28 +66,23 @@ export const PRODUCTION_WORKSPACE_VIEWS = [
     description: '运行任务编排、引擎切换与过程监控',
   },
   {
-    key: 'timeseries_runs',
-    label: '时序InSAR 运行',
-    description: '当前默认接入 SBAS 流程，后续可扩展 PS-InSAR / SBAS-InSAR',
+    key: 'sbas_insar_production',
+    label: 'SBAS-InSAR Production',
+    description: 'Gamma IPTA SBAS stack production, velocity maps, quality metrics, and monitor-point curves',
   },
   {
     key: 'dinsar_products',
     label: 'D-InSAR 产物',
     description: '结果提取、标准目录发布与产物编目',
   },
-  {
-    key: 'timeseries_products',
-    label: '时序InSAR 产物',
-    description: '当前统一登记时序产物，后续兼容多类型时序成果',
-  },
 ];
 
 export const PRODUCTION_WORKSPACE_ENTRY_TO_VIEW = Object.freeze({
   [PRODUCTION_WORKSPACE_TAB]: 'dinsar_runs',
   dinsar_production: 'dinsar_runs',
   dinsar_products: 'dinsar_products',
-  ps_production: 'timeseries_runs',
-  ps_products: 'timeseries_products',
+  ps_production: 'sbas_insar_production',
+  ps_products: 'sbas_insar_production',
 });
 
 export const PRODUCTION_WORKSPACE_ROUTE_TABS = new Set([

frontend/src/hooks/useDinsarOperations.js

@@ -11,7 +11,7 @@ import { normalizePagePayload } from '../utils/appHelpers';
 import { normalizeTaskStatus } from '../utils/appUiHelpers';
 import { DEFAULT_LIST_PAGE_SIZE } from '../config/appConstants';
 
-const NON_BLOCKING_TASK_TYPES = new Set(['UNPACK_ARCHIVES', 'UNPACK_SENTINEL1', 'SCAN_ASSET_INVENTORY', 'COPY_DATA']);
+const NON_BLOCKING_TASK_TYPES = new Set(['UNPACK_ARCHIVES', 'UNPACK_SENTINEL1', 'GF3_UNPACK', 'SCAN_ASSET_INVENTORY', 'COPY_DATA']);
 
 export default function useDinsarOperations({
     onCleanupDinsarLayers,
@@ -229,6 +229,12 @@ export default function useDinsarOperations({
             } else if (taskStatus === 'FAILED') {
                 addLog('error', `LT-1 解包失败: ${taskInfo.message || '未知错误'}`);
             }
+        } else if (taskInfo.task_type === 'GF3_UNPACK') {
+            if (taskStatus === 'COMPLETED') {
+                addLog('success', taskInfo.message || 'GF3 解包完成。');
+            } else if (taskStatus === 'FAILED') {
+                addLog('error', `GF3 解包失败: ${taskInfo.message || '未知错误'}`);
+            }
         }
     };
 

frontend/src/hooks/useGlobalTaskControl.js

@@ -2,7 +2,7 @@ import { useCallback, useEffect, useRef, useState } from 'react';
 import apiClient from '../api/client';
 import { normalizeTaskStatus } from '../utils/appUiHelpers';
 
-const NON_BLOCKING_TASK_TYPES = new Set(['UNPACK_ARCHIVES', 'UNPACK_SENTINEL1', 'SCAN_ASSET_INVENTORY', 'COPY_DATA']);
+const NON_BLOCKING_TASK_TYPES = new Set(['UNPACK_ARCHIVES', 'UNPACK_SENTINEL1', 'GF3_UNPACK', 'SCAN_ASSET_INVENTORY', 'COPY_DATA']);
 
 const isTaskNonBlocking = (taskId, taskType, nonBlockingTaskIds = []) => (
   NON_BLOCKING_TASK_TYPES.has(String(taskType || '').toUpperCase())