Larger s3 area.

call_efast.py

@@ -148,8 +148,47 @@ def prepare_s3(season, site_position, site_name, date_range=None):
             with rasterio.open(composite_path, "w", **profile) as dst:
                 dst.write(composite)
 
-    _, _, reproject_and_crop_s3 = _import_efast()
-    reproject_and_crop_s3(temp_composite_dir, s2_prepared_dir, s3_preprocessed_dir)
+    # Reproject S3 to match S2 REFL bounds (full coverage) instead of DIST_CLOUD bounds
+    # This ensures fusion covers the same area as S2 and dimensions match
+    sen2_ref_paths = list(s2_prepared_dir.glob("*REFL.tif"))
+    if len(sen2_ref_paths) == 0:
+        raise ValueError(f"No REFL files found in {s2_prepared_dir}")
+
+    # Get bounds from REFL file (full coverage, matches S2)
+    with rasterio.open(sen2_ref_paths[0]) as s2_ref:
+        target_bounds = s2_ref.bounds
+        target_crs = s2_ref.crs
+        s2_resolution = abs(s2_ref.transform[0])
+        s3_resolution = s2_resolution * RESOLUTION_RATIO
+        width = int((target_bounds.right - target_bounds.left) / s3_resolution)
+        height = int((target_bounds.top - target_bounds.bottom) / s3_resolution)
+        s3_transform = rasterio.transform.from_bounds(
+            target_bounds.left,
+            target_bounds.bottom,
+            target_bounds.right,
+            target_bounds.top,
+            width,
+            height,
+        )
+
+    # Reproject each S3 composite to match S2 REFL bounds
+    sen3_paths = list(temp_composite_dir.glob("*.tif"))
+    for sen3_path in sen3_paths:
+        vrt_options = {
+            "transform": s3_transform,
+            "height": height,
+            "width": width,
+            "crs": target_crs,
+            "resampling": Resampling.cubic,
+        }
+        with rasterio.open(sen3_path) as s3_src:
+            with WarpedVRT(s3_src, **vrt_options) as vrt:
+                name = sen3_path.name
+                outfile = s3_preprocessed_dir / name
+                profile = vrt.profile.copy()
+                profile.update({"dtype": "float32", "nodata": 0, "driver": "GTiff"})
+                rio_shutil.copy(vrt, outfile, **profile)
+
     shutil.rmtree(temp_composite_dir)
 
 

download_s3.py

@@ -1,4 +1,5 @@
 import os
+import time
 from pathlib import Path
 from datetime import datetime
 from dotenv import load_dotenv
@@ -11,7 +12,7 @@ from rasterio.transform import from_bounds
 
 load_dotenv()
 
-BBOX_SIZE = 0.011
+BBOX_SIZE = 0.016  # Larger than S2 to ensure full coverage including padded pixels
 
 
 def _get_bbox(lon, lat):
@@ -131,10 +132,28 @@ def download_s3(season, site_position, site_name, date_range=None):
     ).resample_spatial(projection=32632)
 
     output_file = output_dir / "s3_data.nc"
-    print("[S3] Downloading NetCDF...")
-    datacube.download(str(output_file), format="NetCDF")
+    print(f"[S3] Downloading NetCDF to {output_file}...")
+    print(f"[S3] Temporal extent: {start_date} to {end_date}")
+    print(f"[S3] Spatial extent: {spatial_extent}")
+    print(f"[S3] Bands: {openeo_bands}")
+    print("[S3] This may take several minutes depending on data volume...")
+
+    start_time = time.time()
+    try:
+        datacube.download(str(output_file), format="NetCDF")
+        elapsed = time.time() - start_time
+        print(f"[S3] Download completed in {elapsed:.1f} seconds")
+    except Exception as e:
+        elapsed = time.time() - start_time
+        print(f"[S3] Download failed after {elapsed:.1f} seconds: {e}")
+        raise
 
     print("[S3] Processing NetCDF...")
+    process_start = time.time()
     _process_netcdf(output_file, output_dir, bands, openeo_bands)
+    process_elapsed = time.time() - process_start
+    print(f"[S3] Processing completed in {process_elapsed:.1f} seconds")
+
+    print(f"[S3] Removing temporary NetCDF file...")
     os.remove(output_file)
     print("[S3] Completed")

ndvi.py

@@ -126,6 +126,10 @@ def _process_ndvi_files(
         return
 
     for geotiff_file in geotiff_files:
+        # Skip DIST_CLOUD files silently (single-band distance-to-clouds, not suitable for NDVI)
+        if "DIST_CLOUD" in geotiff_file.name:
+            continue
+
         # Check if file has enough bands (need at least 4 for RED and NIR)
         try:
             with rasterio.open(geotiff_file) as src:

run.py

@@ -14,14 +14,14 @@ def run_pipeline(season, site_position, site_name):
     try:
         # print(f"Downloading data for {site_name}, {season}")
         # download_s2(season, site_position, site_name)
-        # download_s3(season, site_position, site_name)
+        download_s3(season, site_position, site_name)
 
         # print(f"Generating NDVI for raw data: {site_name}, {season}")
-        # generate_ndvi_raw(season, site_position, site_name)
-        # create_ndvi_timeseries_raw(season, site_position, site_name)
+        generate_ndvi_raw(season, site_position, site_name)
+        create_ndvi_timeseries_raw(season, site_position, site_name)
 
         # print(f"Detecting clouds for {site_name}, {season}")
-        # detect_clouds(season, site_name)
+        detect_clouds(season, site_name)
 
         print(f"Preparing data for EFAST fusion for {site_name}, {season}")
         prepare_s2(season, site_position, site_name)