Update app.py

app.py

@@ -292,16 +292,17 @@ def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selec
             return None
     
     # Filter collection by date and area first
+    # Apply spatial filtering
     collection = ee.ImageCollection(dataset_id) \
         .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
         .filterBounds(roi)
     
-    st.write(f"After initial filtering: {collection.size().getInfo()} images")
-    
-    # Apply pixel cloud masking if threshold > 0
+    # Apply cloud filtering if applicable
     if pixel_cloud_threshold > 0:
         collection = preprocess_collection(collection, pixel_cloud_threshold)
         st.write(f"After cloud masking: {collection.size().getInfo()} images")
+
+     st.write(f"After initial filtering: {collection.size().getInfo()} images")
     
     if aggregation_period.lower() == 'custom (start date to end date)':
         collection = aggregate_data_custom(collection)
@@ -377,14 +378,38 @@ def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id,
     progress_text = st.empty()
     start_time = time.time()
     
-    raw_collection = ee.ImageCollection(dataset_id) \
-        .filterDate(ee.Date(start_date_str), ee.Date(end_date_str))
+    # Apply spatial filtering
+    if roi is not None:
+        raw_collection = ee.ImageCollection(dataset_id) \
+            .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
+            .filterBounds(roi)
+    else:
+        raw_collection = ee.ImageCollection(dataset_id) \
+            .filterDate(ee.Date(start_date_str), ee.Date(end_date_str))
     
     st.write(f"Original Collection Size: {raw_collection.size().getInfo()}")
+    st.write(f"Original Collection Size: {ee.ImageCollection(dataset_id).filterDate(ee.Date(start_date_str), ee.Date(end_date_str)).size().getInfo()}")
+    st.write(f"Filtered Collection Size (After Spatial Filtering): {raw_collection.size().getInfo()}")
     
-    if tile_cloud_threshold > 0 or pixel_cloud_threshold > 0:
+    if pixel_cloud_threshold > 0:
+        raw_collection = preprocess_collection(raw_collection, pixel_cloud_threshold)
+        st.write(f"Filtered Collection Size (After Cloud Masking): {raw_collection.size().getInfo()}")
+        
+    # if tile_cloud_threshold > 0 or pixel_cloud_threshold > 0:
+    #     raw_collection = preprocess_collection(raw_collection, pixel_cloud_threshold)
+    #     st.write(f"Preprocessed Collection Size: {raw_collection.size().getInfo()}")
+    # Apply cloud filtering
+    if imagery_base == "Sentinel" and "Sentinel-2" in sub_options[sub_selection]:
+        pixel_cloud_threshold = st.slider(
+            "Select Maximum Pixel-Based Cloud Coverage Threshold (%)",
+            min_value=0,
+            max_value=100,
+            value=5,
+            step=5,
+            help="Individual pixels with cloud coverage exceeding this threshold will be masked."
+        )
         raw_collection = preprocess_collection(raw_collection, pixel_cloud_threshold)
-        st.write(f"Preprocessed Collection Size: {raw_collection.size().getInfo()}")
+        st.write(f"Filtered Collection Size (After Cloud Masking): {raw_collection.size().getInfo()}")
     
     with ThreadPoolExecutor(max_workers=10) as executor:
         futures = []