feat: 优化HLS处理逻辑，避免数据属性丢失的问题.

HLS_SuPER/HLS_PER.py

@@ -52,9 +52,11 @@ def open_hls(
     Some HLS Landsat scenes have the metadata in the wrong location.
     """
     # Open using rioxarray
-    da = rxr.open_rasterio(url, chunks=chunk_size, mask_and_scale=False).squeeze(
+    da_org = rxr.open_rasterio(url, chunks=chunk_size, mask_and_scale=False).squeeze(
         "band", drop=True
     )
+    # (Add) 复制源数据进行后续操作, 以便最后复制源数据属性信息
+    da = da_org.copy()
 
     # (Add) 读取波段名称
     split_asset = url.split("/")[-1].split(".")
@@ -81,6 +83,14 @@ def open_hls(
             else:
                 da = da * 0.0001
             # Remove Scale Factor After Scaling - Prevents Double Scaling
+            # (Add) 缩放计算后会丢源属性和坐标系, 需要复制源数据坐标系与属性
+            # 先根据是否裁剪情况判断需要复制的坐标系
+            if clip:
+                da.rio.write_crs(da_org.rio.crs, inplace=True)
+            else:
+                da.rio.write_crs(roi.crs, inplace=True)
+            # 再复制源数据属性
+            da.attrs = da_org.attrs.copy()
             da.attrs["scale_factor"] = 1.0
 
         # Add Scale Factor to Attributes Manually - This will overwrite/add if the data is missing.
@@ -90,7 +100,8 @@ def open_hls(
                 da.attrs["scale_factor"] = 0.01
             else:
                 da.attrs["scale_factor"] = 0.0001
-
+    # 销毁源数据
+    del da_org
     return da
 
 
@@ -175,10 +186,10 @@ def process_granule(
             quality_output_name = create_output_name(quality_url, band_dict)
             quality_output_file = f"{output_dir}/{quality_output_name}"
             # Check if quality asset is already processed
+            # Open Quality Layer
+            qa_da = open_hls(quality_url, roi, clip, scale, chunk_size)
             if not os.path.isfile(quality_output_file):
                 # Write Output
-                # Open Quality Layer
-                qa_da = open_hls(quality_url, roi, clip, scale, chunk_size)
                 qa_da.rio.to_raster(raster_path=quality_output_file, driver="COG")
             else:
                 logging.info(

HLS_SuPER/HLS_Su.py

@@ -32,7 +32,7 @@ def hls_search(
 
     # (Add) 根据瓦片ID过滤影像
     if tile_id:
-        results = hls_tileid_filter(results, tile_id)
+        results = tileid_filter(results, tile_id)
 
     # Filter by cloud cover
     if cloud_cover:
@@ -52,9 +52,11 @@ def hls_search(
     return selected_results_urls
 
 
-def hls_tileid_filter(results, tile_id):
+def tileid_filter(results, tile_id):
     """
     (Add) 基于给定的瓦片ID过滤earthaccess检索的数据结果
+
+    实测可过滤数据集: HLS.L30, HLS.S30, MCD43A4
     """
 
     tile_ids = []