feat: 更新经过多轮测试使用后的稳定版本.

HLS_SuPER/HLS_PER.py

@@ -7,7 +7,8 @@ This module contains functions to conduct subsetting and quality filtering of
 search results.
 -------------------------------------------------------------------------------
 Authors: Cole Krehbiel, Mahsa Jami, and Erik Bolch
-Last Updated: 2024-09-18
+Editor: Hong Xie
+Last Updated: 2025-01-06
 ===============================================================================
 """
 
@@ -21,6 +22,7 @@ import xarray as xr
 import rioxarray as rxr
 import dask.distributed
 
+
 def create_output_name(url, band_dict):
     """
     Uses HLS default naming scheme to generate an output name with common band names.
@@ -42,7 +44,9 @@ def create_output_name(url, band_dict):
     return output_name
 
 
-def open_hls(url, roi=None, scale=True, chunk_size=dict(band=1, x=512, y=512)):
+def open_hls(
+    url, roi=None, clip=False, scale=True, chunk_size=dict(band=1, x=512, y=512)
+):
     """
     Generic Function to open an HLS COG and clip to ROI. For consistent scaling, this must be done manually.
     Some HLS Landsat scenes have the metadata in the wrong location.
@@ -52,23 +56,40 @@ def open_hls(url, roi=None, scale=True, chunk_size=dict(band=1, x=512, y=512)):
         "band", drop=True
     )
 
-    # Reproject ROI and Clip if ROI is provided
+    # (Add) 读取波段名称
-    if roi is not None:
+    split_asset = url.split("/")[-1].split(".")
+    asset_name = split_asset[1]
+    band_name = split_asset[-2]
+
+    # Reproject ROI and Clip if ROI is provided and clip is True
+    if roi is not None and clip:
         roi = roi.to_crs(da.spatial_ref.crs_wkt)
         da = da.rio.clip(roi.geometry.values, roi.crs, all_touched=True)
 
+    # (Add) 即使大部分影像已经被缩放且填补了缺失值, 但可能仍然有些影像需要进行手动在本地GIS软件中进行缩放和填补缺失值
     # Apply Scale Factor if desired for non-quality layer
-    if scale and "Fmask" not in url:
+    if band_name != "Fmask":
-        # Mask Fill Values
+        if scale:
-        da = xr.where(da == -9999, np.nan, da)
+            # Mask Fill Values
-        # Scale Data
+            da = xr.where(da == -9999, np.nan, da)
-        da = da * 0.0001
+            # Scale Data
-        # Remove Scale Factor After Scaling - Prevents Double Scaling
+            # (Add) 除质量层, 以及 L30 的两个热红外波段外, 其他光谱波段缩放因子均为 0.0001
-        da.attrs["scale_factor"] = 1.0
+            # (Add) L30 的两个热红外波段缩放因子为 0.01
+            # NOTE: 需要注意的是热红外此时未被改名
+            if (band_name == "B10" or band_name == "B11") and (asset_name == "L30"):
+                da = da * 0.01
+            else:
+                da = da * 0.0001
+            # Remove Scale Factor After Scaling - Prevents Double Scaling
+            da.attrs["scale_factor"] = 1.0
 
-    # Add Scale Factor to Attributes Manually - This will overwrite/add if the data is missing.
+        # Add Scale Factor to Attributes Manually - This will overwrite/add if the data is missing.
-    if not scale and "Fmask" not in url:
+        # (Add) 若要手动缩放, 则需要手动添加缩放因子
-        da.attrs["scale_factor"] = 0.0001
+        else:
+            if (band_name == "B10" or band_name == "B11") and (asset_name == "L30"):
+                da.attrs["scale_factor"] = 0.01
+            else:
+                da.attrs["scale_factor"] = 0.0001
 
     return da
 
@@ -91,6 +112,7 @@ def create_quality_mask(quality_data, bit_nums: list = [0, 1, 2, 3, 4, 5]):
 def process_granule(
     granule_urls,
     roi,
+    clip,
     quality_filter,
     scale,
     output_dir,
@@ -100,6 +122,31 @@ def process_granule(
 ):
     """
     Processes a list of HLS asset urls for a single granule.
+
+    args:
+        granule_urls (list): List of HLS asset urls to process.
+
+        roi (geopandas.GeoDataFrame): ROI to filter data.
+
+        clip (bool): If True, ROI will be clipped to the image.
+
+        quality_filter (bool): If True, quality layer will be used to mask data.
+
+        scale (bool): If True, data will be scaled to reflectance.
+
+        output_dir (str): Directory to save output files.
+
+        band_dict (dict): Dictionary of band names and asset names.
+
+        bit_nums (list): List of bit numbers to use for quality mask.
+        - 0: Cirrus
+        - 1: Cloud
+        - 2: Adjacent to cloud/shadow
+        - 3: Cloud shadow
+        - 4: Snow/ice
+        - 5: Water
+
+        chunk_size (dict): Dictionary of chunk sizes for dask.
     """
 
     # Setup Logging
@@ -116,6 +163,7 @@ def process_granule(
     ):
 
         # First Handle Quality Layer
+        # (Add) 简化原有的冗余处理, 仅处理质量层, 并最后移除质量层下载url
         if quality_filter:
             # Generate Quality Layer URL
             split_asset = granule_urls[0].split("/")[-1].split(".")
@@ -123,31 +171,26 @@ def process_granule(
             quality_url = (
                 f"{'/'.join(granule_urls[0].split('/')[:-1])}/{'.'.join(split_asset)}"
             )
-
             # Check if File exists in Output Directory
-            output_name = create_output_name(quality_url, band_dict)
+            quality_output_name = create_output_name(quality_url, band_dict)
-            output_file = f"{output_dir}/{output_name}"
+            quality_output_file = f"{output_dir}/{quality_output_name}"
-
-            # Open Quality Layer
-            qa_da = open_hls(quality_url, roi, scale, chunk_size)
-
             # Check if quality asset is already processed
-            if not os.path.isfile(output_file):
+            if not os.path.isfile(quality_output_file):
                 # Write Output
-                qa_da.rio.to_raster(raster_path=output_file, driver="COG")
+                # 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(
-                    f"Existing file {output_name} found in {output_dir}. Skipping."
+                    f"Existing quality file {quality_output_name} found in {output_dir}."
                 )
-
-            # Remove Quality Layer from Granule Asset List if Present
-            granule_urls = [asset for asset in granule_urls if asset != quality_url]
-
             # Create Quality Mask
+            # TODO: 掩膜数组的存在可能会造成Dask内存的溢出, 需要优化
             qa_mask = create_quality_mask(qa_da, bit_nums=bit_nums)
+            # (Add) 若设置 quality_filter=True, 则在生成质量掩码后, 需要移除质量层, 避免后续重复处理
+            granule_urls = [url for url in granule_urls if "Fmask" not in url]
 
         # Process Remaining Assets
-
         for url in granule_urls:
             # Check if File exists in Output Directory
             output_name = create_output_name(url, band_dict)
@@ -156,14 +199,20 @@ def process_granule(
             # Check if scene is already processed
             if not os.path.isfile(output_file):
                 # Open Asset
-                da = open_hls(url, roi, scale, chunk_size)
+                da = open_hls(url, roi, clip, scale, chunk_size)
 
                 # Apply Quality Mask if Desired
                 if quality_filter:
                     da = da.where(~qa_mask)
 
                 # Write Output
-                da.rio.to_raster(raster_path=output_file, driver="COG")
+                if "FMASK" in output_name:
+                    da.rio.to_raster(raster_path=output_file, driver="COG")
+                else:
+                    # (Add) 固定输出为 float32 类型, 否则会默认 float64 类型
+                    da.rio.to_raster(
+                        raster_path=output_file, driver="COG", dtype="float32"
+                    )
             else:
                 logging.info(
                     f"Existing file {output_name} found in {output_dir}. Skipping."

HLS_SuPER/HLS_Su.py

@@ -6,7 +6,8 @@ This module contains functions related to searching and preprocessing HLS data.
 -------------------------------------------------------------------------------                        
 Authors: Mahsa Jami, Cole Krehbiel, and Erik Bolch
 Contact: lpdaac@usgs.gov   
-Last Updated: 2024-09-18                                    
+Editor: Hong Xie
+Last Updated: 2025-01-06                                    
 ===============================================================================
 """
 
@@ -16,7 +17,9 @@ import earthaccess
 
 
 # Main function to search and filter HLS data
-def hls_search(roi: list, band_dict: dict, dates=None, cloud_cover=None, log=False):
+def hls_search(
+    roi: list, band_dict: dict, dates=None, cloud_cover=None, tile_id=None, log=False
+):
     """
     This function uses earthaccess to search for HLS data using an roi and temporal parameter, filter by cloud cover and delivers a list of results urls for the selected bands.
     """
@@ -27,6 +30,10 @@ def hls_search(roi: list, band_dict: dict, dates=None, cloud_cover=None, log=Fal
         temporal=dates,
     )
 
+    # (Add) 根据瓦片ID过滤影像
+    if tile_id:
+        results = hls_tileid_filter(results, tile_id)
+
     # Filter by cloud cover
     if cloud_cover:
         results = hls_cc_filter(results, cloud_cover)
@@ -45,6 +52,23 @@ def hls_search(roi: list, band_dict: dict, dates=None, cloud_cover=None, log=Fal
     return selected_results_urls
 
 
+def hls_tileid_filter(results, tile_id):
+    """
+    (Add) 基于给定的瓦片ID过滤earthaccess检索的数据结果
+    """
+
+    tile_ids = []
+    for result in results:
+        # 从json中检索瓦片ID，转换为字符串并放入数组中
+        tmp_id = str(result["meta"]["native-id"].split(".")[2])
+        tile_ids.append(tmp_id)
+    tile_ids = np.array(tile_ids)
+    # 根据瓦片ID找到对应的索引
+    tile_id_indices = np.where(tile_ids == tile_id)
+    # 根据索引过滤结果
+    return [results[i] for i in tile_id_indices[0]]
+
+
 # Filter earthaccess results based on cloud cover threshold
 def hls_cc_filter(results, cc_threshold):
     """

HLS_SuPER/HLS_SuPER.py

@@ -4,7 +4,8 @@
 HLS Subsetting, Processing, and Exporting Reformatted Data Prep Script                         
 Authors: Cole Krehbiel, Mahsa Jami, and Erik Bolch
 Contact: lpdaac@usgs.gov
-Last Updated: 2024-09-18  
+Editor: Hong Xie
+Last Updated: 2025-01-06 
 ===============================================================================
 """
 
@@ -53,6 +54,23 @@ def parse_arguments():
         " ",
     )
 
+    # (Add) clip: 裁剪参数, 默认为 False, 表示不裁剪, 如果为 True, 则表示裁剪
+    parser.add_argument(
+        "-clip",
+        choices=["True", "False"],
+        required=False,
+        help="(Optional) If provided, the script will clip the output to the ROI.",
+        default="False",
+    )
+
+    # (Add) tile: HLS 的瓦片 ID
+    parser.add_argument(
+        "-tile",
+        type=str,
+        required=False,
+        help="(Optional) Tile ID for spatial subset. If provided, the script will search for the tile ID based on the ROI.",
+    )
+
     # dir: Directory to save the files to
     parser.add_argument(
         "-dir",
@@ -117,7 +135,7 @@ def parse_arguments():
         choices=["True", "False"],
         required=False,
         help="Flag to apply scale factor to layers before exporting output files. This is generally unecessary as most applications will scale automatically.",
-        default="False",
+        default="True",
     )
 
     # of: output file format
@@ -221,6 +239,14 @@ def format_dates(start, end):
     return dates
 
 
+def format_tile_id(tile_id):
+    """
+    (Add) 格式化tile_id参数
+    """
+    tile_id = tile_id.strip("'").strip('"')
+    return str(tile_id)
+
+
 def format_cloud_cover(cc):
     try:
         cc = int(cc.strip("'").strip('"'))
@@ -464,6 +490,10 @@ def main():
     roi, vl = format_roi(args.roi)
     logging.info("Region of Interest formatted successfully")
 
+    # (Add) 格式化 clip 参数
+    clip = str_to_bool(args.clip)
+    logging.info(f"Clip to ROI: {clip}")
+
     # Set Output Directory
     if args.dir is not None:
         output_dir = os.path.normpath(args.dir.strip("'").strip('"')) + os.sep
@@ -485,6 +515,10 @@ def main():
     cc = format_cloud_cover(args.cc)
     logging.info(f"Cloud Cover Filter <= {cc}")
 
+    # (Add) 格式化 Tile ID 参数
+    tile = format_tile_id(args.tile)
+    logging.info(f"Tile ID: {tile}")
+
     # Quality Filtering
     qf = str_to_bool(args.qf)
     logging.info(f"Quality Filtering: {qf}")
@@ -533,20 +567,27 @@ def main():
     else:
         logging.info("Searching for data...")
         results_urls = hls_search(
-            roi=vl, band_dict=band_dict, dates=dates, cloud_cover=cc
+            roi=vl, band_dict=band_dict, dates=dates, cloud_cover=cc, tile_id=tile
         )
         logging.info(f"Writing search results to {results_urls_file}")
         with open(results_urls_file, "w") as file:
             json.dump(results_urls, file)
 
+    results_count = len(results_urls)
     total_assets = sum(len(sublist) for sublist in results_urls)
 
+    filter_descriptions = []
     if cc:
-        logging.info(
+        filter_descriptions.append("cloud")
-            f"{len(results_urls)} granules remain after cloud filtering. {total_assets} assets will be processed."
+    if tile:
-        )
+        filter_descriptions.append("tile")
+    if filter_descriptions:
+        filter_log = f"{results_count} granules remain after {' and '.join(filter_descriptions)} filtering. {total_assets} assets will be processed."
     else:
-        logging.info(f"{total_assets} assets will be processed.")
+        filter_log = (
+            f"{results_count} granules remain. {total_assets} assets will be processed."
+        )
+    logging.info(filter_log)
 
     # Confirm Processing
     if not confirm_action("Do you want to proceed with processing? (y/n)"):
@@ -588,11 +629,12 @@ def main():
         dask.delayed(process_granule)(
             granule_url,
             roi=roi,
+            clip=clip,
             quality_filter=qf,
             scale=scale,
             output_dir=cog_dir,
             band_dict=band_dict,
-            bit_nums=[0, 1, 2, 3, 4, 5],
+            bit_nums=[1, 3],
             chunk_size=chunk_size,
         )
         for granule_url in results_urls
@@ -612,8 +654,8 @@ def main():
         logging.info("Timeseries Dataset Created. Removing Temporary Files...")
         shutil.rmtree(cog_dir)
 
-    # Add: 下载影像数据按照DOY归档
+    # (Add) 下载影像数据按照DOY归档
-    logging.info("开始对已下载影像按照DOY日期进行归档.")
+    logging.info("开始将已下载的HLS影像按照DOY日期进行归档")
     files_collection(output_dir)
     logging.info("归档完成!")
 

README.md

@@ -13,7 +13,7 @@
 
 ### 1.2 配置环境变量
 
-- 为了在控制台中直接使用conda命令, 需要将安装的相关目录配置到Path环境变量中。
+- 为了在控制台中直接使用conda命令，需要将安装的相关目录配置到Path环境变量中。
 
 ```
 D:\program\miniforge3
@@ -98,9 +98,11 @@ mamba activate lpdaac_windows
 - 需要注意的是，密码中最好不要出 `@/#/$/%` 等符号，爬取时可能会出错。
 - 单个用户每秒限制最多100次请求，参考自：https://forum.earthdata.nasa.gov/viewtopic.php?t=3734
 
-### 3.2 爬取云端数据并在内存中进行预处理
+### 3.2 脚本可用参数
 
 - `-roi`：感兴趣区，需要按照 **左下右上** 的逆时针顺序设置点坐标
+- `-clip`：是否对影像进行裁剪，默认 `False`
+- `-tile`：HLS影像瓦片ID，例如 `T49RGQ`
 - `-dir`：输出目录，必须是已存在的目录
 - `-start`：开始时间，格式为 `YYYY-MM-DD`
 - `-end`：结束时间，格式为 `YYYY-MM-DD`
@@ -110,14 +112,28 @@ mamba activate lpdaac_windows
 - `-qf`：是否使用质量波段过滤云/云阴影像元，默认 `True`
 - `-scale`：是否对影像使用缩放因子，默认 `True`
 
-- 爬取所有光谱波段
+### 3.3 爬取云端数据并在内存中进行预处理示例
+
+- 爬取 L30 与 S30 的核心光谱波段：仅按照感兴趣区，瓦片ID，起止时间以及产品名称筛选影像，不进行云量筛选影像，对影像进行去云掩膜处理
 
 ```sh
-python .\\HLS_SuPER\\HLS_SuPER.py -roi '113.10114,30.62845,114.24349,31.59081' -dir .\\data\\HLS\\L30 -start 2024-01-01 -end 2024-01-31 -prod HLSL30 -bands COASTAL-AEROSOL,BLUE,GREEN,RED,NIR1,SWIR1,SWIR2,CIRRUS,TIR1,TIR2,Fmask -cc 70 -qf True -scale True
+python .\\HLS_SuPER\\HLS_SuPER.py -roi '112.9834,30.5286,114.32373,31.64448' -tile T49RGQ -dir .\\data\\HLS\\ALL -start 2024-01-01 -end 2024-01-31 -prod both -bands BLUE,GREEN,RED,NIR1,SWIR1,SWIR2,Fmask -scale True
 ```
 
-- 仅爬取必要的核心波段
+- 爬取 L30 的所有波段：按照感兴趣区，瓦片ID，起止时间以及产品名称筛选影像，过滤云量小于70% 的影像，对影像进行去云掩膜处理
 
 ```sh
-python .\\HLS_SuPER\\HLS_SuPER.py -roi '113.10114,30.62845,114.24349,31.59081' -dir .\\data\\HLS\\L30\\subset -start 2024-01-01 -end 2024-01-31 -prod HLSL30 -bands BLUE,GREEN,RED,NIR1,SWIR1,SWIR2 -cc 70 -qf True -scale True
+python .\\HLS_SuPER\\HLS_SuPER.py -roi '112.9834,30.5286,114.32373,31.64448' -tile T49RGQ -dir .\\data\\HLS\\L30\\subset -start 2024-01-01 -end 2024-01-31 -prod HLSL30 -bands COASTAL-AEROSOL,BLUE,GREEN,RED,NIR1,SWIR1,SWIR2,CIRRUS,TIR1,TIR2,Fmask -cc 70 -scale True
+```
+
+- 仅爬取 L30 的热红外波段：仅按照感兴趣区，瓦片ID，起止时间以及产品名称筛选影像，不进行云量筛选影像，对影像进行去云掩膜处理
+
+```sh
+python .\\HLS_SuPER\\HLS_SuPER.py -roi '112.9834,30.5286,114.32373,31.64448' -tile T49RGQ -dir .\\data\\HLS\\L30\\TIR -start 2024-01-01 -end 2024-01-31 -prod HLSL30 -bands TIR1,TIR2,Fmask -scale True
+```
+
+- 【测试用】不进行云量筛选，直接爬取 L30 2024 年暑期光谱波段与热红外波段
+
+```sh
+python .\\HLS_SuPER\\HLS_SuPER.py -roi '112.9834,30.5286,114.32373,31.64448' -tile T49RGQ -dir .\\data\\HLS\\L30\\subset\\2024 -start 2024-06-01 -end 2024-08-31 -prod HLSL30 -bands BLUE,GREEN,RED,NIR1,SWIR1,SWIR2,TIR1,TIR2,Fmask -scale True
 ```