feat: 优化项目路径处理和代码结构以及MODIS下载处理逻辑.

.gitignore

@@ -9,3 +9,5 @@ data/
 *.tif
 
 *.tiff
+
+*.ipynb

DATA_SuPER/DEM_SuPER.py

@@ -22,7 +22,7 @@ Step2: Process DEM data
 
 -------------------------------------------------------------------------------                        
 Authors: Hong Xie
-Last Updated: 2025-07-03
+Last Updated: 2025-08-05
 ===============================================================================
 """
 
@@ -39,7 +39,7 @@ import geopandas as gpd
 import numpy as np
 from rioxarray import open_rasterio
 
-sys.path.append("D:/NASA_EarthData_Script")
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from utils.common_utils import setup_dask_environment, clip_image, mosaic_images
 from HLS_SuPER.HLS_Su import earthdata_search
@@ -183,14 +183,14 @@ def process_granule(
                     dem.rio.write_crs("EPSG:4326", inplace=True)
                     dem.attrs["scale_factor"] = 1
                 dem_raster_list.append(dem)
-            if len(dem_raster_list) > 1:
+            if len(dem_raster_list) >= 1:
                 if name == "slope" or name == "aspect":
                     dem_mosaiced = mosaic_images(dem_raster_list, nodata=-9999)
                 else:
                     dem_mosaiced = mosaic_images(dem_raster_list, nodata=-32768)
-            if roi is not None and clip:
+                if roi is not None and clip:
-                dem_mosaiced = clip_image(dem_mosaiced, roi)
+                    dem_mosaiced = clip_image(dem_mosaiced, roi, clip_by_box=True)
-            dem_mosaiced.rio.to_raster(output_file, driver="COG", compress="DEFLATE")
+                dem_mosaiced.rio.to_raster(output_file, driver="COG", compress="DEFLATE")
         except Exception as e:
             logging.error(f"Error processing files in {name}: {e}")
             return False
@@ -211,13 +211,12 @@ def main(region: list, asset_name: list, tile_id: str):
     unzip_dir = os.path.join(download_dir, "UNZIP")
     output_dir = os.path.join(output_root_dir, "TIF", tile_id)
     os.makedirs(unzip_dir, exist_ok=True)
+    os.makedirs(output_dir, exist_ok=True)
     results_urls_file = f"{output_root_dir}\\NASADEM_{tile_id}_results_urls.json"
-    if not os.path.isfile(results_urls_file):
+    # 默认覆盖上一次检索记录
-        results_urls = earthdata_search(asset_name, roi=bbox)
+    results_urls = earthdata_search(asset_name, roi=bbox)
-        with open(results_urls_file, "w") as f:
+    with open(results_urls_file, "w") as f:
-            json.dump(results_urls, f)
+        json.dump(results_urls, f)
-    else:
-        results_urls = json.load(open(results_urls_file))
     # 构造待解压的文件列表
     zip_file_list = [os.path.join(download_dir, os.path.basename(result[0])) for result in results_urls]
 

DATA_SuPER/GPM_SuPER.py

@@ -28,7 +28,7 @@ import logging
 import earthaccess
 from xarray import open_dataset
 
-sys.path.append("D:/NASA_EarthData_Script")
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from utils.common_utils import setup_dask_environment
 from HLS_SuPER.HLS_Su import earthdata_search

DATA_SuPER/MODIS_SuPER.py

@@ -6,7 +6,7 @@ For example, MCD43A3, MCD43A4, MOD11A1.
 
 -------------------------------------------------------------------------------
 Authors: Hong Xie
-Last Updated: 2025-07-15
+Last Updated: 2025-09-11
 ===============================================================================
 """
 
@@ -20,9 +20,14 @@ import rioxarray as rxr
 import dask.distributed
 import geopandas as gpd
 
-sys.path.append("D:/NASA_EarthData_Script")
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
-from utils.common_utils import clip_image, reproject_image, setup_dask_environment
+from utils.common_utils import (
+    clip_image,
+    reproject_image,
+    setup_dask_environment,
+    setup_logging,
+)
 from HLS_SuPER.HLS_Su import earthdata_search
 
 
@@ -117,7 +122,15 @@ def open_modis(file_path, prod_name):
         raise ValueError(f"Unknown MODIS product: {prod_name}.")
 
 
-def process_modis(download_file, prod_name, roi, clip=True, scale=True, target_crs=None, output_file=None):
+def process_modis(
+    download_file,
+    prod_name,
+    roi,
+    clip=True,
+    scale=True,
+    target_crs=None,
+    output_file=None,
+):
     """
     对 MODIS 数据进行预处理, 包括裁剪, 重投影和缩放.
     """
@@ -127,13 +140,14 @@ def process_modis(download_file, prod_name, roi, clip=True, scale=True, target_c
     if roi is not None and clip:
         modis = clip_image(modis, roi)
 
-    if target_crs is not None:
+    if target_crs is not None and modis is not None:
         modis = reproject_image(modis, target_crs)
+        # 重投影后再裁剪一次
+        if roi is not None and clip:
+            modis = clip_image(modis, roi)
 
-    # 重投影后再裁剪一次
+    if modis.isnull().all():
-    if roi is not None and clip:
+        logging.error(f"Processing {download_file}. Roi area all pixels are nodata.")
-        modis = clip_image(modis, roi)
-
     if scale:
         # 缩放计算后会丢源属性和坐标系, 需要先备份源数据属性信息
         org_attrs = modis.attrs
@@ -165,14 +179,9 @@ def process_granule(
     clip,
     scale,
     output_dir,
-    target_crs="EPSG:4326",
     tile_id=None,
+    target_crs="EPSG:4326",
 ):
-    logging.basicConfig(
-        level=logging.INFO,
-        format="%(levelname)s:%(asctime)s ||| %(message)s",
-        handlers=[logging.StreamHandler(sys.stdout)],
-    )
 
     download_hdf_name = os.path.basename(granule_urls[0])
     # 获取名称与日期
@@ -191,7 +200,7 @@ def process_granule(
         out_tif_name = f"MODIS.{prod_name}.{tile_id}.{date}.NBRDF.tif"
     else:
         out_tif_name = download_hdf_name.replace(".hdf", ".tif")
-    # 除 MCD43A4 需用于光谱指数计算外, MOD11A1 日间温度与 MCD43A4 反照率无需再按日期归档
+    # 除 MCD43A4 需用于光谱指数计算外, MOD11A1 日间温度与 MCD43A3 反照率无需再按日期归档
     if prod_name == "MOD11A1" or prod_name == "MCD43A3":
         output_path = os.path.join(output_dir, "TIF")
     else:
@@ -199,25 +208,29 @@ def process_granule(
     os.makedirs(output_path, exist_ok=True)
     output_file = os.path.join(output_path, out_tif_name)
 
-    if not os.path.isfile(output_file):
+    # Step1: 下载 HDF 文件
-        # Step1: 下载 HDF 文件
+    if not os.path.isfile(download_file):
-        if not os.path.isfile(download_file):
-            try:
-                earthaccess.download(granule_urls, download_path)
-            except Exception as e:
-                logging.error(f"Error downloading {download_file}: {e}")
-                return
-        else:
-            logging.warning(f"{download_file} already exists. Skipping.")
-
-        # Step2: 处理 HDF 文件
         try:
-            process_modis(download_file, prod_name, roi, clip, scale, target_crs, output_file)
+            earthaccess.download(granule_urls, download_path)
+        except Exception as e:
+            logging.error(f"Error downloading {download_file}: {e}")
+            return
+    else:
+        logging.warning(f"{download_file} already exists. Skipping.")
+
+    # Step2: 处理 HDF 文件
+    if not os.path.isfile(output_file):
+        try:
+            process_modis(
+                download_file, prod_name, roi, clip, scale, target_crs, output_file
+            )
+            logging.info(f"Processed {output_file} successfully.")
         except Exception as e:
             os.remove(download_file)
             logging.info(f"Removed corrupted file {download_file}. Retrying download.")
-            process_granule(granule_urls, roi, clip, scale, output_dir, target_crs, tile_id)
+            process_granule(
-        logging.info(f"Processed {output_file} successfully.")
+                granule_urls, roi, clip, scale, output_dir, target_crs, tile_id
+            )
     else:
         logging.warning(f"{output_file} already exists. Skipping.")
 
@@ -229,6 +242,7 @@ def main(
     years: list,
     dates: tuple[str, str],
     tile_id: str,
+    target_crs: str,
     output_root_dir: str,
 ):
     bbox = tuple(list(region.total_bounds))
@@ -255,20 +269,9 @@ def main(
     with open(results_urls_file, "w") as f:
         json.dump(results_urls, f)
 
-    # 配置日志, 首次配置生效, 后续嵌套配置无效
-    logging.basicConfig(
-        level=logging.INFO,  # 级别为INFO及以上的日志会被记录
-        format="%(levelname)s:%(asctime)s ||| %(message)s",
-        handlers=[
-            logging.StreamHandler(sys.stdout),  # 输出到控制台
-            logging.FileHandler(
-                f"{output_dir}\\{asset_name}_{tile_id}_SuPER.log"
-            ),  # 输出到日志文件
-        ],
-    )
-
     client = dask.distributed.Client(timeout=60, memory_limit="8GB")
     client.run(setup_dask_environment)
+    client.run(setup_logging)
     all_start_time = time.time()
     for year in years:
         year_results_dir = os.path.join(output_dir, year)
@@ -276,6 +279,11 @@ def main(
             year_results_dir, f"{asset_name}_{modis_tile_id}_{year}_results_urls.json"
         )
         year_results = json.load(open(year_results_file))
+        # 配置主进程日志
+        logs_file = os.path.join(
+            year_results_dir, f"{asset_name}_{tile_id}_{year}_SuPER.log"
+        )
+        setup_logging(logs_file)
         client.scatter(year_results)
 
         start_time = time.time()
@@ -287,14 +295,21 @@ def main(
                 clip=True,
                 scale=True,
                 output_dir=year_results_dir,
-                target_crs="EPSG:32649",
                 tile_id=tile_id,
+                target_crs=target_crs,
             )
             for granule_url in year_results
         ]
         dask.compute(*tasks)
 
         total_time = time.time() - start_time
+        # Dask任务结束后读取dask_worker.txt日志文件内容, 并注入到logs_file中
+        with open(logs_file, "a") as f:
+            with open("dask_worker.log", "r") as f2:
+                f.write(f2.read())
+        # 随后清空dask_worker.txt文件
+        with open("dask_worker.log", "w") as f:
+            f.write("")
         logging.info(
             f"{year} MODIS {asset_name} Downloading complete and proccessed. Total time: {total_time} seconds"
         )
@@ -303,6 +318,9 @@ def main(
     logging.info(
         f"All MODIS {asset_name} Downloading complete and proccessed. Total time: {all_total_time} seconds"
     )
+    # 最后删除dask_worker.log文件
+    os.remove("dask_worker.log")
+    return
 
 
 if __name__ == "__main__":
@@ -310,12 +328,22 @@ if __name__ == "__main__":
     # region = gpd.read_file("./data/vectors/wuling_guanqu_polygon.geojson")
     tile_id = "49REL"
     region = gpd.read_file(f"./data/vectors/{tile_id}.geojson")
-    # asset_name = "MOD11A1"
+    target_crs = "EPSG:32649"
+    asset_name = "MOD11A1"
     # asset_name = "MCD43A3"
-    asset_name = "MCD43A4"
+    # asset_name = "MCD43A4"
     modis_tile_id = "h27v06"
     # 示例文件名称:  MCD43A4.A2024001.h27v05.061.2024010140610.hdf
-    years = ["2024", "2023", "2022"]
+    years = ["2024"]
     dates = ("03-01", "10-31")
     output_root_dir = ".\\data\\MODIS\\"
-    main(region, asset_name, modis_tile_id, years, dates, tile_id, output_root_dir)
+    main(
+        region,
+        asset_name,
+        modis_tile_id,
+        years,
+        dates,
+        tile_id,
+        target_crs,
+        output_root_dir,
+    )

DATA_SuPER/S1_SAR_SuPER.py

@@ -48,7 +48,7 @@ import numpy as np
 import xarray as xr
 from rioxarray import open_rasterio
 
-sys.path.append("D:/NASA_EarthData_Script")
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from utils.common_utils import setup_dask_environment, clip_image, mosaic_images
 from HLS_SuPER.HLS_Su import earthdata_search

DATA_SuPER/SMAP_SuPER.py

@@ -28,7 +28,7 @@ import h5py
 from osgeo import gdal
 import xarray as xr
 
-sys.path.append("D:/NASA_EarthData_Script")
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from utils.common_params import EASE2_GRID_PARAMS, EPSG
 from utils.common_utils import (

DATA_SuPER/insitu_SuPER.py

@@ -55,7 +55,7 @@ import logging
 import time
 from datetime import datetime, timedelta
 
-sys.path.append("D:\NASA_EarthData_Script")
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 
 class getInsituData:

HLS_SuPER/HLS_SuPER.py

@@ -5,7 +5,7 @@ HLS Subsetting, Processing, and Exporting Reformatted Data Prep Script
 Authors: Cole Krehbiel, Mahsa Jami, and Erik Bolch
 Contact: lpdaac@usgs.gov
 Editor: Hong Xie
-Last Updated: 2025-01-15 
+Last Updated: 2025-09-05
 ===============================================================================
 """
 
@@ -24,11 +24,12 @@ import json
 
 import earthaccess
 from shapely.geometry import box
+from shapely.geometry.polygon import orient
 import geopandas as gpd
 from datetime import datetime as dt
 import dask.distributed
 
-sys.path.append("D:/NASA_EarthData_Script")
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 from utils.common_utils import setup_dask_environment
 from HLS_Su import hls_search
@@ -169,7 +170,15 @@ def parse_arguments():
 
     return parser.parse_args()
 
-
+def ensure_ccw(geom):
+    """
+    Ensure the exterior ring of the polygon is counterclockwise.
+    """
+    if geom.exterior.is_ccw:
+        return geom  # Already counterclockwise
+    else:
+        return orient(geom, sign=1.0)  # Make it counterclockwise
+    
 def format_roi(roi):
     """
     Determines if submitted ROI is a file or bbox coordinates.
@@ -190,22 +199,20 @@ def format_roi(roi):
                 logging.info(
                     "Multiple polygons detected. Creating single geometry of external coordinates."
                 )
-                single_geometry = roi.unary_union.convex_hull
+                single_geometry = roi.union_all().convex_hull
                 roi = gpd.GeoDataFrame(geometry=[single_geometry], crs=roi.crs)
             # Check if ROI is in Geographic CRS, if not, convert to it
-            if roi.crs.is_geographic:
+            if not roi.crs.is_geographic:
-                # List Vertices in correct order for search
+                roi = roi.to_crs("EPSG:4326")
-                # (Add) 使用外包矩形坐标作为检索使用的坐标
-                minx, miny, maxx, maxy = roi.total_bounds
-                bounding_box = box(minx, miny, maxx, maxy)
-                vertices_list = list(bounding_box.exterior.coords)
-
-            else:
-                roi_geographic = roi.to_crs("EPSG:4326")
                 logging.info(
                     "Note: ROI submitted is being converted to Geographic CRS (EPSG:4326)"
                 )
-                vertices_list = list(roi_geographic.geometry[0].exterior.coords)
+            roi['geometry'] = roi['geometry'].apply(ensure_ccw)
+            # List Vertices in correct order for search
+            # (Add) 使用外包矩形坐标简化提交检索时使用的坐标
+            minx, miny, maxx, maxy = roi.total_bounds
+            bounding_box = box(minx, miny, maxx, maxy)
+            vertices_list = list(bounding_box.exterior.coords)
         except (FileNotFoundError, ValueError):
             sys.exit(
                 f"The GeoJSON/shapefile is either not valid or could not be found.\nPlease double check the name and provide the absolute path to the file or make sure that it is located in {os.getcwd()}"
@@ -217,6 +224,7 @@ def format_roi(roi):
 
         # Convert bbox to a geodataframe for clipping
         roi = gpd.GeoDataFrame(geometry=[box(*bbox)], crs="EPSG:4326")
+        roi['geometry'] = roi['geometry'].apply(ensure_ccw) 
 
         vertices_list = list(roi.geometry[0].exterior.coords)
 
@@ -248,6 +256,8 @@ def format_tile_id(tile_id):
     """
     (Add) 格式化tile_id参数
     """
+    if tile_id is None:
+        return None
     tile_id = tile_id.strip("'").strip('"')
     return str(tile_id)
 
@@ -481,6 +491,7 @@ def main():
         # Defaults to the current directory
         output_dir = os.getcwd() + os.sep
 
+    os.makedirs(output_dir, exist_ok=True)
     logging.info(f"Output directory set to: {output_dir}")
 
     # Format/Validate Dates

README.md

@@ -2,32 +2,34 @@
 
 ## 仓库说明
 
-1) EARTHDATA 说明
+1. EARTHDATA 说明
-  - NASA 计划于 2026 年底将公开数据全部集成进 EARTHDATA 中
+
-  - HLS 数据集：NASA 多部门将Landsat8/9与Sentinel-2A/B统一协调至30m，划分为：
+- NASA 计划于 2026 年底将公开数据全部集成进 EARTHDATA 中
-    - L30（Landsat8/9，包含其独有的2个热红外波段，并已处理为表面亮温℃）
+- HLS 数据集：NASA 多部门将 Landsat8/9 与 Sentinel-2A/B 统一协调至 30m，划分为：
-    - S30（Sentinel-2A/B，包含其独有的4个红边波段）
+  - L30（Landsat8/9，包含其独有的 2 个热红外波段，并已处理为表面亮温 ℃）
-    - 对于核心六波段（Blue/Green/Red/NIR/SWIR1/SWIR2）可直接合并使用，合并后湖北地区单格网观测频率约为3-4d
+  - S30（Sentinel-2A/B，包含其独有的 4 个红边波段）
-2) 使用说明
+  - 对于核心六波段（Blue/Green/Red/NIR/SWIR1/SWIR2）可直接合并使用，合并后湖北地区单格网观测频率约为 3-4d
-  - 本仓库基于官方原始脚本修复了实际使用中存在的bug，并添加了更多可控制参数
+
-  - 爬取时不要使用魔法工具
+2. 使用说明
-  - 实测单格网全年影像爬取+预处理约1h
+
-  - 单用户每秒限制100次请求
+- 本仓库基于官方原始脚本修复了实际使用中存在的 bug，并添加了更多可控制参数
+- 爬取时不要使用魔法工具
+- 实测单格网全年影像爬取+预处理约 1h
+- 单用户每秒限制 100 次请求
 
 ## 1 安装基础环境
 
 ### 1.1 miniforge
 
-- miniforge是结合conda与mamba的最小化包，比Anaconda和Miniconda更快更轻量，并且配置命令与原conda基本一致，支持直接使用mamba命令。
+- miniforge 是结合 conda 与 mamba 的最小化包，比 Anaconda 和 Miniconda 更快更轻量，并且配置命令与原 conda 基本一致，支持直接使用 mamba 命令。
-- 简而言之，环境配置效率上，miniforge > Mambaforge (202407已废弃) > Miniconda + Mamba > Miniconda > Anaconda
+- 简而言之，环境配置效率上，miniforge > Mambaforge (202407 已废弃) > Miniconda + Mamba > Miniconda > Anaconda
 
 - 官方仓库地址：https://github.com/conda-forge/miniforge
 - 官方下载地址：https://conda-forge.org/download/
 
-
 ### 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
@@ -37,14 +39,14 @@ D:\program\miniforge3\Library\bin
 
 ### 1.3 配置权限
 
-- 详细配置与miniconda相同，图文教程地址：https://gis-xh.github.io/my-note/python/01conda/Win11-Miniconda-install/
+- 详细配置与 miniconda 相同，图文教程地址：https://gis-xh.github.io/my-note/python/01conda/Win11-Miniconda-install/
-- Windows环境下，需要设置虚拟环境文件夹的访问权限为所有用户可访问，否则会出现无法读取虚拟环境文件的问题
+- Windows 环境下，需要设置虚拟环境文件夹的访问权限为所有用户可访问，否则会出现无法读取虚拟环境文件的问题
 - 具体地：
   - 设置`D:\program\miniforge3\env`目录为所有用户可访问，具体操作为：右键点击文件夹 -> 属性 -> 安全 -> 编辑 -> 添加 -> 添加所有用户 -> 全选 -> 应用 -> 确定
 
 ### 1.4 配置镜像源
 
-- 生成下载源文件的配置文件 (若已经安装过Anaconda/Miniconda，则无需执行此步骤)
+- 生成下载源文件的配置文件 (若已经安装过 Anaconda/Miniconda，则无需执行此步骤)
 
 ```sh
 conda config --set show_channel_urls yes
@@ -57,7 +59,7 @@ envs_dirs:
   - D:\program\miniforge3\envs
   - 其他路径地址(可选，创建虚拟环境时将会按照顺序查找)
 channels:
-  - defaults
+  - conda-forge
 show_channel_urls: true
 channel_alias: https://mirrors.tuna.tsinghua.edu.cn/anaconda
 default_channels:
@@ -75,6 +77,12 @@ custom_channels:
   simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
 ```
 
+- 清理镜像缓存
+
+```sh
+conda clean -i
+```
+
 ### 1.5 初始化 conda
 
 - 打开控制台，初始化 PowerShell 与 CMD
@@ -89,20 +97,33 @@ conda init cmd.exe
 
 ## 2 运行环境配置
 
-### 2.1 使用mamba创建并激活虚拟环境
+### 2.1 使用 mamba 创建并激活虚拟环境
 
-- 克隆虚拟环境 (Windows环境下推荐)
+- 克隆虚拟环境 (完整复刻运行环境所有依赖)
 
 ```sh
-mamba env create -f setup/lpdaac_windows.yml
+mamba env create -f setup/lpdaac.yml
+```
+
+- 克隆虚拟环境 (复刻主要依赖环境-部分依赖可能会更新)
+
+```sh
+mamba env create -f setup/environment.yml
 ```
 
 - 激活虚拟环境
 
 ```sh
-mamba activate lpdaac_windows
+mamba activate lpdaac
 ```
 
+- 导出当前虚拟环境
+
+```sh
+mamba env export > setup/lpdaac.yml
+```
+
+
 ## 3 设计思路
 
 ### 3.1 数据组织
@@ -146,11 +167,10 @@ data/
 
 ### 3.2 NASA Earthdata 账户准备
 
-- 参考自NASA官网示例Demo：https://github.com/nasa/LPDAAC-Data-Resources/blob/main/setup/setup_instructions_python.md
+- 参考自 NASA 官网示例 Demo：https://github.com/nasa/LPDAAC-Data-Resources/blob/main/setup/setup_instructions_python.md
 - 首次运行爬取命令时，需要输入用户名和密码，用户名和密码可以在 [Earthdata](https://urs.earthdata.nasa.gov/) 注册获取。
 - 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
+- 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
-
 
 ## 4 使用示例
 
@@ -160,7 +180,7 @@ data/
 
 - `-roi`：感兴趣区，需要按照 **左下右上** 的逆时针顺序设置点坐标，同时还支持 `shp` 与 `geojson/json` 格式文件
 - `-clip`：是否对影像进行裁剪，默认 `False`
-- `-tile`：HLS影像瓦片ID，例如 `T49RGQ`
+- `-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`
@@ -172,19 +192,19 @@ data/
 
 #### 4.1.2 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
+- 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 '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
 ```
 
-- 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
+- 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 '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
+- 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 .\\data\\vectors\\49REL.geojson -tile T49REL -dir .\\data\\HLS\\2024 -start 2024-06-01 -end 2024-09-01 -prod HLSL30 -bands TIR1,TIR2
@@ -198,4 +218,4 @@ python .\\HLS_SuPER\\HLS_SuPER.py -roi .\\data\\vectors\\49REL.geojson -tile T49
 
 ### 4.2 其他数据
 
-v1.0: 直接运行`DATA_SuPER/`目录下所需数据对应的*.py文件即可.
+v1.0: 直接运行`DATA_SuPER/`目录下所需数据对应的\*.py 文件即可.

setup/environment.yml

@@ -0,0 +1,29 @@
+name: lpdaac
+channels:
+  - conda-forge
+dependencies:
+  - dask
+  - earthaccess
+  - fiona
+  - gdal
+  - geopandas
+  - geoviews
+  - h5netcdf
+  - h5py
+  - harmony-py
+  - hvplot
+  - jupyter
+  - jupyter_bokeh
+  - jupyterlab
+  - libgdal-hdf4
+  - odc-stac
+  - pyresample
+  - pystac-client
+  - python=3.12
+  - rasterio
+  - ray-default
+  - rioxarray
+  - scikit-image
+  - seaborn
+  - spectral
+  - selenium

setup/lpdaac.yml

@@ -0,0 +1,437 @@
+name: lpdaac
+channels:
+  - conda-forge
+dependencies:
+  - _libavif_api=1.3.0=h57928b3_0
+  - _openmp_mutex=4.5=2_gnu
+  - affine=2.4.0=pyhd8ed1ab_1
+  - aiobotocore=2.23.2=pyhe01879c_1
+  - aiohappyeyeballs=2.6.1=pyhd8ed1ab_0
+  - aiohttp=3.12.15=py312h05f76fc_0
+  - aiohttp-cors=0.8.1=pyhd8ed1ab_0
+  - aioitertools=0.12.0=pyhd8ed1ab_1
+  - aiosignal=1.4.0=pyhd8ed1ab_0
+  - annotated-types=0.7.0=pyhd8ed1ab_1
+  - anyio=4.10.0=pyhe01879c_0
+  - aom=3.9.1=he0c23c2_0
+  - argon2-cffi=25.1.0=pyhd8ed1ab_0
+  - argon2-cffi-bindings=25.1.0=py312he06e257_0
+  - arrow=1.3.0=pyhd8ed1ab_1
+  - asciitree=0.3.3=py_2
+  - asttokens=3.0.0=pyhd8ed1ab_1
+  - async-lru=2.0.5=pyh29332c3_0
+  - attrs=25.3.0=pyh71513ae_0
+  - aws-c-auth=0.9.0=h467f71e_16
+  - aws-c-cal=0.9.2=hef2a5b8_1
+  - aws-c-common=0.12.4=hfd05255_0
+  - aws-c-compression=0.3.1=ha8a2810_6
+  - aws-c-event-stream=0.5.5=h16d2062_1
+  - aws-c-http=0.10.2=h909f643_3
+  - aws-c-io=0.21.0=h20b9e97_1
+  - aws-c-mqtt=0.13.1=h8a47558_4
+  - aws-c-s3=0.8.3=hcc9d52c_1
+  - aws-c-sdkutils=0.2.4=ha8a2810_1
+  - aws-checksums=0.2.7=ha8a2810_2
+  - aws-crt-cpp=0.32.10=h16ee0b7_3
+  - aws-sdk-cpp=1.11.510=h41ea3a3_14
+  - babel=2.17.0=pyhd8ed1ab_0
+  - beautifulsoup4=4.13.4=pyha770c72_0
+  - bleach=6.2.0=pyh29332c3_4
+  - bleach-with-css=6.2.0=h82add2a_4
+  - blosc=1.21.6=hfd34d9b_1
+  - bokeh=3.7.3=pyhd8ed1ab_0
+  - botocore=1.39.8=pyge310_1234567_0
+  - bounded-pool-executor=0.0.3=pyhd8ed1ab_0
+  - branca=0.8.1=pyhd8ed1ab_0
+  - brotli=1.1.0=h2466b09_3
+  - brotli-bin=1.1.0=h2466b09_3
+  - brotli-python=1.1.0=py312h275cf98_3
+  - bzip2=1.0.8=h2466b09_7
+  - c-ares=1.34.5=h2466b09_0
+  - c-blosc2=2.19.1=h3cf07e4_0
+  - ca-certificates=2025.8.3=h4c7d964_0
+  - cached-property=1.5.2=hd8ed1ab_1
+  - cached_property=1.5.2=pyha770c72_1
+  - cachetools=5.5.2=pyhd8ed1ab_0
+  - cartopy=0.25.0=py312hc128f0a_0
+  - certifi=2025.8.3=pyhd8ed1ab_0
+  - cffi=1.17.1=py312h4389bb4_0
+  - cftime=1.6.4=py312h1a27103_1
+  - charls=2.4.2=h1537add_0
+  - charset-normalizer=3.4.3=pyhd8ed1ab_0
+  - click=8.2.1=pyh7428d3b_0
+  - click-plugins=1.1.1.2=pyhd8ed1ab_0
+  - cligj=0.7.2=pyhd8ed1ab_2
+  - cloudpickle=3.1.1=pyhd8ed1ab_0
+  - colorama=0.4.6=pyhd8ed1ab_1
+  - colorcet=3.1.0=pyhd8ed1ab_1
+  - colorful=0.5.6=pyhd8ed1ab_0
+  - comm=0.2.3=pyhe01879c_0
+  - configobj=5.0.9=pyhd8ed1ab_1
+  - contourpy=1.3.3=py312hf90b1b7_1
+  - cpython=3.12.11=py312hd8ed1ab_0
+  - cryptography=45.0.6=py312h84d000f_0
+  - curlify=2.2.1=pyh44b312d_0
+  - cycler=0.12.1=pyhd8ed1ab_1
+  - cytoolz=1.0.1=py312h4389bb4_0
+  - dask=2025.7.0=pyhe01879c_0
+  - dask-core=2025.7.0=pyhe01879c_1
+  - datashader=0.18.2=pyhd8ed1ab_0
+  - dav1d=1.2.1=hcfcfb64_0
+  - debugpy=1.8.16=py312ha1a9051_0
+  - decorator=5.2.1=pyhd8ed1ab_0
+  - defusedxml=0.7.1=pyhd8ed1ab_0
+  - deprecated=1.2.18=pyhd8ed1ab_0
+  - distlib=0.4.0=pyhd8ed1ab_0
+  - distributed=2025.7.0=pyhe01879c_0
+  - donfig=0.8.1.post1=pyhd8ed1ab_1
+  - earthaccess=0.14.0=pyhd8ed1ab_0
+  - exceptiongroup=1.3.0=pyhd8ed1ab_0
+  - executing=2.2.0=pyhd8ed1ab_0
+  - fasteners=0.19=pyhd8ed1ab_1
+  - filelock=3.18.0=pyhd8ed1ab_0
+  - fiona=1.10.1=py312h6e88f47_3
+  - folium=0.20.0=pyhd8ed1ab_0
+  - fonttools=4.59.0=py312h05f76fc_0
+  - fqdn=1.5.1=pyhd8ed1ab_1
+  - freetype=2.13.3=h57928b3_1
+  - freexl=2.0.0=hf297d47_2
+  - frozenlist=1.7.0=py312hfdf67e6_0
+  - fsspec=2025.7.0=pyhd8ed1ab_0
+  - gdal=3.10.3=py312h07de9ea_12
+  - geopandas=1.1.1=pyhd8ed1ab_0
+  - geopandas-base=1.1.1=pyha770c72_0
+  - geos=3.13.1=h9ea8674_0
+  - geotiff=1.7.4=h86c3423_2
+  - geoviews=1.14.0=hd8ed1ab_0
+  - geoviews-core=1.14.0=pyha770c72_0
+  - giflib=5.2.2=h64bf75a_0
+  - google-api-core=2.25.1=pyhd8ed1ab_0
+  - google-auth=2.40.3=pyhd8ed1ab_0
+  - googleapis-common-protos=1.70.0=pyhd8ed1ab_0
+  - grpcio=1.71.0=py312h18946f6_1
+  - h11=0.16.0=pyhd8ed1ab_0
+  - h2=4.2.0=pyhd8ed1ab_0
+  - h5netcdf=1.6.4=pyhd8ed1ab_0
+  - h5py=3.14.0=nompi_py312h6cc2a29_100
+  - harmony-py=1.2.0=pyhd8ed1ab_0
+  - hdf4=4.2.15=h5557f11_7
+  - hdf5=1.14.6=nompi_he30205f_103
+  - holoviews=1.21.0=pyhd8ed1ab_0
+  - hpack=4.1.0=pyhd8ed1ab_0
+  - httpcore=1.0.9=pyh29332c3_0
+  - httpx=0.28.1=pyhd8ed1ab_0
+  - hvplot=0.12.0=pyhd8ed1ab_0
+  - hyperframe=6.1.0=pyhd8ed1ab_0
+  - idna=3.10=pyhd8ed1ab_1
+  - imagecodecs=2025.8.2=py312h2f18ccd_0
+  - imageio=2.37.0=pyhfb79c49_0
+  - importlib-metadata=8.7.0=pyhe01879c_1
+  - importlib-resources=6.5.2=pyhd8ed1ab_0
+  - importlib_resources=6.5.2=pyhd8ed1ab_0
+  - ipykernel=6.30.1=pyh3521513_0
+  - ipython=9.4.0=pyh6be1c34_0
+  - ipython_pygments_lexers=1.1.1=pyhd8ed1ab_0
+  - ipywidgets=8.1.7=pyhd8ed1ab_0
+  - isoduration=20.11.0=pyhd8ed1ab_1
+  - jedi=0.19.2=pyhd8ed1ab_1
+  - jinja2=3.1.6=pyhd8ed1ab_0
+  - jmespath=1.0.1=pyhd8ed1ab_1
+  - joblib=1.5.1=pyhd8ed1ab_0
+  - json5=0.12.0=pyhd8ed1ab_0
+  - jsonpointer=3.0.0=py312h2e8e312_1
+  - jsonschema=4.25.0=pyhe01879c_0
+  - jsonschema-specifications=2025.4.1=pyh29332c3_0
+  - jsonschema-with-format-nongpl=4.25.0=he01879c_0
+  - jupyter=1.1.1=pyhd8ed1ab_1
+  - jupyter-lsp=2.2.6=pyhe01879c_0
+  - jupyter_bokeh=4.0.5=pyhd8ed1ab_1
+  - jupyter_client=8.6.3=pyhd8ed1ab_1
+  - jupyter_console=6.6.3=pyhd8ed1ab_1
+  - jupyter_core=5.8.1=pyh5737063_0
+  - jupyter_events=0.12.0=pyh29332c3_0
+  - jupyter_server=2.16.0=pyhe01879c_0
+  - jupyter_server_terminals=0.5.3=pyhd8ed1ab_1
+  - jupyterlab=4.4.5=pyhd8ed1ab_0
+  - jupyterlab_pygments=0.3.0=pyhd8ed1ab_2
+  - jupyterlab_server=2.27.3=pyhd8ed1ab_1
+  - jupyterlab_widgets=3.0.15=pyhd8ed1ab_0
+  - jxrlib=1.1=hcfcfb64_3
+  - kerchunk=0.2.7=pyhd8ed1ab_0
+  - kiwisolver=1.4.8=py312hf90b1b7_1
+  - krb5=1.21.3=hdf4eb48_0
+  - lark=1.2.2=pyhd8ed1ab_1
+  - lazy-loader=0.4=pyhd8ed1ab_2
+  - lcms2=2.17=hbcf6048_0
+  - lerc=4.0.0=h6470a55_1
+  - libabseil=20250127.1=cxx17_h4eb7d71_0
+  - libaec=1.1.4=h20038f6_0
+  - libarchive=3.8.1=gpl_h1ca5a36_100
+  - libarrow=20.0.0=h7ea4809_8_cuda
+  - libarrow-acero=20.0.0=h7d8d6a5_8_cuda
+  - libarrow-dataset=20.0.0=h7d8d6a5_8_cuda
+  - libarrow-substrait=20.0.0=hb76e781_8_cuda
+  - libavif16=1.3.0=hf2698fe_0
+  - libblas=3.9.0=34_h5709861_mkl
+  - libbrotlicommon=1.1.0=h2466b09_3
+  - libbrotlidec=1.1.0=h2466b09_3
+  - libbrotlienc=1.1.0=h2466b09_3
+  - libcblas=3.9.0=34_h2a3cdd5_mkl
+  - libcrc32c=1.1.2=h0e60522_0
+  - libcurl=8.14.1=h88aaa65_0
+  - libdeflate=1.24=h76ddb4d_0
+  - libevent=2.1.12=h3671451_1
+  - libexpat=2.7.1=hac47afa_0
+  - libffi=3.4.6=h537db12_1
+  - libfreetype=2.13.3=h57928b3_1
+  - libfreetype6=2.13.3=h0b5ce68_1
+  - libgcc=15.1.0=h1383e82_4
+  - libgdal-core=3.10.3=h228a343_12
+  - libgdal-hdf4=3.10.3=ha47b6c4_12
+  - libgomp=15.1.0=h1383e82_4
+  - libgoogle-cloud=2.36.0=hf249c01_1
+  - libgoogle-cloud-storage=2.36.0=he5eb982_1
+  - libgrpc=1.71.0=h8c3449c_1
+  - libhwloc=2.12.1=default_h88281d1_1000
+  - libhwy=1.2.0=h1d1702c_0
+  - libiconv=1.18=h135ad9c_1
+  - libjpeg-turbo=3.1.0=h2466b09_0
+  - libjxl=0.11.1=ha161b08_2
+  - libkml=1.3.0=h538826c_1021
+  - liblapack=3.9.0=34_hf9ab0e9_mkl
+  - liblzma=5.8.1=h2466b09_2
+  - libnetcdf=4.9.2=nompi_ha45073a_118
+  - libparquet=20.0.0=ha850022_8_cuda
+  - libpng=1.6.50=h7351971_1
+  - libprotobuf=5.29.3=hd33f5f0_2
+  - libre2-11=2025.06.26=habfad5f_0
+  - librttopo=1.1.0=hbfc9ebc_18
+  - libsodium=1.0.20=hc70643c_0
+  - libspatialite=5.1.0=h378fb81_14
+  - libsqlite=3.50.4=hf5d6505_0
+  - libssh2=1.11.1=h9aa295b_0
+  - libthrift=0.21.0=hbe90ef8_0
+  - libtiff=4.7.0=h05922d8_5
+  - libutf8proc=2.10.0=hff4702e_0
+  - libwebp-base=1.6.0=h4d5522a_0
+  - libwinpthread=12.0.0.r4.gg4f2fc60ca=h57928b3_9
+  - libxcb=1.17.0=h0e4246c_0
+  - libxml2=2.13.8=h741aa76_1
+  - libzip=1.11.2=h3135430_0
+  - libzlib=1.3.1=h2466b09_2
+  - libzopfli=1.0.3=h0e60522_0
+  - linkify-it-py=2.0.3=pyhd8ed1ab_1
+  - llvm-openmp=20.1.8=hfa2b4ca_1
+  - llvmlite=0.44.0=py312h1f7db74_1
+  - locket=1.0.0=pyhd8ed1ab_0
+  - lz4=4.4.4=py312h032eceb_0
+  - lz4-c=1.10.0=h2466b09_1
+  - lzo=2.10=h6a83c73_1002
+  - mapclassify=2.10.0=pyhd8ed1ab_1
+  - markdown=3.8.2=pyhd8ed1ab_0
+  - markdown-it-py=3.0.0=pyhd8ed1ab_1
+  - markupsafe=3.0.2=py312h31fea79_1
+  - matplotlib-base=3.10.5=py312h0ebf65c_0
+  - matplotlib-inline=0.1.7=pyhd8ed1ab_1
+  - mdit-py-plugins=0.4.2=pyhd8ed1ab_1
+  - mdurl=0.1.2=pyhd8ed1ab_1
+  - minizip=4.0.10=h9fa1bad_0
+  - mistune=3.1.3=pyh29332c3_0
+  - mkl=2024.2.2=h57928b3_16
+  - msgpack-python=1.1.1=py312hd5eb7cc_0
+  - multidict=6.6.3=py312h05f76fc_0
+  - multimethod=2.0=pyhd8ed1ab_0
+  - multipledispatch=0.6.0=pyhd8ed1ab_1
+  - munkres=1.1.4=pyhd8ed1ab_1
+  - narwhals=2.0.1=pyhe01879c_0
+  - nbclient=0.10.2=pyhd8ed1ab_0
+  - nbconvert-core=7.16.6=pyh29332c3_0
+  - nbformat=5.10.4=pyhd8ed1ab_1
+  - nest-asyncio=1.6.0=pyhd8ed1ab_1
+  - netcdf4=1.7.2=nompi_py312hf8617a8_102
+  - networkx=3.5=pyhe01879c_0
+  - notebook=7.4.5=pyhd8ed1ab_0
+  - notebook-shim=0.2.4=pyhd8ed1ab_1
+  - numba=0.61.2=py312hdcac391_1
+  - numcodecs=0.15.1=py312h72972c8_0
+  - numpy=2.2.6=py312h3150e54_0
+  - odc-geo=0.5.0rc1=pyhd8ed1ab_0
+  - odc-loader=0.5.1=pyhd8ed1ab_0
+  - odc-stac=0.4.0=pyhd8ed1ab_0
+  - opencensus=0.11.3=pyhd8ed1ab_1
+  - opencensus-context=0.1.3=py312h2e8e312_3
+  - openjpeg=2.5.3=h24db6dd_1
+  - openssl=3.5.2=h725018a_0
+  - opentelemetry-api=1.36.0=pyhd8ed1ab_0
+  - opentelemetry-exporter-prometheus=0.48b0=pyhd8ed1ab_0
+  - opentelemetry-proto=1.36.0=pyhd8ed1ab_0
+  - opentelemetry-sdk=1.36.0=pyhd8ed1ab_0
+  - opentelemetry-semantic-conventions=0.57b0=pyh3cfb1c2_0
+  - orc=2.1.2=h35764e3_0
+  - outcome=1.3.0.post0=pyhd8ed1ab_1
+  - overrides=7.7.0=pyhd8ed1ab_1
+  - packaging=25.0=pyh29332c3_1
+  - pandas=2.3.1=py312hc128f0a_0
+  - pandocfilters=1.5.0=pyhd8ed1ab_0
+  - panel=1.7.5=pyhd8ed1ab_0
+  - param=2.2.1=pyhd8ed1ab_0
+  - parso=0.8.4=pyhd8ed1ab_1
+  - partd=1.4.2=pyhd8ed1ab_0
+  - patsy=1.0.1=pyhd8ed1ab_1
+  - pcre2=10.45=h99c9b8b_0
+  - pickleshare=0.7.5=pyhd8ed1ab_1004
+  - pillow=11.3.0=py312hfb502af_0
+  - pip=25.2=pyh8b19718_0
+  - platformdirs=4.3.8=pyhe01879c_0
+  - pockets=0.9.1=pyhd8ed1ab_1
+  - pqdm=0.2.0=pyhd8ed1ab_1
+  - progressbar2=4.2.0=pyhd8ed1ab_0
+  - proj=9.6.2=h7990399_1
+  - prometheus_client=0.22.1=pyhd8ed1ab_0
+  - prompt-toolkit=3.0.51=pyha770c72_0
+  - prompt_toolkit=3.0.51=hd8ed1ab_0
+  - propcache=0.3.1=py312h31fea79_0
+  - proto-plus=1.26.1=pyhd8ed1ab_0
+  - protobuf=5.29.3=py312h275cf98_0
+  - psutil=7.0.0=py312h4389bb4_0
+  - pthread-stubs=0.4=h0e40799_1002
+  - pure_eval=0.2.3=pyhd8ed1ab_1
+  - py-spy=0.4.1=h77a83cd_0
+  - pyarrow=20.0.0=py312h2e8e312_0
+  - pyarrow-core=20.0.0=py312h607bf26_0_cuda
+  - pyasn1=0.6.1=pyhd8ed1ab_2
+  - pyasn1-modules=0.4.2=pyhd8ed1ab_0
+  - pycparser=2.22=pyh29332c3_1
+  - pyct=0.5.0=pyhd8ed1ab_1
+  - pydantic=2.11.7=pyh3cfb1c2_0
+  - pydantic-core=2.33.2=py312h8422cdd_0
+  - pygments=2.19.2=pyhd8ed1ab_0
+  - pykdtree=1.4.3=py312h196c9fc_0
+  - pyogrio=0.11.0=py312h6e88f47_0
+  - pyopenssl=25.1.0=pyhd8ed1ab_0
+  - pyparsing=3.2.3=pyhe01879c_2
+  - pyproj=3.7.1=py312h5ea471a_1
+  - pyresample=1.34.2=py312h275cf98_0
+  - pyshp=3.0.0=pyhd8ed1ab_0
+  - pysocks=1.7.1=pyh09c184e_7
+  - pystac=1.13.0=pyhd8ed1ab_0
+  - pystac-client=0.9.0=pyhd8ed1ab_0
+  - python=3.12.11=h3f84c4b_0_cpython
+  - python-cmr=0.13.0=pyhff2d567_1
+  - python-dateutil=2.9.0.post0=pyhe01879c_2
+  - python-dotenv=0.20.0=pyhd8ed1ab_0
+  - python-fastjsonschema=2.21.1=pyhd8ed1ab_0
+  - python-json-logger=2.0.7=pyhd8ed1ab_0
+  - python-tzdata=2025.2=pyhd8ed1ab_0
+  - python-utils=3.9.1=pyhff2d567_1
+  - python_abi=3.12=8_cp312
+  - pytz=2025.2=pyhd8ed1ab_0
+  - pyu2f=0.1.5=pyhd8ed1ab_1
+  - pyviz_comms=3.0.6=pyhd8ed1ab_0
+  - pywavelets=1.9.0=py312h196c9fc_0
+  - pywin32=311=py312h829343e_0
+  - pywinpty=2.0.15=py312h275cf98_0
+  - pyyaml=6.0.2=py312h31fea79_2
+  - pyzmq=27.0.1=py312h5b324a9_0
+  - qhull=2020.2=hc790b64_5
+  - rasterio=1.4.3=py312ha172ac9_1
+  - rav1e=0.7.1=ha073cba_3
+  - ray-core=2.47.1=py312h4ed40c6_0
+  - ray-default=2.47.1=py312h7553cdf_0
+  - re2=2025.06.26=h3dd2b4f_0
+  - referencing=0.36.2=pyh29332c3_0
+  - requests=2.32.4=pyhd8ed1ab_0
+  - rfc3339-validator=0.1.4=pyhd8ed1ab_1
+  - rfc3986-validator=0.1.1=pyh9f0ad1d_0
+  - rfc3987-syntax=1.1.0=pyhe01879c_1
+  - rioxarray=0.19.0=pyhd8ed1ab_0
+  - rpds-py=0.27.0=py312hdabe01f_0
+  - rsa=4.9.1=pyhd8ed1ab_0
+  - s3fs=2025.7.0=pyhd8ed1ab_0
+  - scikit-image=0.25.2=py312h72972c8_1
+  - scikit-learn=1.7.1=py312h91ac024_0
+  - scipy=1.16.0=py312h1416ca1_0
+  - seaborn=0.13.2=hd8ed1ab_3
+  - seaborn-base=0.13.2=pyhd8ed1ab_3
+  - selenium=4.34.2=pyhe01879c_0
+  - selenium-manager=4.34.0=h18a1a76_0
+  - send2trash=1.8.3=pyh5737063_1
+  - setproctitle=1.3.6=py312h4389bb4_0
+  - setuptools=80.9.0=pyhff2d567_0
+  - shapely=2.0.7=py312h3f81574_1
+  - six=1.17.0=pyhe01879c_1
+  - smart_open=7.3.0.post1=pyhe01879c_0
+  - snappy=1.2.2=h7fa0ca8_0
+  - sniffio=1.3.1=pyhd8ed1ab_1
+  - snuggs=1.4.7=pyhd8ed1ab_2
+  - sortedcontainers=2.4.0=pyhd8ed1ab_1
+  - soupsieve=2.7=pyhd8ed1ab_0
+  - spectral=0.24=pyhd8ed1ab_0
+  - sphinxcontrib-napoleon=0.7=pyhd8ed1ab_1
+  - sqlite=3.50.4=hdb435a2_0
+  - stack_data=0.6.3=pyhd8ed1ab_1
+  - statsmodels=0.14.5=py312h196c9fc_0
+  - svt-av1=3.0.2=he0c23c2_0
+  - tbb=2021.13.0=h18a62a1_2
+  - tblib=3.1.0=pyhd8ed1ab_0
+  - terminado=0.18.1=pyh5737063_0
+  - threadpoolctl=3.6.0=pyhecae5ae_0
+  - tifffile=2025.6.11=pyhd8ed1ab_0
+  - tinycss2=1.4.0=pyhd8ed1ab_0
+  - tinynetrc=1.3.1=pyhd8ed1ab_0
+  - tk=8.6.13=h2c6b04d_2
+  - tomli=2.2.1=pyhe01879c_2
+  - toolz=1.0.0=pyhd8ed1ab_1
+  - tornado=6.5.2=py312he06e257_0
+  - tqdm=4.67.1=pyhd8ed1ab_1
+  - traitlets=5.14.3=pyhd8ed1ab_1
+  - trio=0.30.0=py312h2e8e312_0
+  - trio-websocket=0.12.2=pyh29332c3_0
+  - types-python-dateutil=2.9.0.20250809=pyhd8ed1ab_0
+  - typing-extensions=4.14.1=h4440ef1_0
+  - typing-inspection=0.4.1=pyhd8ed1ab_0
+  - typing_extensions=4.14.1=pyhe01879c_0
+  - typing_utils=0.1.0=pyhd8ed1ab_1
+  - tzdata=2025b=h78e105d_0
+  - uc-micro-py=1.0.3=pyhd8ed1ab_1
+  - ucrt=10.0.22621.0=h57928b3_1
+  - ujson=5.10.0=py312h275cf98_1
+  - unicodedata2=16.0.0=py312h4389bb4_0
+  - universal_pathlib=0.2.6=pyhd8ed1ab_1
+  - uri-template=1.3.0=pyhd8ed1ab_1
+  - uriparser=0.9.8=h5a68840_0
+  - urllib3=2.5.0=pyhd8ed1ab_0
+  - vc=14.3=h41ae7f8_31
+  - vc14_runtime=14.44.35208=h818238b_31
+  - vcomp14=14.44.35208=h818238b_31
+  - virtualenv=20.33.1=pyhd8ed1ab_0
+  - virtualizarr=1.2.0=pyhd8ed1ab_0
+  - vs2015_runtime=14.44.35208=h38c0c73_31
+  - wcwidth=0.2.13=pyhd8ed1ab_1
+  - webcolors=24.11.1=pyhd8ed1ab_0
+  - webencodings=0.5.1=pyhd8ed1ab_3
+  - websocket-client=1.8.0=pyhd8ed1ab_1
+  - wheel=0.45.1=pyhd8ed1ab_1
+  - widgetsnbextension=4.0.14=pyhd8ed1ab_0
+  - win_inet_pton=1.1.0=pyh7428d3b_8
+  - winpty=0.4.3=4
+  - wrapt=1.17.2=py312h4389bb4_0
+  - wsproto=1.2.0=pyhd8ed1ab_1
+  - xarray=2025.7.1=pyhd8ed1ab_0
+  - xerces-c=3.2.5=he0c23c2_2
+  - xorg-libxau=1.0.12=h0e40799_0
+  - xorg-libxdmcp=1.1.5=h0e40799_0
+  - xyzservices=2025.4.0=pyhd8ed1ab_0
+  - yaml=0.2.5=h6a83c73_3
+  - yarl=1.20.1=py312h31fea79_0
+  - zarr=2.18.7=pyhd8ed1ab_0
+  - zeromq=4.3.5=ha9f60a1_7
+  - zfp=1.0.1=he0c23c2_2
+  - zict=3.0.0=pyhd8ed1ab_1
+  - zipp=3.23.0=pyhd8ed1ab_0
+  - zlib=1.3.1=h2466b09_2
+  - zlib-ng=2.2.5=h1608b31_0
+  - zstandard=0.23.0=py312h4389bb4_2
+  - zstd=1.5.7=hbeecb71_2
+  - pip:
+      - beautifulsoup4==4.12.3

setup/lpdaac_windows.yml

@@ -1,426 +0,0 @@
-name: lpdaac_windows
-channels:
-  - defaults
-  - conda-forge
-dependencies:
-  - affine=2.4.0=pyhd8ed1ab_0
-  - aiobotocore=2.7.0=pyhd8ed1ab_1
-  - aiofiles=22.1.0=pyhd8ed1ab_0
-  - aiohttp=3.8.6=py310h8d17308_1
-  - aiohttp-cors=0.7.0=py_0
-  - aioitertools=0.11.0=pyhd8ed1ab_0
-  - aiosignal=1.3.1=pyhd8ed1ab_0
-  - aiosqlite=0.19.0=pyhd8ed1ab_0
-  - ansicon=1.89.0=py310h5588dad_7
-  - anyio=4.0.0=pyhd8ed1ab_0
-  - argon2-cffi=23.1.0=pyhd8ed1ab_0
-  - argon2-cffi-bindings=21.2.0=py310h8d17308_4
-  - arrow=1.3.0=pyhd8ed1ab_0
-  - asciitree=0.3.3=py_2
-  - asttokens=2.4.1=pyhd8ed1ab_0
-  - async-timeout=4.0.3=pyhd8ed1ab_0
-  - aws-c-auth=0.7.3=h0127223_1
-  - aws-c-cal=0.6.1=hfb91821_1
-  - aws-c-common=0.9.0=hcfcfb64_0
-  - aws-c-compression=0.2.17=h04c9df6_2
-  - aws-c-event-stream=0.3.1=h495bb32_4
-  - aws-c-http=0.7.11=hf013885_4
-  - aws-c-io=0.13.32=he824701_1
-  - aws-c-mqtt=0.9.3=h64f41f2_1
-  - aws-c-s3=0.3.14=hb8b96c7_1
-  - aws-c-sdkutils=0.1.12=h04c9df6_1
-  - aws-checksums=0.1.17=h04c9df6_1
-  - aws-crt-cpp=0.21.0=hf1ed06d_5
-  - aws-sdk-cpp=1.10.57=heb7cc7f_19
-  - babel=2.13.1=pyhd8ed1ab_0
-  - backports=1.0=pyhd8ed1ab_3
-  - backports.functools_lru_cache=1.6.5=pyhd8ed1ab_0
-  - beautifulsoup4=4.12.2=pyha770c72_0
-  - bleach=6.1.0=pyhd8ed1ab_0
-  - blessed=1.19.1=pyh95a074a_2
-  - blosc=1.21.5=hdccc3a2_0
-  - bokeh=3.3.0=pyhd8ed1ab_0
-  - boto3=1.28.64=pyhd8ed1ab_0
-  - botocore=1.31.64=pyhd8ed1ab_0
-  - bounded-pool-executor=0.0.3=pyhd8ed1ab_0
-  - branca=0.7.0=pyhd8ed1ab_1
-  - brotli=1.0.9=hcfcfb64_9
-  - brotli-bin=1.0.9=hcfcfb64_9
-  - brotli-python=1.0.9=py310h00ffb61_9
-  - bzip2=1.0.8=hcfcfb64_5
-  - c-ares=1.21.0=hcfcfb64_0
-  - ca-certificates=2025.1.31=h56e8100_0
-  - cached-property=1.5.2=hd8ed1ab_1
-  - cached_property=1.5.2=pyha770c72_1
-  - cachetools=5.3.2=pyhd8ed1ab_0
-  - cairo=1.18.0=h1fef639_0
-  - cartopy=0.22.0=py310hecd3228_1
-  - certifi=2025.1.31=pyhd8ed1ab_0
-  - cffi=1.16.0=py310h8d17308_0
-  - cfitsio=4.3.0=h9b0cee5_0
-  - cftime=1.6.3=py310h3e78b6c_0
-  - charset-normalizer=3.3.2=pyhd8ed1ab_0
-  - click=8.1.7=win_pyh7428d3b_0
-  - click-plugins=1.1.1=py_0
-  - cligj=0.7.2=pyhd8ed1ab_1
-  - cloudpickle=3.0.0=pyhd8ed1ab_0
-  - colorama=0.4.6=pyhd8ed1ab_0
-  - colorcet=3.0.1=pyhd8ed1ab_0
-  - colorful=0.5.4=pyhd8ed1ab_0
-  - comm=0.1.4=pyhd8ed1ab_0
-  - configobj=5.0.8=pyhd8ed1ab_0
-  - contourpy=1.2.0=py310h232114e_0
-  - cryptography=41.0.5=py310h6e82f81_0
-  - cycler=0.12.1=pyhd8ed1ab_0
-  - cytoolz=0.12.2=py310h8d17308_1
-  - dask=2023.10.1=pyhd8ed1ab_0
-  - dask-core=2023.10.1=pyhd8ed1ab_0
-  - datashader=0.16.0=pyhd8ed1ab_0
-  - debugpy=1.8.0=py310h00ffb61_1
-  - decorator=5.1.1=pyhd8ed1ab_0
-  - defusedxml=0.7.1=pyhd8ed1ab_0
-  - distlib=0.3.7=pyhd8ed1ab_0
-  - distributed=2023.10.1=pyhd8ed1ab_0
-  - entrypoints=0.4=pyhd8ed1ab_0
-  - exceptiongroup=1.1.3=pyhd8ed1ab_0
-  - executing=2.0.1=pyhd8ed1ab_0
-  - expat=2.5.0=h63175ca_1
-  - fasteners=0.17.3=pyhd8ed1ab_0
-  - filelock=3.13.1=pyhd8ed1ab_0
-  - fiona=1.9.5=py310h65cc672_0
-  - folium=0.15.0=pyhd8ed1ab_0
-  - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
-  - font-ttf-inconsolata=3.000=h77eed37_0
-  - font-ttf-source-code-pro=2.038=h77eed37_0
-  - font-ttf-ubuntu=0.83=hab24e00_0
-  - fontconfig=2.14.2=hbde0cde_0
-  - fonts-conda-ecosystem=1=0
-  - fonts-conda-forge=1=0
-  - fonttools=4.44.0=py310h8d17308_0
-  - fqdn=1.5.1=pyhd8ed1ab_0
-  - freetype=2.12.1=hdaf720e_2
-  - freexl=2.0.0=h8276f4a_0
-  - frozenlist=1.4.0=py310h8d17308_1
-  - fsspec=2023.10.0=pyhca7485f_0
-  - gdal=3.7.3=py310haa9213b_2
-  - geopandas=0.14.0=pyhd8ed1ab_1
-  - geopandas-base=0.14.0=pyha770c72_1
-  - geos=3.12.0=h1537add_0
-  - geotiff=1.7.1=hcf4a93f_14
-  - geoviews=1.11.0=pyhd8ed1ab_0
-  - geoviews-core=1.11.0=pyha770c72_0
-  - gettext=0.21.1=h5728263_0
-  - gitdb=4.0.11=pyhd8ed1ab_0
-  - gitpython=3.1.40=pyhd8ed1ab_0
-  - google-api-core=2.13.0=pyhd8ed1ab_0
-  - google-auth=2.23.4=pyhca7485f_0
-  - googleapis-common-protos=1.61.0=pyhd8ed1ab_0
-  - gpustat=1.1.1=pyhd8ed1ab_0
-  - grpcio=1.54.3=py310h8020be6_0
-  - h5netcdf=1.3.0=pyhd8ed1ab_0
-  - h5py=3.10.0=nompi_py310h20f5850_100
-  - hdf4=4.2.15=h5557f11_7
-  - hdf5=1.14.2=nompi_h73e8ff5_100
-  - holoviews=1.18.1=pyhd8ed1ab_0
-  - hvplot=0.9.0=pyhd8ed1ab_0
-  - icu=73.2=h63175ca_0
-  - idna=3.4=pyhd8ed1ab_0
-  - imagecodecs-lite=2019.12.3=py310h3e78b6c_7
-  - imageio=2.31.5=pyh8c1a49c_0
-  - importlib-metadata=6.8.0=pyha770c72_0
-  - importlib_metadata=6.8.0=hd8ed1ab_0
-  - intel-openmp=2023.2.0=h57928b3_50497
-  - ipykernel=6.26.0=pyha63f2e9_0
-  - ipython=8.17.2=pyh5737063_0
-  - ipython_genutils=0.2.0=py_1
-  - ipywidgets=8.1.1=pyhd8ed1ab_0
-  - isoduration=20.11.0=pyhd8ed1ab_0
-  - jedi=0.19.1=pyhd8ed1ab_0
-  - jinja2=3.1.2=pyhd8ed1ab_1
-  - jinxed=1.2.0=pyh95a074a_0
-  - jmespath=1.0.1=pyhd8ed1ab_0
-  - joblib=1.3.2=pyhd8ed1ab_0
-  - json5=0.9.14=pyhd8ed1ab_0
-  - jsonpointer=2.4=py310h5588dad_3
-  - jsonschema=4.19.2=pyhd8ed1ab_0
-  - jsonschema-specifications=2023.7.1=pyhd8ed1ab_0
-  - jsonschema-with-format-nongpl=4.19.2=pyhd8ed1ab_0
-  - jupyter=1.0.0=pyhd8ed1ab_10
-  - jupyter-resource-usage=1.0.1=pyhd8ed1ab_0
-  - jupyter-server-mathjax=0.2.6=pyh5bfe37b_1
-  - jupyter_bokeh=3.0.7=pyhd8ed1ab_0
-  - jupyter_client=7.4.9=pyhd8ed1ab_0
-  - jupyter_console=6.6.3=pyhd8ed1ab_0
-  - jupyter_core=5.5.0=py310h5588dad_0
-  - jupyter_events=0.9.0=pyhd8ed1ab_0
-  - jupyter_server=2.10.0=pyhd8ed1ab_0
-  - jupyter_server_fileid=0.9.0=pyhd8ed1ab_0
-  - jupyter_server_terminals=0.4.4=pyhd8ed1ab_1
-  - jupyter_server_ydoc=0.8.0=pyhd8ed1ab_0
-  - jupyter_ydoc=0.2.4=pyhd8ed1ab_0
-  - jupyterlab=3.6.6=pyhd8ed1ab_0
-  - jupyterlab-geojson=3.4.0=pyhd8ed1ab_0
-  - jupyterlab-git=0.44.0=pyhd8ed1ab_0
-  - jupyterlab_pygments=0.2.2=pyhd8ed1ab_0
-  - jupyterlab_server=2.25.1=pyhd8ed1ab_0
-  - jupyterlab_widgets=3.0.9=pyhd8ed1ab_0
-  - kealib=1.5.2=ha10e780_1
-  - kiwisolver=1.4.5=py310h232114e_1
-  - krb5=1.21.2=heb0366b_0
-  - lazy_loader=0.3=pyhd8ed1ab_0
-  - lcms2=2.15=h67d730c_3
-  - lerc=4.0.0=h63175ca_0
-  - libabseil=20230125.3=cxx17_h63175ca_0
-  - libaec=1.1.2=h63175ca_1
-  - libarchive=3.7.2=h6f8411a_0
-  - libarrow=12.0.1=he3e0f11_8_cpu
-  - libblas=3.9.0=19_win64_mkl
-  - libboost-headers=1.82.0=h57928b3_6
-  - libbrotlicommon=1.0.9=hcfcfb64_9
-  - libbrotlidec=1.0.9=hcfcfb64_9
-  - libbrotlienc=1.0.9=hcfcfb64_9
-  - libcblas=3.9.0=19_win64_mkl
-  - libcrc32c=1.1.2=h0e60522_0
-  - libcurl=8.4.0=hd5e4a3a_0
-  - libdeflate=1.19=hcfcfb64_0
-  - libevent=2.1.12=h3671451_1
-  - libexpat=2.5.0=h63175ca_1
-  - libffi=3.4.2=h8ffe710_5
-  - libgdal=3.7.3=h3217549_2
-  - libglib=2.78.1=he8f3873_0
-  - libgoogle-cloud=2.12.0=h00b2bdc_1
-  - libgrpc=1.54.3=ha177ca7_0
-  - libhwloc=2.9.3=default_haede6df_1009
-  - libiconv=1.17=h8ffe710_0
-  - libjpeg-turbo=3.0.0=hcfcfb64_1
-  - libkml=1.3.0=haf3e7a6_1018
-  - liblapack=3.9.0=19_win64_mkl
-  - libnetcdf=4.9.2=nompi_h8284064_112
-  - libpng=1.6.39=h19919ed_0
-  - libpq=16.3=hab9416b_0
-  - libprotobuf=3.21.12=h12be248_2
-  - librttopo=1.1.0=h92c5fdb_14
-  - libsodium=1.0.18=h8d14728_1
-  - libspatialindex=1.9.3=h39d44d4_4
-  - libspatialite=5.1.0=hbf340bc_1
-  - libsqlite=3.44.0=hcfcfb64_0
-  - libssh2=1.11.0=h7dfc565_0
-  - libthrift=0.18.1=h06f6336_2
-  - libtiff=4.6.0=h6e2ebb7_2
-  - libutf8proc=2.8.0=h82a8f57_0
-  - libwebp-base=1.3.2=hcfcfb64_0
-  - libxcb=1.15=hcd874cb_0
-  - libxml2=2.12.7=h283a6d9_1
-  - libzip=1.10.1=h1d365fa_3
-  - libzlib=1.2.13=hcfcfb64_5
-  - linkify-it-py=2.0.0=pyhd8ed1ab_0
-  - llvmlite=0.41.1=py310hb84602e_0
-  - locket=1.0.0=pyhd8ed1ab_0
-  - lz4=4.3.2=py310hbbb2075_1
-  - lz4-c=1.9.4=hcfcfb64_0
-  - lzo=2.10=he774522_1000
-  - m2w64-gcc-libgfortran=5.3.0=6
-  - m2w64-gcc-libs=5.3.0=7
-  - m2w64-gcc-libs-core=5.3.0=7
-  - m2w64-gmp=6.1.0=2
-  - m2w64-libwinpthread-git=5.0.0.4634.697f757=2
-  - mapclassify=2.6.1=pyhd8ed1ab_0
-  - markdown=3.5.1=pyhd8ed1ab_0
-  - markdown-it-py=3.0.0=pyhd8ed1ab_0
-  - markupsafe=2.1.3=py310h8d17308_1
-  - matplotlib-base=3.8.1=py310hc9baf74_0
-  - matplotlib-inline=0.1.6=pyhd8ed1ab_0
-  - mdit-py-plugins=0.4.0=pyhd8ed1ab_0
-  - mdurl=0.1.0=pyhd8ed1ab_0
-  - minizip=4.0.2=h5bed578_0
-  - mistune=3.0.2=pyhd8ed1ab_0
-  - mkl=2023.2.0=h6a75c08_50496
-  - msgpack-python=1.0.6=py310h232114e_0
-  - msys2-conda-epoch=20160418=1
-  - multidict=6.0.4=py310h8d17308_1
-  - multimethod=1.9.1=pyhd8ed1ab_0
-  - multipledispatch=0.6.0=py_0
-  - munch=4.0.0=pyhd8ed1ab_0
-  - munkres=1.1.4=pyh9f0ad1d_0
-  - nbclassic=1.0.0=pyhb4ecaf3_1
-  - nbclient=0.8.0=pyhd8ed1ab_0
-  - nbconvert=7.11.0=pyhd8ed1ab_0
-  - nbconvert-core=7.11.0=pyhd8ed1ab_0
-  - nbconvert-pandoc=7.11.0=pyhd8ed1ab_0
-  - nbdime=3.2.1=pyhd8ed1ab_0
-  - nbformat=5.9.2=pyhd8ed1ab_0
-  - nest-asyncio=1.5.8=pyhd8ed1ab_0
-  - netcdf4=1.6.5=nompi_py310h6477780_100
-  - networkx=3.2.1=pyhd8ed1ab_0
-  - notebook=6.5.6=pyha770c72_0
-  - notebook-shim=0.2.3=pyhd8ed1ab_0
-  - numba=0.58.1=py310h9ccaf4f_0
-  - numcodecs=0.12.1=py310h00ffb61_0
-  - numpy=1.26.0=py310hf667824_0
-  - nvidia-ml-py=12.535.133=pyhd8ed1ab_0
-  - opencensus=0.11.3=pyhd8ed1ab_0
-  - opencensus-context=0.1.3=py310h5588dad_2
-  - openjpeg=2.5.0=h3d672ee_3
-  - openssl=3.4.1=ha4e3fda_0
-  - orc=1.9.0=hada7b9e_1
-  - overrides=7.4.0=pyhd8ed1ab_0
-  - packaging=23.2=pyhd8ed1ab_0
-  - pandas=2.1.2=py310hecd3228_0
-  - pandoc=3.1.3=h57928b3_0
-  - pandocfilters=1.5.0=pyhd8ed1ab_0
-  - panel=1.3.1=pyhd8ed1ab_0
-  - param=2.0.1=pyhca7485f_0
-  - parso=0.8.3=pyhd8ed1ab_0
-  - partd=1.4.1=pyhd8ed1ab_0
-  - pcre2=10.40=h17e33f8_0
-  - pexpect=4.8.0=pyh1a96a4e_2
-  - pickleshare=0.7.5=py_1003
-  - pillow=10.1.0=py310h1e6a543_0
-  - pip=23.3.1=pyhd8ed1ab_0
-  - pixman=0.42.2=h63175ca_0
-  - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_1
-  - platformdirs=3.11.0=pyhd8ed1ab_0
-  - poppler=23.10.0=hc2f3c52_0
-  - poppler-data=0.4.12=hd8ed1ab_0
-  - postgresql=16.3=h7f155c9_0
-  - pqdm=0.2.0=pyhd8ed1ab_0
-  - proj=9.3.0=he13c7e8_2
-  - prometheus_client=0.18.0=pyhd8ed1ab_0
-  - prompt-toolkit=3.0.39=pyha770c72_0
-  - prompt_toolkit=3.0.39=hd8ed1ab_0
-  - protobuf=4.21.12=py310h00ffb61_0
-  - psutil=5.9.5=py310h8d17308_1
-  - pthread-stubs=0.4=hcd874cb_1001
-  - pthreads-win32=2.9.1=hfa6e2cd_3
-  - ptyprocess=0.7.0=pyhd3deb0d_0
-  - pure_eval=0.2.2=pyhd8ed1ab_0
-  - py-spy=0.3.14=h975169c_0
-  - pyarrow=12.0.1=py310hd1a9178_8_cpu
-  - pyasn1=0.5.0=pyhd8ed1ab_0
-  - pyasn1-modules=0.3.0=pyhd8ed1ab_0
-  - pycparser=2.21=pyhd8ed1ab_0
-  - pyct=0.4.6=py_0
-  - pyct-core=0.4.6=py_0
-  - pydantic=1.10.13=py310h8d17308_1
-  - pygments=2.16.1=pyhd8ed1ab_0
-  - pykdtree=1.3.9=py310h3e78b6c_1
-  - pyopenssl=23.3.0=pyhd8ed1ab_0
-  - pyparsing=3.1.1=pyhd8ed1ab_0
-  - pyproj=3.6.1=py310hebb2149_4
-  - pyresample=1.27.1=py310hecd3228_2
-  - pyshp=2.3.1=pyhd8ed1ab_0
-  - pysocks=1.7.1=pyh0701188_6
-  - pystac=1.9.0=pyhd8ed1ab_0
-  - pystac-client=0.7.5=pyhd8ed1ab_0
-  - python=3.10.13=h4de0772_0_cpython
-  - python-dateutil=2.8.2=pyhd8ed1ab_0
-  - python-fastjsonschema=2.18.1=pyhd8ed1ab_0
-  - python-json-logger=2.0.7=pyhd8ed1ab_0
-  - python-tzdata=2023.3=pyhd8ed1ab_0
-  - python_abi=3.10=4_cp310
-  - pytz=2023.3.post1=pyhd8ed1ab_0
-  - pyu2f=0.1.5=pyhd8ed1ab_0
-  - pyviz_comms=2.3.2=pyhd8ed1ab_0
-  - pywavelets=1.4.1=py310h3e78b6c_1
-  - pywin32=306=py310h00ffb61_2
-  - pywinpty=2.0.12=py310h00ffb61_0
-  - pyyaml=6.0.1=py310h8d17308_1
-  - pyzmq=24.0.1=py310hcd737a0_1
-  - qtconsole-base=5.5.0=pyha770c72_0
-  - qtpy=2.4.1=pyhd8ed1ab_0
-  - rasterio=1.3.9=py310h4d3659c_0
-  - ray-core=2.7.1=py310h139b6d1_0
-  - ray-default=2.7.1=py310h5588dad_0
-  - re2=2023.03.02=hd4eee63_0
-  - referencing=0.30.2=pyhd8ed1ab_0
-  - requests=2.31.0=pyhd8ed1ab_0
-  - rfc3339-validator=0.1.4=pyhd8ed1ab_0
-  - rfc3986-validator=0.1.1=pyh9f0ad1d_0
-  - rioxarray=0.15.0=pyhd8ed1ab_0
-  - rpds-py=0.12.0=py310h87d50f1_0
-  - rsa=4.9=pyhd8ed1ab_0
-  - rtree=1.1.0=py310h1cbd46b_0
-  - s3fs=2023.10.0=pyhd8ed1ab_0
-  - s3transfer=0.7.0=pyhd8ed1ab_0
-  - scikit-image=0.20.0=py310h1c4a608_1
-  - scikit-learn=1.3.2=py310hfd2573f_1
-  - scipy=1.11.3=py310hf667824_1
-  - send2trash=1.8.2=pyh08f2357_0
-  - setproctitle=1.3.3=py310h8d17308_0
-  - setuptools=68.2.2=pyhd8ed1ab_0
-  - shapely=2.0.2=py310h839b4a8_0
-  - six=1.16.0=pyh6c4a22f_0
-  - smart_open=6.4.0=pyhd8ed1ab_0
-  - smmap=5.0.0=pyhd8ed1ab_0
-  - snappy=1.1.10=hfb803bf_0
-  - sniffio=1.3.0=pyhd8ed1ab_0
-  - snuggs=1.4.7=py_0
-  - sortedcontainers=2.4.0=pyhd8ed1ab_0
-  - soupsieve=2.5=pyhd8ed1ab_1
-  - spectral=0.23.1=pyh1a96a4e_0
-  - sqlite=3.44.0=hcfcfb64_0
-  - stack_data=0.6.2=pyhd8ed1ab_0
-  - tbb=2021.10.0=h91493d7_2
-  - tblib=2.0.0=pyhd8ed1ab_0
-  - terminado=0.17.0=pyh08f2357_0
-  - threadpoolctl=3.2.0=pyha21a80b_0
-  - tifffile=2020.6.3=py_0
-  - tiledb=2.16.3=h1ffc264_3
-  - tinycss2=1.2.1=pyhd8ed1ab_0
-  - tinynetrc=1.3.1=pyhd8ed1ab_0
-  - tk=8.6.13=h5226925_1
-  - tomli=2.0.1=pyhd8ed1ab_0
-  - toolz=0.12.0=pyhd8ed1ab_0
-  - tornado=6.3.3=py310h8d17308_1
-  - tqdm=4.66.1=pyhd8ed1ab_0
-  - traitlets=5.13.0=pyhd8ed1ab_0
-  - types-python-dateutil=2.8.19.14=pyhd8ed1ab_0
-  - typing_utils=0.1.0=pyhd8ed1ab_0
-  - tzdata=2023c=h71feb2d_0
-  - uc-micro-py=1.0.1=pyhd8ed1ab_0
-  - ucrt=10.0.22621.0=h57928b3_0
-  - unicodedata2=15.1.0=py310h8d17308_0
-  - uri-template=1.3.0=pyhd8ed1ab_0
-  - uriparser=0.9.7=h1537add_1
-  - urllib3=1.26.18=pyhd8ed1ab_0
-  - vc=14.3=h64f974e_17
-  - vc14_runtime=14.36.32532=hdcecf7f_17
-  - virtualenv=20.21.0=pyhd8ed1ab_0
-  - vs2015_runtime=14.36.32532=h05e6639_17
-  - wcwidth=0.2.9=pyhd8ed1ab_0
-  - webcolors=1.13=pyhd8ed1ab_0
-  - webencodings=0.5.1=pyhd8ed1ab_2
-  - wheel=0.41.3=pyhd8ed1ab_0
-  - widgetsnbextension=4.0.9=pyhd8ed1ab_0
-  - win_inet_pton=1.1.0=pyhd8ed1ab_6
-  - winpty=0.4.3=4
-  - wrapt=1.16.0=py310h8d17308_0
-  - xarray=2023.10.1=pyhd8ed1ab_0
-  - xerces-c=3.2.4=h63175ca_3
-  - xorg-libxau=1.0.11=hcd874cb_0
-  - xorg-libxdmcp=1.1.3=hcd874cb_0
-  - xyzservices=2023.10.1=pyhd8ed1ab_0
-  - xz=5.2.6=h8d14728_0
-  - y-py=0.5.5=py310h87d50f1_2
-  - yaml=0.2.5=h8ffe710_2
-  - yarl=1.9.2=py310h8d17308_1
-  - ypy-websocket=0.8.2=pyhd8ed1ab_0
-  - zarr=2.16.1=pyhd8ed1ab_0
-  - zeromq=4.3.4=h0e60522_1
-  - zict=3.0.0=pyhd8ed1ab_0
-  - zipp=3.17.0=pyhd8ed1ab_0
-  - zlib=1.2.13=hcfcfb64_5
-  - zstd=1.5.5=h12be248_0
-  - pip:
-      - attrs==25.3.0
-      - earthaccess==0.14.0
-      - h11==0.14.0
-      - importlib-resources==6.5.2
-      - outcome==1.3.0.post0
-      - python-cmr==0.13.0
-      - selenium==4.30.0
-      - trio==0.29.0
-      - trio-websocket==0.12.2
-      - typing-extensions==4.12.2
-      - websocket-client==1.8.0
-      - wsproto==1.2.0

utils/common_utils.py

@@ -5,7 +5,7 @@
 
 -------------------------------------------------------------------------------
 Authors: Hong Xie
-Last Updated: 2025-07-07
+Last Updated: 2025-09-11
 ===============================================================================
 """
 
@@ -256,10 +256,11 @@ def setup_logging(log_file: str = "dask_worker.log"):
 
     Parameters
     ----------
+
     log_file : str, optional
         日志文件路径, by default "dask_worker.log"
     """
-    
+
     logging.basicConfig(
         level=logging.INFO,
         format="%(levelname)s:%(asctime)s ||| %(message)s",
@@ -269,6 +270,7 @@ def setup_logging(log_file: str = "dask_worker.log"):
         ],
     )
 
+
 def load_band_as_arr(org_tif_path, band_num=1):
     """
     读取波段数据
@@ -410,15 +412,26 @@ def create_quality_mask(quality_data, bit_nums: list = [0, 1, 2, 3, 4, 5]):
 
 
 def clip_image(
-    image: xr.DataArray | xr.Dataset, roi: gpd.GeoDataFrame, clip_by_box=True
+    image: xr.DataArray | xr.Dataset, roi: gpd.GeoDataFrame = None, clip_by_box=True
-):
+) -> xr.DataArray | xr.Dataset | None:
     """
     Clip Image data to ROI.
 
-    args:
+    Parameters
-        image (xarray.DataArray | xarray.Dataset): 通过 rioxarray.open_rasterio 加载的图像数据.
+    ----------
-        roi (gpd.GeoDataFrame): 感兴趣区数据.
+
-        clip_by_box (bool): 是否使用 bbox 进行裁剪, 默认为 True.
+    image : xarray.DataArray | xarray.Dataset
+        通过 rioxarray.open_rasterio 加载的图像数据.
+    roi : gpd.GeoDataFrame, optional
+        感兴趣区数据.
+    clip_by_box : bool, optional
+        是否使用 bbox 进行裁剪, 默认为 True.
+
+    Returns
+    -------
+
+    xarray.DataArray | xarray.Dataset | None
+        裁剪后的图像数据. 若裁剪后数据全为无效值, 则返回 None.
     """
 
     if roi is None:
@@ -443,15 +456,25 @@ def clip_image(
     return image_cliped
 
 
-def clip_roi_image(file_path: str, grid: gpd.GeoDataFrame) -> xr.DataArray | None:
+def clip_roi_image(
+    file_path: str, grid: gpd.GeoDataFrame = None
+) -> xr.DataArray | None:
     """
     按研究区范围裁剪影像
 
-    args:
+    Parameters
-        file_path (str): 待裁剪影像路径
+    ----------
-        grid (gpd.GeoDataFrame): 格网范围
+
-    return:
+    file_path : str
-        raster_cliped (xr.DataArray): 裁剪后的影像
+        待裁剪影像路径
+    grid : gpd.GeoDataFrame, optional
+        格网范围, 默认为 None.
+
+    Returns
+    -------
+
+    raster_cliped : xr.DataArray
+        裁剪后的影像
     """
     raster = open_rasterio(file_path)
     try:
@@ -487,15 +510,27 @@ def reproject_image(
     target_crs: str = None,
     target_shape: tuple = None,
     target_image: xr.DataArray = None,
-):
+) -> xr.DataArray:
     """
     Reproject Image data to target CRS or target data.
 
-    args:
+    Parameters
-        image (xarray.DataArray): 通过 rioxarray.open_rasterio 加载的图像数据.
+    ----------
-        target_crs (str): Target CRS, eg. EPSG:4326.
+
-        target_shape (tuple): Target shape, eg. (1000, 1000).
+    image : xarray.DataArray
-        target_image (xarray.DataArray): Target image, eg. rioxarray.open_rasterio 加载的图像数据.
+        通过 rioxarray.open_rasterio 加载的图像数据.
+    target_crs : str, optional
+        Target CRS, eg. EPSG:4326.
+    target_shape : tuple, optional
+        Target shape, eg. (1000, 1000).
+    target_image : xarray.DataArray, optional
+        Target image, eg. rioxarray.open_rasterio 加载的图像数据.
+
+    Returns
+    -------
+
+    xarray.DataArray
+        重投影后的图像数据.
     """
     if target_image is not None:
         # 使用 target_image 进行重投影匹配
@@ -506,9 +541,9 @@ def reproject_image(
             target_image.shape[1] == image.shape[1]
             and target_image.shape[2] == image.shape[2]
         ):
-            # 若判断为降尺度/等尺度, 则直接使用 cubic_spline 重采样投影到目标影像
+            # 若判断为降尺度/等尺度, 则直接使用 cubic 双三次插值重采样投影到目标影像
             image_reprojed = image.rio.reproject_match(
-                target_image, resampling=Resampling.cubic_spline
+                target_image, resampling=Resampling.cubic
             )
         else:
             # print("target_image shape is not match with image shape", image.shape, "to", target_image.shape)
@@ -520,7 +555,7 @@ def reproject_image(
         # 使用 target_crs 进行重投影
         reproject_kwargs = {
             "dst_crs": target_crs,
-            "resampling": Resampling.cubic_spline,
+            "resampling": Resampling.cubic,
         }
         if target_shape is not None:
             reproject_kwargs["shape"] = target_shape
@@ -563,7 +598,7 @@ def mosaic_images(
             tif_list,
             nodata=nodata,
             method=method,
-            resampling=Resampling.cubic_spline,
+            resampling=Resampling.cubic,
         )
         # 将结果重新构建为 xarray 数据集
         # 单张SAR影像直接读取 transform: 233400.0 30.0 0.0 3463020.0 0.0 -30.0

utils/sr2rgb.py

@@ -0,0 +1,104 @@
+"""
+将 COG 格式的 Red, Green, Blue 单波段地表反射率图像合成为 RGB 图像
+
+1. 对比度线性拉伸至 0-255;
+2. 合并波段;
+3. 保存为 uint8 格式 RGB 图像;
+"""
+
+import os
+import xarray as xr
+from rioxarray import open_rasterio
+import numpy as np
+
+
+def sr2rgb(red_path: str, green_path: str, blue_path: str, output_path: str) -> None:
+    """
+    将红、绿、蓝三个单波段地表反射率图像合成为RGB图像
+
+    参数:
+        red_path (str): 红色波段文件路径
+        green_path (str): 绿色波段文件路径
+        blue_path (str): 蓝色波段文件路径
+        output_path (str): 输出RGB图像路径
+    """
+    # 检查文件是否存在
+    for path in [red_path, green_path, blue_path]:
+        if not os.path.exists(path):
+            raise FileNotFoundError(f"文件不存在: {path}")
+
+    # 读取三个波段数据
+    red_band = open_rasterio(red_path, masked=True).squeeze(dim="band", drop=True)
+    green_band = open_rasterio(green_path, masked=True).squeeze(dim="band", drop=True)
+    blue_band = open_rasterio(blue_path, masked=True).squeeze(dim="band", drop=True)
+    # 暂存元数据
+    y_coords = red_band.y
+    x_coords = red_band.x
+    crs = red_band.rio.crs
+    transform = red_band.rio.transform()
+
+    def stretch_band(band):
+        """
+        线性拉伸到0-255范围
+        """
+        # 处理NaN值与负值
+        band_no_nan = np.where(np.isnan(band), 0, band)
+        band_no_nan = np.where(band_no_nan < 0, 0, band_no_nan)
+        band_min = np.min(band_no_nan)
+        band_max = np.max(band_no_nan)
+        # 避免除零错误
+        if band_max == band_min:
+            stretched = np.zeros_like(band_no_nan, dtype=np.uint8)
+        else:
+            stretched = ((band_no_nan - band_min) / (band_max - band_min) * 255).astype(
+                np.uint8
+            )
+        return stretched
+
+    red_stretched = stretch_band(red_band.values)
+    green_stretched = stretch_band(green_band.values)
+    blue_stretched = stretch_band(blue_band.values)
+    # 合并三个波段为RGB图像
+    rgb_array = xr.DataArray(
+        np.dstack((red_stretched, green_stretched, blue_stretched)),
+        dims=("y", "x", "band"),
+        coords={"band": [1, 2, 3], "y": y_coords, "x": x_coords},
+    )
+    # 转置维度顺序以符合rioxarray要求
+    rgb_array = rgb_array.transpose("band", "y", "x")
+    # 写入元数据
+    rgb_array.rio.write_crs(crs, inplace=True)
+    rgb_array.rio.write_transform(transform, inplace=True)
+    rgb_array.rio.write_nodata(0, inplace=True)
+    # 保存为TIFF文件
+    rgb_array.rio.to_raster(output_path, dtype="uint8")
+    print(f"RGB图像已保存到: {output_path}")
+    return
+
+
+if __name__ == "__main__":
+    # tif_dir = "D:\\NASA_EarthData_Script\\data\\HLS\\2024\\2024012"
+    # red_path = os.path.join(
+    #     tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.RED.subset.tif"
+    # )
+    # green_path = os.path.join(
+    #     tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.GREEN.subset.tif"
+    # )
+    # blue_path = os.path.join(
+    #     tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.BLUE.subset.tif"
+    # )
+    # output_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2024012T031101.v2.0.RGB.tif")
+
+    tif_dir = "D:\\NASA_EarthData_Script\\data\\HLS\\2025\\2025011"
+    red_path = os.path.join(
+        tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.RED.subset.tif"
+    )
+    green_path = os.path.join(
+        tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.GREEN.subset.tif"
+    )
+    blue_path = os.path.join(
+        tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.BLUE.subset.tif"
+    )
+    output_path = os.path.join(tif_dir, "HLS.S30.T49RGP.2025011T031009.v2.0.RGB.tif")
+
+    sr2rgb(red_path, green_path, blue_path, output_path)