669 lines
21 KiB
Python
669 lines
21 KiB
Python
# -*- coding: utf-8 -*-
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"""
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===============================================================================
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HLS Subsetting, Processing, and Exporting Reformatted Data Prep Script
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Authors: Cole Krehbiel, Mahsa Jami, and Erik Bolch
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Contact: lpdaac@usgs.gov
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Editor: Hong Xie
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Last Updated: 2025-01-06
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===============================================================================
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"""
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# Possible Future Improvements:
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# TODO Improve CF-1.6 NetCDF Compliance
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# TODO Improve behavior around deletion of cogs when a netcdf is requested
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# TODO Add ZARR as output option
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import argparse
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import sys
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import os
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import shutil
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import logging
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import time
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import json
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import earthaccess
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from shapely.geometry import polygon, box
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import geopandas as gpd
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from datetime import datetime as dt
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import dask.distributed
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from HLS_Su import hls_search
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from HLS_PER import process_granule, create_timeseries_dataset
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def parse_arguments():
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"""
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Function to parse command line input arguments.
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"""
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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description="Performs Spatial/Temporal/Band Subsetting, Processing, and Customized Exporting for HLS V2.0 files",
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)
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# roi: Region of interest as shapefile, geojson, or comma separated LL Lon, LL Lat, UR Lon, UR Lat
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parser.add_argument(
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"-roi",
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type=str,
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required=True,
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help="(Required) Region of Interest (ROI) for spatial subset. \
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Valid inputs are: (1) a geojson or shapefile (absolute path to file required if not in same directory as this script), or \
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(2) bounding box coordinates: 'LowerLeft_lon,LowerLeft_lat,UpperRight_lon,UpperRight_lat'\
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NOTE: Negative coordinates MUST be written in single quotation marks '-120,43,-118,48'\
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NOTE 2: If providing an absolute path with spaces in directory names, please use double quotation marks "
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" ",
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)
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# (Add) clip: 裁剪参数, 默认为 False, 表示不裁剪, 如果为 True, 则表示裁剪
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parser.add_argument(
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"-clip",
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choices=["True", "False"],
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required=False,
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help="(Optional) If provided, the script will clip the output to the ROI.",
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default="False",
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)
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# (Add) tile: HLS 的瓦片 ID
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parser.add_argument(
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"-tile",
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type=str,
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required=False,
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help="(Optional) Tile ID for spatial subset. If provided, the script will search for the tile ID based on the ROI.",
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)
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# dir: Directory to save the files to
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parser.add_argument(
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"-dir",
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required=False,
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help="Directory to export output HLS files to.",
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default=os.getcwd(),
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)
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# start: Start Date
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parser.add_argument(
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"-start",
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required=False,
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help="Start date for time period of interest: valid format is yyyy-mm-dd (e.g. 2020-10-20).",
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default="2014-04-03",
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)
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# end: End Date
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parser.add_argument(
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"-end",
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required=False,
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help="Start date for time period of interest: valid format is yyyy-mm-dd (e.g. 2022-10-24).",
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default=dt.today().strftime("%Y-%m-%d"),
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)
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# prod: product(s) desired to be downloaded
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parser.add_argument(
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"-prod",
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choices=["HLSS30", "HLSL30", "both"],
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required=False,
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help="Desired product(s) to be subset and processed.",
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default="both",
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)
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# layers: layers desired to be processed within the products selected
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parser.add_argument(
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"-bands",
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required=False,
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help="Desired layers to be processed. Valid inputs are ALL, COASTAL-AEROSOL, BLUE, GREEN, RED, RED-EDGE1, RED-EDGE2, RED-EDGE3, NIR1, SWIR1, SWIR2, CIRRUS, TIR1, TIR2, WATER-VAPOR, FMASK, VZA, VAA, SZA, SAA. To request multiple layers, provide them in comma separated format with no spaces. Unsure of the names for your bands?--check out the README which contains a table of all bands and band names.",
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default="ALL",
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)
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# cc: maximum cloud cover (%) allowed to be returned (by scene)
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parser.add_argument(
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"-cc",
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required=False,
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help="Maximum (scene-level) cloud cover (percent) allowed for returned observations (e.g. 35). Valid range: 0 to 100 (integers only)",
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default="100",
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)
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# qf: quality filter flag: filter out poor quality data yes/no
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parser.add_argument(
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"-qf",
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choices=["True", "False"],
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required=False,
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help="Flag to quality filter before exporting output files (see README for quality filtering performed).",
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default="True",
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)
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# sf: scale factor flag: Scale data or leave unscaled yes/no
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parser.add_argument(
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"-scale",
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choices=["True", "False"],
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required=False,
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help="Flag to apply scale factor to layers before exporting output files. This is generally unecessary as most applications will scale automatically.",
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default="True",
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)
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# of: output file format
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parser.add_argument(
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"-of",
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choices=["COG", "NC4", "ZARR"],
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required=False,
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help="Define the desired output file format",
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default="COG",
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)
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# chunksize: chunk size for processing with dask
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parser.add_argument(
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"-cs",
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type=str,
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help="Chunksize for processing scenes with dask in format 'band,x,y'. This is used to provide chunk_size argument to rioxarray.open_rasterio to improve processing speed.\
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For example: '1,512,512' (native hls chunk size) provides better performance for ROIs that fall within a single scene, while '1,3600,3600' (full HLS scene) provides better performance for \
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larger ROIs that span multiple scenes. The default is '1,512,512', but this can lead to a very large task list for large ROIs.",
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default="1,512,512",
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)
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# logfile: Optional logfile path
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parser.add_argument(
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"-logfile",
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required=False,
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help="Optional path to output logfile. If not provided, logging will only be to the console.",
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)
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return parser.parse_args()
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def format_roi(roi):
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"""
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Determines if submitted ROI is a file or bbox coordinates.
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If a file, opens a GeoJSON or shapefile and creates a list of polygon vertices in the correct order. If the file has multiple polygons it will use a unary union convex hull of the external bounds.
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If bbox coordinates, creates a geodataframe with a single Polygon geometry.
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Returns a geopandas dataframe for clipping and a list of vertices for searching.
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"""
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if os.path.isfile(roi): # and roi.endswith(("geojson", "shp")):
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print(roi)
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try:
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# Open ROI if file
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roi = gpd.read_file(roi)
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if len(roi) > 1:
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# Merge all Polygon geometries and create external boundary
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logging.info(
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"Multiple polygons detected. Creating single geometry of external coordinates."
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)
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single_geometry = roi.unary_union.convex_hull
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roi = gpd.GeoDataFrame(geometry=[single_geometry], crs=roi.crs)
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logging.info(roi)
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# Check if ROI is in Geographic CRS, if not, convert to it
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if roi.crs.is_geographic:
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# List Vertices in correct order for search
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vertices_list = list(roi.geometry[0].exterior.coords)
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else:
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roi_geographic = roi.to_crs("EPSG:4326")
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logging.info(
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"Note: ROI submitted is being converted to Geographic CRS (EPSG:4326)"
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)
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vertices_list = list(roi_geographic.geometry[0].exterior.coords)
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except (FileNotFoundError, ValueError):
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sys.exit(
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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()}"
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)
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else:
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# If bbox coordinates are submitted
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bbox = tuple(map(float, roi.strip("'\"").split(",")))
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print(bbox)
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# Convert bbox to a geodataframe for clipping
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roi = gpd.GeoDataFrame(geometry=[box(*bbox)], crs="EPSG:4326")
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vertices_list = list(roi.geometry[0].exterior.coords)
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return (roi, vertices_list)
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def format_dates(start, end):
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# Strip Quotes
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start = start.strip("'").strip('"')
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end = end.strip("'").strip('"')
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# Convert to datetime
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try:
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start = dt.strptime(start, "%Y-%m-%d")
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end = dt.strptime(end, "%Y-%m-%d")
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except ValueError:
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sys.exit(
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"A date format is not valid. The valid format is ISO 8601: YYYY-MM-DD (e.g. 2020-10-20)"
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)
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if start > end:
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sys.exit(
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f"The Start Date requested: {start} is after the End Date Requested: {end}."
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)
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else:
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dates = (start.strftime("%Y-%m-%d"), end.strftime("%Y-%m-%d"))
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return dates
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def format_tile_id(tile_id):
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"""
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(Add) 格式化tile_id参数
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"""
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tile_id = tile_id.strip("'").strip('"')
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return str(tile_id)
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def format_cloud_cover(cc):
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try:
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cc = int(cc.strip("'").strip('"'))
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except ValueError:
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sys.exit(
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f"{cc} is not a valid input for filtering by cloud cover (e.g. 35). Valid range: 0 to 100 (integers only)"
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)
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# Validate that cc is in the valid range (0-100)
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if cc < 0 or cc > 100:
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sys.exit(
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f"{cc} is not a valid input option for filtering by cloud cover (e.g. 35). Valid range: 0 to 100 (integers only)"
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)
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return cc
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def str_to_bool(value):
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"""
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Converts a string to a boolean.
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Accepts 'True', 'true', '1' as True.
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Accepts 'False', 'false', '0' as False.
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"""
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if isinstance(value, str):
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if value.lower() in ("true", "1"):
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return True
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elif value.lower() in ("false", "0"):
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return False
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raise ValueError(f"Cannot convert {value} to boolean.")
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def create_band_dict(prod, bands):
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"""
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Creates a dictionary of bands and common band names for each collection requested.
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"""
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shortname = {"HLSS30": "HLSS30.v2.0", "HLSL30": "HLSL30.v2.0"}
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# Create a dictionary with product name and shortname
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if prod == "both":
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prods = shortname
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else:
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prods = {prod: shortname[prod]}
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# Strip spacing, quotes, make all upper case and create a list
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bands = bands.strip(" ").strip("'").strip('"').upper()
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band_list = bands.split(",")
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# Create a LUT dict including the HLS product bands mapped to names
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lut = {
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"HLSS30": {
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"COASTAL-AEROSOL": "B01",
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"BLUE": "B02",
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"GREEN": "B03",
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"RED": "B04",
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"RED-EDGE1": "B05",
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"RED-EDGE2": "B06",
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"RED-EDGE3": "B07",
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"NIR-Broad": "B08",
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"NIR1": "B8A",
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"WATER-VAPOR": "B09",
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"CIRRUS": "B10",
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"SWIR1": "B11",
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"SWIR2": "B12",
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"FMASK": "Fmask",
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"VZA": "VZA",
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"VAA": "VAA",
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"SZA": "SZA",
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"SAA": "SAA",
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},
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"HLSL30": {
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"COASTAL-AEROSOL": "B01",
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"BLUE": "B02",
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"GREEN": "B03",
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"RED": "B04",
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"NIR1": "B05",
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"SWIR1": "B06",
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"SWIR2": "B07",
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"CIRRUS": "B09",
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"TIR1": "B10",
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"TIR2": "B11",
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"FMASK": "Fmask",
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"VZA": "VZA",
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"VAA": "VAA",
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"SZA": "SZA",
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"SAA": "SAA",
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},
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}
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# List of all available/acceptable band names
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all_bands = [
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"ALL",
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"COASTAL-AEROSOL",
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"BLUE",
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"GREEN",
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"RED",
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"RED-EDGE1",
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"RED-EDGE2",
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"RED-EDGE3",
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"NIR1",
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"SWIR1",
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"SWIR2",
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"CIRRUS",
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"TIR1",
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"TIR2",
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"WATER-VAPOR",
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"FMASK",
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"VZA",
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"VAA",
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"SZA",
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"SAA",
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]
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# Validate that bands are named correctly
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for b in band_list:
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if b not in all_bands:
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sys.exit(
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f"Band: {b} is not a valid input option. Valid inputs are {all_bands}. To request multiple layers, provide them in comma separated format with no spaces. Unsure of the names for your bands?--check out the README which contains a table of all bands and band names."
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)
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# Set up a dictionary of band names and numbers by product
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band_dict = {}
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for p in prods:
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band_dict[p] = {}
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for b in band_list:
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if b == "ALL":
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band_dict[p] = lut[p]
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else:
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try:
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band_dict[p][b] = lut[p][b]
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except ValueError:
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print(f"Product {p} does not contain band {b}")
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return band_dict
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def format_chunksize(chunksize):
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"""
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Converts comma-separated chunksize string to dictionary.
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"""
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keys = ["band", "x", "y"]
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values = list(map(int, chunksize.strip("'\"").split(",")))
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if len(values) != len(keys):
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raise ValueError(
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"Chunksize must provide band, x and y (3) values separated by commas."
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)
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return dict(zip(keys, values))
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def confirm_action(prompt):
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"""
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Prompts the user to confirm an action.
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"""
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while True:
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response = input(prompt).lower()
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if response in ["y", "yes"]:
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return True
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elif response in ["n", "no"]:
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return False
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else:
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print("Invalid input. Please enter 'y' or 'n'.")
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def setup_dask_environment():
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"""
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Passes RIO environment variables to dask workers for authentication.
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"""
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import os
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import rasterio
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cookie_file_path = os.path.expanduser("~/cookies.txt")
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global env
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gdal_config = {
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"GDAL_HTTP_UNSAFESSL": "YES",
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"GDAL_HTTP_COOKIEFILE": cookie_file_path,
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"GDAL_HTTP_COOKIEJAR": cookie_file_path,
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"GDAL_DISABLE_READDIR_ON_OPEN": "YES",
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"CPL_VSIL_CURL_ALLOWED_EXTENSIONS": "TIF",
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"GDAL_HTTP_MAX_RETRY": "10",
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"GDAL_HTTP_RETRY_DELAY": "0.5",
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"GDAL_HTTP_TIMEOUT": "300",
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}
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env = rasterio.Env(**gdal_config)
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env.__enter__()
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def files_collection(output_dir):
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"""
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将已下载的HLS影像数据按照文件名中的日期进行归档
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"""
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# 获取当前目录下的所有文件
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files = os.listdir(output_dir)
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# 遍历所有文件
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for file in files:
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# 检查文件是否为tif格式
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if file.endswith(".tif"):
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# 提取文件名中的日期
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doy = file.split(".")[3][:7]
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# 构建目标目录路径
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target_dir = os.path.join(output_dir, doy)
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# 如果目标目录不存在，则创建它
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if not os.path.exists(target_dir):
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os.makedirs(target_dir)
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# 移动文件到目标目录
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source_path = os.path.join(output_dir, file)
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target_path = os.path.join(target_dir, file)
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shutil.move(source_path, target_path)
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def main():
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"""
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Main function to run the HLS SuPER script.
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"""
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# Parse arguments
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args = parse_arguments()
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# Configure logging
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log_handlers = [logging.StreamHandler(sys.stdout)]
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if args.logfile:
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log_handlers.append(logging.FileHandler(args.logfile))
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logging.basicConfig(
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level=logging.INFO,
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format="%(levelname)s:%(asctime)s ||| %(message)s",
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handlers=log_handlers,
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)
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# Handle Login Credentials with earthaccess
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earthaccess.login(persist=True)
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# Start Log
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logging.info("HLS SuPER script started")
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# Format ROI
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roi, vl = format_roi(args.roi)
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logging.info("Region of Interest formatted successfully")
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# (Add) 格式化 clip 参数
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clip = str_to_bool(args.clip)
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logging.info(f"Clip to ROI: {clip}")
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|
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# Set Output Directory
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if args.dir is not None:
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output_dir = os.path.normpath(args.dir.strip("'").strip('"')) + os.sep
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else:
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# Defaults to the current directory
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output_dir = os.getcwd() + os.sep
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logging.info(f"Output directory set to: {output_dir}")
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# Format/Validate Dates
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dates = format_dates(args.start, args.end)
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logging.info(f"Date Parameters: {dates}")
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# Create Product/Band Dictionary
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band_dict = create_band_dict(args.prod, args.bands)
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logging.info(f"Products/Bands Selected: {band_dict}")
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# Format Cloud Cover
|
|
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}")
|
|
|
|
# Scale Factor
|
|
scale = str_to_bool(args.scale)
|
|
logging.info(f"Apply Scale Factor: {scale}")
|
|
|
|
# Chunk Size
|
|
chunk_size = format_chunksize(args.cs)
|
|
logging.info(f"Chunk Size: {chunk_size}")
|
|
|
|
# Output File Type
|
|
if args.of not in ["COG", "NC4"]:
|
|
sys.exit(
|
|
f"Output format {args.of} is not a valid output format. Please choose from 'COG', 'NC4'."
|
|
)
|
|
|
|
logging.info(f"Output format: {args.of}")
|
|
|
|
# Search for Data and Save Results
|
|
results_urls_file = os.path.join(output_dir, "hls_super_results_urls.json")
|
|
use_existing_file = False
|
|
|
|
if os.path.isfile(results_urls_file):
|
|
logging.info(f"Results url list already exists in {output_dir}.")
|
|
# Confirm if user wants to use existing file.
|
|
if confirm_action(
|
|
f"Do you want to use the existing results file ({results_urls_file})? (y/n)"
|
|
):
|
|
use_existing_file = True
|
|
|
|
else:
|
|
if not confirm_action(
|
|
"Do you want to overwrite the existing results file? (y/n)"
|
|
):
|
|
sys.exit(
|
|
f"Processing aborted. Please move, rename, or remove existing file: {results_urls_file}."
|
|
)
|
|
|
|
if use_existing_file:
|
|
logging.info("Using existing results file.")
|
|
with open(results_urls_file, "r") as file:
|
|
results_urls = json.load(file)
|
|
|
|
else:
|
|
logging.info("Searching for data...")
|
|
results_urls = hls_search(
|
|
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:
|
|
filter_descriptions.append("cloud")
|
|
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:
|
|
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)"):
|
|
sys.exit("Processing aborted.")
|
|
|
|
# Initialize Dask Cluster
|
|
client = dask.distributed.Client()
|
|
|
|
# Setup Dask Environment (GDAL Configs)
|
|
client.run(setup_dask_environment)
|
|
|
|
logging.info(
|
|
f"Dask environment setup successfully. View dashboard: {client.dashboard_link}."
|
|
)
|
|
|
|
# Scatter Results Results url
|
|
client.scatter(results_urls)
|
|
|
|
# If NC4, create a temporary directory to store COGs
|
|
if args.of == "NC4":
|
|
cog_dir = os.path.join(output_dir, "temp")
|
|
if not os.path.exists(cog_dir):
|
|
os.makedirs(cog_dir, exist_ok=True)
|
|
else:
|
|
if not confirm_action(
|
|
"Temporary directory to store COGs already exists. Use these files to create NC4 outputs? (y/n)"
|
|
):
|
|
sys.exit(
|
|
f"Processing aborted. Please remove existing directory: {cog_dir}."
|
|
)
|
|
|
|
else:
|
|
cog_dir = output_dir
|
|
|
|
# Process Granules
|
|
start_time = time.time()
|
|
logging.info("Processing...")
|
|
tasks = [
|
|
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=[1, 3],
|
|
chunk_size=chunk_size,
|
|
)
|
|
for granule_url in results_urls
|
|
]
|
|
dask.compute(*tasks)
|
|
|
|
# Create Timeseries Dataset if NC4
|
|
if args.of == "NC4":
|
|
logging.info("Creating timeseries dataset...")
|
|
create_timeseries_dataset(cog_dir, output_type=args.of, output_dir=output_dir)
|
|
|
|
# Close Dask Client
|
|
client.close()
|
|
|
|
# Remove Temporary COGs if NC4
|
|
if args.of == "NC4":
|
|
logging.info("Timeseries Dataset Created. Removing Temporary Files...")
|
|
shutil.rmtree(cog_dir)
|
|
|
|
# (Add) 下载影像数据按照DOY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!")
|
|
|
|
# End Timer
|
|
total_time = time.time() - start_time
|
|
logging.info(f"Processing complete. Total time: {round(total_time,2)}s, ")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|