GDAL：重新投影netCDF文件

Question

我正在尝试使用GDAL将netCDF文件转换为EPSG：3857，以便与Mapbox一起使用。这将是.nc到.nc的转换。不光栅。我愿意使用GDAL或其他方法来执行此操作。必须将这些数据重新投影到控制台应用程序之前-这个过程要花几周时间才能找到解决方案-我认为这很简单。

我正在为卫星数据着色。有3个.nc文件（蓝色，红色和红外）经过组合和处理后可以创建彩色图像。从Amazon AWS下载3个文件后，一个python控制台应用程序进行处理并将.jpg转储到同一文件夹。该应用程序的源代码为Located here so you may validate the data。 （因为文件是超高分辨率，所以速度很慢。）

我尝试过的代码是：

gdalwarp -t_srs EPSG:3857 test.nc test-projected.nc

但是，尝试了其他几种变体，但没有任何效果。

我不是这个专业人士，但是我是否应该使用gdalwarp来做到这一点？我只想更改投影-别无其他，因此python应用仍然可以处理数据。它必须能够使用重新投影的文件来创建.jpg。

以下链接是需要转换的数据的示例：

.nc file on AWS > Color Channel 1 (Blue 1km resolution)

.nc file on AWS > Color Channel 2 (Red, Higher 0.5km resolution & larger file size)

.nc file on AWS > Color Channel 3 (Infrared - serves as green)

此外，在线其他人已经通过https://github.com/blaylockbk/pyBKB_v2/tree/master/BB_GOES16上的pyproj模块使用类似的投影来完成此操作。 （与Mapbox一起使用时，我的必须为EPSG：3857）。如果将python代码修改为一次性完成所有操作，那也将很棒。我将赏金作为最后的希望。

我不了解python，所以我一直在尝试GDAL，但是添加到我的源代码中以达到预期结果（或工作GDAL脚本）的有效python代码将获得赏金。

Answer 1

这是我的解决方法：

# -*- coding: utf-8 -*-
"""
Created on Mon Mar  4 17:39:45 2019

@author: Guy Serbin
"""

import os, sys, glob, argparse
from osgeo import gdal, osr
from scipy.misc import imresize

parser = argparse.ArgumentParser(description = 'Script to create CONUS true color image from GOES 16 L1b data.')
parser.add_argument('-i', '--indir', type = str, default = r'C:\Data\Freelancer\DavidHolcomb', help = 'Input directory name.')
parser.add_argument('-o', '--outdir', type = str, default = None, help = 'Output directory name.')
parser.add_argument('-p', '--proj', type = int, default = 3857, help = 'Output projection, must be EPSG number.')
args = parser.parse_args()

if not args.indir:
    print('ERROR: --indir not set. exiting.')
    sys.exit()
elif not os.path.isdir(args.indir):
    print('ERROR: --indir not set to a valid directory path. exiting.')
    sys.exit()

if not args.outdir:
    print('WARNING: --outdir not set. Output will be written to --indir.')
    args.outdir = args.indir

o_srs = osr.SpatialReference()
o_srs.ImportFromEPSG(args.proj)


# based upon code ripped from https://riptutorial.com/gdal/example/25859/read-a-netcdf-file---nc--with-python-gdal

# Path of netCDF file
netcdf_red = glob.glob(os.path.join(args.indir, 'OR_ABI-L1b-RadC-M3C02_G16_s*.nc'))[0]
netcdf_green = glob.glob(os.path.join(args.indir, 'OR_ABI-L1b-RadC-M3C03_G16_s*.nc'))[0]
netcdf_blue = glob.glob(os.path.join(args.indir, 'OR_ABI-L1b-RadC-M3C01_G16_s*.nc'))[0]
baselist = os.path.basename(netcdf_blue).split('_')

outputfilename = os.path.join(args.outdir, 'OR_ABI-L1b-RadC-M3TrueColor_1_G16_{}.tif'.format(baselist[3]))
print('Output file will be: {}'.format(outputfilename))
tempfile = os.path.join(args.outdir, 'temp.tif')

# Specify the layer name to read
layer_name = "Rad"

# Open netcdf file.nc with gdal
print('Opening red band file: {}'.format(netcdf_red))
dsR = gdal.Open("NETCDF:{0}:{1}".format(netcdf_red, layer_name))
print('Opening green band file: {}'.format(netcdf_green))
dsG = gdal.Open("NETCDF:{0}:{1}".format(netcdf_green, layer_name))
print('Opening blue band file: {}'.format(netcdf_blue))
dsB = gdal.Open("NETCDF:{0}:{1}".format(netcdf_blue, layer_name))
red_srs = osr.SpatialReference()
red_srs.ImportFromWkt(dsR.GetProjectionRef())
i_srs = osr.SpatialReference()
i_srs.ImportFromWkt(dsG.GetProjectionRef())
GeoT = dsG.GetGeoTransform()
print(i_srs.ExportToWkt())
red_transform = osr.CoordinateTransformation(red_srs, o_srs)
transform = osr.CoordinateTransformation(i_srs, o_srs)

# Read full data from netcdf

print('Reading red band into memory.')
red = dsR.ReadAsArray(0, 0, dsR.RasterXSize, dsR.RasterYSize)
print('Resizing red band to match green and blue bands.')
red = imresize(red, 50, interp = 'bicubic')
print('Reading green band into memory.')
green = dsG.ReadAsArray(0, 0, dsG.RasterXSize, dsG.RasterYSize)
print('Reading blue band into memory.')
blue = dsB.ReadAsArray(0, 0, dsB.RasterXSize, dsB.RasterYSize)
red[red < 0] = 0
green[green < 0] = 0
blue[blue < 0] = 0

# Stack data and output
print('Stacking data.')
driver = gdal.GetDriverByName('GTiff')
stack = driver.Create('/vsimem/stack.tif', dsB.RasterXSize, dsB.RasterYSize, 3, gdal.GDT_Int16)
stack.SetProjection(i_srs.ExportToWkt())
stack.SetGeoTransform(GeoT)
stack.GetRasterBand(1).WriteArray(red)
stack.GetRasterBand(2).WriteArray(green)
stack.GetRasterBand(3).WriteArray(blue)
print('Warping data to new projection.')
warped = gdal.Warp('/vsimem/warped.tif', stack, dstSRS = o_srs, outputType = gdal.GDT_Int16)

print('Writing output to disk.')

outRaster = gdal.Translate(outputfilename, '/vsimem/warped.tif')

outRaster = None
red = None
green = None
blue = None
tmp_ds = None
dsR = None
dsG = None
dsB = None

print('Processing complete.')

Answer 2

您可以使用rioxarray来执行此操作。这样做的示例如下：https://corteva.github.io/rioxarray/html/examples/reproject.html

以下是针对您的用例的示例：

import rioxarray

xds = rioxarray.open_rasterio("OR_ABI-L1b-RadC-M3C01_G16_s20190621802131_e20190621804504_c20190621804546.nc")

<xarray.Dataset>
Dimensions:      (band: 1, x: 5000, y: 3000)
Coordinates:
  * y            (y) float64 1.584e+06 1.585e+06 ... 4.588e+06 4.589e+06
  * x            (x) float64 -3.627e+06 -3.626e+06 ... 1.381e+06 1.382e+06
  * band         (band) int64 1
    spatial_ref  int64 0
Data variables:
    Rad          (band, y, x) int16 ...
    DQF          (band, y, x) int8 ...

xds.rio.crs

CRS.from_wkt('PROJCS["unnamed",GEOGCS["unknown",DATUM["unnamed",SPHEROID["Spheroid",6378137,298.2572221]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]]],PROJECTION["Geostationary_Satellite"],PARAMETER["central_meridian",-75],PARAMETER["satellite_height",35786023],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],EXTENSION["PROJ4","+proj=geos +lon_0=-75 +h=35786023 +x_0=0 +y_0=0 +ellps=GRS80 +units=m +no_defs +sweep=x"]]')

然后重新投影：

xds_3857 = xds.rio.reproject("epsg:3857")

<xarray.Dataset>
Dimensions:      (band: 1, x: 7693, y: 4242)
Coordinates:
  * x            (x) float64 -1.691e+07 -1.691e+07 ... -5.892e+06 -5.891e+06
  * y            (y) float64 7.714e+06 7.712e+06 ... 1.641e+06 1.64e+06
  * band         (band) int64 1
    spatial_ref  int64 0
Data variables:
    Rad          (band, y, x) int16 1023 1023 1023 1023 ... 1023 1023 1023 1023
    DQF          (band, y, x) int8 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0
Attributes:
    creation_date:  2019-09-25 01:02:54.590053

xds_3857.rio.crs

CRS.from_epsg(3857)

写入netcdf：

xds_3857.to_netcdf("epsg3857.nc")