如何将 netCDF4 文件转换为 geoTiff

Question

I'm currently trying to get Tropomi data in geoTiff format. I downloaded some data in netCDF4 format. This way I obtain three numpy arrays. one with latitude coordinates, one with longitude coordinates and one with carbon-mono-oxide values.

So I have a matrix with values for my raster and of each value I know the longitude and latitude of that respective value.

With this information how can I construct a georeferenced raster?

I read in the data as follows import netCDF4 from netCDF4 import Dataset import numpy as np

file = '/home/daniel/Downloads/S5P_NRTI_L2__CO_____20190430T171319_20190430T171819_08006_01_010301_20190430T175151.nc'

rootgrp = Dataset(file, "r",format="NETCDF4")

lat = rootgrp.groups['PRODUCT']['latitude'][:] 
lon = rootgrp.groups['PRODUCT']['longitude'][:]
carbon = rootgrp.groups['PRODUCT']['carbonmonoxide_total_column'][:]

obtaining 3 matrices with shape (1,290,215)

Now I would like to convert this to a Mercator projected geoTIFF, but I do not know how to go about it.

Answer 1

the gdal_translate option seems to work. But here is an alternative explicit way I did it.

#importing packages
import numpy as np
from scipy import interpolate
from netCDF4 import Dataset
from shapely.geometry import Point
import geopandas as gpd
from geopy.distance import geodesic
import rasterio
import matplotlib.pyplot as plt

#load data 
file = '/home/daniel/Ellipsis/db/downloaded/rawtropomi/S5P_NRTI_L2__CO_____20190430T171319_20190430T171819_08006_01_010301_20190430T175151.nc'
rootgrp = Dataset(file, "r",format="NETCDF4")
lat = rootgrp.groups['PRODUCT']['latitude'][:]
lon = rootgrp.groups['PRODUCT']['longitude'][:]
carbon = rootgrp.groups['PRODUCT']['carbonmonoxide_total_column'][:]
carbon = carbon.filled(0)
lat = lat.filled(-1000)
lon = lon.filled(-1000)

carbon = carbon.flatten()
lat = lat.flatten()
lon = lon.flatten()

#calculate the real distance between corners and get the widht and height in pixels assuming you want a pixel resolution of at least 7 by 7 kilometers
w = max(geodesic((min(lat),max(lon)), (min(lat),min(lon))).meters/7000 , geodesic((max(lat),max(lon)), (max(lat),min(lon))).meters/14000)
h = geodesic((min(lat),max(lon)), (max(lat),max(lon))).meters/14000

# create a geopandas with as its rows the latitude, longitude an the measrument values. transfrom it to the webmercator projection (or projection of your choosing)
points = [Point(xy) for xy in zip(lon, lat)]
crs = {'init': 'epsg:4326'}
data = gpd.GeoDataFrame({'value':carbon}, crs=crs, geometry=points)
data = data.to_crs({'init': 'epsg:3395'})
data['lon'] = data.bounds['maxx'].values
data['lat'] = data.bounds['maxy'].values

#make grid of coordinates. You nee de calculate the coordinate of each pixel in the desired raster
minlon = min(data['lon'])
maxlon = max(data['lon'])
minlat = min(data['lat'])
maxlat = max(data['lat'])

lon_list = np.arange(minlon, maxlon, (maxlon-minlon)/w )
lat_list = np.arange(minlat, maxlat, (maxlat-minlat)/h)

lon_2d, lat_2d = np.meshgrid(lon_list, lat_list)



#use the values in the geopandas dataframe to interpolate values int the coordinate raster
r = interpolate.griddata(points = (data['lon'].values,data['lat'].values), values = data['value'].values, xi = (lon_2d, lat_2d))
r = np.flip(r, axis = 0)

#check result
plt.imshow(r)


#save raster
transform = rasterio.transform.from_bounds(south = minlat, east = maxlon, north =     maxlat, west = minlon, width = r.shape[1], height = r.shape[2]   )
file_out = 'test.tiff'
new_dataset = rasterio.open(file_out , 'w', driver='Gtiff', compress='lzw',
                                    height = r.shape[1], width = r.shape[2],
                                    count= r.shape[0], dtype=str( r.dtype),
                                    crs=   data.crs,
                                    transform= transform)
new_dataset.write(r)
new_dataset.close()

Answer 2

I would suggest looking at this answer here using gdal_translate:

Convert NetCDF (.nc) to GEOTIFF

gdal_translate -of GTiff file.nc test.tiff