Get nearest pixel value from satellite image using latitude longitude coordinates

2
votes

I have a satellite image file. Loaded into dask array. I want to get pixel value (nearest) of a latitude, longitude of interest.

Satellite image is in GEOS projection. I have longitude and latitude information as 2D numpy arrays.

Satellite Image file

I have loaded it into a dask data array

from satpy import Scene
import matplotlib as plt
import os

cwd = os.getcwd()

fn = os.path.join(cwd, 'EUMETSAT_data/1Jan21/MSG1-SEVI-MSG15-0100-NA-20210101185741.815000000Z-20210101185757-1479430.nat')

files = [fn]

scn = Scene(filenames=files, reader='seviri_l1b_native')
scn.load(["VIS006"])
da = scn['VIS006']

This is what the dask array looks like: enter image description here enter image description here

I read lon lats from the area attribute with the help of satpy:

lon, lat = scn['VIS006'].attrs['area'].get_lonlats()
print(lon.shape)
print(lat.shape)

(1179, 808)
(1179, 808)

I get a 2d numpy array each, for longitude and latitude that are coordinates but I can not use them for slicing or selecting.

What is the best practice/method to get nearest lat long, pixel information? How do I project the data onto lat long coordinates that I can then use for indexing to arrive at the pixel value.

At the end, I want to get pixel value (nearest) of lat long of interest.

Thanks in advance!!!

3
pythongisnetcdfpython-xarraysatellite-image
You have the proj parameters of the GEOS projection used by the satellite image. I would do the opposite: convert you lat lon points into that geos projection. From there a simple rounding will give you the nearest pixel(s). – Serge Ballesta

3 Answers

3
votes

The AreaDefinition object you are using (.attrs['area']) has a few methods for getting different coordinate information.

area = scn['VIS006'].attrs['area']
col_idx, row_idx = area.get_xy_from_lonlat(lons, lats)

scn['VIS006'].values[row_idx, col_idx]

Note that row and column are flipped. The get_xy_from_lonlat method should work for arrays or scalars.

There are other methods for getting X/Y coordinates of each pixel if that is what you're interesting in.

1
votes

You can find the location with following:

import numpy as np
px,py = (23.0,55.0) # some location to take out values:

dist = np.sqrt(np.cos(lat*np.pi/180.0)*(lon-px)**2+(lat-py)**2); # this is the distance matrix from point (px,py)
kkout = np.squeeze(np.where(np.abs(dist)==np.nanmin(dist))); # find location where distance is minimum
print(kkout) # you will see the row and column, where to take out data
1
votes

@serge ballesta - thanks for the direction

Answering my own question.

Project the latitude and longitude (platecaree projection) onto the GEOS projection CRS. Find x and y. Use this x and y and nearest select method of xarray to get pixel value from dask array.

import cartopy.crs as ccrs

data_crs = ccrs.Geostationary(central_longitude=41.5, satellite_height=35785831, false_easting=0, false_northing=0, globe=None, sweep_axis='y')

lon = 77.541677 # longitude of interest
lat = 8.079148 # latitude of interst

# lon lat system in 
x, y = data_crs.transform_point(lon, lat, src_crs=ccrs.PlateCarree())

dn = ds.sel(x=x,y=y, method='nearest')