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I am trying to estimate how much area of emerged landmasses is covered by each of the 12 great groups of soils defined in the USDA Soil Taxonomy with rasterio/rioxarray. My results differ quite a bit from previous attempts and I would like to know if anyone can double check my approach. I am suspicious that my approach to introduce minimum distortion in area estimates might be flawed.

The dataset's original projection is EPSG:4326 with a resolution of ~250 m. I first reproject it to an equal-earth projection (i.e., EPSG:8857) using rio.reproject for a given resolution res using the nearest resampling method, then I count unique values with np.unique, I extract the number of pixels for the class of interest and I multiply that by res**2.

# Load data
soilsrc = rio.open_rasterio('data.tif')
# Set resolution 
res = 1000
# Set classes of interest
andIdx = [50, 58, 59, 61,63,64,74,75,76,77,80]
# Reprojection
soilsrcP = soilsrc.rio.reproject(CRS.from_epsg(8857), resolution=res, resampling=Resampling.nearest)
# Get unique values
unique, count = np.unique(soilsrcP, return_counts = True)
# convert to a DataFrame
cts = pd.DataFrame()
cts['values'] = unique
cts['counts'] = count
# Get the area of the class of interest
andosols = cts[cts['values'].isin(andIdx)]['counts']*res**2

Does that make sense conceptually? Besides the resampling bit (which I do for now so I can test the code on my laptop), am I introducing any unnecessary distortion? Can anyone suggest a better way to preserve the area, preferably using Python? I don't see a polygonisation as an option as I think this global dataset is too large for that.

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Conceptually at least, I think what you have here is ok. You are basically finding the area of a given pixel and multiplying it by the number of pixels in that class. Now, this can get a little more complicated for global models as pixel area changes with latitude for geographic projections and reprojecting to a projected system won't be perfect, but an equal area is a good choice.

You can always double check by manually transforming the pixel area in degrees to m and summing those (using code from rsgislib):

ellipse = [6378137.0, 6356752.314245]

radlat = numpy.deg2rad(latitude)

Rsq = (ellipse[0] * numpy.cos(radlat)) ** 2 + (ellipse[1] * numpy.sin(radlat)) ** 2
Mlat = (ellipse[0] * ellipse[1]) ** 2 / (Rsq**1.5)
Nlon = ellipse[0] ** 2 / numpy.sqrt(Rsq)
x_size = numpy.pi / 180 * numpy.cos(radlat) * Nlon * lon_size
y_size = numpy.pi / 180 * Mlat * lat_size

x_size, y_size

However, you will first need an array of the image pixel latitude which you can work out using the geotransform and image size

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  • Thank loads for double checking my sanity. I am still puzzled by the lack of sensitivity/error analysis in the majority of the literature using global data that I am familiar with. Do you know of any approach/textbooks that treat on assessing the uncertainties associated with these reprojections? Surely this must have been assessed somewhere...
    – e5k
    Nov 28, 2022 at 9:27

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