Using this file downloaded from copernicus: S1A_IW_GRDH_1SDV_20220106T002118_20220106T002143_041333_04E9EE_7026.zip
This process worked in a previous version of Snappy from a few years ago but no longer.
The Python code looks like. All the variables are defined as arguments to a Python method and are valid upon calling the code.
from snappy import ProductIO
from snappy import GPF
from snappy import HashMap
import snappy
import jpy
product = ProductIO.readProduct(zip_file)
params = HashMap()
params.put('demResamplingMethod', 'BILINEAR_INTERPOLATION')
params.put('imgResamplingMethod', 'BILINEAR_INTERPOLATION')
params.put('demName', dem_name)
params.put('externalDEMApplyEGM', True)
params.put('pixelSpacingInMeter', resolution)
params.put('sourceBands', 'Amplitude_' + polarisation.upper())
params.put('nodataValueAtSea', False)
params.put('saveDEM', False)
terrain_corrected_product = GPF.createProduct("Terrain-Correction",
params, target_product)
ProductIO.writeProduct(terrain_corrected_product, output_file,
"GeoTIFF-BigTIFF")
Getting differing projections when using Python Snappy interface vs. SNAP UI. All inputs seem to be the same but the resulting projected geotiff when using Snappy is considerably off compared to truth. The SNAP geotiff looks perfect.