umr-lops / l2a-wind-direction-processor Goto Github PK
View Code? Open in Web Editor NEWPython package to generate Level-2A wind direction files from Sentinel-1 TOPS product based on low frequency texture analysis.
License: MIT License
Python package to generate Level-2A wind direction files from Sentinel-1 TOPS product based on low frequency texture analysis.
License: MIT License
looking at the code for basic regression
heading_angle
is not use at all in the "pdf" version.example for a similar processor:
l2wave/2023/307/S1A_IW_WAV__2SDV_20231103T063230_20231103T063257_051049_0627C3_C45D_E00.SAFE/l2a-s1a-iw1-wav-vv-20231103t063230-20231103t063255-051049-0627c3-004-e00.nc
xsarslc
as optional dependencyadd .pbs and prun script from project_rmarquart
If we look at
sigma0
is used for the prediction.sigma0_filt
variable with (denoising and bright target correction) also it is NaN for the tiles with land while sigma0
in the latest version of the L1B product is always defined.sigma0
by sigma0_filt
.default values for sigma0 patches in original network developed by @rmarquarlops are: 44x44 pixels.
These 44x44 pixels matrices are achieved using interface.py
on a 17.6km² tiles at 400 m resolution in azimuth and range:
def get_low_res_tiles_from_L1BSLC(file_path, xspectra = 'intra', posting = {'sample':400,'line':400},
tile_width = {'sample':17600.,'line':17600.}, window='GAUSSIAN', **kwargs):
but it seems that using Level-1B generated with a 17600m² size is leading to have sigma0 patches with NaN on the edges:
While we can get 44x44 sigma0 matrix starting from Level-1B produced on 17.7km² tiles without any NaN:
Question for @rmarquarlops : Should we start from Level-1B product with larger tiles (i.e. 17.7km² instead of 17.6km²)?
Having NaN in the sigma0 matrix does not prevent to do the prediction but the results are completely different compare to matrix without NaN.
We want:
The processor should not output something else than vv polarization subswath files.
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