My understanding is that stackSentinel.py reads the kml information. So we should read this information into geopandas and use the geopanda function to do the intersection:
https://geopandas.org/set_operations.html#
My hope is that we can write a function check_coverage.py that returns a geopanda-calculated overlap region:
The overlap region among all dates (based on the preview kml files):
South North East West
36.42009 35.45052 89.130302 91.556358
But don't spend too much time on it . Stop if you come to the conclusion that it is hopeless.
I had several cases where I thought there were data but then it could not find any overlap (I believe Bolnay ascending). The first goal is to find out how much a problem this is.
The next step would be to open the Sentinel files using Sentinel-1_TOPS.py and read that lat/long information into geopandas, but that would be much more complicated.
Here the stackSentinel.py coverage issue. The first case clearly shows one problem: ’North’ is more south than ’South’.
https://github.com/geodesymiami/rsmas_insar/issues/402 https://github.com/geodesymiami/rsmas_insar/issues/401
My understanding is that stackSentinel.py reads the kml information. So we should read this information into geopandas and use the geopanda function to do the intersection: https://geopandas.org/set_operations.html# My hope is that we can write a function
check_coverage.pythat returns a geopanda-calculated overlap region:The overlap region among all dates (based on the preview kml files): South North East West 36.42009 35.45052 89.130302 91.556358
But don't spend too much time on it . Stop if you come to the conclusion that it is hopeless.
I had several cases where I thought there were data but then it could not find any overlap (I believe Bolnay ascending). The first goal is to find out how much a problem this is.
The next step would be to open the Sentinel files using Sentinel-1_TOPS.py and read that lat/long information into geopandas, but that would be much more complicated.