Interferogram network from slc stack with stackSentienl.py?

[Jesse-kearse]

Is there an easy way to leverage an existing SLC stack to get a network of interferograms using stackSentinel.py? without having to redo many of the steps in the topsStack workflow?

I have generated many SLC stacks using topsStack / stackSentinel.py using the following command:

stackSentinel.py -s /path/to/safe_files -d /path/to/dem -b bbox_SNWE -n '1'  -m 20190605 -a /aux -o /orbits -C NESD -W slc

which uses the following workflow:

run_01_unpack_topo_reference
run_02_unpack_secondary_slc
run_03_average_baseline
run_04_extract_burst_overlaps
run_05_overlap_geo2rdr
run_06_overlap_resample
run_07_pairs_misreg
run_08_timeseries_misreg
run_09_fullBurst_geo2rdr
run_10_fullBurst_resample
run_11_extract_stack_valid_region
run_12_merge_reference_secondary_slc
run_13_grid_baseline

This has worked perfectly for my needs thus far, however, I would now like to use the same SAR timeseries as input for MintPy, and I am wondering if there exists an efficient way to use stackSentinel.py to generate a network of ifgs using the existing SLC stack? or whether I need to generate an entirely new stackSentinel.py with the "-W interferogram" option.

Any insight would be greatly appreciated.

[keepfighting666]

hello, I am using Sentinel-1 data to process SLC data. When checking the standard deviation of the range registration, I found it to be between 0.4 and 1.0, while the azimuth registration standard deviation is around 0.003. Does this situation require parameter adjustments and re-registration? If adjustments are needed, how should I modify the parameters? The parameters I am currently using are cc: 0.7 and snr: 10, with all other parameters set to their defaults. Did you deal with the same problem? Thanks a lot.

[esrivas17]

Hi @Jesse-kearse , did you manage to solve this issue? I am currently in the same situation, and I dont want to redo everything again