SES-08. Comparing DInSAR time series software and their compatibility with RMACC resources: MintPy and MSBASv3
The recent growth in access to C-band Synthetic Aperture Radar (SAR) data through the European Space Agency (ESA) Sentinel-1A/B satellites and the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission provides increased opportunities for differential interferometric synthetic aperture radar (DInSAR) monitoring. In 2020 we developed a container which allowed use of dockerized InSAR scientific computing environment (ISCE) (Rosen 2012) software from 2019, allowing us to rapidly generate DInSAR pairs from Sentinel-1 imagery using the ISCE processing software at ~10 meter resolution in the Rocky Mountain Advanced Computing Cluster (RMACC). Here we compare results from an updated ISCE workflow with a stack processor (Fattahi 2017), from a newly generated container running on RMACC against the previous version of ISCE. The new workflow is compatible with the MintPy (Yunjun 2019) DInSAR time series software, which I compare with the previous time series processed with Multidimensional Small Baseline Subset (MSBAS) (Samsonov 2013). Results from both workflows are used to create a time series of subsidence for Lagos, Nigeria, where rapid urban growth has led to accelerated subsidence throughout the city in recent years. The new results also incorporate European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric corrections and a newly available Ionospheric correction.