Automated Python workflow for generating Sentinel-1 PSI and SBAS interferometric stacks using SNAP on the Geospatial Computing Platform

Abstract

SNAP (Sentinel Application Platform) is an ESA open-source package distinguished by its stability and user-friendly interface, especially while conducting interferometric SAR (InSAR) processing. However, SNAP-ESA is limited by the lack of a flexible algorithm to generate InSAR time series stacks for both Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques. Moreover, another limitation is the computational requirement to generate InSAR time series interferometric stacks for the available data time span over large areas. In this research, we introduce an innovative automated Python Workflow built upon SNAP-ESA, namely SNAPWF. SNAPWF integrates the capabilities of open-source ASF-search and SNAP-ESA software, enabling network graph generation for PSI and SBAS. The generated network graphs are then utilized to generate the InSAR stacks using SNAP-ESA flexible Graph Processing Framework (GPF) through the Graph Processing Tool (GPT). SNAPWF has the capability to export the interferometric stacks to different file formats that enable further analysis in other available software packages. We implemented and tested SNAPWF on a dedicated geospatial cloud computing platform (GCP). The results demonstrated its capability to generate complete interferometric stacks for Sentinel-1 scenes for PSI and SBAS implemented for a study area across Kenya and Tanzania in 6 hours for one year of data. Moreover, the performance test results showed the possible utilization of the variable resources to accelerate the processing steps.