Automated Python workflow for generating Sentinel-1 PSI and SBAS interferometric stacks using SNAP on geospatial computing platform

Amira Zaki*, Ling Chang, Irene Manzella, Mark van der Meijde, Serkan Girgin, Hakan Tanyas, Islam Fadel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
22 Downloads (Pure)

Abstract

Detecting and monitoring surface deformation using radar satellite data is vital in geohazard assessment. Sentinel-1 has provided unprecedented spatial and temporal resolution, but data processing is complicated and poses computational challenges. Although software and tools exist, each with its own limitations. SNAP-ESA is notable for its user-friendly interface and stable performance in Interferometric Synthetic Aperture Radar (InSAR). However, SNAP-ESA lacks a flexible approach for generating interferometric time series stacks for Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) techniques and faces computational challenges over large areas. Here, we present an automated Python workflow, SNAPWF, using SNAP-ESA to enable efficient PSI and SBAS interferometric time series stacks generation using flexible network graphs. SNAPWF has been implemented on a dedicated geospatial computing platform, enabling efficient performance over large areas. Results confirm its ability to generate PSI and SBAS interferometric stacks using full Sentinel-1 scenes and achieve results comparable to existing software.

Original languageEnglish
Article number106075
JournalEnvironmental Modelling and Software
Volume178
DOIs
Publication statusPublished - Jul 2024

Keywords

  • InSAR
  • Interferometric stacks
  • Open-source tools
  • PSI
  • SBAS
  • ITC-HYBRID

Fingerprint

Dive into the research topics of 'Automated Python workflow for generating Sentinel-1 PSI and SBAS interferometric stacks using SNAP on geospatial computing platform'. Together they form a unique fingerprint.

Cite this