Spatiotemporal processes of land subsidence along the Texas coastline estimated with satellite geodetic techniques of InSAR and GNSS
Abstract
Land subsidence in coastal areas may cause increased risks to near-shore infrastructures, resilience of marine ecosystems, and coastal flooding damages. Multiple locations along the Texas Gulf Coast have been recognized among most subsided hotspots in the United States, and large-scale high-resolution mapping of coastal subsidence is significant in providing knowledge of land movement processes along the Texas coastline. Advances in the synthetic aperture radar (SAR) image acquisition and processing make the interferometric SAR (InSAR) technique a powerful tool for fulfilling this purpose. This study adopted persistent scatter (PS) InSAR, a commonly used multi-temporal InSAR (MT-InSAR) technique, to process Sentinel-1 SAR imagery between 2016 and 2022 across the Texas coastline. Specifically, five stacks of single look complex (SLC) imagery in the ascending orbit were utilized to have a full coverage of the entire study area. The vertical-vertical (VV) polarization data was used with the PS InSAR technique for every sub-swath, which overlaps coastal zones, across all stacks of SAR imagery. Land deformation information was extracted on the PSs that possessed stable phase amplitude. Because the MT-InSAR technique can only provide land deformation relative to a reference point and date for each sub-swath processing, high-accuracy positioning solutions from continuously operated GNSS (cGNSS) stations over the study area were used to align and validate the PS-InSAR results along the line-of-sight (LOS) direction. The study mapped the spatiotemporal processes of land deformation over the entire Texas Gulf Coast, and the results are anticipated to provide decision-making supports to pertinent federal, state, and/or local stakeholders who are engaged in coastal environmental conservation and sustainable development.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFMGC25F0749Q