MAPPING LAND SUBSIDENCE ALONG THE COASTAL BEND OF TEXAS USING RADAR INTERFEROMETRIC TECHNIQUES

Land deformation such as uplift and subsidence are well known problems affecting the Texas coastal bend, especially compounded with recent sea level rise. However, being able to quantify these phenomena is often challenging. While land deformation can be measured by in-situ methods such as Global Positioning System (GPS), it is often limited by finances and manpower. Additionally, it is spatially limited since there are relatively few data points acquired. InSAR (Interferometric Synthetic Aperture Radar) uses free and readily available satellite data that provides accurate, spatially homogeneous land deformation rates over selected temporal ranges. With this high-resolution spatiotemporal dataset, it is possible to further investigate the controlling factors of land deformation. We are currently using SENTINEL-1 data from October 2016 to June of 2019 to create land subsidence time series in the coastal bend region of Texas. A total of 111 ascending SENTINEL-1 scenes are being processed in SARscape to quantify spatiotemporal variability in land subsidence rates. An additional 153 descending SENTINEL-1 scenes will be also investigated. Both SBAS (Small Baseline Subset) and PS (Persistent Scatterers) interferometric techniques are being used. Once processed, the radar-derived land subsidence rates data will be validated and calibrated against those extracted from Continuously Operating Reference Station (CORS) GPS stations. Once validated, factors controlling the observed subsidence rates will be investigated. The generated subsidence rates and locations will be correlated spatially and temporally with surface and subsurface lithology, geologic structures (e.g., fault locations), and fluid (groundwater and hydrocarbon) extraction rates and locations. The results of this research will help the scientific community better understand the rate at which subsidence is occurring, factors affecting land subsidence, as well as provide a useful dataset for hazard prediction studies in the Coastal Bend area.