Exploring Spatiotemporal Relationships between InSAR-derived Land Subsidence and Satellite-based Hydrological Variables

Abstract: Shabestar basin in the East Azerbaijan province, Northwest Iran, where irrigation is the main groundwater consumer, has experienced large-scale subsidence and groundwater deletion, which poses a threat to the local agricultural activities, economic development, and food security. With the emergency of mitigating the risk, satisfying future demand for groundwater, and improving resilience considering climate change, this study proposes a satellite-based approach to explore the spatio-temporal relationships between measured subsidence and hydrological variables in the basin to assist groundwater management strategy. We investigated ground subsidence in the basin using the SBAS-InSAR technique based on series of Sentinel-1A TOPS Synthetic Aperture Radar (SAR) images acquired from 22 January 2016 to 21 October 2020 along ascending and descending tracks. The study showed average subsidence rates ranged from -97.5 mm/year to 10 mm/year in the basin after decomposing line-of-sight velocity fields to vertical components. The prominent subsidence was found in the eastern and western portions of the basin, and the maximum average subsidence rate was detected in the eastern part of the basin near Nazarlu. Correlation analysis between the surface subsidence and potential driving factors, including the actual evapotranspiration (ETa), land surface temperature (LST), the normalized difference vegetation index (NDVI), precipitation (P), and soil water index (SWI), revealed a significant relationship between the first three variables and observed subsidence by InSAR. A multivariate long short-term memory (LSTM) network was established to investigate the importance of the first three variables and predict subsidence in the near future. The result quantitatively revealed that the agricultural practice had a major impact on subsidence occurrence in the basin. Furthermore, our findings indicated that the area is estimated to continue subsiding dramatically in the next five years. This study fills the gap in the local groundwater monitoring system using satellite-based data and artificial intelligence and contributes to the local groundwater management by providing insights into the main drivers of groundwater-induced subsidence.