Interoperability of -Band Sentinel-1 SAR and GRACE Satellite Sensors on PSInSAR-Based Urban Surface Subsidence Mapping of Varanasi, India<i/>

Abstract

The Sentinel-1 is an active synthetic aperture radar (SAR) satellite with a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> -band SAR sensor operating at a center frequency of 5.405 GHz and a wavelength of 5.55 cm. With the availability of freely available SAR datasets from Sentinel-1, the persistent scatterer SAR interferometry (PSInSAR)-based urban surface subsidence mapping has become easy. However, apart from geological causes, the major cause of urban surface subsidence is the over-exploitation of groundwater that results in piezometric pressure loss in the aquifers resulting in net subsidence. With the Gravity Recovery and Climate Experiment (GRACE) satellite sensor, the groundwater level fluctuations can be very easily studied temporally. But the coarse spatial resolution of GRACE data makes the study of groundwater fluctuations difficult to study for smaller watersheds. This study aims to correlate the PSInSAR average surface line of sight (LOS) displacement from Sentinel-1, with the average groundwater fluctuations from the GRACE sensor temporally from May 2017 to February 2022. The study also compared the correlation between PSInSAR displacement in both VV and VH polarizations and observed the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> values to be 0.63 and 0.65 for VV and VH polarizations, respectively, with GRACE data. After that, using the displacement and groundwater level fluctuation data, an estimation of a gravimetric anomaly due to a decrease in groundwater level was carried out for Varanasi city. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> , mean absolute error (MAE), and root-mean-square error (RMSE) were observed to be 0.84, 1.23, and 2.4, respectively, in gravimetric anomaly estimation, thus giving sufficient acceptance for interoperability of the two sensors.