Using machine learning techniques to reconstruct the signal observed by the GRACE mission based on AMSR-E microwave data

Abstract

This study delves into the synergy between remote sensing and satellite gravimetry, focusing on the utilization of Advanced Microwave Scanning Radiometer (AMSR-E) data for modeling delta Total Water Storage (ΔTWS) values derived from the GRACE mission. Various machine learning algorithms were employed to investigate the concordance between Gravity Recovery and Climate Experiment (GRACE) and AMSR-E observations. Despite the limited correlation in circumpolar permafrost areas, ΔTWS was successfully modeled with an accuracy of a Root Mean Square Error (RMSE) of 3.5 cm. The Amazon region exhibited a notable model error, attributed to significant ΔTWS amplitude; the overall model quality was affirmed by Normalized Root Mean Square Error (NRMSE) and Nash-Sutcliffe Efficiency (NSE) metrics. Importantly, the effectiveness of AMSR-E Soil Moisture (SM) data, encompassing C (frequency of 4–8 GHz) and X (frequency of 8–12 GHz) ranges (~0.04 m and ~0.03 m wavelength, respectively) in modeling ΔTWS, even in heavily forested equatorial regions, was demonstrated.

Keywords

• grace
• amsr-e
• total water storage anomalies
• soil moisture
• remote sensors