Correction of the Penetration Bias for InSAR DEM via Synergetic AI-Physical Modeling: A Greenland Case Study

Abstract

Rapid changes in the Greenland Ice Sheet require precise elevation monitoring to understand ice dynamics and predict sea level rise. X-band Interferometric Synthetic Aperture Radar (InSAR) has the potential for this purpose but is limited by microwave signal penetration biases, which can be a few meters. We present a novel hybrid modeling approach that integrates machine learning (ML) with physical models to enhance the estimation of the elevation bias in InSAR data at X-band. Our method addresses the limitations of traditional physical modeling techniques by parameterizing the vertical structure function using a ML model. This approach combines machine learning as input for the physical model. The results demonstrate the improvements in correcting elevation biases, thus increasing the accuracy of X-band InSAR DEMs over Greenland. This advancement has the potential for more precise elevation estimation and ice-sheet monitoring.