On the Potential of Bistatic Insar Features for Monitoring Ice Sheets Properties and Estimating Surface Elevation Bias

A crucial facet of ice sheet monitoring involves delineating distinct layers within the snowpack, known as snow facies, each characterized by unique physical properties. Variations in melt levels and snow properties across these facies exert influence on the radar wave penetration of spaceborne synthetic aperture radar (SAR) systems. This, in turn, affects the estimation of the radar mean phase center, a critical parameter for generating digital elevation models (DEMs), introducing penetration bias and leading to an underestimation of the surface topographic height. Accurate estimation of this bias is pivotal for reducing uncertainties in determining snow depth, ice thickness, and glacier mass balance through DEM differencing. In this paper, we explore the use of bistatic interferometric SAR features (InSAR) to monitor changes in the snow properties of the Greenland ice sheet (GIS), establishing links between these changes and anticipated variations in the radar penetration bias. We propose to combine machine learning-based models for snow facies segmentation and surface elevation bias estimation to achieve a more accurate estimation of the latter, while unveiling the importance of each feature for minimizing the prediction error. The surface elevation bias is estimated using a random forest baseline model based on TanDEM-X InSAR data and IceBridge laser-altimeter measurements acquired during the boreal winter season of 2010/11 in Greenland, achieving a coefficient of determination of R
²
= 84% and an RMSE of 0.70 m. Furthermore, we show that the derived snow facies are the most important feature for the final prediction.