Physically Informed Bayesian Retrieval of SWE and Snow Depth in Forested Areas from Airborne X And Ku-Band SAR Measurements (2511.18564v1)

Abstract: This study presents a coupled physical statistical framework for retrieving snow water equivalent (SWE) in forested areas using dual frequency X and Ku band SAR observations. The method combines a multilayer snow hydrology model (MSHM) with microwave propagation and backscatter models, and includes a canopy parameterization based on a modified Water Cloud Model that accounts for canopy closure. The framework is applied to airborne SnowSAR measurements over Grand Mesa, Colorado, and evaluated against snow pit SWE and LiDAR snow depth from the SnowEx'17 campaign. Prior distributions of snowpack properties are generated with MSHM forced by numerical weather prediction, and vegetation and soil parameters are initialized from Ku HH observations under frozen conditions and interpolated from open to nearby forested areas using kriging. Successful SWE and snow depth retrievals in forested pixels are obtained where relative backscatter residuals are below 30% for incidence angles between 30 and 50 degrees, capturing both the mean and variance of snowpack distributions. For 90 m forested pixels, the snow depth RMSE is 0.033 m (less than 8% of maximum pit SWE), with improved spatial patterns relative to hydrology only simulations. Performance degrades in highly heterogeneous land cover such as mixed forest and wetlands and along canopy and water boundaries due to uncertainty in canopy closure, although absolute snow depth differences remain below 10% and 20% for about 62% and 82% of pixels, respectively. Retrievals at 30 m resolution for one flight further reduce spatial errors and increase the fraction of low error pixels by about 78% at a 10% absolute error threshold, demonstrating the feasibility of dual frequency Bayesian SWE retrievals in forested landscapes by combining physical modeling with SAR observations.