Physical-Statistical Retrieval of SWE and Snow Depth in Forested Areas from Airborne X And Ku-Band SAR Measurements

<jats:p>Abstract. This study presents a physical-statistical Snow Water Equivalent (SWE) retrieval framework in forested areas using dual-frequency X- and Ku-band SAR airborne measurements. The methodology builds on previous work coupling snow hydrology and microwave propagation and backscatter models and introduces a parameterization of microwave propagation and scattering within the forest canopy based on the Water Cloud Model (WCM) modified to account for canopy closure effects. The retrieval framework was applied to SnowSAR measurements from four flights over Grand Mesa, Colorado and its performance was evaluated against snow pit observations and LiDAR snow depth measurements. Prior distributions of snowpack properties were generated using a multilayer snow hydrology model (MSHM) forced with Numerical Weather Prediction (NWP) analysis data. SAR measurements were spatially averaged to 90 m and 30 m resolution for retrieval. Prior distributions of vegetation and ground parameters were initialized using Ku-HH measurements, with effective soil and vegetation parameters estimated for frozen conditions. Soil parameters were estimated in open areas and spatially interpolated to nearby forested areas using ordinary kriging. SWE and snow depth retrievals for forested pixels at 90 m resolution were considered successful by accepting a relative residual backscatter (RRB) tolerance in the Bayesian optimization up to 30 % for individual pixels with incidence angles between 30°–50° along SnowSAR flight paths. Successful retrievals capture both the mean and spatial variance of snowpack properties across the Grand Mesa plateau consistent with the LiDAR survey with RRB generally below 5 %. Validation against collocated LiDAR snow depth and snow pit SWE measurements from the SnowEx ’17 campaign show a root mean square error (RMSE) of 0.033 m (&lt; 8 % of maximum SWE for pits) and improved spatial patterns compared to snow hydrology predictions driven by NWP alone. The errors are larger in pixels with mixed land-cover (e.g., forest-grassland boundaries, land-margins of frozen ponds and lakes, and mixed forest) due to increased uncertainty in the estimation of vegetation parameters. Absolute relative differences (ARD) between LiDAR snow depth and SAR snow depth retrievals are below 10 % for 62 % of the forested pixels at 90 m resolution, and the fraction of successful retrievals increases to 82 % for ARD &lt; 20 %. Retrievals at 30 m resolution achieve dramatic improvement with 78 % of the retrievals for ARD &lt; 10 % due to reduced mixed land-cover uncertainty. These results demonstrate the feasibility of dual-frequency Bayesian SWE retrieval in forested landscapes by combining physical modeling with remote sensing at high spatial resolution enabled by SAR.</jats:p>