Quantifying Snow-Ground Backscatter Uncertainty: A Bayesian Approach Using Multifrequency SAR and In-Situ Observations

Snowpack plays a vital role in Earth’s water cycle, especially in mountain regions where it serves as a major source of freshwater. Accurate estimation of snowpack microwave backscatter is critical for retrieving key physical properties of snow, such as snow depth (SD) and snow water equivalent (SWE), typically modeled using radiative transfer models (RTMs). Among the various sources of uncertainty in RTM simulations, snow-ground reflectivity—used as a boundary condition—plays a critical role in influencing the ac-curacy of simulated backscatter. This study leverages high-resolution X- and Ku-band SAR backscatter aircraft measurements using SWESARR and SnowSAR from NASA’s SnowEx campaigns, co-located with in-situ snow pit observations in Grand Mesa, Colorado, to estimate the parameters governing the estimation of the snow-ground reflectivity and quantify the uncertainties associated with them. Focusing on the snow-ground interface, we compare multiple soil reflectivity models to assess the sensitivity of backscatter to key ground parameters such as surface roughness, moisture content, and specular to total reflectivity ratio (STRR). At X-band, increasing ground surface roughness reduced the simulated backscatter by ~1.5 dB across the tested range, and increasing the specular to total reflectivity ratio (STRR) produced an additional ~1.0 dB decrease. A Bayesian MCMC parameter optimization was used to estimate each parameter, and the posterior distributions were then analyzed to quantify the uncertainties. The retrieval sensitivity to the specular to total reflectivity ratio (STRR) is minimized in the 0.6-0.7 range and it can be fixed at 0.65 without having discernible impact. The Bayesian inversion reveals that extreme parameter values act as diagnostic indicators of unmodeled complexity rather than retrieval failures, with representativeness error often dominating over instrument noise. The study highlights the importance of the snow-ground backscatter boundary condition in forward modeling of snowpack backscatter and provides robust guidance on parameter ranges to reduce uncertainty in RTMs, ultimately aiding SWE and SD retrieval from active microwave observations. While this study relied on Grand Mesa, the framework developed here, along with the model uncertainty, is broadly applicable to other snow-dominated mountain regions where active microwave observations can be used for snowpack monitoring.