A physical-statistical retrieval framework to estimate swe from x and ku-band sar observations

A physical-statistical framework to estimate Snow Water Equivalent ( SWE) and Snow depth (SD) from SAR measurements was implemented and applied to SnowSAR flight-line data collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA and averaged to 90 m resolution. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, SWE and SD and physical model parameters. To reduce the number of physical parameters, the snowpack is represented by two layers only. Retrievals compare well with pit observations with good performance in deep snow and residual errors less than 8% for SnowSAR incidence angles > 30 degrees.