Evaluating the Performance of Sentinel-1 SAR Derived Snow Depth Retrievals Over the Extratropical Andes Cordillera

Abstract

Monitoring and estimating mountain snowpack mass over regional scales is still a challenge because of the inadequacy of observational networks in capturing spatiotemporal variability, and limitations in remotely sensed retrievals. Recent work using C-band synthetic aperture radar (SAR) backscatter data from the Sentinel-1 satellite mission has shown good promise for tracking mountain snow depth over specific northern hemisphere ranges, although the broader potential is still unknown. Here, we extend the new Sentinel-1 based modeling framework beyond the northern hemisphere by only utilizing globally available input data, and evaluate different model parametrization and model performance over the Chilean and Argentine Andes mountains, which contain the largest mountain snowpack in the southern hemisphere. The accuracy of Sentinel-1 snow depth estimates is evaluated against an extensive in situ network available for the region. Satellite-retrieved snow depth is found to have poorer performance across the Andes than observed for northern hemisphere mountain ranges because of greater sensitivity to evergreen forest cover and shallower snowpacks. The algorithm does offer some skill but performance is variable and site-dependent. Algorithm performance is best over regions with limited evergreen forest cover (15%) and snow depths greater than 0.75 m, although the retrievals over-estimate snow depth across most sites. Systemic errors for specific snow classes and across different snow depths are shown, highlighting specific areas in need of further investigation and development.