Assimilation of L-band interferometric synthetic aperture radar (InSAR) snow depth retrievals for improved snowpack quantification

Abstract

The integration of snow hydrology models and remote sensing observations via data assimilation is a promising method to capture the dynamics of seasonal snowpacks at a high spatial resolution and to reduce uncertainty with respect to snow water resources. In this study, we employ an interferometric synthetic aperture radar (InSAR) technique to quantify snow depth change using modeled snow density and assimilate the referenced and calibrated retrievals into the Multilayer Snow Hydrology Model (MSHM). Although the impact of assimilating snow depth change is local in space and time, the impact on snowpack mass properties (snow depth or snow water equivalent, SWE) is cumulative, and the InSAR retrievals are valuable to improve snowpack simulation and to capture the spatial and temporal variability in snow depth or SWE. Details on the estimation algorithm of InSAR snow depth or SWE changes, referencing, and calibration prove to be important to minimize errors during data assimilation.