Assimilation of L-band 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 high spatial resolution and reduce uncertainty with respect to snow water resources. In this study, we employ a modified interferometric Synthetic Aperture Radar (InSAR) technique to quantify snow depth change using modeled snow density and assimilate the referenced and calibrated retrievals into a 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 SWE) is cumulative, and the InSAR retrievals are valuable to improve snowpack simulation and capture the spatial and temporal variability of snow depth or SWE. Details in the estimation algorithm of InSAR snow depth or SWE changes, referencing and calibration prove to be important to minimize errors during data assimilation.