Mapping the spatial distribution and time evolution of snow water equivalent with passive microwave measurements

Abstract

This paper presents an algorithm that estimates the spatial distribution as well as temporal evolution and snow water equivalent and snow depth based on passive remote sensing measurements. It combines the inversion of passive microwave remote sensing measurements with snow accumulation and melt model predictions to yield improved estimates of snow depth and snow water equivalent, at a pixel resolution of 10km. In the inversion, snow grain size evolution is constrained based on pattern matching by using the local snow temperature history. This algorithm is applied to produce spatial snow map of Upper Rio Grande basin in Colorado. The simulation result of this algorithm is compared with that of snow hydrology model and linear regression method. The comparison with the ground truth measurements from 4 SNOwpack TELemetry (SNOTEL) sites in the basin shows that this algorithm is able to improve the estimates of snow depth and snow water equivalent.