Assimilation of space-borne microwave radiometer and discrete ground-based data in snow depth mapping: a case study for northern Eurasia

A new technique to retrieve snow depth (SD) information by assimilating time series of passive microwave radiometer data to SD estimates interpolated from discrete ground-based observations is presented. The technique is based on Bayesian (statistical) inversion of an analytical brightness temperature model. The data assimilation is demonstrated for the boreal forests and sub-arctic regions of Eurasia using SSM/I observations from 22 test stations around Russia, covering a time period from November 1993 to the beginning of April 1994. The obtained results indicate that the data assimilation improves the performance of regional SD estimation when compared with the case of only using the SD estimates interpolated from a discrete ground-based observation network.