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Abstract. Snow is an important freshwater resource that impacts the health and well-being of communities, the economy, and sustains ecosystems of the cryosphere. This is why there is a need for a spaceborne Earth observation mission to monitor global snow conditions. Environment and Climate Change Canada, in partnership with the Canadian Space Agency, is developing a new Ku-band synthetic aperture radar mission to retrieve snow water equivalent (SWE) at a nominal resolution of 500 m, and weekly coverage of the cryosphere. Here, we present the concept of the SWE retrieval algorithm for this proposed satellite mission. It is shown that by combining a priori knowledge of snow conditions from a land surface model, like the Canadian Soil Vegetation Snow version 2 model (SVS-2), in a Markov Chain Monte Carlo (MCMC) Bayesian model coupled with the Snow Microwave Radiative Transfer model (SMRT), we can retrieve SWE with an RMSE of 15.8 mm (16.4 %) and a MCMC-retrieved SWE uncertainty of 23.4 mm (25.2 %). To achieve this accuracy, a larger uncertainty in the a priori grain size estimation is required, since this variable is known to be underestimated within SVS-2 and has a considerable impact on the microwave scattering properties of snow. It is also shown that adding four observations from different incidence angles improves the accuracy of the SWE retrieval because these observations are sensitive to different scattering mechanisms of the snowpack. These results validate the mission concept of the proposed Canadian satellite mission.
Montpetit et al. (Fri,) studied this question.