The Potential for Estimating Snow Depth With QuikScat Data and a Snow Physical Model

Abstract

Active microwave remote sensing is a promising tool for global snow water equivalent (SWE) mapping. However, many studies have shown that more information is needed to estimate the SWE accurately. A very important problem is characterizing the snow grain size and quantitatively separating the effects of grain size and snow mass on the backscattering magnitude. In this letter, QuikScat backscattering coefficient data are used to estimate snow depth, with the snow grain size, density, and temperature estimated from the snow thermal model, driven by the Global Land Data Assimilation System forcing data. Considering the spatial resolution and the incident angle of the enhanced resolution QuikScat data, the estimation is applied to flat farm land and grass land. The snow thermal simulated snow grain size was found to be well correlated with the QuikScat measurements of the effective scattering coefficient, and the relationship between them is calibrated using data from one site in 2008–2009. Then, this calibrated relationship is used to estimate the snow depth at other sites. The results show that the snow thermal model simulated grain size can be used to improve the snow depth estimation from active microwave remote sensing.