On Estimation of snow water equivalence using SIR-C/X-SAR

Snow water equivalence, which is the product of snow density and depth, is the most important parameter in snow hydrology. This paper demonstrates the algorithms for estimating dry snow density, depth, grain size, underground dielectric constant and surface RMS height using multi-frequency and –polarization SAR (SIR-C/X-SAR) measurements. The algorithms were developed based on the numerically simulated backscattering coefficients. We used L-band VV and HH to estimate snow density and the under-ground surface parameters: dielectric constant and roughness RMS height. The under-ground surface can be either soil or rock. Then, C-band VV, HH and X-band VV are used to estimate snow depth and grain size.