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, under-ground dielectric constant and surface RMS height using multi- frequency and -polarization SAR measurements. The algorithms were developed based on the numerically simulated backscattering coefficients. We use L-band VV and HH to estimate snow density and the underground surface parameters: dielectric constant and roughness RMS height. The underground 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. The validation from the field snow density measurements averaged from the top and bottom snow layers indicate that an absolute and relative accuracy of 0.042 gcm-3 and 13.15 percent can be expected. The comparison with the ground scatterometer measurements showed RMSE of 4.1 percent by volume for solid moisture estimation and 0.42 cm for the surface RMS height with this newly developed algorithms. The validation by using three SIR-C/X- SAR image data indicated that this algorithm performed well for incidence angle greater than 30 degrees with RMES 34 cm and 0.27 mm for estimation of snow depth and ice optical equivalent particle radius, respectively.
Jiancheng Shi, Jiancheng Shi,
"Estimation of snow water equivalence using SIR-C/X-SAR", Proc. SPIE 3503, Microwave Remote Sensing of the Atmosphere and Environment, (19 August 1998); doi: 10.1117/12.319499; https://doi.org/10.1117/12.319499