Radar polarimetric modeling and observations of snow-covered terrain

Snow cover has been shown to be a critical component of local and regional influence on climate system and weather forecasting. In hydrological investigations, modeling and forecasting of snowmelt runoff requires information about snowpack properties and their spatial variability. Active sensors provide the best opportunities for measuring snow water equivalency in alpine topography. But understanding the relationships between radar signature and snow properties needs to be improved.; The main purpose of this research is to characterize the snowpack parameters from SAR measurements and to develop quantitative algorithms to measure snowpack parameters such as snow covered area, depth, density, grain size, and liquid water content for SIR-C, EOS-SAR, and Aircraft Radar.; In this study, the polarimetric radar model of a snow covered terrain, for general conditions, has been developed. The model predictions match the measured co-polarized backscattering coefficients fairly well for wet snow, from which the algorithms of liquid water content retrieval can be developed. The specific polarization signatures from C-band can be used effectively to map the different liquid water content snow and glacier covered area without requiring any topographic information. Inversion algorithms for the estimation of snowpack parameters, such as snow density, grain size, depth, and ground dielectric condition, are discussed based on the model simulation.