Improved snow wetness estimation from fully polarimetric SAR image

Snow wetness is an important parameter for forecasting of snow avalanche and snow melt run off modeling in cragged areas specifically Himalayan region of India. In this paper a new snow wetness estimation approach is used for fully polarimetric Synthetic Aperture Radar (SAR) data. In this new methodology Freeman surface scattering and Cloude volume scattering components are introduced, which account for all independent relative polarimetric phase parameters of the coherency matrix. These parameters have been inverted into surface and volume dielectric constant of the snow respectively. A generalized spheroidal shape is considered as a snow particle structure for volume scattering model. The estimated snow wetness is validated using the field data, which was collected, synchronized with the satellite pass. The results were also compared with the Shi and Dozier [1] inversion model based snow wetness.