Study of snow is an important domain of research in hydrology and meteorology. It has been demonstrated that snow physical properties can be retrieved using active microwave sensors. This requires an understanding of the interaction between electromagnetic (EM) waves with natural media. The objective of this work is two-fold: to study numerically all physical forward models concerning the EM wave interaction with snow and to develop an inverse scattering algorithm to estimate snow depth based on radar backscattering measurements at different frequencies and incidence angles. For the first part, the goal is to solve the scattering calculations by means of the well-known electromagnetic simulator Ansoft High Frequency Structure Simulator (HFSS). The numerical simulations include: the effective permittivity of snow, surface scattering phenomena in layered homogeneous media (air-snow-ground) with rough interfaces, and volume scattering phenomena when treating snow as a dense random media. For the second part, the study is extended to develop a retrieval method to estimate snow thickness over ground from backscattering observations at L- and X-band using multiple incidence angles. The return signal from snow over ground is influenced by: surface scattering, volume scattering, and the noise effects of the radar system. So, the backscattering coefficient from the medium is modelled statistically by including a white Gaussian noise into the simulation. This inversion algorithm estimates first the snow density using L-band co-polarized backscattering coefficient at normal incidence and then retrieves the snow depth from X-band co-polarized backscattering coefficients using dual incidence angles.
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