Balanced inversion of radar cross section data for snow-covered sea ice dielectric profile reconstruction

Climate change is altering the weather patterns on a global scale. This is most evident in the arctic where the consequence is a lower ice thickness and smaller areal extend. Monitoring arctic environment through non-destructive microwave remote sensing techniques has a key role in understanding the risks and benefits of the future arctic. To this end, we have recently developed a nonlinear inverse scattering algorithm to reconstruct snow-covered sea ice dielectric profiles from microwave normalized radar cross section (NRCS) data [Firoozy et. al., Submitted to IEEE TGRS]. Specifically, this inversion algorithm utilizes the collected multi-polarimetric multi-frequency NRCS data to reconstruct the relative complex permittivity and thickness of snow and ice layers above the ocean, and the roughness parameters thereof. These reconstructed parameters are important as they govern the electromagnetic scattering response of snow-covered sea ice.