Radar cross section data inversion for snow-covered sea ice remote sensing
This thesis reports on my Ph.D. research in the area of microwave remote sensing of the Arctic. The main objective of this research is to reconstruct the dielectric profile of the snow-covered sea ice, and indirectly retrieve some of its geophysical and thermodynamic properties. To meet this objective, a nonlinear electromagnetic inverse scattering algorithm is developed that consists of forward and inverse solvers. The input to this algorithm is the normalized radar cross section (NRCS) data collected by radar systems from the snow-covered sea ice profile. The proposed inversion algorithm iteratively minimizes a discrepancy between the measured and simulated NRCS data to achieve an accurate reconstruction. Two main challenges associated with this inverse problem are its ill-posedness and its limited available scattering data. To tackle these, the utilization of appropriate regularization and weighting schemes as well as the incorporation of prior information into the inversion algorithm are employed. These include the utilization of (i) appropriate weighting factors for the misfit cost function, (ii) more sensitive NRCS data with respect to the unknown parameters, (iii) further parametrization of the profile based on the expected distribution, (iv) time-series NRCS data to better initialize the inversion process, and (v) NRCS data collected by the satellite and on-site scatterometer to be inverted simultaneously for profile reconstruction. The experimental data utilized are collected by the author in collaboration with the Centre for Earth Observation Science. These measurements are performed on (i) the artificially-grown sea ice in the Sea-ice Environmental Research Facility, located at the University of Manitoba during winter 2014, and (ii) the landfast sea ice located in the Arctic (Cambridge Bay, Nunavut) during May 2014. The measurement procedure includes NRCS data collection through an on-site C-band scatterometer and a spaceborne SAR satellite and physical sampling of the snow and sea ice. The proposed electromagnetic inverse scattering algorithm is utilized to invert these experimental data sets, as well as some synthetic data sets. It will be shown that the use of various techniques developed in this thesis in conjunction with the developed inversion algorithm results in reasonable snow-covered sea ice profile reconstruction.