| Summary: | Spaceborne Synthetic Aperture Radar (SAR) is the primary choice for sea ice monitoring due to its all-weather, day-and-night capability and regular delivery of high resolution images. This thesis presents methods for estimation of sea ice concentration and drift, a multi-sensor study of Baltic Sea ice radar signatures and an interferometric study of landfast sea ice. The ice concentration is determined by combining spatial autocorrelation and backscatter intensity in a neural network, which is trained with ice charts for the Baltic Sea. The root-mean-square error of the estimated concentration is found to be 7 percentage points for a spatially uniform distribution of ice concentrations. The ice drift is estimated from sequential SAR images by combining two methods: 1) areal matching by phase correlation, and 2) feature-based matching by detection and tracking of individual floes. The first method includes a module to resolve rotation. The second method is designed to assist tracking in the marginal ice zone and processes images in two steps: Firstly, by segmentation based on intensity thresholding to obtain objects corresponding to floes, and secondly, by feature tracking of floe outlines. A thresholding method that locates the antimode of imbalanced bimodal distributions is introduced, as well as a novel method for handling of aggregated floes. Assessment against manual measurements showed that up to 98% of drift vectors were estimated correctly, though the number varies with image pair and internal settings. Further, the ice drift algorithm is applied to C-band SAR images from a two-week spring period in the Fram Strait, and the measured ice drift is compared to drift modelled by the ice-ocean model HIROMB. The model is shown to overestimate the drift by a factor 1-2.5, attributable to an underestimated ice thickness, and to exhibit a 10°-30° offset in drift direction, independent of the drift direction.The thesis also evaluates the usefulness of spaceborne SAR sensors for ice charting. The traditionally ...
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