| Summary: | In this work operational sea ice synthetic aperture radar (SAR) data products were improved and developed. A SAR instrument is transmitting electromagnetic radiation at certain wavelengths and measures the radiation which is scattered back towards the instrument from the target, in our case sea and sea ice. The measured backscattering is converted to an image describing the target area through complex signal processing. The images, however, differ from optical images, i.e. photographs, and their visual interpretation is not straightforward. The main idea in this work has been to deliver the essential SAR-based sea ice information to end-users (typically on ships) in a compact and user-friendly format. The operational systems at Finnish Institute of Marine Research (FIMR) are currently based on the data received from a Canadian SAR-satellite, Radarsat-1. The operational sea ice classification, developed by the author with colleagues, has been further developed. One problem with the SAR data is typically that the backscattering varies depending on the incidence angle. The incidence angle is the angle in which the transmitted electromagnetic wave meets the target surface and it varies within each SAR image and between different SAR images depending on the measuring geometry. To improve this situation, an incidence angle correction algorithm to normalize the backscattering over the SAR incidence angle range for Baltic Sea ice has been developed as part of this work. The algorithm is based on SAR backscattering statistics over the Baltic Sea. To locate different sea ice areas in SAR images, a SAR segmentation algorithm based on pulse-coupled neural networks has been developed and tested. The parameters have been tuned suitable for the operational data in use at FIMR. The sea ice classification is based on this segmentation and the classification is segment-wise rather than pixel-wise. To improve SAR-based distinguishing between sea ice and open water an open water detection algorithm based on segmentation and local ...
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