Copernicus Sentinel-1 and Deep Learning help advance sea ice information service - News - Sentinel Online

A Danish R&D project is developing an automatic sea ice product service, which can meet the increased demands for better and more timely sea ice information, using the extensive amount of free and available data from the Copernicus Sentinel satellites, along with novel machine learning techniques for satellite data fusion and sea-ice information retrieval. Manual ice-charting from multi-sensor satellite data has been used for decades, but it is a time-consuming process and delays the delivery of satellite based information to end users. There is a need for automated ice observations from satellite data delivered directly to users or assimilated into ice forecast models, in order to meet the increased demands for better and more timely sea ice information, to improve efficiency and safety of marine operations in polar regions. The Danish Meteorological Institute (DMI), the Technical University of Denmark and Harnvig Arctic & Maritime have initiated the project Automated Sea Ice Products (ASIP) – funded by the Innovation Fund Denmark. The ASIP vision for designing an automatic and robust sea ice classification scheme is to merge imagery from the Sentinel-1 satellites of the European Union's Copernicus programme with other satellite sensor data that have complementary capabilities, such as passive microwave data from AMSR2 (Advanced Microwave Scanning Radiometer 2) and in the future the Copernicus Imaging Microwave Radiometer (CIMR), to better resolve the ambiguities that can occur in SAR imagery of sea ice.