Sea Ice Types and Sea Water Distinction in the Arctic Using CFOSAT SWIM Data

Sea ice recognition is one of the main tasks for sea ice monitoring in the Arctic and it also serves the detection of other ocean phenomena. The Surface Wave Investigation and Monitoring instrument (SWIM) on the China-France Oceanography Satellite (CFOSAT) is a new type of sensor with a small incidence angle detection mode which is different from traditional remote sensors. In this study, a novel method of sea ice types and sea water distinction is proposed. In our previous study, waveform features are introduced in the sea ice classification, such as the maximum power (MAX), the backscattering power (BSP), and the pulse peakiness (PP), the KNN classifier obtains high accuracies. In this research, using SWIM data from November 2019 to April 2020, various feature combinations and binary tree method are applied and validated for sea ice types (the first year ice and multi-year ice) and sea water distinction. Compared with sea ice charts of the Arctic and Antarctic Research Institute (AARI), the overall accuracy of distinction is about 83%, while the overall accuracy of sea ice and sea water separation is up to 99% and better than 97% for each small incidence angle. Our work can be applied for sea ice monitoring and sea ice product generation of microwave remote sensing at small incident angles.