Refining Polarimetric Classification Methods for Deriving Sea Ice Labels from Synthetic Aperture Radar Data

Current changes in climate conditions are causing rapid reductions in Arctic sea ice. This loss of sea ice has environmental implications as well as economic and global security implications, especially in regards to Arctic navigation. Rapid changes in sea ice influence when an Arctic route can become navigable, and thus understanding such changes is crucial for operation planning and routing. The Arctic's spatial extent and severe environment are well suited for observation via satellite remote sensing, and the problem space lends itself well to a supervised machine learning approach. However, such an approach is limited by the lack of labeled sea ice data sets with both the spatial and temporal density and resolution to pair with image data. In this study, we develop methods to derive sea ice labels directly from satellite synthetic aperture radar (SAR) data. We use single-look complex data from the Sentinel-1 constellation collected in the Extra-Wide swath mode, which has optimal imaging parameters for sea ice observation. We expand on existing methods of deriving labels from SAR data using H-a plane polarimetric classification techniques by examining additional polarimetric parameters. We then develop new classification rules by training a decision tree classifier using a labeled data set made available by the National Snow & Ice Data Center. We focus our analysis on data collected in Summer of 2020 and Summer and Fall of 2021 covering the Greenland and Barents Seas, Central Arctic Ocean, and Baffin Bay.