Dual-polarization GNSS reflectometry for FYI–MYI sea ice type classification using microsatellite observations

Accurate sea ice type classification is essential for polar climate monitoring, ocean–climate interaction studies, and safe navigation. Global Navigation Satellite System Reflectometry (GNSS-R) provides all-weather, high-temporal-resolution sea ice observations with low-power bistatic microwave measurements. The hypothesis of this study is that dual circular polarization GNSS-R observations from small satellites, which simultaneously capture left-hand circularly polarized (LHCP) and right-hand circularly polarized (RHCP) reflected signals, provide additional information for characterizing sea ice scattering properties. Using dual-polarization observations from the Tianmu-1 satellite constellation, we extract features from Delay Doppler Maps (DDMs) and perform sea ice type classification with an eXtreme Gradient Boosting (XGBoost) model. Results show that incorporating dual-polarization features improves performance compared with LHCP-only observations. In the Arctic, first-year ice (FYI) and multi-year ice (MYI) are classified with recall rates of 74.79 % and 69.04 %, with an overall accuracy of 74.29 %. In the Antarctic, FYI and MYI recall rates are 73.64 % and 74.85 %, respectively, with an overall accuracy of 73.71 %. MYI recall improves by approximately 1.7 % in the Arctic and 4.5 % in the Antarctic, while maintaining stable FYI performance. The performance in the Arctic is affected by attitude maneuvers in latitudes > 80°N. These results demonstrate that dual circular polarization GNSS-R provides complementary information that enhances sea ice type classification and supports its potential for next-generation GNSS-R missions.