Sea ice type classification based on random forest machine learning with Cryosat-2 altimeter data

Abstract

Sea ice type is the most sensitive variables in Arctic ice monitoring and its detailed information is essential for ice situation evaluation, climate prediction and vessels navigating. In this study, we analyzed the different sea ice types with the Cryosat-2 (CS-2) SAR mode waveform data. The waveform of CS-2 data was describe by a set of parameters: pulse peakiness (PP), leading-edge width (LeW), trailing-edge width (TeW), stack standard deviation (SSD) and Maximum value of the echo waveform (Max)] and backscatter coefficient (Sigma0). Random forest (RF) classifier was chosen to classify ice type and the classification results were compared with Arctic and Antarctic Research Institute (AARI) operational ice charts. The results show that 85% of the Arctic surface type can be correctly classified from November 2015 to May 2016, 83% of the FYI can be correctly identified which is the domain ice type in Arctic. In comparison with Bayesian and K nearest-neighbor classifiers, the classification accuracy of RF increased by 5% and 3% respectively.