Journal Article

Arctic Sea Ice Classification Based on CFOSAT SWIM Data at Multiple Small Incidence Angles

Yan, Ran, Chen, Ping, Shi, Lijian, Xu, Ying, Wang, Jin, Zhang, Jie, Zhang, Xi, Liu, Meijie, Zhong, Shilei

2022

Overview

Sea ice type is the key parameter of Arctic sea ice monitoring. Microwave remote sensors with medium incidence and normal incidence modes are the primary detection methods for sea ice types. 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 that is different from traditional remote sensors. The method of sea ice detection using SWIM data is also under development. The research reported here concerns ice classification using SWIM data in the Arctic from October 2019 to April 2020. Six waveform features are extracted from the SWIM echo data at small incidence angles, then the distinguishing capabilities of a single feature are analyzed using the Kolmogorov-Smirnov distance. The classifiers of the k-nearest neighbor and support vector machine are established and chosen based on single features. Moreover, sea ice classification based on multi-feature combinations is carried out using the chosen KNN classifier, and optimal combinations are developed. Compared with sea ice charts, the overall accuracy is up to 81% using the optimal classifier and a multi-feature combination at 2°. The results reveal that SWIM data can be used to classify sea water and sea ice types. Moreover, the optimal multi-feature combinations with the KNN method are applied to sea ice classification in the local regions. The classification results are analyzed using Sentinel-1 SAR images. In general, it is concluded that these multifeature combinations with the KNN method are effective in sea ice classification using SWIM data. Our work confirms the potential of sea ice classification based on the new SWIM sensor, and highlight the new sea ice monitoring technology and application of remote sensing at small incidence angles.

• Publisher: MDPI AG
• Subject: Accuracy / angle of incidence / Arctic / Arctic Ocean / Arctic region / Classification / Classifiers / Feature extraction / Ice / Image classification / Incidence angle / k-nearest neighbor method / K-nearest neighbors algorithm / Methods / Monitoring instruments / Oceanography / Precipitation / Remote monitoring / Remote sensing / Remote sensors / Satellites / Sea ice / sea ice classification / Seawater / Sensors / small incidence angles / Support vector machines / Surface waves / surface waves investigation and monitoring (SWIM) / Synthetic aperture radar / waveform features / Waveforms