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

Abstract

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.

Sea ice recognition for CFOSAT SWIM at multiple small incidence angles in the Arctic

“…The overall accuracy (OA) and F1 score (F1s) that are defined as seen in 'Supplementary Material' file are used to evaluate the abilities of classifiers for sea ice recognition. The introduction of classifiers is in terms of our previous work and other researches (Liu et al, 2015;Rinne and Similä, 2016;Shen et al, 2017a;Shen et al, 2017b;Jiang et al, 2019;Liu et al, 2022), so three classifiers are chosen for sea ice and sea water separation in this study, including the random forest (RF), k-nearest neighbors (KNN) and support vector machine (SVM). Then, the optimal classifier is chosen from the three ones, which is used to obtain the best feature combination.…”

“…In the discussion of our previous study, the inverse mean power (IMP) was useful to discriminate FYI and MYI (Liu et al, 2022). In this study, IMP is also introduced for sea ice and sea water separation.…”

“…According to our previous work and other researches, eleven waveform features are extracted to assess the echo characteristics of SWIM at six small incidence angles (Laxon, 1994;Zygmuntowska et al, 2013;Rinne and Similä, 2016;Shen et al, 2017b;Shu et al, 2019;Liu et al, 2022). These features reflect the different waveform characteristics, for example, the power, structure, and overall characteristics of echo waveforms.…”

“…Our previous study indicated that LEW and TEW did not yield satisfactory results, and the echo power (MAX) and waveform shape (TEW) could be combined to improve the effects on distinguishing between FYI and MYI (Liu et al, 2022). In this study, we expand this method to separate sea ice and sea water.…”

“…Thus, SWIM data with the new observation mode can be used for sea ice recognition and contribute to sea ice monitoring methods and operational techniques. Our previous study focused on sea ice type classification (Liu et al, 2021;Liu et al, 2022), and a method to distinguish between sea ice and sea water was not fully established. In addition, two new features (Inverse mean power, IMP; Trailing edge slope, TES) were introduced for first-year ice (FYI) and multiyear ice (MYI) separation, but these features were not used for sea water recognition.…”