Sea ice recognition for CFOSAT SWIM at multiple small incidence angles in the Arctic
Sea ice recognition for CFOSAT SWIM at multiple small incidence angles in the Arctic
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DOI：
10.3389/fmars.2022.986228
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Meijie Liu;Ranran Yan;Xi Zhang;Ying Xu;Ping Chen;Yongsen Zhao;Yu Guo;Yangeng Chen;Xiaohan Zhang;Shengxu Li
中科院分区：
文献类型：
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作者：
Meijie Liu;Ranran Yan;Xi Zhang;Ying Xu;Ping Chen;Yongsen Zhao;Yu Guo;Yangeng Chen;Xiaohan Zhang;Shengxu Li
海冰识别是北极海冰监测的主要任务之一，也被应用于其他海洋现象的检测。表面波调查与监测（SWIM）仪器作为一种创新的遥感器，在多个小入射角下工作，不同于现有的用于海冰监测的中等和正常入射模式的传感器。小入射角下的海冰识别研究很少。此外，SWIM使用海冰和海水的区分标志来从海浪产品中去除海冰。因此，本研究基于2020年10月至2021年4月的SWIM数据，重点研究北极海冰识别。首先提取11个特征，并应用于使用累积概率分布（CPD）和互信息度量（MIM）的波形特征的分析。然后，随机森林（RF），k-最近邻（KNN）和支持向量机（SVM）的分类器，并评估其海冰识别的能力。最佳分类器是具有欧几里德距离和k等于11的KNN方法。基于KNN方法，利用特征组合对海冰和海水进行分离，选择最优组合。因此，在每个小入射角的最佳分类器特征组装建立，最高的整体准确率达到97.1%。此外，还研究了最优分类器-特征组合的应用，取得了较好的效果。这些组件在短期和长期的海冰识别中具有很高的精度，总体精度大于93.1%。因此，该方法满足SWIM消除海冰影响的要求。此外，海冰的范围和边缘可以提取从SWIM海冰识别结果在高水平的精度大于94.8%。结果表明，基于SWIM数据的最优分类器-特征组合体现了SWIM方法在海冰识别中的有效性。本文的工作不仅突出了小入射角遥感海冰监测新技术，而且研究了SWIM数据在海冰服务中的应用。
Sea ice recognition is one of the main tasks for sea ice monitoring in the Arctic and is also applied for the detection of other ocean phenomena. The Surface Wave Investigation and Monitoring (SWIM) instrument, as an innovative remote sensor that operates at multiple small incidence angles, is different from existing sensors with moderate and normal incidence modes for sea ice monitoring. Sea ice recognition at small incidence angles has rarely been studied. Moreover, SWIM uses a discrimination flag of sea ice and sea water to remove sea ice from sea wave products. Therefore, this research focuses on sea ice recognition in the Arctic based on SWIM data from October 2020 to April 2021. Eleven features are first extracted, and applied for the analysis of the waveform characteristics using the cumulative probability distribution (CPD) and mutual information measurement (MIM). Then, random forest (RF), k-nearest neighbor (KNN) and support vector machine (SVM) classifiers are built, and their abilities of sea ice recognition are assessed. The optimal classifier is the KNN method with Euclidean distance and k equal to 11. Feature combinations are also used to separate sea ice and sea water based on the KNN method to select the optimal combination. Thus, the optimal classifier-feature assembly at each small incidence angle is established, and the highest overall accuracy reaches 97.1%. Moreover, the application of the optimal classifier–feature assemblies is studied, and its performance is fairly good. These assemblies yield high accuracies in the short- and long-term periods of sea ice recognition, and the overall accuracies are greater than 93.1%. So, the proposed method satisfies the SWIM requirement of removing the sea ice effect. Moreover, sea ice extents and edges can be extracted from SWIM sea ice recognition results at a high level of precision greater than 94.8%. As a result, the optimal classifier–feature assemblies based on SWIM data express the effectiveness of the SWIM approach in sea ice recognition. Our work not only highlights the new sea ice monitoring technology of remote sensing at small incidence angles, but also studies the application of SWIM data in sea ice services.