Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data
Sea ice thickness is one of the essential characteristics of sea ice. Sea ice lead detection is the key to sea ice thickness estimation from radar altimetry data. This research studies ten different surface type classification methods, including supervised learning, unsupervised learning, and thresh...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/2/516 |
| _version_ | 1827622186917036032 |
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| author | Wenqing Zhong Maofei Jiang Ke Xu Yongjun Jia |
| author_facet | Wenqing Zhong Maofei Jiang Ke Xu Yongjun Jia |
| author_sort | Wenqing Zhong |
| collection | DOAJ |
| description | Sea ice thickness is one of the essential characteristics of sea ice. Sea ice lead detection is the key to sea ice thickness estimation from radar altimetry data. This research studies ten different surface type classification methods, including supervised learning, unsupervised learning, and threshold methods, being applied to the HY-2B radar altimeter data collected in October 2019 in the Arctic Ocean. The Sentinel-1 Synthetic Aperture Radar (SAR) images were used for training and validation of the classifiers. Compared with other classifiers, the supervised Bagging ensemble learning classifier showed excellent and robust performance with overall accuracy up to 95.69%. In order to assess the performance of the Bagging classifier in practical applications, lead fractions from January 2019 to March 2021 based on the HY-2B radar altimeter data were mapped using the trained Bagging classifier and compared to the CryoSat-2 L2I data product. The results of the lead fraction showed the monthly variability of ice lead, and the ice lead had a reasonable spatial distribution and was consistent with CryoSat-2 L2I data products. According to these results, the Bagging classifier can provide an essential reference for future studies of Arctic sea ice thickness and sea level estimation from HY-2B radar altimeter data. |
| first_indexed | 2024-03-09T11:18:58Z |
| format | Article |
| id | doaj.art-3ba74f21d4c34b1389e728dc36175ec0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:18:58Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3ba74f21d4c34b1389e728dc36175ec02023-12-01T00:23:02ZengMDPI AGRemote Sensing2072-42922023-01-0115251610.3390/rs15020516Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter DataWenqing Zhong0Maofei Jiang1Ke Xu2Yongjun Jia3The CAS Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences (CAS), Beijing 100190, ChinaThe CAS Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences (CAS), Beijing 100190, ChinaThe CAS Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences (CAS), Beijing 100190, ChinaKey Laboratory of Space Ocean Remote Sensing and Application, MNR, Beijing 100049, ChinaSea ice thickness is one of the essential characteristics of sea ice. Sea ice lead detection is the key to sea ice thickness estimation from radar altimetry data. This research studies ten different surface type classification methods, including supervised learning, unsupervised learning, and threshold methods, being applied to the HY-2B radar altimeter data collected in October 2019 in the Arctic Ocean. The Sentinel-1 Synthetic Aperture Radar (SAR) images were used for training and validation of the classifiers. Compared with other classifiers, the supervised Bagging ensemble learning classifier showed excellent and robust performance with overall accuracy up to 95.69%. In order to assess the performance of the Bagging classifier in practical applications, lead fractions from January 2019 to March 2021 based on the HY-2B radar altimeter data were mapped using the trained Bagging classifier and compared to the CryoSat-2 L2I data product. The results of the lead fraction showed the monthly variability of ice lead, and the ice lead had a reasonable spatial distribution and was consistent with CryoSat-2 L2I data products. According to these results, the Bagging classifier can provide an essential reference for future studies of Arctic sea ice thickness and sea level estimation from HY-2B radar altimeter data.https://www.mdpi.com/2072-4292/15/2/516HY-2Bradar altimetersea ice leadclassification methodsea ice thickness |
| spellingShingle | Wenqing Zhong Maofei Jiang Ke Xu Yongjun Jia Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data Remote Sensing HY-2B radar altimeter sea ice lead classification method sea ice thickness |
| title | Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data |
| title_full | Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data |
| title_fullStr | Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data |
| title_full_unstemmed | Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data |
| title_short | Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data |
| title_sort | arctic sea ice lead detection from chinese hy 2b radar altimeter data |
| topic | HY-2B radar altimeter sea ice lead classification method sea ice thickness |
| url | https://www.mdpi.com/2072-4292/15/2/516 |
| work_keys_str_mv | AT wenqingzhong arcticseaiceleaddetectionfromchinesehy2bradaraltimeterdata AT maofeijiang arcticseaiceleaddetectionfromchinesehy2bradaraltimeterdata AT kexu arcticseaiceleaddetectionfromchinesehy2bradaraltimeterdata AT yongjunjia arcticseaiceleaddetectionfromchinesehy2bradaraltimeterdata |