{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T13:21:22Z","timestamp":1726060882368},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,15]],"date-time":"2023-01-15T00:00:00Z","timestamp":1673740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41906199"]},{"name":"Foundation of Key Laboratory of Space Ocean Remote Sensing and Application, MNR","award":["E0C01kA340"]},{"name":"Youth Innovation Project of National Space Science Center of Chinese Academy of Sciences","award":["EOPD40012S"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"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.<\/jats:p>","DOI":"10.3390\/rs15020516","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T09:31:32Z","timestamp":1673861492000},"page":"516","source":"Crossref","is-referenced-by-count":5,"title":["Arctic Sea Ice Lead Detection from Chinese HY-2B Radar Altimeter Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Wenqing","family":"Zhong","sequence":"first","affiliation":[{"name":"The CAS Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences (CAS), Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4688-6260","authenticated-orcid":false,"given":"Maofei","family":"Jiang","sequence":"additional","affiliation":[{"name":"The CAS Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences (CAS), Beijing 100190, China"}]},{"given":"Ke","family":"Xu","sequence":"additional","affiliation":[{"name":"The CAS Key Laboratory of Microwave Remote Sensing, National Space Science Center, Chinese Academy of Sciences (CAS), Beijing 100190, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9579-1217","authenticated-orcid":false,"given":"Yongjun","family":"Jia","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Ocean Remote Sensing and Application, MNR, Beijing 100049, China"},{"name":"National Satellite Ocean Application Service, Beijing 100082, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1023\/A:1005695109752","article-title":"The Response of Arctic Sea Ice to Global Change","volume":"46","author":"Lemke","year":"2000","journal-title":"Clim. 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