{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:23:32Z","timestamp":1760145812398,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,26]],"date-time":"2024-08-26T00:00:00Z","timestamp":1724630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"research program \u201cImpact and Response of Antarctic Seas to Climate Change, IRASCC2020-2022\u201d from the Chinese Arctic and Antarctic Administration (CAA), Ministry of Natural Resources of the People\u2019s Republic of China","award":["IRASCC 01-02-05C"],"award-info":[{"award-number":["IRASCC 01-02-05C"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sea ice is a crucial component of the global climate system. The China\u2013French Ocean Satellite Scatterometer (CFOSAT\/SCAT, CSCAT) employs an innovative rotating fan beam system. This study applied principal component analysis (PCA) to extract classification features and developed an ensemble machine learning approach for sea ice detection. PCA identified key features from CSCAT\u2019s backscatter information, representing outer and sweet swath observations. The ensemble model\u2019s performances (OA and Kappa) for the Northern and Southern Hemispheres were 0.930, 0.899, and 0.844, 0.747, respectively. CSCAT achieved an accuracy of over 0.9 for close ice and open water but less than 0.3 for open ice, with misclassification of open ice as closed ice. The sea ice extent discrepancy between CSCAT and the National Snow and Ice Data Center (NSIDC) was \u22120.06 \u00b1 0.36 million km2 in the Northern Hemisphere and \u22120.03 \u00b1 0.48 million km2 in the Southern Hemisphere. CSCAT\u2019s sea ice closely matched synthetic aperture radar (SAR) imagery, indicating effective sea ice and open water differentiation. CSCAT accurately distinguished sea ice from open water but struggled with open ice classification, with misclassifications in the Arctic\u2019s Greenland Sea and Hudson Bay, and the Antarctic\u2019s sea ice\u2013water boundary.<\/jats:p>","DOI":"10.3390\/rs16173148","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T03:51:06Z","timestamp":1724730666000},"page":"3148","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Ensemble Machine Learning Approach for Sea Ice Monitoring Using CFOSAT\/SCAT Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6220-1751","authenticated-orcid":false,"given":"Yanping","family":"Luo","sequence":"first","affiliation":[{"name":"Fisheries College, Ocean University of China, Qingdao 266003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8548-0223","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Fisheries College, Ocean University of China, Qingdao 266003, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2193-2205","authenticated-orcid":false,"given":"Chuanyang","family":"Huang","sequence":"additional","affiliation":[{"name":"Fisheries College, Ocean University of China, Qingdao 266003, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6112-1417","authenticated-orcid":false,"given":"Fangcheng","family":"Han","sequence":"additional","affiliation":[{"name":"Fisheries College, Ocean University of China, Qingdao 266003, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1002\/2016RG000532","article-title":"Atmosphere-ocean-ice interactions in the Amundsen Sea Embayment, West Antarctica","volume":"55","author":"Turner","year":"2017","journal-title":"Rev. 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