{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T10:54:28Z","timestamp":1787223268836,"version":"build-2736575974"},"reference-count":33,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,22]],"date-time":"2024-12-22T00:00:00Z","timestamp":1734825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871085"],"award-info":[{"award-number":["41871085"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021YFC3001000"],"award-info":[{"award-number":["2021YFC3001000"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["311021004"],"award-info":[{"award-number":["311021004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["G2014-02-06"],"award-info":[{"award-number":["G2014-02-06"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["41871085"],"award-info":[{"award-number":["41871085"]}]},{"name":"National Key R&amp;D Program of China","award":["2021YFC3001000"],"award-info":[{"award-number":["2021YFC3001000"]}]},{"name":"National Key R&amp;D Program of China","award":["311021004"],"award-info":[{"award-number":["311021004"]}]},{"name":"National Key R&amp;D Program of China","award":["G2014-02-06"],"award-info":[{"award-number":["G2014-02-06"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["41871085"],"award-info":[{"award-number":["41871085"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["2021YFC3001000"],"award-info":[{"award-number":["2021YFC3001000"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["311021004"],"award-info":[{"award-number":["311021004"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["G2014-02-06"],"award-info":[{"award-number":["G2014-02-06"]}]},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences","award":["41871085"],"award-info":[{"award-number":["41871085"]}]},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences","award":["2021YFC3001000"],"award-info":[{"award-number":["2021YFC3001000"]}]},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences","award":["311021004"],"award-info":[{"award-number":["311021004"]}]},{"name":"State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences","award":["G2014-02-06"],"award-info":[{"award-number":["G2014-02-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The retrieval of global significant wave height (SWH) data is crucial for maritime navigation, aquaculture safety, and oceanographic research. Leveraging the high temporal resolution and spatial coverage of Cyclone Global Navigation Satellite System (CYGNSS) data, machine learning models have shown promise in SWH retrieval. However, existing models struggle with accuracy under high-SWH conditions and discard a significant number of such observations due to low quality, which limits their effectiveness in global SWH retrieval, particularly for monitoring tropical cyclone (TC) events. To address this, this study proposes a daily global SWH retrieval framework through the enhanced eXtreme Gradient Boosting model (XGBoost-SC), which incorporates Cumulative Distribution Function (CDF) matching to introduce prior distribution information and reduce errors for SWH values exceeding 3 m. An enhanced loss function is employed to improve accuracy and mitigate the distribution bias in low-SWH retrieval induced by CDF matching. The results were tested over one million sample points and validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) SWH product. With the help of CDF matching, XGBoost-SC outperformed all models, significantly reducing RMSE and bias while improving the retrieval capability for high SWHs. For SWH values between 3\u20136 m, the RMSE and bias were 0.94 m and \u22120.44 m, and for values above 6 m, they were 2.79 m and \u22122.0 m. The enhanced performance of XGBoost-SC for large SWHs was further confirmed in TC conditions over the Western North Pacific and in the Western Atlantic Ocean. This study provides a reference for large-scale SWH retrieval, particularly under TC conditions.<\/jats:p>","DOI":"10.3390\/rs16244782","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:13:38Z","timestamp":1734945218000},"page":"4782","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Significant Wave Height Retrieval in Tropical Cyclone Conditions Using CYGNSS Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4882-0928","authenticated-orcid":false,"given":"Xiangyang","family":"Han","sequence":"first","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"Guangdong Provincial Engineering Research Center for Public Security and Disasters, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3128-7633","authenticated-orcid":false,"given":"Xianwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"Guangdong Provincial Engineering Research Center for Public Security and Disasters, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9568-7076","authenticated-orcid":false,"given":"Zhi","family":"He","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"Guangdong Provincial Engineering Research Center for Public Security and Disasters, Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8350-7613","authenticated-orcid":false,"given":"Jinhua","family":"Wu","sequence":"additional","affiliation":[{"name":"China Railway Siyuan Survey and Design Group Co., Ltd., Wuhan 430063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yang, J., Zhang, J., Jia, Y., Fan, C., and Cui, W. 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