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In order to prove the methodology with a robust dataset, five-year normalized radar cross section (NRCS) measurements from the advanced scatterometer (ASCAT), a well-known side-looking radar sensor, are used to train the model. In situ wind data from direct buoy observations, instead of reanalysis wind data or model results, are used as the ground truth in the OPEN model. The model is applied to retrieve sea surface winds from two independent data sets, ASCAT and Sentinel-1 SAR data, and has been well-validated using buoy measurements from the National Oceanic and Atmospheric Administration (NOAA) and China Meteorological Administration (CMA), and the ASCAT coastal wind product. The comparison between the OPEN model and four C-band model (CMOD) versions (CMOD4, CMOD-IFR2, CMOD5.N, and CMOD7) further indicates the good performance of the proposed model for C-band SAR sensors. It is anticipated that the use of high-resolution SAR data together with the new wind speed retrieval method can provide continuous and accurate ocean wind products in the future.<\/jats:p>","DOI":"10.3390\/rs14092269","type":"journal-article","created":{"date-parts":[[2022,5,8]],"date-time":"2022-05-08T23:27:25Z","timestamp":1652052445000},"page":"2269","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Neural Network Method for Retrieving Sea Surface Wind Speed for C-Band SAR"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9672-617X","authenticated-orcid":false,"given":"Peng","family":"Yu","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"},{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National Engineering Research Centre of Geo-Spatial Information Technology, Fuzhou University, Fuzhou 350002, China"}]},{"given":"Wenxiang","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, National Engineering Research Centre of Geo-Spatial Information Technology, Fuzhou University, Fuzhou 350002, China"}]},{"given":"Xiaojing","family":"Zhong","sequence":"additional","affiliation":[{"name":"College of Harbour and Coastal Engineering, Jimei University, Xiamen 361021, China"}]},{"given":"Johnny A.","family":"Johannessen","sequence":"additional","affiliation":[{"name":"Nansen Environmental and Remote Sensing Center and Geophysical Institute, University of Bergen, N-5006 Bergen, Norway"}]},{"given":"Xiao-Hai","family":"Yan","sequence":"additional","affiliation":[{"name":"Center for Remote Sensing, College of Earth, Ocean and Environment, University of Delaware, Newark, DE 19716, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6578-6970","authenticated-orcid":false,"given":"Xupu","family":"Geng","sequence":"additional","affiliation":[{"name":"Fujian Engineering Research Center for Ocean Remote Sensing Big Data, Xiamen University, Xiamen 361005, China"}]},{"given":"Yuanrong","family":"He","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1303-9820","authenticated-orcid":false,"given":"Wenfang","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Sun Yat-sen University, Guangzhou 510080, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2308","DOI":"10.1029\/JC091iC02p02308","article-title":"Structure of the surface wind field from the Seasat SAR","volume":"91","author":"Gerling","year":"1986","journal-title":"J. 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