{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:03:16Z","timestamp":1774933396956,"version":"3.50.1"},"reference-count":65,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,30]],"date-time":"2022-07-30T00:00:00Z","timestamp":1659139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Postdoctoral Innovative Talent Support Program","award":["BX20200039"],"award-info":[{"award-number":["BX20200039"]}]},{"name":"Postdoctoral Innovative Talent Support Program","award":["2021M700319"],"award-info":[{"award-number":["2021M700319"]}]},{"name":"Postdoctoral Innovative Talent Support Program","award":["42104031"],"award-info":[{"award-number":["42104031"]}]},{"name":"Postdoctoral Innovative Talent Support Program","award":["BARI2003"],"award-info":[{"award-number":["BARI2003"]}]},{"name":"Postdoctoral Fund","award":["BX20200039"],"award-info":[{"award-number":["BX20200039"]}]},{"name":"Postdoctoral Fund","award":["2021M700319"],"award-info":[{"award-number":["2021M700319"]}]},{"name":"Postdoctoral Fund","award":["42104031"],"award-info":[{"award-number":["42104031"]}]},{"name":"Postdoctoral Fund","award":["BARI2003"],"award-info":[{"award-number":["BARI2003"]}]},{"name":"National Natural Science Foundation of China","award":["BX20200039"],"award-info":[{"award-number":["BX20200039"]}]},{"name":"National Natural Science Foundation of China","award":["2021M700319"],"award-info":[{"award-number":["2021M700319"]}]},{"name":"National Natural Science Foundation of China","award":["42104031"],"award-info":[{"award-number":["42104031"]}]},{"name":"National Natural Science Foundation of China","award":["BARI2003"],"award-info":[{"award-number":["BARI2003"]}]},{"name":"BeiDou Technology Achievement Transformation and Industrialization of Beihang","award":["BX20200039"],"award-info":[{"award-number":["BX20200039"]}]},{"name":"BeiDou Technology Achievement Transformation and Industrialization of Beihang","award":["2021M700319"],"award-info":[{"award-number":["2021M700319"]}]},{"name":"BeiDou Technology Achievement Transformation and Industrialization of Beihang","award":["42104031"],"award-info":[{"award-number":["42104031"]}]},{"name":"BeiDou Technology Achievement Transformation and Industrialization of Beihang","award":["BARI2003"],"award-info":[{"award-number":["BARI2003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this study, we investigate sea state estimation from spaceborne GNSS-R. Due to the complex scattering of electromagnetic waves on the rough sea surface, the neural network approach is adopted to develop an algorithm to derive significant wave height (SWH) from CYGNSS data. Eighty-nine million pieces of CYGNSS data from September to November 2020 and the co-located ECMWF data are employed to train a three-hidden-layer neural network. Ten variables are considered as the input parameters of the neural network. Without the auxiliary of the wind speed, the SWH retrieved using the trained neural network exhibits a bias and an RMSE of \u22120.13 and 0.59 m with respect to ECMWF data. When considering wind speed as the input, the bias and RMSE were reduced to \u22120.09 and 0.49 m, respectively. When the incidence angle ranges from 35\u00b0 to 65\u00b0 and the SNR is above 7 dB, the retrieval performance is better than that obtained using other values. The measurements derived from the \u201cBlock III\u201d satellite offer worse results than those derived from other satellites. When the distance is considered as an input parameter, the retrieval performances for the areas near the coast are significantly improved. A soft data filter is used to synchronously improve the precision and ensure the desired sample number. The RMSEs of the retrieved SWH are reduced to 0.45 m and 0.41 m from 0.59 m and 0.49 m, and only 16.0% and 14.9% of the samples are removed. The retrieved SWH also shows a clear agreement with the co-located buoy and Jason-3 altimeter data.<\/jats:p>","DOI":"10.3390\/rs14153666","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T04:04:00Z","timestamp":1659326640000},"page":"3666","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Retrieval and Assessment of Significant Wave Height from CYGNSS Mission Using Neural Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9046-3388","authenticated-orcid":false,"given":"Feng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"given":"Dongkai","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5702-5474","authenticated-orcid":false,"given":"Lei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beihang University, Beijing 100191, China"},{"name":"School of Information Science and Engineering, Shandong Agricultural University, No. 61, Daizong Road, Tai\u2019an 271018, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/0029-8018(92)90035-3","article-title":"Wave direction measurements using pitch-roll buoys","volume":"19","author":"Steele","year":"1992","journal-title":"Ocean. 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