{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T11:26:33Z","timestamp":1771413993958,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T00:00:00Z","timestamp":1692835200000},"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":["62031005"],"award-info":[{"award-number":["62031005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research.<\/jats:p>","DOI":"10.3390\/rs15174149","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:23:40Z","timestamp":1692872620000},"page":"4149","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The SSR Brightness Temperature Increment Model Based on a Deep Neural Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhongkai","family":"Wen","sequence":"first","affiliation":[{"name":"College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"},{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}]},{"given":"Huan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"},{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}]},{"given":"Weiping","family":"Shu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}]},{"given":"Liqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}]},{"given":"Lei","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}]},{"given":"Xiang","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}]},{"given":"Yashi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}]},{"given":"Jingjing","family":"Ren","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9142-5036","authenticated-orcid":false,"given":"Shuang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}]},{"given":"Qingjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113411","DOI":"10.1016\/j.rse.2022.113411","article-title":"Deviations of satellite-measured sea surface salinity caused by environmental factors and their regional dependence","volume":"285","author":"Ouyang","year":"2023","journal-title":"Remote Sens. 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