Exploiting Frequency-Domain Information of GNSS Reflectometry for Sea Surface Wind Speed Retrieval | |
Chen, Keran2; Zhou, Yuan2; Li, Shuoshi2; Wang, Ping2; Li, Xiaofeng1,3 | |
刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
2023 | |
卷号 | 61页码:13 |
关键词 | Cyclone global navigation satellite system (CyGNSS) deep learning frequency domain global navigation satellite system reflectometry (GNSS-R) wind speed retrieval |
ISSN号 | 0196-2892 |
DOI | 10.1109/TGRS.2023.3284849 |
通讯作者 | Zhou, Yuan(zhouyuan@tju.edu.cn) ; Li, Xiaofeng(xiaofeng.li@ieee.org) |
英文摘要 | Global navigation satellite system reflectometry (GNSS-R) delay-Doppler map (DDM) measures the sea surface roughness, which has recently been applied to retrieve sea surface wind speed. However, current studies on GNSS-R wind speed retrieval only use the spatial domain of the DDM without considering the variation patterns in the map, which is regarded as frequency-domain information of the map. In this study, we propose a joint frequency & ahat;"spatial domain network (FSNet) based on reflectivity data provided by the cyclone global navigation satellite system (CyGNSS) mission. We construct a matchup dataset between the CyGNSS satellite data and the European Centre for Medium-Range Weather Forecasts (ECMWF) model data from 1 January 2018 to 31 December 2019. The wind speed range is 0-25 m/s. Using the proposed FSNet, frequency and spatial features are simultaneously extracted. The frequency-domain feature supplements the spatial-domain information of the mid- and high-level features in the neural network. Rather than directly concatenating the frequency-domain features with the spatial-domain features, we designed a feature fusion module (FFM) to fuse frequency and spatial features for wind speed retrieval adaptively. Experiments show that our FSNet wind speed retrieval has a root mean square error (RMSE) of 1.63 m/s for a wind range of 0-25 m/s. This accuracy is 25.4% better than the operational algorithm provided by the CyGNSS Level 2 wind speed product. For a higher wind range of 16-25 m/s, FSNet performed even better, improving the RMSE by 31%. |
资助项目 | National Key Research and Development Program of China[2020YFC1523204] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19060101] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42040401] ; Key Research and Development Project of Shandong Province[2019JZZY010102] ; CAS Program[Y9KY04101L] ; National Natural Science Foundation of China[62171320] ; National Natural Science Foundation of China[U2006211] |
WOS关键词 | OCEAN |
WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:001021331900002 |
内容类型 | 期刊论文 |
源URL | [http://ir.qdio.ac.cn/handle/337002/182478] |
专题 | 海洋研究所_海洋环流与波动重点实验室 |
通讯作者 | Zhou, Yuan; Li, Xiaofeng |
作者单位 | 1.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 2.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 3.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Keran,Zhou, Yuan,Li, Shuoshi,et al. Exploiting Frequency-Domain Information of GNSS Reflectometry for Sea Surface Wind Speed Retrieval[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2023,61:13. |
APA | Chen, Keran,Zhou, Yuan,Li, Shuoshi,Wang, Ping,&Li, Xiaofeng.(2023).Exploiting Frequency-Domain Information of GNSS Reflectometry for Sea Surface Wind Speed Retrieval.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61,13. |
MLA | Chen, Keran,et al."Exploiting Frequency-Domain Information of GNSS Reflectometry for Sea Surface Wind Speed Retrieval".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023):13. |
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