Exploiting Frequency-Domain Information of GNSS Reflectometry for Sea Surface Wind Speed Retrieval

doi:10.1109/TGRS.2023.3284849

IOCAS-IR > 海洋环流与波动重点实验室

	Exploiting Frequency-Domain Information of GNSS Reflectometry for Sea Surface Wind Speed Retrieval
	Chen, Keran 1; Zhou, Yuan 1; Li, Shuoshi 1; Wang, Ping 1; Li, Xiaofeng 2,3
	2023
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	61 页码:13
通讯作者	Zhou, Yuan([email protected]) ; Li, Xiaofeng([email protected])
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%.
关键词	Cyclone global navigation satellite system (CyGNSS) deep learning frequency domain global navigation satellite system reflectometry (GNSS-R) wind speed retrieval
DOI	10.1109/TGRS.2023.3284849
收录类别	SCI
语种	英语
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研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001021331900002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS关键词	OCEAN
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/182479
专题	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.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China
通讯作者单位	海洋环流与波动重点实验室
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.