Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network

doi:10.3390/rs13020195

IOCAS-IR > 海洋环流与波动重点实验室

	Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network
	Wang, He 1,2; Yang, Jingsong 2,3; Zhu, Jianhua 1; Ren, Lin 2,3; Liu, Yahao 4,5,6; Li, Weiwei 1; Chen, Chuntao 7
	2021
发表期刊	REMOTE SENSING
卷号	13 期号:2 页码:18
通讯作者	Wang, He([email protected])
摘要	Sea state estimation from wide-swath and frequent-revisit scatterometers, which are providing ocean winds in the routine, is an attractive challenge. In this study, state-of-the-art deep learning technology is successfully adopted to develop an algorithm for deriving significant wave height from Advanced Scatterometer (ASCAT) aboard MetOp-A. By collocating three years (2016-2018) of ASCAT measurements and WaveWatch III sea state hindcasts at a global scale, huge amount data points (>8 million) were employed to train the multi-hidden-layer deep learning model, which has been established to map the inputs of thirteen sea state related ASCAT observables into the wave heights. The ASCAT significant wave height estimates were validated against hindcast dataset independent on training, showing good consistency in terms of root mean square error of 0.5 m under moderate sea condition (1.0-5.0 m). Additionally, reasonable agreement is also found between ASCAT derived wave heights and buoy observations from National Data Buoy Center for the proposed algorithm. Results are further discussed with respect to sea state maturity, radar incidence angle along with the limitations of the model. Our work demonstrates the capability of scatterometers for monitoring sea state, thus would advance the use of scatterometers, which were originally designed for winds, in studies of ocean waves.
关键词	Advanced Scatterometer (ASCAT) significant wave height WaveWatch III deep learning multi-hidden-layer neural network
DOI	10.3390/rs13020195
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFC1401007]
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing
WOS记录号	WOS:000611561800001
出版者	MDPI
引用统计	被引频次：13[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/337002/170052
专题	海洋环流与波动重点实验室
通讯作者	Wang, He
作者单位	1.Natl Ocean Technol Ctr, Minist Nat Resources, Tianjin 300112, Peoples R China 2.Second Inst Oceanog, State Key Lab Satellite Ocean Environm Dynam, Minist Nat Resources, Hangzhou 310012, Peoples R China 3.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 5.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 6.Qingdao Natl Lab Marine Sci & Technol, Lab Ocean & Climate Dynam, Qingdao 266237, Peoples R China 7.Yantai Univ, Sch Ocean, Yantai 264005, Peoples R China
推荐引用方式 GB/T 7714	Wang, He,Yang, Jingsong,Zhu, Jianhua,et al. Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network[J]. REMOTE SENSING,2021,13(2):18.
APA	Wang, He.,Yang, Jingsong.,Zhu, Jianhua.,Ren, Lin.,Liu, Yahao.,...&Chen, Chuntao.(2021).Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network.REMOTE SENSING,13(2),18.
MLA	Wang, He,et al."Estimation of Significant Wave Heights from ASCAT Scatterometer Data via Deep Learning Network".REMOTE SENSING 13.2(2021):18.

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