IOCAS-IR  > 海洋环流与波动重点实验室
Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model
Zhang, Xudong1,2; Wang, Haoyu1,2; Wang, Shuo3; Liu, Yanliang4,5; Yu, Weidong6,7; Wang, Jing8; Xu, Qing8; Li, Xiaofeng1,2
2022-04-01
Source PublicationREMOTE SENSING OF ENVIRONMENT
ISSN0034-4257
Volume272Pages:15
Corresponding AuthorLi, Xiaofeng(lixf@qdio.ac.cn)
AbstractInternal waves (IW) are characterized by a large-amplitude, long-wave crest, and long-propagation distance. They are widespread in the global ocean. Amplitude is an essential IW parameter and is difficult to derive from the IW surface signatures in satellite images. A laboratory experiment and combined satellite/in-situ measurements were carried out to build two internal wave datasets (888 pairs of lab data and 121 pairs of synchronous in-situ data and satellite images). To efficiently use the lab data, we implemented a transfer learning model to retrieve IW amplitude from satellite images. The model is a purely data-driven model pre-trained with lab data and re-trained with satellite/in-situ data. A short connection was incorporated into the transfer learning framework to reduce information loss. Bias correction was adopted to improve the model performance. After the correction, the root mean square error (RMSE) of the estimated IW amplitude decreased from 12.09 m (17.84 m) to 9.59 m (11.59 m), the mean relative error decreased from 21% (27%) to 18% (16%), and the correlation coefficients improved from 0.81 (0.72) to 0.89 (0.90) on the test (training) dataset. For IWs with amplitude exceeding 100 m, the model can be expected to get an absolute error of 10 m. The mean relative error decreased with the increase in IW amplitudes. Comparisons with other algorithms demonstrate that the proposed model is efficient for IW studies. We applied the model to 156 satellite images containing IW signatures in the Andaman Sea, finding that large-amplitude IWs were mainly located at the water depth between 200 m and 1000 m on the continental slope. When considering one-pixel input errors for the peak-to-peak (PP) distance, the model shows large tolerance with the errors. Compared with the KdV equation-based method, the developed model was more accurate.
KeywordInternal wave Amplitude Transfer learning Remote sensing In-situ measurement Laboratory experiment
DOI10.1016/j.rse.2022.112940
Indexed BySCI
Language英语
Funding ProjectCAS (Chinese Academy of Sciences) Program[Y9KY04101L] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000] ; Key Project of Center for Ocean Mega-Science[COMS2019R02] ; National Natural Science Foundation for Young Scientists of China[41906157] ; National Natural Science Foundation of China[U2006211] ; National Natural Science Foundation of China[42090044] ; National Natural Science Foundation of China[41776183] ; Major scientific and technological innovation projects in Shandong Province[2019JZZY010102] ; UK Royal Society International Exchange Project[IES\R1\211036]
WOS Research AreaEnvironmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000759731300003
PublisherELSEVIER SCIENCE INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/178141
Collection海洋环流与波动重点实验室
Corresponding AuthorLi, Xiaofeng
Affiliation1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, 7 Nanhai Rd, Qingdao 266071, Peoples R China
2.Chinese Acad Sci, Ctr Ocean Megasci, 7 Nanhai Rd, Qingdao 266071, Peoples R China
3.Univ Birmingham, Sch Comp Sci, Birmingham, W Midlands, England
4.Minist Nat Resources, Inst Oceanog 1, Ctr Ocean & Climate Res, Qingdao, Peoples R China
5.Pilot Natl Lab Marine Sci & Technol Qingdao, Lab Reg Oceanog & Numer Modeling, Qingdao, Peoples R China
6.Sun Yat Sen Univ, Sch Atmospher Sci, Zhuhai, Peoples R China
7.Sun Yat Sen Univ, Minist Educ, Key Lab Trop Atmosphere Ocean Syst, Guangzhou, Peoples R China
8.Ocean Univ China, Facult Informat Sci & Engn, Qingdao, Peoples R China
First Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences;  Center for Ocean Mega-Science, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Oceanology, Chinese Academy of Sciences;  Center for Ocean Mega-Science, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Xudong,Wang, Haoyu,Wang, Shuo,et al. Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model[J]. REMOTE SENSING OF ENVIRONMENT,2022,272:15.
APA Zhang, Xudong.,Wang, Haoyu.,Wang, Shuo.,Liu, Yanliang.,Yu, Weidong.,...&Li, Xiaofeng.(2022).Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model.REMOTE SENSING OF ENVIRONMENT,272,15.
MLA Zhang, Xudong,et al."Oceanic internal wave amplitude retrieval from satellite images based on a data-driven transfer learning model".REMOTE SENSING OF ENVIRONMENT 272(2022):15.
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