Institutional Repository of Key Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
| 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 Publication | REMOTE SENSING OF ENVIRONMENT
 |
| ISSN | 0034-4257 |
| Volume | 272Pages:15 |
| Corresponding Author | Li, Xiaofeng(lixf@qdio.ac.cn) |
| Abstract | Internal 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. |
| Keyword | Internal wave Amplitude Transfer learning Remote sensing In-situ measurement Laboratory experiment |
| DOI | 10.1016/j.rse.2022.112940 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | CAS (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 Area | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS Subject | Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS ID | WOS:000759731300003 |
| Publisher | ELSEVIER SCIENCE INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.qdio.ac.cn/handle/337002/178141 |
| Collection | 海洋环流与波动重点实验室 |
| Corresponding Author | Li, Xiaofeng |
| Affiliation | 1.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 Affilication | Institute of Oceanology, Chinese Academy of Sciences; Center for Ocean Mega-Science, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute 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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