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  2. 海洋研究所
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  4. 海洋环流与波动重点实验室
Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction

文献类型：期刊论文

作者Jiang, Jiawei4,5; Wang, Jun1,6; Liu, Yiping7; Huang, Chao4; Jiang, Qiufu4; Feng, Liqiang2; Wan, Liying3; Zhang, Xiangguang4,5
刊名REMOTE SENSING
出版日期2024-06-01
卷号16期号:12页码:18
关键词temperature structure prediction temperature structure reconstruction spatiotemporal window ocean satellite observations spatiotemporal attention mechanism
DOI10.3390/rs16122243
通讯作者Zhang, Xiangguang(zxg@qdio.ac.cn)
英文摘要In this study, we investigate the feasibility of using historical remote sensing data to predict the future three-dimensional subsurface ocean temperature structure. We also compare the performance differences between predictive models and real-time reconstruction models. Specifically, we propose a multi-scale residual spatiotemporal window ocean (MSWO) model based on a spatiotemporal attention mechanism, to predict changes in the subsurface ocean temperature structure over the next six months using satellite remote sensing data from the past 24 months. Our results indicate that predictions made using historical remote sensing data closely approximate those made using historical in situ data. This finding suggests that satellite remote sensing data can be used to predict future ocean structures without relying on valuable in situ measurements. Compared to future predictive models, real-time three-dimensional structure reconstruction models can learn more accurate inversion features from real-time satellite remote sensing data. This work provides a new perspective for the application of artificial intelligence in oceanography for ocean structure reconstruction.
WOS关键词OCEAN MODEL
资助项目Technology Support Talent Program of the Chinese Academy of Sciences[E4KY31] ; Chinese Academy of Sciences pilot project[XDB42000000] ; Major Science and Technology Infrastructure Maintenance and Reconstruction Project of the Chinese Academy of Sciences[DSS-WXGZ-2022] ; National Key Research and Development Program[2021YFC3101504] ; National Natural Science Foundation of China[42176030] ; High Level Innovative Talent Project of NUDT
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001256118000001
出版者MDPI
源URL[http://ir.qdio.ac.cn/handle/337002/186449]  
专题海洋研究所_海洋环流与波动重点实验室
通讯作者Zhang, Xiangguang
作者单位1.Hunan Key Lab Marine Detect Technol, Changsha 410073, Peoples R China
2.Chinese Acad Sci, Inst Oceanol, Ocean Big Data Ctr, Qingdao 266071, Peoples R China
3.Natl Marine Environm Forecasting Ctr, Key Lab Res Marine Hazards Forecasting, Beijing 100081, Peoples R China
4.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
6.Natl Univ Def Technol, Coll Meteorol & Oceanog, Changsha 410073, Peoples R China
7.China Geol Survey, Yantai Ctr Coastal Zone Geol Survey, Yantai 264000, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Jiawei,Wang, Jun,Liu, Yiping,et al. Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction[J]. REMOTE SENSING,2024,16(12):18.
APA Jiang, Jiawei.,Wang, Jun.,Liu, Yiping.,Huang, Chao.,Jiang, Qiufu.,...&Zhang, Xiangguang.(2024).Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction.REMOTE SENSING,16(12),18.
MLA Jiang, Jiawei,et al."Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction".REMOTE SENSING 16.12(2024):18.

入库方式： OAI收割

来源：海洋研究所

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