Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature Prediction
Accurate prediction of sea surface temperature (SST), a crucial indicator of global climate and ecosystem changes, holds significant economic and social benefits. Deep learning has shown preliminary success in modeling the dynamic spatial-temporal dependencies within SST signals, yet it remains chal...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10844304/ |
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| author | Zhenxiang Bai Zhengya Sun Bojie Fan An-An Liu Zhiqiang Wei Bo Yin |
| author_facet | Zhenxiang Bai Zhengya Sun Bojie Fan An-An Liu Zhiqiang Wei Bo Yin |
| author_sort | Zhenxiang Bai |
| collection | DOAJ |
| description | Accurate prediction of sea surface temperature (SST), a crucial indicator of global climate and ecosystem changes, holds significant economic and social benefits. Deep learning has shown preliminary success in modeling the dynamic spatial-temporal dependencies within SST signals, yet it remains challenging to obtain precise SSTs due to the inherent variabilities across multiple temporal and spatial scales, driven by distinct physical processes. In this paper, we propose a novel multi-scale spatio-temporal attention network, named MUSTAN, tailored for the SST prediction problem. MUSTAN achieves multi-scale fusion through a progressive scale expansion paradigm, where sub-scale representations are iteratively merged with its counterpart scale units, enabling the propagation of fine-scale SST changes across broader scales. For each scale, MUSTAN introduces temporal attention to characterize dynamic SST patterns in different ocean regions, and spatial attention to capture intricate SST evolution interplay among these regions. Extensive experiments conducted on datasets from the Bohai Sea, Yellow Sea, and South China Sea consistently validate the effectiveness and superiority of our design, outperforming the state-of-the-art methods on SST prediction tasks. |
| format | Article |
| id | doaj-art-dde619a7fbd24bffa45ef86d634ea301 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-dde619a7fbd24bffa45ef86d634ea3012025-08-20T03:10:46ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01185866587710.1109/JSTARS.2025.353112210844304Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature PredictionZhenxiang Bai0https://orcid.org/0009-0002-6381-5015Zhengya Sun1https://orcid.org/0000-0003-2381-6363Bojie Fan2An-An Liu3https://orcid.org/0000-0001-5755-9145Zhiqiang Wei4https://orcid.org/0000-0002-2830-8301Bo Yin5https://orcid.org/0000-0001-6318-0174Department of Computer Science and Technology, Ocean University of China, Qingdao, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaDepartment of Computer Science and Technology, Ocean University of China, Qingdao, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin, ChinaDepartment of Computer Science and Technology, Ocean University of China, Qingdao, ChinaDepartment of Computer Science and Technology, Ocean University of China, Qingdao, ChinaAccurate prediction of sea surface temperature (SST), a crucial indicator of global climate and ecosystem changes, holds significant economic and social benefits. Deep learning has shown preliminary success in modeling the dynamic spatial-temporal dependencies within SST signals, yet it remains challenging to obtain precise SSTs due to the inherent variabilities across multiple temporal and spatial scales, driven by distinct physical processes. In this paper, we propose a novel multi-scale spatio-temporal attention network, named MUSTAN, tailored for the SST prediction problem. MUSTAN achieves multi-scale fusion through a progressive scale expansion paradigm, where sub-scale representations are iteratively merged with its counterpart scale units, enabling the propagation of fine-scale SST changes across broader scales. For each scale, MUSTAN introduces temporal attention to characterize dynamic SST patterns in different ocean regions, and spatial attention to capture intricate SST evolution interplay among these regions. Extensive experiments conducted on datasets from the Bohai Sea, Yellow Sea, and South China Sea consistently validate the effectiveness and superiority of our design, outperforming the state-of-the-art methods on SST prediction tasks.https://ieeexplore.ieee.org/document/10844304/Data modelingdeep learningmultiscale spatio-temporal predictionsea surface temperature (SST) |
| spellingShingle | Zhenxiang Bai Zhengya Sun Bojie Fan An-An Liu Zhiqiang Wei Bo Yin Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature Prediction IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Data modeling deep learning multiscale spatio-temporal prediction sea surface temperature (SST) |
| title | Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature Prediction |
| title_full | Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature Prediction |
| title_fullStr | Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature Prediction |
| title_full_unstemmed | Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature Prediction |
| title_short | Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature Prediction |
| title_sort | multiscale spatio temporal attention network for sea surface temperature prediction |
| topic | Data modeling deep learning multiscale spatio-temporal prediction sea surface temperature (SST) |
| url | https://ieeexplore.ieee.org/document/10844304/ |
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