Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction

doi:10.3390/rs16122243

IOCAS-IR > 海洋环流与波动重点实验室

	Multi-Scale Window Spatiotemporal Attention Network for Subsurface Temperature Prediction and Reconstruction
	Jiang, Jiawei 1,2; Wang, Jun 3,4; Liu, Yiping 5; Huang, Chao1 ; Jiang, Qiufu1 ; Feng, Liqiang 6; Wan, Liying 7; Zhang, Xiangguang 1,2
	2024-06-01
发表期刊	REMOTE SENSING
卷号	16 期号:12 页码:18
通讯作者	Zhang, Xiangguang(zxg@qdio.ac.cn)
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.
关键词	temperature structure prediction temperature structure reconstruction spatiotemporal window ocean satellite observations spatiotemporal attention mechanism
DOI	10.3390/rs16122243
收录类别	SCI
语种	英语
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
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001256118000001
出版者	MDPI
WOS关键词	OCEAN MODEL
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/186450
专题	海洋环流与波动重点实验室
通讯作者	Zhang, Xiangguang
作者单位	1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Natl Univ Def Technol, Coll Meteorol & Oceanog, Changsha 410073, Peoples R China 4.Hunan Key Lab Marine Detect Technol, Changsha 410073, Peoples R China 5.China Geol Survey, Yantai Ctr Coastal Zone Geol Survey, Yantai 264000, Peoples R China 6.Chinese Acad Sci, Inst Oceanol, Ocean Big Data Ctr, Qingdao 266071, Peoples R China 7.Natl Marine Environm Forecasting Ctr, Key Lab Res Marine Hazards Forecasting, Beijing 100081, Peoples R China
第一作者单位	海洋环流与波动重点实验室
通讯作者单位	海洋环流与波动重点实验室
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.