{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:06:23Z","timestamp":1784736383943,"version":"3.55.0"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFF0704000"],"award-info":[{"award-number":["2021YFF0704000"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2068"],"award-info":[{"award-number":["U22A2068"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20320"],"award-info":[{"award-number":["U23A20320"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Project of Shandong Province, China","award":["2020JMRH0201"],"award-info":[{"award-number":["2020JMRH0201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/jstars.2025.3531122","type":"journal-article","created":{"date-parts":[[2025,1,17]],"date-time":"2025-01-17T13:36:50Z","timestamp":1737121010000},"page":"5866-5877","source":"Crossref","is-referenced-by-count":17,"title":["Multiscale Spatio-Temporal Attention Network for Sea Surface Temperature Prediction"],"prefix":"10.1109","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6381-5015","authenticated-orcid":false,"given":"Zhenxiang","family":"Bai","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Ocean University of China, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2381-6363","authenticated-orcid":false,"given":"Zhengya","family":"Sun","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bojie","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Ocean University of China, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5755-9145","authenticated-orcid":false,"given":"An-An","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Tianjin University, Tianjin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2830-8301","authenticated-orcid":false,"given":"Zhiqiang","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Ocean University of China, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6318-0174","authenticated-orcid":false,"given":"Bo","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Ocean University of China, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/s00382-017-3751-5"},{"key":"ref2","article-title":"Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach","volume-title":"Remote Sens. Environ.","volume":"233","author":"Xiao","year":"2019"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1126\/science.288.5467.847"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/lgrs.2017.2733548"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-019-0236-x"},{"key":"ref6","first-page":"217","article-title":"Data-driven modeling of surface temperature anomaly and solar activity trends","volume-title":"Environ. Modelling Softw.","volume":"37","author":"Friedel","year":"2012"},{"issue":"20","key":"ref7","first-page":"7943","article-title":"Sea surface temperature warming patterns and future vegetation change","volume-title":"J. Climate","volume":"28","author":"Rauscher","year":"2015"},{"issue":"2","key":"ref8","first-page":"125","article-title":"An artificial neural network method for detecting red tides with noaa avhrr imagery","volume":"7","author":"Lou","year":"2003","journal-title":"J. Remote Sens.-Beijing-"},{"issue":"16","key":"ref9","first-page":"2184","article-title":"Atmospheric response patterns associated with tropical forcing","volume-title":"J. Climate","volume":"15","author":"Hoerling","year":"2002"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1080\/01431161.2010.485139"},{"key":"ref11","first-page":"94","article-title":"Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean","volume-title":"Ecological Inform.","volume":"36","author":"Salles","year":"2016"},{"key":"ref12","first-page":"485","article-title":"Impact of common sea surface temperature anomalies on global drought and pluvial frequency","volume-title":"J. Climate","volume":"23","author":"Findell","year":"2010"},{"key":"ref13","first-page":"1001","article-title":"Abnormal warm sea-surface temperature in the Indian Ocean, active potential vorticity over the Tibetan plateau, and severe flooding along the Yangtze river in summer 2020","volume-title":"Quart. J. Roy. Meteorological Soc.","volume":"148","author":"Ma","year":"2022"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-023-09970-5"},{"issue":"23","key":"ref15","first-page":"6047","article-title":"Tropical atlantic SST prediction with coupled oceanAtmosphere GCMs","volume-title":"J. Climate","volume":"19","author":"Stockdale","year":"2006"},{"key":"ref16","first-page":"1545","article-title":"ENSO and ENSO-related predictability. Part I: Prediction of equatorial Pacific sea surface temperature with a hybrid coupled oceanatmosphere model","volume-title":"J. Climate","volume":"6","author":"Barnett","year":"1993"},{"key":"ref17","first-page":"45","article-title":"Statistical prediction of sea-surface temperature over the tropical atlantic","volume-title":"Int. J. Climatol.","volume":"24","author":"Repelli","year":"2004"},{"key":"ref18","first-page":"1715","article-title":"Prediction of sea surface temperature by combining numerical and neural techniques","volume-title":"J. Atmospheric Ocean. Technol.","volume":"33","author":"Patil","year":"2016"},{"key":"ref19","article-title":"Deep-learning model for sea surface temperature prediction near the Korean peninsula","volume-title":"Deep Sea Res. Part II, Topical Stud. Oceanogr.","volume":"208","author":"Choi","year":"2023"},{"key":"ref20","article-title":"Estimation of ocean subsurface thermal structure from surface parameters: A neural network approach","volume-title":"Geophysical Res. Lett.","volume":"31","author":"Ali","year":"2004"},{"key":"ref21","article-title":"Sea surface temperature prediction with memory graph convolutional networks","volume-title":"IEEE Geosci. Remote Sens. Lett.","volume":"19","author":"Zhang","year":"2022"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2024.3415821"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s00382-024-07348-2"},{"issue":"7","key":"ref24","article-title":"Prediction of sea surface temperature using U-Net based model","volume-title":"Remote Sens.","volume":"16","author":"Ren","year":"2024"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3425514"},{"key":"ref26","article-title":"Network-based identification and characterization of teleconnections on different scales","volume-title":"Sci. Rep.","volume":"9","author":"Agarwal","year":"2019"},{"issue":"1","key":"ref27","article-title":"MSSTNet: A multi-scale spatiotemporal prediction neural network for precipitation nowcasting","volume-title":"Remote Sens.","volume":"15","author":"Ye","year":"2023"},{"key":"ref28","article-title":"MS-LSTM: Exploring spatiotemporal multiscale representations in video prediction domain","volume-title":"Appl. Soft Comput.","volume":"147","author":"Ma","year":"2023"},{"issue":"22","key":"ref29","article-title":"GLSNN network: A multi-scale spatiotemporal prediction model for urban traffic flow","volume-title":"Sensors","volume":"22","author":"Cai","year":"2022"},{"key":"ref30","article-title":"A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data","volume-title":"Environ. Model. Softw.","volume":"120","author":"Xiao","year":"2019"},{"key":"ref31","article-title":"Time-series graph network for sea surface temperature prediction","volume-title":"Big Data Res.","volume":"25","author":"Sun","year":"2021"},{"issue":"14","key":"ref32","article-title":"A graph memory neural network for sea surface temperature prediction","volume-title":"Remote Sens.","volume":"15","author":"Liang","year":"2023"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2024.3357191"},{"issue":"10","key":"ref34","article-title":"MIMO: A unified spatio-temporal model for multi-scale sea surface temperature prediction","volume-title":"Remote Sens.","volume":"14","author":"Hou","year":"2022"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3597937"},{"key":"ref36","article-title":"TemproNet: A transformer-based deep learning model for seawater temperature prediction","volume-title":"Ocean Eng.","volume":"293","author":"Chen","year":"2024"},{"key":"ref37","first-page":"10012","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","volume-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis. (ICCV)","author":"Liu"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2780843"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2931728"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICME52920.2022.9859659"},{"key":"ref42","article-title":"Scaleformer: Iterative multi-scale refining transformers for time series forecasting","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Shabani","year":"2023"},{"key":"ref43","article-title":"SimMTM: A simple pre-training framework for masked time-series modeling","volume-title":"Proc. 37th Conf. Neural Inf. Process. Syst.","author":"Dong","year":"2023"}],"container-title":["IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/4609443\/10766875\/10844304.pdf?arnumber=10844304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T21:19:10Z","timestamp":1771449550000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10844304\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/jstars.2025.3531122","relation":{},"ISSN":["1939-1404","2151-1535"],"issn-type":[{"value":"1939-1404","type":"print"},{"value":"2151-1535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}