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Predicting SST at different temporal scales benefits various applications, from short-term SST prediction for weather forecasting to long-term SST prediction for analyzing El Ni\u00f1o\u2013Southern Oscillation (ENSO). However, existing approaches for SST prediction train separate models for different temporal scales, which is inefficient and cannot take advantage of the correlations among the temperatures of different scales to improve the prediction performance. In this work, we propose a unified spatio-temporal model termed the Multi-In and Multi-Out (MIMO) model to predict SST at different scales. MIMO is an encoder\u2013decoder model, where the encoder learns spatio-temporal features from the SST data of multiple scales, and fuses the learned features with a Cross Scale Fusion (CSF) operation. The decoder utilizes the learned features from the encoder to adaptively predict the SST of different scales. To our best knowledge, this is the first work to predict SST at different temporal scales simultaneously with a single model. According to the experimental evaluation on the Optimum Interpolation SST (OISST) dataset, MIMO achieves the state-of-the-art prediction performance.<\/jats:p>","DOI":"10.3390\/rs14102371","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"2371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["MIMO: A Unified Spatio-Temporal Model for Multi-Scale Sea Surface Temperature Prediction"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2331-1333","authenticated-orcid":false,"given":"Siyun","family":"Hou","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Shanghai 200082, China"},{"name":"Project Management Office of China National Scientific Seafloor Observatory, Tongji University, Shanghai 200082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8768-6740","authenticated-orcid":false,"given":"Wengen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Shanghai 200082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianying","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Shanghai 200082, China"},{"name":"Project Management Office of China National Scientific Seafloor Observatory, Tongji University, Shanghai 200082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuigeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Intelligent Information Processing, Shanghai 200438, China"},{"name":"School of Computer Science, Fudan University, Shanghai 200438, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jihong","family":"Guan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Shanghai 200082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rufu","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University, Shanghai 200082, China"},{"name":"State Key Laboratory of Marine Geology, Tongji University, Shanghai 200082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Project Management Office of China National Scientific Seafloor Observatory, Tongji University, Shanghai 200082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pisano, A., Marullo, S., Artale, V., Falcini, F., Yang, C., Leonelli, F.E., Santoleri, R., and Buongiorno Nardelli, B. 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