{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T13:58:24Z","timestamp":1771509504614,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,5,27]],"date-time":"2022-05-27T00:00:00Z","timestamp":1653609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1811464"],"award-info":[{"award-number":["U1811464"]}],"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":["U21A6001"],"award-info":[{"award-number":["U21A6001"]}],"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":["No OOST2021-03"],"award-info":[{"award-number":["No OOST2021-03"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the CAS Key Laboratory of Science and Technology on Operational Oceanography Open Project Funding","award":["U1811464"],"award-info":[{"award-number":["U1811464"]}]},{"name":"the CAS Key Laboratory of Science and Technology on Operational Oceanography Open Project Funding","award":["U21A6001"],"award-info":[{"award-number":["U21A6001"]}]},{"name":"the CAS Key Laboratory of Science and Technology on Operational Oceanography Open Project Funding","award":["No OOST2021-03"],"award-info":[{"award-number":["No OOST2021-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ocean observation is essential for studying ocean dynamics, climate change, and carbon cycles. Due to the difficulty and high cost of in situ observations, existing ocean observations are inadequate, and satellite observations are mostly surface observations. Previous work has not adequately considered the spatio-temporal correlation within the ocean itself. This paper proposes a new method\u2014convolutional long short-term memory network (ConvLSTM)\u2014for the inversion of the ocean subsurface temperature and salinity fields with the sea surface satellite observations (sea surface temperature, sea surface salinity, sea surface height, and sea surface wind) and subsurface Argo reanalyze data. Given the time dependence and spatial correlation of the ocean dynamic parameters, the ConvLSTM model can improve inversion models\u2019 robustness and generalizability by considering ocean variability\u2019s significant spatial and temporal correlation characteristics. Taking the 2018 results as an example, our average inversion results in an overall normalized root mean square error (NRMSE) of 0.0568 \u00b0C\/0.0027 PSS and a correlation coefficient (R) of 0.9819\/0.9997 for subsurface temperature (ST)\/subsurface salinity (SS). The results show that SSTA, SSSA SSHA, and SSWA together are valuable parameters for obtaining accurate ST\/SS estimates, and the use of multiple channels in shallow seas is effective. This study demonstrates that ConvLSTM is superior in modeling the subsurface temperature and salinity fields, fully taking global ocean data\u2019s spatial and temporal correlation into account, and outperforms the classic random forest and LSTM approaches in predicting subsurface temperature and salinity fields.<\/jats:p>","DOI":"10.3390\/rs14112587","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T02:30:06Z","timestamp":1653964206000},"page":"2587","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Inversion of Ocean Subsurface Temperature and Salinity Fields Based on Spatio-Temporal Correlation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0130-3340","authenticated-orcid":false,"given":"Tao","family":"Song","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Campus de Montegancedo, Boadilla del Monte, 28660 Madrid, Spain"}]},{"given":"Wei","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1462-4696","authenticated-orcid":false,"given":"Fan","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"DAMO Academy, Alibaba Group, Hangzhou 310056, China"}]},{"given":"Jiarong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Runsheng","family":"Han","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Danya","family":"Xu","sequence":"additional","affiliation":[{"name":"Guangdong Laboratory of Marine Science and Engineering, Zhuhai 519080, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,27]]},"reference":[{"key":"ref_1","unstructured":"Bindoff, N.L., Willebrand, J., Artale, V., Cazenave, A., Gregory, J.M., Gulev, S., Hanawa, K., Le Quere, C., Levitus, S., and Nojiri, Y. 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