{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:00:56Z","timestamp":1774677656025,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T00:00:00Z","timestamp":1720137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFE0203500"],"award-info":[{"award-number":["2022YFE0203500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022B1212050003"],"award-info":[{"award-number":["2022B1212050003"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["SCSIO2023QY01"],"award-info":[{"award-number":["SCSIO2023QY01"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["SCSIO2023HC07"],"award-info":[{"award-number":["SCSIO2023HC07"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["SCSIO202201"],"award-info":[{"award-number":["SCSIO202201"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Science and Technology Planning Project of Guangdong Province, China","award":["2022YFE0203500"],"award-info":[{"award-number":["2022YFE0203500"]}]},{"name":"The Science and Technology Planning Project of Guangdong Province, China","award":["2022B1212050003"],"award-info":[{"award-number":["2022B1212050003"]}]},{"name":"The Science and Technology Planning Project of Guangdong Province, China","award":["SCSIO2023QY01"],"award-info":[{"award-number":["SCSIO2023QY01"]}]},{"name":"The Science and Technology Planning Project of Guangdong Province, China","award":["SCSIO2023HC07"],"award-info":[{"award-number":["SCSIO2023HC07"]}]},{"name":"The Science and Technology Planning Project of Guangdong Province, China","award":["SCSIO202201"],"award-info":[{"award-number":["SCSIO202201"]}]},{"name":"Chinese Academy of Sciences (CAS) Key Technology Talent Program of 2024","award":["2022YFE0203500"],"award-info":[{"award-number":["2022YFE0203500"]}]},{"name":"Chinese Academy of Sciences (CAS) Key Technology Talent Program of 2024","award":["2022B1212050003"],"award-info":[{"award-number":["2022B1212050003"]}]},{"name":"Chinese Academy of Sciences (CAS) Key Technology Talent Program of 2024","award":["SCSIO2023QY01"],"award-info":[{"award-number":["SCSIO2023QY01"]}]},{"name":"Chinese Academy of Sciences (CAS) Key Technology Talent Program of 2024","award":["SCSIO2023HC07"],"award-info":[{"award-number":["SCSIO2023HC07"]}]},{"name":"Chinese Academy of Sciences (CAS) Key Technology Talent Program of 2024","award":["SCSIO202201"],"award-info":[{"award-number":["SCSIO202201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sea Surface Temperature (SST) is of great importance to study several major phenomena due to ocean interactions with other earth systems. Previous studies on SST based on statistical inference methods were less accurate for longer prediction lengths. A considerable number of studies in recent years involve machine learning for SST modeling. These models were able to mitigate this problem to some length by modeling SST patterns and trends. Sequence analysis by decomposition is used for SST forecasting in several studies. Ensemble Empirical Mode Decomposition (EEMD) has been proven in previous studies as a useful method for this. The application of EEMD in spatiotemporal modeling has been introduced as Multidimensional EEMD (MEEMD). The aim of this study is to employ fast MEEMD methods to decompose the SST spatiotemporal dataset and apply a Convolutional Long Short-Term Memory (ConvLSTM)-based model to model and forecast SST. The results show that the fast MEEMD method is capable of enhancing spatiotemporal SST modeling compared to the Linear Inverse Model (LIM) and ConvLSTM model without decomposition. The model was further validated by making predictions from April to May 2023 and comparing them to original SST values. There was a high consistency between predicted and real SST values.<\/jats:p>","DOI":"10.3390\/rs16132468","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T11:46:05Z","timestamp":1720179965000},"page":"2468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Application of Fast MEEMD\u2013ConvLSTM in Sea Surface Temperature Predictions"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0511-4469","authenticated-orcid":false,"given":"R. W. W. M. U. P.","family":"Wanigasekara","sequence":"first","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Zhenqiu","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"}]},{"given":"Weiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"}]},{"given":"Yao","family":"Luo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0054-6516","authenticated-orcid":false,"given":"Gang","family":"Pan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Robles-Tamayo, C.M., Valdez-Holgu\u00edn, J.E., Garc\u00eda-Morales, R., Figueroa-Preciado, G., Herrera-Cervantes, H., L\u00f3pez-Mart\u00ednez, J., and Enr\u00edquez-Oca\u00f1a, L.F. 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