{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T19:27:43Z","timestamp":1777404463993,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,20]],"date-time":"2024-04-20T00:00:00Z","timestamp":1713571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41976165"],"award-info":[{"award-number":["41976165"]}]},{"name":"National Natural Science Foundation of China","award":["2022HS038"],"award-info":[{"award-number":["2022HS038"]}]},{"name":"Hillstone Networks Project of Network Security","award":["41976165"],"award-info":[{"award-number":["41976165"]}]},{"name":"Hillstone Networks Project of Network Security","award":["2022HS038"],"award-info":[{"award-number":["2022HS038"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Predicting the mesoscale eddies in the ocean is crucial for advancing our understanding of the ocean and climate systems. Establishing spatio-temporal correlation among input data is a significant challenge in mesoscale eddy prediction tasks, especially for deep learning techniques. In this paper, we first present a deep learning solution based on a video prediction model to capture the spatio-temporal correlation and predict future sea surface height data accurately. To enhance the performance of the model, we introduced a novel metadata embedding module that utilizes neural networks to fuse remote sensing metadata with input data, resulting in increased accuracy. To the best of our knowledge, our model outperforms the state-of-the-art method for predicting sea level anomalies. Consequently, a mesoscale eddy detection algorithm will be applied to the predicted sea surface height data to generate mesoscale eddies in future. The proposed solution achieves competitive results, indicating that the prediction error for the eddy center position is 5.6 km for a 3-day prediction and 13.6 km for a 7-day prediction.<\/jats:p>","DOI":"10.3390\/rs16081466","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T03:57:07Z","timestamp":1713758227000},"page":"1466","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Metadata-Enhanced Deep Learning Method for Sea Surface Height and Mesoscale Eddy Prediction"],"prefix":"10.3390","volume":"16","author":[{"given":"Rongjie","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Teacher Education, Nanjing University of Information Science and Technology, Nanjing 211800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biao","family":"Song","sequence":"additional","affiliation":[{"name":"School of Software, Nanjing University of Information Science and Technology, Nanjing 211800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1167-3991","authenticated-orcid":false,"given":"Zhongfeng","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Marine Science, Nanjing University of Information Science and Technology, Nanjing 211800, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[{"name":"Nanjing Institute of Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5305","DOI":"10.1029\/JC095iC04p05305","article-title":"Mesoscale eddy diffusion, particle sinking, and the interpretation of sediment trap data","volume":"95","author":"Siegel","year":"1990","journal-title":"J. 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