{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:51:13Z","timestamp":1784299873364,"version":"3.55.0"},"reference-count":56,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,8]],"date-time":"2022-07-08T00:00:00Z","timestamp":1657238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["SML2020SP007"],"award-info":[{"award-number":["SML2020SP007"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["311020004"],"award-info":[{"award-number":["311020004"]}]},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)","award":["41806004"],"award-info":[{"award-number":["41806004"]}]},{"name":"National Natural Science Foundation of China","award":["SML2020SP007"],"award-info":[{"award-number":["SML2020SP007"]}]},{"name":"National Natural Science Foundation of China","award":["311020004"],"award-info":[{"award-number":["311020004"]}]},{"name":"National Natural Science Foundation of China","award":["41806004"],"award-info":[{"award-number":["41806004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sea surface temperature (SST) is an important physical factor in the interaction between the ocean and the atmosphere. Accurate monitoring and prediction of the temporal and spatial distribution of SST are of great significance in dealing with climate change, disaster prevention, disaster reduction, and marine ecological protection. This study establishes a prediction model of sea surface temperature for the next five days in the East China Sea using long-term and short-term memory neural networks (LSTM). It investigates the influence of different parameters on prediction accuracy. The sensitivity experiment results show that, based on the same training data, the length of the input data of the LSTM model can improve the model\u2019s prediction performance to a certain extent. However, no obvious positive correlation is observed between the increase in the input data length and the improvement of the model\u2019s prediction accuracy. On the contrary, the LSTM model\u2019s performance decreases with the prediction length increase. Furthermore, the single-point prediction results of the LSTM model for the estuary of the Yangtze River, Kuroshio, and the Pacific Ocean are accurate. In particular, the prediction results of the point in the Pacific Ocean are the most accurate at the selected four points, with an RMSE of 0.0698 \u00b0C and an R2 of 99.95%. At the same time, the model in the Pacific region is migrated to the East China Sea. The model was found to have good mobility and can well represent the long-term and seasonal trends of SST in the East China Sea.<\/jats:p>","DOI":"10.3390\/rs14143300","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:06:21Z","timestamp":1657497981000},"page":"3300","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Prediction of Sea Surface Temperature in the East China Sea Based on LSTM Neural Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiaoyan","family":"Jia","sequence":"first","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiyan","family":"Ji","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Han","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8724-7820","authenticated-orcid":false,"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqing","family":"Han","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiayan","family":"Lin","sequence":"additional","affiliation":[{"name":"Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/S0034-4257(02)00103-7","article-title":"Remote sensing of Southern Ocean sea surface temperature: Implications for marine biophysical models","volume":"84","author":"Sumner","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1126\/science.288.5467.847","article-title":"Satellite Measurements of Sea Surface Temperature through Clouds","volume":"288","author":"Wentz","year":"2000","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7943","DOI":"10.1175\/JCLI-D-14-00528.1","article-title":"Sea Surface Temperature Warming Patterns and Future Vegetation Change","volume":"28","author":"Rauscher","year":"2015","journal-title":"J. Clim."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ecoinf.2016.10.004","article-title":"Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean","volume":"36","author":"Salles","year":"2016","journal-title":"Ecol. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.rse.2017.03.008","article-title":"Temporal trends in sea surface temperature gradients in the South Atlantic Ocean","volume":"194","author":"Bouali","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1126\/science.275.5302.957","article-title":"Twentieth-Century Sea Surface Temperature Trends","volume":"275","author":"Cane","year":"1997","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.rse.2016.10.035","article-title":"Validation of satellite sea surface temperature analyses in the Beaufort Sea using UpTempO buoys","volume":"187","author":"Castro","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8144","DOI":"10.1038\/s41598-017-08146-z","article-title":"Decadal trends in Red Sea maximum surface temperature","volume":"7","author":"Chaidez","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1126\/science.1185435","article-title":"Tropical ocean temperatures over the past 3.5 million years","volume":"328","author":"Herbert","year":"2010","journal-title":"Science"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1038\/nclimate3304","article-title":"Distinct global warming rates tied to multiple ocean surface temperature changes","volume":"7","author":"Yao","year":"2017","journal-title":"Nat. Clim. Change"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1016\/j.watres.2006.11.053","article-title":"Ecological anomalies in the East China Sea: Impacts of the Three Gorges Dam?","volume":"41","author":"Jiao","year":"2007","journal-title":"Water Res."},{"key":"ref_12","first-page":"3","article-title":"Analysis and Forecast of Sea Surface Temperature Field for the East China Sea and the adjacent waters","volume":"1","author":"Du","year":"1986","journal-title":"Mar. Forecast."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e16518","DOI":"10.1029\/2020JC016518","article-title":"Drivers of Marine Heatwaves in the East China Sea and the South Yellow Sea in Three Consecutive Summers During 2016\u20132018","volume":"125","author":"Gao","year":"2020","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101243","DOI":"10.1016\/j.dynatmoce.2021.101243","article-title":"Impact of Surface forcing on simulating Sea Surface Temperature in the Indian Ocean\u2014A study using Regional Ocean Modeling System (ROMS)","volume":"95","author":"Tiwari","year":"2021","journal-title":"Dyn. Atmos. Ocean."},{"key":"ref_15","first-page":"1","article-title":"Sea Surface Temperature Simulation of Tropical and North Pacific Basins Using a Hybrid Coordinate Ocean Model (HYCOM)","volume":"10","author":"Gao","year":"2008","journal-title":"Mar. Sci. Bull."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1119\/1.1941791","article-title":"An Introduction to Physical Oceanography","volume":"30","author":"Arx","year":"2005","journal-title":"Am. J. Phys."},{"key":"ref_17","first-page":"s2","article-title":"An introduction to GODAE OceanView","volume":"8","author":"Bell","year":"2015","journal-title":"J. Oper. Oceanogr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2701","DOI":"10.1029\/1999GL011107","article-title":"Forecasts of tropical Pacific SST and sea level using a Markov model","volume":"27","author":"Xue","year":"2000","journal-title":"Geophys. Res. Lett."},{"key":"ref_19","unstructured":"Laepple, T., and Jewson, S. (2007). Five year ahead prediction of Sea Surface Temperature in the Tropical Atlantic: A comparison between IPCC climate models and simple statistical methods. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s00382-004-0390-4","article-title":"Predictability of Indian Ocean sea surface temperature using canonical correlation analysis","volume":"22","author":"Collins","year":"2004","journal-title":"Clim. Dyn."},{"key":"ref_21","first-page":"9","article-title":"Review of Research on Data Mining in Application of Meteorological Forecasting","volume":"33","author":"Peng","year":"2015","journal-title":"J. Arid. Meteorol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1002\/we.295","article-title":"Wind farm power prediction: A data-mining approach","volume":"12","author":"Kusiak","year":"2009","journal-title":"Wind Energy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.rse.2014.08.012","article-title":"Mapping maximum urban air temperature on hot summer days","volume":"154","author":"Ho","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.1016\/j.solener.2010.05.009","article-title":"The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data","volume":"84","author":"Behrang","year":"2010","journal-title":"Sol. Energy"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s00704-012-0661-7","article-title":"Least squares support vector machine for short-term prediction of meteorological time series","volume":"111","author":"Mellit","year":"2012","journal-title":"Theor. Appl. Climatol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2016.11.002","article-title":"High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat\/GLAS observations","volume":"123","author":"Yue","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1016\/j.scitotenv.2018.12.297","article-title":"Estimation of spatiotemporal PM 1.0 distributions in China by combining PM 2.5 observations with satellite aerosol optical depth","volume":"658","author":"Zang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1175\/1520-0442(1998)011<0029:FEEANN>2.0.CO;2","article-title":"Forecasting ENSO Events: A Neural Network\u2013Extended EOF Approach","volume":"11","author":"Tangang","year":"1998","journal-title":"J. Clim."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s003820050156","article-title":"Forecasting the equatorial Pacific sea surface temperatures by neural network models","volume":"13","author":"Tangang","year":"1997","journal-title":"Clim. Dyn."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7511","DOI":"10.1029\/97JC03414","article-title":"Forecasting regional sea surface temperatures in the tropical Pacific by neural network models, with wind stress and sea level pressure as predictors","volume":"103","author":"Tangang","year":"1998","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.neunet.2006.01.004","article-title":"Neural network forecasts of the tropical Pacific sea surface temperatures","volume":"19","author":"Wu","year":"2006","journal-title":"Neural Netw."},{"key":"ref_32","first-page":"52","article-title":"Comparison of the accuracy of SST estimates by artificial neural networks (ANN) and other quantitative methods using radiolarian data from the Antarctic and Pacific Oceans","volume":"2","author":"Gupta","year":"2009","journal-title":"Earth Sci. India"},{"key":"ref_33","first-page":"210","article-title":"Predictability of sea surface temperature anomalies in the Indian Ocean using artificial neural networks","volume":"35","author":"Tripathi","year":"2006","journal-title":"Indian J. Mar. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"302479","DOI":"10.1155\/2013\/302479","article-title":"Predicting Sea Surface Temperatures in the North Indian Ocean with Nonlinear Autoregressive Neural Networks","volume":"2013","author":"Patil","year":"2013","journal-title":"Int. J. Oceanogr."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s10236-017-1032-9","article-title":"Prediction of daily sea surface temperature using efficient neural networks","volume":"67","author":"Patil","year":"2017","journal-title":"Ocean. Dyn."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1260\/1759-3131.4.2.133","article-title":"Using Artificial Neural Networks to Forecast Monthly and Seasonal Sea Surface Temperature Anomalies in the Western Indian Ocean","volume":"4","author":"Mahongo","year":"2013","journal-title":"Int. J. Ocean. Clim. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4214","DOI":"10.1080\/01431161.2018.1454623","article-title":"Prediction of daily sea surface temperature using artificial neural networks","volume":"39","author":"Aparna","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hou, S., Li, W., Liu, T., Zhou, S., Guan, J., Qin, R., and Wang, Z. (2022). MIMO: A Unified Spatio-Temporal Model for Multi-Scale Sea Surface Temperature Prediction. Remote Sens., 14.","DOI":"10.3390\/rs14102371"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/LGRS.2017.2733548","article-title":"Prediction of Sea Surface Temperature Using Long Short-Term Memory","volume":"14","author":"Zhang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1007\/s42452-020-03239-3","article-title":"Prediction of sea surface temperatures using deep learning neural networks","volume":"2","author":"Sarkar","year":"2020","journal-title":"SN Appl. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kim, M., Yang, H., and Kim, J. (2020). Sea Surface Temperature and High Water Temperature Occurrence Prediction Using a Long Short-Term Memory Model. Remote Sens., 12.","DOI":"10.3390\/rs12213654"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"12040","DOI":"10.1088\/1755-1315\/658\/1\/012040","article-title":"Sea surface temperature prediction model based on long and short-term memory neural network","volume":"658","author":"Li","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"111358","DOI":"10.1016\/j.rse.2019.111358","article-title":"Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach","volume":"233","author":"Xiao","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"104501","DOI":"10.1016\/j.envsoft.2019.104502","article-title":"A spatiotemporal deep learning model for sea surface temperature field prediction using time-series satellite data","volume":"120","author":"Xiao","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wei, L., Guan, L., Qu, L., and Guo, D. (2020). Prediction of Sea Surface Temperature in the China Seas Based on Long Short-Term Memory Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12172697"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"100237","DOI":"10.1016\/j.bdr.2021.100237","article-title":"Time-Series Graph Network for Sea Surface Temperature Prediction","volume":"25","author":"Sun","year":"2021","journal-title":"Big Data Res."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Pan, X., Jiang, T., Sui, B., Liu, C., and Sun, W. (2020). Monthly and Quarterly Sea Surface Temperature Prediction Based on Gated Recurrent Unit Neural Network. J. Mar. Sci. Eng., 8.","DOI":"10.3390\/jmse8040249"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.rse.2010.10.017","article-title":"Roberts-Jones, J.; Fiedler, E.; Wimmer, W. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) system","volume":"116","author":"Donlon","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_50","first-page":"88","article-title":"A comparison analysis of six sea surface temperature products","volume":"35","author":"Jiang","year":"2013","journal-title":"Acta Oceanol. Sin."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_53","first-page":"2121","article-title":"Adaptive Subgradient Methods for Online Learning and Stochastic Optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_54","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_55","first-page":"11","article-title":"The Kuroshio. Part I. Physical features","volume":"28","author":"Su","year":"1990","journal-title":"Oceanogr. Mar. Biol. Annu. Rev."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/0198-0149(91)90070-V","article-title":"Ocean heat transport across 24\u00b0N in the Pacific","volume":"38","author":"Bryden","year":"1991","journal-title":"Deep Sea Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3300\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:46:45Z","timestamp":1760140005000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3300"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,8]]},"references-count":56,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143300"],"URL":"https:\/\/doi.org\/10.3390\/rs14143300","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,8]]}}}