{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:00:47Z","timestamp":1774627247844,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T00:00:00Z","timestamp":1646870400000},"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":["No. 61802424"],"award-info":[{"award-number":["No. 61802424"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No. 2018YFC1406206"],"award-info":[{"award-number":["No. 2018YFC1406206"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sea surface temperature (SST) has important practical value in ocean related fields. Numerical prediction is a common method for forecasting SST at present. However, the forecast results produced by the numerical forecast models often deviate from the actual observation data, so it is necessary to correct the bias of the numerical forecast products. In this paper, an SST correction approach based on the Convolutional Long Short-Term Memory (ConvLSTM) network with multiple attention mechanisms is proposed, which considers the spatio-temporal relations in SST data. The proposed model is appropriate for correcting SST numerical forecast products by using satellite remote sensing data. The approach is tested in the region of the South China Sea and reduces the root mean squared error (RMSE) to 0.35 \u00b0C. Experimental results reveal that the proposed approach is significantly better than existing models, including traditional statistical methods, machine learning based methods, and deep learning methods.<\/jats:p>","DOI":"10.3390\/rs14061339","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T20:19:10Z","timestamp":1646943550000},"page":"1339","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Hybrid Deep Learning Model for the Bias Correction of SST Numerical Forecast Products Using Satellite Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Tonghan","family":"Fei","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Binghu","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}]},{"given":"Xiang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Junxing","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2066-6064","authenticated-orcid":false,"given":"Yan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4334-3882","authenticated-orcid":false,"given":"Huizan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Weimin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"150202132719008","DOI":"10.1175\/JCLI-D-14-00334.1","article-title":"The leading mode of observed and cmip5 enso-residual sea surface temperatures and associated changes in indo-pacific climate","volume":"28","author":"Funk","year":"2015","journal-title":"J. Clim."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1080\/01490419.2015.1010757","article-title":"Integrative analysis of altika-ssha, modis-sst, and ocm-chlorophyll signatures for fisheries applications","volume":"38","author":"Solanki","year":"2015","journal-title":"Mar. Geod."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/LGRS.2017.2780843","article-title":"A cfcc-lstm model for sea surface temperature prediction","volume":"15","author":"Yang","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6047","DOI":"10.1175\/JCLI3947.1","article-title":"Tropical atlantic sst prediction with coupled ocean-atmosphere gcms","volume":"19","author":"Stockdale","year":"2006","journal-title":"J. Clim."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1080\/10020070708541038","article-title":"An improvement of the too cold tongue in the tropical pacific with the development of an ocean-wave-atmosphere coupled numerical model","volume":"17","author":"Song","year":"2007","journal-title":"Prog. Nat. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2915","DOI":"10.1007\/s00382-013-1901-y","article-title":"Oceanic origin of southeast tropical atlantic biases","volume":"43","author":"Xu","year":"2014","journal-title":"Clim. Dyn."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ocemod.2006.03.005","article-title":"Effect of determining initial conditions by four-dimensional variational data assimilation on storm surge forecasting","volume":"14","author":"Peng","year":"2006","journal-title":"Ocean Model."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1007\/s00376-018-8003-z","article-title":"Optimal initial error growth in the prediction of the kuroshio large meander based on a high-resolution regional ocean model","volume":"35","author":"Li","year":"2018","journal-title":"Adv. Atmos. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9197","DOI":"10.1002\/2014GL062472","article-title":"Trends in the predictive performance of raw ensemble weather forecasts","volume":"41","author":"Hemri","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1175\/2008WAF2222126.1","article-title":"Dynamical properties of mos forecasts: Analysis of the ecmwf operational forecasting system","volume":"23","author":"Vannitsem","year":"2010","journal-title":"Weather Forecast."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8384","DOI":"10.1175\/JCLI-D-13-00481.1","article-title":"Statistical downscaling multimodel forecasts for seasonal precipitation and surface temperature over the southeastern united states","volume":"27","author":"Tian","year":"2014","journal-title":"J. Clim."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.atmosres.2007.08.006","article-title":"Correction of 2m-temperature forecasts using kalman filtering technique","volume":"87","author":"Libonati","year":"2008","journal-title":"Atmos. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4837","DOI":"10.1175\/MWR-D-17-0084.1","article-title":"Adaptive kalman filtering for postprocessing ensemble numerical weather predictions","volume":"145","author":"Pelosi","year":"2017","journal-title":"Mon. Weather Rev."},{"key":"ref_14","first-page":"16","article-title":"Temporal and spatial distribution of short-time heavy rain of Sichuan Basin in summer","volume":"35","author":"Wang","year":"2015","journal-title":"Plateau Mt. Meteorol. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Yu, Y., Zhang, W., Luo, T., and Wang, X. (2019). Cloud detection from fy-4a\u2019s geostationary interferometric infrared sounder using machine learning approaches. Remote Sens., 11.","DOI":"10.3390\/rs11243035"},{"key":"ref_16","first-page":"114270Z","article-title":"Correction model for the temperature of numerical weather prediction by SVM","volume":"11427","author":"Zeng","year":"2020","journal-title":"Second Target Recognit. Artif. Intell. Summit Forum"},{"key":"ref_17","first-page":"105","article-title":"Neural network bp model approximation and prediction of complicated weather systems","volume":"15","author":"Zhang","year":"2001","journal-title":"Acta Meteorol. Sin."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"118376","DOI":"10.1016\/j.atmosenv.2021.118376","article-title":"A deep convolutional neural network model for improving WRF forecasts","volume":"253","author":"Sayeed","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1109\/TGRS.2018.2865429","article-title":"Gaussian Process Regression for Arctic Coastal Erosion Forecasting","volume":"57","author":"Kupilik","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2004.11.015","article-title":"Oil spill detection by satellite remote sensing","volume":"95","author":"Brekke","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yu, Y., Yang, X., Zhang, W., Duan, B., Cao, X., and Leng, H. (2017). Assimilation of sentinel-1 derived sea surface winds for typhoon forecasting. Remote Sens., 9.","DOI":"10.3390\/rs9080845"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, R., Zhang, W., and Wang, X. (2020). Machine learning in tropical cyclone forecast modeling: A review. Atmosphere, 11.","DOI":"10.3390\/atmos11070676"},{"key":"ref_23","first-page":"1445","article-title":"A survey on deep learning for natural language processing","volume":"42","author":"Xi","year":"2016","journal-title":"Acta Autom. Sin."},{"key":"ref_24","first-page":"1096","article-title":"Unsupervised feature learning for audio classification using convolutional deep belief networks","volume":"22","author":"Lee","year":"2009","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sattar, N.S., and Arifuzzaman, S. (2020, January 10\u201313). Community Detection using Semi-supervised Learning with Graph Convolutional Network on GPUs. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378123"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jain, V., Murray, J.F., Roth, F., Turaga, S., Zhigulin, V., Briggman, K.L., Helmstaedter, M.N., Denk, W., and Seung, H.S. (2007, January 14\u201321). Supervised Learning of Image Restoration with Convolutional Networks. Proceedings of the 2007 IEEE 11th International Conference on Computer Vision, Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4408909"},{"key":"ref_27","unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., and Woo, W.C. (2015). Convolutional Lstm Network: A Machine Learning Approach for Precipitation Nowcasting, MIT Press."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"90069","DOI":"10.1109\/ACCESS.2020.2993874","article-title":"Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","first-page":"1","article-title":"Prediction of sea ice motion with convolutional long short-term memory networks","volume":"99","author":"Petrou","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s10707-019-00355-0","article-title":"A hybrid cnn-lstm model for typhoon formation forecasting","volume":"23","author":"Chen","year":"2019","journal-title":"GeoInformatica"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Winona, A.Y., and Adytia, D. (2020, January 5\u20136). Short Term Forecasting of Sea Level by Using LSTM with Limited Historical Data. Proceedings of the 2020 International Conference on Data Science and Its Applications (ICoDSA), Bandung, Indonesia.","DOI":"10.1109\/ICoDSA50139.2020.9213025"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kun, X., Shan, T., Yi, T., and Chao, C. (2021, January 11\u201313). Attention-based long short-term memory network temperature prediction model. Proceedings of the 2021 7th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO), Guangzhou, China.","DOI":"10.1109\/CMMNO53328.2021.9467533"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1126\/science.285.5433.1548","article-title":"Improved weather and seasonal climate forecasts from multimodel superensemble","volume":"285","author":"Krishnamurti","year":"1999","journal-title":"Science"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1175\/MWR-D-11-00308.1","article-title":"A two-stage quality control method for 2-m temperature observations using biweight means and a progressive eof analysis","volume":"141","author":"Xu","year":"2013","journal-title":"Mon. Weather Rev."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, X., Gao, S., Wang, T., Li, Y., and Ren, P. (2020, January 5\u201330). Correcting Predictions from Simulating Wave Nearshore Model via Gaussian Process Regression. Proceedings of the Global Oceans 2020: Singapore\u2014U.S. Gulf Coast, Biloxi, MS, USA.","DOI":"10.1109\/IEEECONF38699.2020.9389333"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Doroshenko, A., Shpyg, V., and Kushnirenko, R. (2020, January 25\u201327). Machine Learning to Improve Numerical Weather Forecasting. Proceedings of the 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT), Kyiv, Ukraine.","DOI":"10.1109\/ATIT50783.2020.9349325"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, X., Li, X., Zhu, J., Xu, Z., and Yu, K. (2021, January 11\u201314). A local similarity-preserving framework for nonlinear dimensionality reduction with neural networks. Proceedings of the The 26th International Conference on Database Systems for Advanced Applications (Dasfaa 2021), Tai Pei, China.","DOI":"10.1007\/978-3-030-73197-7_25"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104842","DOI":"10.1016\/j.cageo.2021.104842","article-title":"Random-forest based adjusting method for wind forecast of WRF model","volume":"55","author":"Wang","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"eaba1482","DOI":"10.1126\/sciadv.aba1482","article-title":"Purely satellite data\u2013driven deep learning forecast of complicated tropical instability waves","volume":"6","author":"Zheng","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1016\/j.oceaneng.2003.05.003","article-title":"Improving wave predictions with artificial neural networks","volume":"31","author":"Makarynskyy","year":"2004","journal-title":"Ocean Eng."},{"key":"ref_41","unstructured":"Xu, X., Liu, Y., Chao, H., Luo, Y., Chu, H., and Chen, L. (2019). Towards a precipitation bias corrector against noise and maldistribution. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, T., Gao, S., Xu, J., Li, Y., Li, P., and Ren, P. (2018, January 28\u201331). Correcting Predictions from Oceanic Maritime Numerical Models via Residual Learning. Proceedings of the 2018 OCEANS\u2014MTS\/IEEE Kobe Techno-Ocean. (OTO), Kobe, Japan.","DOI":"10.1109\/OCEANSKOBE.2018.8558835"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1175\/MWR-D-18-0187.1","article-title":"Neural networks for post-processing ensemble weather forecasts","volume":"146","author":"Rasp","year":"2018","journal-title":"Mon. Weather Rev."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1109\/JOE.2016.2521222","article-title":"Neural-network-based data assimilation to improve numerical ocean wave forecast","volume":"4","author":"Deshmukh","year":"2016","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2072","DOI":"10.1175\/1520-0442(2000)013<2072:COSEIC>2.0.CO;2","article-title":"Correction of systematic errors in coupled gcm forecasts","volume":"13","author":"Yang","year":"2000","journal-title":"J. Clim."},{"key":"ref_46","first-page":"5","article-title":"Study on the correction of SST prediction of HYCOM","volume":"35","author":"Han","year":"2018","journal-title":"Mar. Forecast."},{"key":"ref_47","first-page":"59","article-title":"Study on the correction of SST prediction in South China Sea using remotely sensed SST","volume":"39","author":"Zhang","year":"2020","journal-title":"J. Trop. Oceanogr."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3d convolutional neural networks for human action recognition","volume":"35","author":"Ji","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_50","first-page":"2204","article-title":"Recurrent models of visual attention","volume":"2","author":"Mnih","year":"2014","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_51","first-page":"88","article-title":"An oceanic general circulation model framed in hybrid isopycnic-cartesian coordinates","volume":"4","author":"Bleck","year":"2002","journal-title":"Ocean Modeling"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"32","DOI":"10.5670\/oceanog.2014.66","article-title":"US Navy Operational Global Ocean and Arctic Ice Prediction Systems","volume":"27","author":"Metzger","year":"2014","journal-title":"Oceanography"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5473","DOI":"10.1175\/2007JCLI1824.1","article-title":"Daily High-Resolution-Blended Analyses for Sea Surface Temperature","volume":"20","author":"Reynolds","year":"2007","journal-title":"J. Clim."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"43","DOI":"10.4031\/MTSJ.52.3.7","article-title":"Evaluation of Satellite-Derived SST Products in Identifying the Rapid Temperature Drop on the West Florida Shelf Associated With Hurricane Irma","volume":"52","author":"Liu","year":"2018","journal-title":"Mar. Technol. Soc. J."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1339\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:34:02Z","timestamp":1760135642000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,10]]},"references-count":54,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061339"],"URL":"https:\/\/doi.org\/10.3390\/rs14061339","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,10]]}}}