{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T21:31:01Z","timestamp":1779917461536,"version":"3.53.1"},"reference-count":68,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Scientific Research Program of the Shanghai Science and Technology Commission","award":["18DZ2253900"],"award-info":[{"award-number":["18DZ2253900"]}]},{"name":"the Scientific Research Program of the Shanghai Science and Technology Commission","award":["EMW201909"],"award-info":[{"award-number":["EMW201909"]}]},{"name":"the Scientific Research Program of the Shanghai Science and Technology Commission","award":["202102245031"],"award-info":[{"award-number":["202102245031"]}]},{"name":"the open fund of the Key Laboratory for Information Science of Electromagnetic Waves, Fudan University","award":["18DZ2253900"],"award-info":[{"award-number":["18DZ2253900"]}]},{"name":"the open fund of the Key Laboratory for Information Science of Electromagnetic Waves, Fudan University","award":["EMW201909"],"award-info":[{"award-number":["EMW201909"]}]},{"name":"the open fund of the Key Laboratory for Information Science of Electromagnetic Waves, Fudan University","award":["202102245031"],"award-info":[{"award-number":["202102245031"]}]},{"name":"the University\u2013Industry Collaborative Education Program Initiated by the Ministry of Education","award":["18DZ2253900"],"award-info":[{"award-number":["18DZ2253900"]}]},{"name":"the University\u2013Industry Collaborative Education Program Initiated by the Ministry of Education","award":["EMW201909"],"award-info":[{"award-number":["EMW201909"]}]},{"name":"the University\u2013Industry Collaborative Education Program Initiated by the Ministry of Education","award":["202102245031"],"award-info":[{"award-number":["202102245031"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The prediction of oceanic features is always an important issue in oceanography, where deep learning has been proven to be a useful tool. In this study, we applied the improved U-net model to predict the monthly sea surface salinity (SSS) over the western Pacific (WP) Ocean, and the model was designed to use the SSSs from six consecutive months to predict the SSS in the next month. The monthly satellite-based SSSs in 2015\u20132020 were used for model training, and the data collected after January 2021 were used to evaluate the model\u2019s predictive abilities. The results showed that the predicted SSSs represented the general patterns of SSSs over the WP region. However, the small-scale features were smoothed out in the model, and the temporal variations were also not well captured, especially over the East China Sea and Yellow Sea (ECS&amp;YS) region. To further evaluate the potential of the U-net model, a more specific model was conducted for the ECS&amp;YS region (Domain 2), which successfully predicted both spatial and temporal variations in the SSSs, including the spreading and retreating of the low-salinity tongue. Based on the comparison between the two domains and sensitivity experiments, we found that the prediction biases were contributed by the spatial distributions of the SSSs, the domain size, and the filter numbers. In addition, further multi-step prediction experiments suggested that our U-net model could also be used for long-time prediction, and we have examined up to five months. Overall, this study demonstrated the great ability and potential of the U-net model for predicting SSS, even though only a few trainable data are available.<\/jats:p>","DOI":"10.3390\/rs15061684","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T02:36:22Z","timestamp":1679366182000},"page":"1684","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A Modified U-Net Model for Predicting the Sea Surface Salinity over the Western Pacific Ocean"],"prefix":"10.3390","volume":"15","author":[{"given":"Xuewei","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1878-2974","authenticated-orcid":false,"given":"Ning","family":"Zhao","sequence":"additional","affiliation":[{"name":"Japan Agency for Marine-Earth Science and Technology, Yokosuka 2370061, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Han","sequence":"additional","affiliation":[{"name":"College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China"},{"name":"Shanghai Engineering Research Center of Estuarine and Oceanographic Mapping, Shanghai 201306, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mu, Z.Y., Zhang, W.M., Wang, P.Q., Wang, H.Z., and Yang, X.F. (2019). Assimilation of SMOS sea surface salinity in the regional ocean model for South China Sea. Remote Sens., 11.","DOI":"10.3390\/rs11080919"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"6789","DOI":"10.1080\/01431160802227313","article-title":"A new algorithm for microwave radiometer remote sensing of sea surface salinity without influence of wind","volume":"29","author":"Yin","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7811","DOI":"10.1002\/2014JC009960","article-title":"SMOS sea surface salinity signals of tropical instability waves","volume":"119","author":"Yin","year":"2014","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1175\/JCLI-D-14-00435.1","article-title":"Satellite and Argo observed surface salinity variations in the tropical Indian ocean and their association with the Indian Ocean dipole mode","volume":"28","author":"Du","year":"2015","journal-title":"J. Clim."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1175\/JAS-D-12-0283.1","article-title":"Modulation of Seasonal Precipitation over the tropical western\/central Pacific by convectively coupled mixed Rossby-gravity waves","volume":"70","author":"Horinouchi","year":"2013","journal-title":"J. Atmos. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7057","DOI":"10.1029\/2018GL078662","article-title":"Detection of intraseasonal oscillations in SMAP salinity in the Bay of Bengal","volume":"45","author":"Subrahmanyam","year":"2018","journal-title":"Geophys. Res. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5513","DOI":"10.1175\/JCLI-D-16-0626.1","article-title":"In search of fingerprints of the recent intensification of the ocean water cycle","volume":"30","author":"Vinogradova","year":"2017","journal-title":"J. Clim."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"9591","DOI":"10.1002\/2017JC013333","article-title":"Modulation of the Ganges-Brahmaputra River plume by the Indian Ocean Dipole and eddies inferred from satellite observations","volume":"122","author":"Fournier","year":"2017","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6069","DOI":"10.1175\/JCLI-D-20-0716.1","article-title":"Interrelationships of sea surface salinity, Chlorophyll-\u03b1 con-centration, and sea surface temperature near the Antarctic Ice Edge","volume":"34","author":"Comiso","year":"2021","journal-title":"J. Clim."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1007\/s12524-016-0637-7","article-title":"Predicting sea surface salinity using an improved genetic algorithm combining operation tree method","volume":"45","author":"Chen","year":"2017","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1175\/JTECH-D-19-0168.1","article-title":"A novel dual path gated recurrent unit model for sea surface salinity prediction","volume":"37","author":"Song","year":"2020","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.rse.2012.04.008","article-title":"Remotely sensed estimates of surface salinity in the Chesapeake Bay: A statistical approach","volume":"123","author":"Urquhart","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"23729","DOI":"10.1007\/s11042-020-08976-6","article-title":"A review of object detection based on deep learning","volume":"79","author":"Xiao","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1016\/j.neucom.2007.12.036","article-title":"Learning long-term dependencies with recurrent neural networks","volume":"71","author":"Schaefer","year":"2008","journal-title":"Neurocomputing"},{"key":"ref_18","unstructured":"Pascanu, R., Mikolov, T., and Bengio, Y. (2013, January 16\u201321). On the difficulty of training recurrent neural networks. Proceedings of the 30th International Conference on Machine Learning\u2014Volume 28, Atlanta, GA, USA."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"3797","DOI":"10.3390\/s18113797","article-title":"TD-LSTM: Temporal dependence-based LSTM networks for marine temperature prediction","volume":"18","author":"Liu","year":"2018","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"104502","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_22","doi-asserted-by":"crossref","first-page":"905848","DOI":"10.3389\/fmars.2022.905848","article-title":"Seven-day sea surface temperature prediction using a 3DConv-LSTM model","volume":"9","author":"Li","year":"2022","journal-title":"Front. Mar. Sci."},{"key":"ref_23","unstructured":"Shi, X.J., Chen, Z.R., Wan, H., Yeung, D.Y., and Wong, W.K. (2015, January 7\u201312). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/29.21701","article-title":"Phoneme recognition using time-delay neural networks","volume":"37","author":"Waibel","year":"1989","journal-title":"IEEE Trans. Acoust."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6029","DOI":"10.3233\/JIFS-179185","article-title":"A deep learning approach to predict the spatial and temporal distribution of flight delay in network","volume":"37","author":"Ai","year":"2019","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ma, C.Y., Li, S.Q., Wang, A.N., Yang, J.E., and Chen, G. (2019). Altimeter observation-based eddy nowcasting using an improved Conv-LSTM network. Remote Sens., 11.","DOI":"10.3390\/rs11070783"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"6865","DOI":"10.1109\/TGRS.2019.2909057","article-title":"Prediction of sea ice motion with convolutional long short-term memory networks","volume":"57","author":"Petrou","year":"2019","journal-title":"IEEE Trans Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","article-title":"Convolutional neural networks: An overview and application in radiology","volume":"9","author":"Yamashita","year":"2018","journal-title":"Insights Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lou, R., Lv, Z., Dang, S.P., Su, T.Y., and Li, X.F. (2021). Application of machine learning in ocean data. Multimed. Syst., 1\u201310.","DOI":"10.1007\/s00530-020-00733-x"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3368","DOI":"10.1080\/01431161.2019.1701724","article-title":"Spatial-temporal predictions of sst time series in china\u2019s offshore waters using a regional convolution long short-term memory (RC-LSTM) network","volume":"41","author":"Xu","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Pan, X.L., Jiang, T., Sui, B.K., Liu, C.X., and Sun, W.F. (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_32","doi-asserted-by":"crossref","first-page":"112465","DOI":"10.1016\/j.rse.2021.112465","article-title":"Predicting subsurface thermohaline structure from remote sensing data based on long short-term memory neural networks","volume":"260","author":"Su","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e2022GL097904","DOI":"10.1029\/2022GL097904","article-title":"Short-Term Precipitation Prediction for Contiguous United States Using Deep Learning","volume":"49","author":"Chen","year":"2022","journal-title":"Geophys. Res. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"L11603","DOI":"10.1029\/2007GL029888","article-title":"Prediction of sea surface temperatures in the western Mediterranean Sea by neural networks using satellite observations","volume":"34","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"249","DOI":"10.3989\/scimar.03621.19K","article-title":"A new space technology for ocean observation: The SMOS mission","volume":"76","author":"Font","year":"2012","journal-title":"Sci. Mar."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111769","DOI":"10.1016\/j.rse.2020.111769","article-title":"Sea surface salinity estimates from spaceborne L-band radiometers: An overview of the first decade of observation (2010\u20132019)","volume":"242","author":"Reul","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T.T. (2015, January 5\u20139). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lguensat, R., Sun, R.M., Fablet, P., Tandeo, E., Mason, M., and Chen, G. (2018, January 22\u201327). EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddies. Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing IGARSS, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518411"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jiao, L.B., Huo, L.Z., and Hu, C.M.T. (2020). Refined UNet: UNet-based refinement network for cloud and shadow precise segmentation. Remote Sens., 12.","DOI":"10.3390\/rs12122001"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Fan, Y., Rui, X., Zhang, G., Yu, T., Xu, X., and Poslad, S. (2021). Feature Merged Network for Oil Spill Detection Using SAR Images. Remote Sens., 13.","DOI":"10.3390\/rs13163174"},{"key":"ref_41","unstructured":"Cardoso, M.J., and Arbel, T. (2017, January 14). 3D Randomized Connection Network with Graph-Based Inference. Proceedings of the 3rd MICCAI International Workshop on Deep Learning in Medical Image Analysis (DLMIA), 7th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS), Quebec, QC, Canada. Lecture Notes in Computer Science."},{"key":"ref_42","unstructured":"Bates, R., Irving, B.J., Markelc, B., Kaeppler, J., Muschel, R., Grau1, V., and Schnabell, J.A. (2017). Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks. arXiv."},{"key":"ref_43","first-page":"3221","article-title":"An efficient model for the prediction of SMAP sea surface salinity using machine learning approaches in the Persian Gulf","volume":"41","author":"Hasanlou","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.patrec.2021.01.036","article-title":"SmaAt-UNet: Precipitation nowcasting using a small attention-UNet architecture","volume":"145","author":"Trebing","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_45","first-page":"9856669","article-title":"Fault Recognition Method Based on Attention Mechanism and the 3D-UNet","volume":"2022","author":"Yu","year":"2022","journal-title":"Comput. Intel. Neurosci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, C.T., and Guo, X. (2020). Changing Asia-Pacific Marginal Seas. Atmosphere, Earth, Ocean & Space, Springer.","DOI":"10.1007\/978-981-15-4886-4"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1016\/j.csr.2006.05.003","article-title":"A salinity front in the southern East China Sea separating the Chinese coastal and Taiwan Strait waters from Kuroshio waters","volume":"26","author":"Chen","year":"2006","journal-title":"Cont. Shelf. Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e2020JC016914","DOI":"10.1029\/2020JC016914","article-title":"Impacts of salinity variation on the mixed-layer processes and sea surface temperature in the Kuroshio-Oyashio confluence region","volume":"126","author":"Kido","year":"2021","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8008","DOI":"10.1029\/2001JC000893","article-title":"Sea surface salinity changes in the East China Sea during 1997\u20132001: Influence of the Yangtze River","volume":"107","author":"Delcroix","year":"2002","journal-title":"J. Geophys. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.csr.2010.10.012","article-title":"Effects of the Changjiang river discharge on sea surface warming in the Yellow and East China Seas in summer","volume":"31","author":"Park","year":"2011","journal-title":"Cont. Shelf. Res."},{"key":"ref_51","unstructured":"Remote Sensing Systems (RSS) (2019). SMAP Sea Surface Salinity Products, PO.DAAC. Version 4.0."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Fournier, S., Le, T., Tang, W.Q., Steele, M., and Olmedo, E. (2019). Evaluation and intercomparison of SMOS, Aquarius, and SMAP sea surface salinity products in the Arctic Ocean. Remote Sens., 11.","DOI":"10.3390\/rs11243043"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1029\/2019JC014937","article-title":"Comparison of satellite-derived sea surface salinity products from SMOS, Aquarius, and SMAP","volume":"124","author":"Bao","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_54","unstructured":"Meissner, T., Wentz, F.J., Manaster, R., and Lindsley, A. (2019). Remote Sensing Systems SMAP Ocean Surface Salinities [Level 2C, Level 3 Running 8-Day, Level 3 Monthly, Remote Sensing Systems. Available online: www.remss.com\/missions\/smap."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1049\/ell2.12084","article-title":"Network slimming for compressed: Ensuing cardiac cine MRI","volume":"57","author":"Park","year":"2021","journal-title":"Electron. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1007\/s12524-021-01312-x","article-title":"Open-Pit mining area segmentation of remote sensing images based on DUSegNet","volume":"49","author":"Xie","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2480","DOI":"10.1049\/iet-ipr.2019.1248","article-title":"Dynamic gesture recognition based on feature fusion network and variant ConvLSTM","volume":"14","author":"Peng","year":"2020","journal-title":"IET Image Process."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"111","DOI":"10.3319\/TAO.2004.15.2.111(O)","article-title":"Effects of reduced Yangtze River discharge on the circulation of surrounding seas","volume":"15","author":"Lee","year":"2004","journal-title":"Terr. Atmos. Ocean. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13131-020-1542-z","article-title":"Validation and application of soil moisture active passive sea surface salinity observation over the Changjiang River Estuary","volume":"39","author":"Wu","year":"2020","journal-title":"Acta Oceanol. Sin."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1109\/LGRS.2019.2947170","article-title":"Prediction of 3-D ocean temperature by multilayer convolutional LSTM","volume":"17","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.3389\/fmars.2018.00343","article-title":"The Influence of Riverine Nutrients in Niche Partitioning of Phytoplankton Communities\u2014A Contrast Between the Amazon River Plume and the Changjiang (Yangtze) River Diluted Water of the East China Sea","volume":"5","author":"Gomes","year":"2018","journal-title":"Front. Mar. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1007\/s00254-008-1266-4","article-title":"Influence of the Three Gorges Project on saltwater intrusion in the Yangtze River Estuary","volume":"56","author":"An","year":"2009","journal-title":"Environ. Geol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Guillou, N., Chaplain, G., and Petton, S. (2022). Predicting sea surface salinity in a tidal estuary with machine learning. Oceanologia.","DOI":"10.1016\/j.oceano.2022.07.007"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Dossa, A.N., Alory, G., daSilva, A.C., Dahunsi, A.M., and Bertrand, A. (2021). Global Analysis of Coastal Gradients of Sea Surface Salinity. Remote Sens., 13.","DOI":"10.3390\/rs13132507"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1023\/A:1015876701363","article-title":"The Current System in the Yellow and East China Seas","volume":"58","author":"Ichikawa","year":"2002","journal-title":"J. Oceanogr."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"105403","DOI":"10.1016\/j.envsoft.2022.105403","article-title":"Extensive study of recurrent neural network architectures with a multivariate approach for water quality assessment in complex coastal lagoon environments: A case study on the Venice Lagoon","volume":"154","author":"Aslan","year":"2022","journal-title":"Environ. Model. Softw."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"932932","DOI":"10.3389\/fclim.2022.932932","article-title":"A deep learning model for forecasting global monthly mean sea surface temperature anomalies","volume":"4","author":"Taylor","year":"2022","journal-title":"Front. Clim."},{"key":"ref_68","first-page":"12514","article-title":"D2CL: A Dense Dilated Convolutional LSTM Model for Sea Surface Temperature Prediction","volume":"14","author":"Hou","year":"2021","journal-title":"IEEE J.-STARS"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1684\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:59:19Z","timestamp":1760122759000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1684"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,21]]},"references-count":68,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061684"],"URL":"https:\/\/doi.org\/10.3390\/rs15061684","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,21]]}}}