{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T18:28:30Z","timestamp":1777832910785,"version":"3.51.4"},"reference-count":56,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T00:00:00Z","timestamp":1711584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CNES","award":["R-S22\/DU-0003-023-92"],"award-info":[{"award-number":["R-S22\/DU-0003-023-92"]}]},{"name":"CNES","award":["DOS0200558\/00"],"award-info":[{"award-number":["DOS0200558\/00"]}]},{"DOI":"10.13039\/100016308","name":"French Public Bank of Investment (BPIFrance)","doi-asserted-by":"publisher","award":["R-S22\/DU-0003-023-92"],"award-info":[{"award-number":["R-S22\/DU-0003-023-92"]}],"id":[{"id":"10.13039\/100016308","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016308","name":"French Public Bank of Investment (BPIFrance)","doi-asserted-by":"publisher","award":["DOS0200558\/00"],"award-info":[{"award-number":["DOS0200558\/00"]}],"id":[{"id":"10.13039\/100016308","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Real-time reconstruction of ocean surface currents is a challenge due to the complex, non-linear dynamics of the ocean, the small number of in situ measurements, and the spatio-temporal heterogeneity of satellite altimetry observations. To address this challenge, we introduce HIRES-CURRENTS-Net, an operational real-time convolutional neural network (CNN) model for daily ocean current reconstruction. This study focuses on the Mediterranean Sea, a region where operational models have great difficulty predicting surface currents. Notably, our model showcases higher accuracy compared to commonly used alternative methods. HIRES-CURRENTS-Net integrates high-resolution measurements from the infrared or visible spectrum\u2014high resolution Sea Surface Temperature (SST) or chlorophyll (CHL) images\u2014in addition to the low-resolution Sea Surface Height (SSH) maps derived from satellite altimeters. In the first stage, we apply a transfer learning method which uses a high-resolution numerical model to pre-train our CNN model on simulated SSH and SST data with synthetic clouds. The observation of System Simulation Experiments (OSSEs) offers us a sufficient training dataset with reference surface currents at very high resolution, and a model trained on this data can then be applied to real data. In the second stage, to enhance the real-time operational performance of our model over previous methods, we fine-tune the CNN model on real satellite data using a novel pseudo-labeling strategy. We validate HIRES-CURRENTS-Net on real data from drifters and demonstrate that our data-driven approach proves effective for real-time sea surface current reconstruction with potential operational applications such as ship routing.<\/jats:p>","DOI":"10.3390\/rs16071182","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T12:09:40Z","timestamp":1711627780000},"page":"1182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Ocean Satellite Data Fusion for High-Resolution Surface Current Maps"],"prefix":"10.3390","volume":"16","author":[{"given":"Alisa","family":"Kugusheva","sequence":"first","affiliation":[{"name":"AMPHITRITE, X-Novation Center, \u00c9cole Polytechnique, 91128 Palaiseau, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hannah","family":"Bull","sequence":"additional","affiliation":[{"name":"AMPHITRITE, X-Novation Center, \u00c9cole Polytechnique, 91128 Palaiseau, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evangelos","family":"Moschos","sequence":"additional","affiliation":[{"name":"AMPHITRITE, X-Novation Center, \u00c9cole Polytechnique, 91128 Palaiseau, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Artemis","family":"Ioannou","sequence":"additional","affiliation":[{"name":"AMPHITRITE, X-Novation Center, \u00c9cole Polytechnique, 91128 Palaiseau, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Briac","family":"Le Vu","sequence":"additional","affiliation":[{"name":"AMPHITRITE, X-Novation Center, \u00c9cole Polytechnique, 91128 Palaiseau, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7882-493X","authenticated-orcid":false,"given":"Alexandre","family":"Stegner","sequence":"additional","affiliation":[{"name":"AMPHITRITE, X-Novation Center, \u00c9cole Polytechnique, 91128 Palaiseau, France"},{"name":"Laboratoire de M\u00e9t\u00e9orologie Dynamique, Institut Pierre-Simon-Laplace (CNRS), Ecole Polytechnique, 91128 Palaiseau, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"key":"ref_1","unstructured":"Fu, L., and Cazenave, A. (2001). Satellite Altimetry and Earth Sciences: A Handbook of Techniques and Applications, Academic Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"232","DOI":"10.3389\/fmars.2019.00232","article-title":"Global Observations of Fine-Scale Ocean Surface Topography With the Surface Water and Ocean Topography (SWOT) Mission","volume":"6","author":"Morrow","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7220","DOI":"10.1029\/2018JC014140","article-title":"Up to What Extent Can We Characterize Ocean Eddies Using Present-Day Gridded Altimetric Products?","volume":"123","author":"Amores","year":"2018","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e2021JC017475","DOI":"10.1029\/2021JC017475","article-title":"Cyclone-Anticyclone Asymmetry of Eddy Detection on Gridded Altimetry Product in the Mediterranean Sea","volume":"126","author":"Stegner","year":"2021","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ioannou, A., Moschos, E., Le Vu, B., and Stegner, A. (2023, January 7\u20138). Short-Term Optimal Ship Routing via Reliable Satellite Current Data. Proceedings of the NAME International Symposium on Ship Operations, Management and Economics, Athens, Greece.","DOI":"10.5957\/SOME-2023-044"},{"key":"ref_6","first-page":"9870950","article-title":"Recent Developments in Artificial Intelligence in Oceanography","volume":"2022","author":"Dong","year":"2022","journal-title":"Ocean-Land Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Buongiorno Nardelli, B., Cavaliere, D., Charles, E., and Ciani, D. (2022). Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms. Remote Sens., 14.","DOI":"10.3390\/rs14051159"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4204214","DOI":"10.1109\/TGRS.2023.3268006","article-title":"Multimodal 4DVarNets for the reconstruction of sea surface dynamics from SST-SSH synergies","volume":"61","author":"Fablet","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chassignet, E.P., Pascual, A., Tintore, J., and Verron, J. (2018). New Frontiers in Operational Oceanography, GODAE OceanView.","DOI":"10.17125\/gov2018"},{"key":"ref_10","first-page":"51","article-title":"The GODAE\/Mercator-Ocean global ocean forecasting system: Results, applications and prospects","volume":"1","author":"Derval","year":"2008","journal-title":"J. Oper. Oceanogr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.5194\/os-14-1093-2018","article-title":"Recent updates to the Copernicus Marine Service global ocean monitoring and forecasting real-time 1\/12\u2218 high-resolution system","volume":"14","author":"Lellouche","year":"2018","journal-title":"Ocean Sci."},{"key":"ref_12","unstructured":"Jullien, S., Caillaud, M., Benshila, R., Bordois, L., Cambon, G., Dumas, F., Gentil, S.L., Lemari\u00e9, F., Marchesiello, P., and Theetten, S. (2022). CROCO Technical and Numerical Documentation. Zenodo, Technical Note."},{"key":"ref_13","unstructured":"Brodeau, L., Sommer, J.L., and Albert, A. (2020). Ocean-next\/eNATL60: Material describing the set-up and the assessment of NEMO-eNATL60 simulations. Zenodo, Technical Note."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4193","DOI":"10.5194\/gmd-15-4193-2022","article-title":"The Regional Coupled Suite (RCS-IND1): Application of a flexible regional coupled modelling framework to the Indian region at kilometre scale","volume":"15","author":"Castillo","year":"2022","journal-title":"Geosci. Model Dev."},{"key":"ref_15","first-page":"i","article-title":"Satellite altimetry","volume":"Volume 69","author":"Chelton","year":"2001","journal-title":"International Geophysics"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1080\/01490419.2010.491031","article-title":"Estimating mean sea level change from the TOPEX and Jason altimeter missions","volume":"33","author":"Nerem","year":"2010","journal-title":"Mar. Geod."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.asr.2021.01.022","article-title":"Altimetry for the future: Building on 25 years of progress","volume":"68","author":"Abdalla","year":"2021","journal-title":"Adv. Space Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Evensen, G. (2009). Data Assimilation: The Ensemble Kalman Filter, Springer.","DOI":"10.1007\/978-3-642-03711-5"},{"key":"ref_19","unstructured":"Cressie, N. (2015). Statistics for Spatial Data, John Wiley & Sons."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.5194\/os-15-1207-2019","article-title":"DUACS DT2018: 25 years of reprocessed sea level altimetry products","volume":"15","author":"Taburet","year":"2019","journal-title":"Ocean Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"691955","DOI":"10.3389\/fmars.2021.691955","article-title":"Assessing the impact of the assimilation of swot observations in a global high-resolution analysis and forecasting system part 1: Methods","volume":"8","author":"Benkiran","year":"2021","journal-title":"Front. Mar. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/978-3-319-32449-4_6","article-title":"The SWOT mission and its capabilities for land hydrology","volume":"55","author":"Biancamaria","year":"2016","journal-title":"Remote Sens. Water Resour."},{"key":"ref_23","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_24","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_25","unstructured":"Li, Z., Yang, W., Peng, S., and Liu, F. (2020). A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","unstructured":"Yamanaka, J., Kuwashima, S., and Kurita, T. (2017, January 14\u201318). Fast and accurate image super resolution by deep CNN with skip connection and network in network. Proceedings of the Neural Information Processing: 24th International Conference\u2014ICONIP 2017, Guangzhou, China. Proceedings, Part II 24."},{"key":"ref_28","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18P."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Moschos, E., Stegner, A., Le Vu, B., and Schwander, O. (2022, January 17\u201322). Real-Time Validation of Operational Ocean Models Via Eddy-Decting Deep Neural Networks. Proceedings of the IGARSS 2022\u20132022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883253"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Moschos, E., Kugusheva, A., Coste, P., and Stegner, A. (2023, January 2\u20137). Computer Vision for Ocean Eddy Detection in Infrared Imagery. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV56688.2023.00633"},{"key":"ref_31","first-page":"1500605","article-title":"Oceanic Eddy Identification Using Pyramid Split Attention U-Net With Remote Sensing Imagery","volume":"20","author":"Zhao","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"302","DOI":"10.2112\/SI90-038.1","article-title":"U-Net convolutional neural network model for deep red tide learning using GOCI","volume":"90","author":"Kim","year":"2019","journal-title":"J. Coast. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2782","DOI":"10.1109\/JSTARS.2022.3162387","article-title":"AlgaeNet: A deep-learning framework to detect floating green algae from optical and SAR imagery","volume":"15","author":"Gao","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5339","DOI":"10.1109\/JSTARS.2021.3076109","article-title":"Sea ice concentration estimation: Using passive microwave and SAR data with a U-net and curriculum learning","volume":"14","author":"Radhakrishnan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","first-page":"4010205","article-title":"Development of a dual-attention U-Net model for sea ice and open water classification on SAR images","volume":"19","author":"Ren","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","first-page":"4713","article-title":"Image super-resolution via iterative refinement","volume":"45","author":"Saharia","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e2019JC015827","DOI":"10.1029\/2019JC015827","article-title":"Spatial and temporal variability of the North Atlantic eddy field from two kilometric-resolution ocean models","volume":"125","author":"Ajayi","year":"2020","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ciani, D., Charles, E., Buongiorno Nardelli, B., Rio, M.H., and Santoleri, R. (2021). Ocean currents reconstruction from a combination of altimeter and ocean colour data: A feasibility study. Remote Sens., 13.","DOI":"10.3390\/rs13122389"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e2020MS002302","DOI":"10.1029\/2020MS002302","article-title":"Application of symmetric instability parameterization in the Coastal and Regional Ocean Community Model (CROCO)","volume":"13","author":"Dong","year":"2021","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"102174","DOI":"10.1016\/j.ocemod.2023.102174","article-title":"Downscaling of ocean fields by fusion of heterogeneous observations using deep learning algorithms","volume":"182","author":"Thiria","year":"2023","journal-title":"Ocean Model."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Archambault, T., Filoche, A., Charantonnis, A., and B\u00e9r\u00e9ziat, D. (2023, January 19\u201321). Multimodal Unsupervised Spatio-Temporal Interpolation of satellite ocean altimetry maps. Proceedings of the VISAPP, Lisboa, Portugal.","DOI":"10.5220\/0011620100003417"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"e2022MS003589","DOI":"10.1029\/2022MS003589","article-title":"Synthesizing sea surface temperature and satellite altimetry observations using deep learning improves the accuracy and resolution of gridded sea surface height anomalies","volume":"15","author":"Martin","year":"2023","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Martin, S., Manucharyan, G., and Klein, P. (2024). Deep Learning Improves Global Satellite Observations of Ocean Eddy Dynamics. EarthArXiv Eprints.","DOI":"10.31223\/X5W676"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"12865","DOI":"10.1029\/JC091iC11p12865","article-title":"An objective method for computing advective surface velocities from sequential infrared satellite images","volume":"91","author":"Emery","year":"1986","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1175\/1520-0426(1990)007<0852:EOTMCC>2.0.CO;2","article-title":"Evaluation of the maximum cross-correlation method of estimating sea surface velocities from sequential satellite images","volume":"7","author":"Tokmakian","year":"1990","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"9653","DOI":"10.1029\/92JC00734","article-title":"Comparison of velocity estimates from advanced very high resolution radiometer in the coastal transition zone","volume":"97","author":"Kelly","year":"1992","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Isern-Fontanet, J., Chapron, B., Lapeyre, G., and Klein, P. (2006). Potential use of microwave sea surface temperatures for the estimation of ocean currents. Geophys. Res. Lett., 33.","DOI":"10.1029\/2006GL027801"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.1029\/2001GL013368","article-title":"Chlorophyll variability in eastern boundary currents","volume":"28","author":"Thomas","year":"2001","journal-title":"Geophys. Res. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sokolov, S., and Rintoul, S.R. (2007). On the relationship between fronts of the Antarctic Circumpolar Current and surface chlorophyll concentrations in the Southern Ocean. J. Geophys. Res. Ocean., 112.","DOI":"10.1029\/2006JC004072"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"8195","DOI":"10.1002\/2014JC010111","article-title":"Regional variations in the influence of mesoscale eddies on near-surface chlorophyll","volume":"119","author":"Gaube","year":"2014","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_51","unstructured":"Cutolo, E., Pascual, A., Ruiz, S., Zarokanellos, N., and Fablet, R. (2022). CLOINet: Ocean state reconstructions through remote-sensing, in-situ sparse observations and Deep Learning. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8913","DOI":"10.1029\/2019JC015031","article-title":"Cyclostrophic corrections of AVISO\/DUACS surface velocities and its application to mesoscale eddies in the Mediterranean Sea","volume":"124","author":"Ioannou","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2012.10.012","article-title":"High and Ultra-High resolution processing of satellite Sea Surface Temperature data over Southern European Seas in the framework of MyOcean project","volume":"129","author":"Tronconi","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1175\/JTECH-D-15-0160.1","article-title":"The challenge of using future SWOT data for oceanic field reconstruction","volume":"33","author":"Gaultier","year":"2016","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_55","unstructured":"Liu, J., Tang, J., and Wu, G. (2021). AdaDM: Enabling Normalization for Image Super-Resolution. arXiv."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Murugesan, B., Sarveswaran, K., Shankaranarayana, S.M., Ram, K., and Sivaprakasam, M. (2019). Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation. arXiv.","DOI":"10.1109\/EMBC.2019.8857339"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1182\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:20:09Z","timestamp":1760106009000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,28]]},"references-count":56,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16071182"],"URL":"https:\/\/doi.org\/10.3390\/rs16071182","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,28]]}}}