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Oceanogr."},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2024.171365","article-title":"Can three-dimensional nitrate\n                        structure be reconstructed from surface information with artificial\n                        intelligence\u2014A proof-of-concept study","volume":"924","author":"Yang","year":"2024","journal-title":"Sci. Total Environ."},{"issue":"6","key":"ref9","doi-asserted-by":"crossref","first-page":"4590","DOI":"10.1109\/TGRS.2020.3016473","article-title":"A machine learning approach to estimate surface\n                        chlorophyll a concentrations in global oceans from satellite\n                        measurements","volume":"59","author":"Hu","year":"2021","journal-title":"IEEE\n                        Trans. Geosci. Remote Sens."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.cageo.2014.10.016","article-title":"Multivariable integration method for estimating sea\n                        surface salinity in coastal waters from in situ data and remotely sensed\n                        data using random forest algorithm","volume":"75","author":"Liu","year":"2015","journal-title":"Comput.\n                        Geosci."},{"key":"ref11","doi-asserted-by":"crossref","DOI":"10.1016\/j.rse.2021.112316","article-title":"Improving satellite retrieval\n                        of oceanic particulate organic carbon concentrations using machine learning\n                        methods","volume":"256","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1021\/acs.est.3c08833"},{"issue":"13","key":"ref13","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.3390\/rs11131598","article-title":"Estimating subsurface thermohaline structure of the\n                        global ocean using surface remote sensing\n                    observations","volume":"11","author":"Su","year":"2019","journal-title":"Remote Sens."},{"key":"ref14","doi-asserted-by":"crossref","DOI":"10.1016\/j.jag.2021.102440","article-title":"Super-resolution of subsurface temperature field from\n                        remote sensing observations based on machine\n                    learning","volume":"102","author":"Su","year":"2021","journal-title":"Int. J. Appl. Earth Observ.\n                        Geoinformation"},{"issue":"7743","key":"ref15","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process\n                        understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"issue":"3","key":"ref16","doi-asserted-by":"crossref","first-page":"388","DOI":"10.3390\/w13030388","article-title":"Neural network approach to retrieving ocean\n                        subsurface temperatures from surface parameters observed by\n                        satellites","volume":"13","author":"Cheng","year":"2021","journal-title":"Water"},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.1109\/TGRS.2021.3109979","article-title":"Reconstructing high-resolution ocean subsurface and\n                        interior temperature and salinity anomalies from satellite\n                        observations","volume":"60","author":"Meng","year":"2022","journal-title":"IEEE\n                        Trans. Geosci. Remote Sens."},{"issue":"19","key":"ref18","doi-asserted-by":"crossref","first-page":"4890","DOI":"10.3390\/rs14194890","article-title":"Spatiotemporal prediction of monthly sea subsurface\n                        temperature fields using a 3D U-Net-based model","volume":"14","author":"Sun","year":"2022","journal-title":"Remote Sens."},{"issue":"13","key":"ref19","doi-asserted-by":"crossref","first-page":"3198","DOI":"10.3390\/rs14133198","article-title":"Subsurface temperature reconstruction for the global\n                        ocean from 1993 to 2020 using satellite observations and deep\n                        learning","volume":"14","author":"Su","year":"2022","journal-title":"Remote Sens."},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2024.3509616"},{"issue":"10","key":"ref21","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1109\/TKDE.2017.2720168","article-title":"Theory-guided data science: A\n                        new paradigm for scientific discovery from data","volume":"29","author":"Karpatne","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref22","article-title":"Physics-guided neural networks (PGNN): An application\n                        in lake temperature modeling","author":"Daw","year":"2017","journal-title":"arXiv:1710.11431"},{"issue":"7","key":"ref23","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1109\/TNNLS.2021.3123968","article-title":"Physics-guided generative adversarial networks for\n                        sea subsurface temperature prediction","volume":"34","author":"Meng","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"9","key":"ref24","doi-asserted-by":"crossref","first-page":"1728","DOI":"10.3390\/jmse11091728","article-title":"Predicting ocean temperature in high-frequency\n                        internal wave area with physics-guided deep learning: A case study from the\n                        South China Sea","volume":"11","author":"Wu","year":"2023","journal-title":"J. Mar. Sci. 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Syst."},{"issue":"12","key":"ref40","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1038\/s41558-020-00918-2","article-title":"Increasing ocean stratification over the past\n                        half-century","volume":"10","author":"Li","year":"2020","journal-title":"Nature Climate\n                    Change"},{"key":"ref41","article-title":"Internal waves in a randomly stratified\n                        ocean","author":"McGorman","year":"1972"},{"key":"ref42","first-page":"32","volume-title":"The International Thermodynamic Equation of\n                        Seawater, 2010: Calculation and Use of Thermodynamic Properties","year":"2010"},{"issue":"4","key":"ref43","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1162\/EVCO_a_00128","article-title":"Pareto front estimation for decision\n                        making","volume":"22","author":"Giagkiozis","year":"2014","journal-title":"Evol. Comput."},{"issue":"1","key":"ref44","doi-asserted-by":"crossref","first-page":"165","DOI":"10.5194\/essd-8-165-2016","article-title":"A long-term record of blended satellite and in situ\n                        sea-surface temperature for climate monitoring, modeling and environmental\n                        studies","volume":"8","author":"Banzon","year":"2016","journal-title":"Earth Syst. Sci. Data"},{"issue":"8","key":"ref45","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1175\/JCLI-D-20-0166.1","article-title":"Improvements of the daily\n                        optimum interpolation sea surface temperature (DOISST) version\n                        2.1","volume":"34","author":"Huang","year":"2021","journal-title":"J. Climate"},{"issue":"4","key":"ref46","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1175\/1520-0442(1993)006<0768:IOMPAO>2.0.CO;2","article-title":"Impact of Mount Pinatubo aerosols on\n                        satellite-derived sea surface temperatures","volume":"6","author":"Reynolds","year":"1993","journal-title":"J. Climate"},{"key":"ref47","volume-title":"CCI Sea Surface Salinity Data","author":"Boutin","year":"2019"},{"key":"ref48","volume-title":"RSS\n                        Cross-Calibrated Multi-Platform (CCMP) Monthly Ocean Vector Wind Analysis on\n                        0.25 Deg Grid, Version 3.0","author":"Mears"},{"issue":"2","key":"ref49","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1175\/2010BAMS2946.1","article-title":"A cross-calibrated,\n                        multiplatform ocean surface wind velocity product for meteorological and\n                        oceanographic applications","volume":"92","author":"Atlas","year":"2011","journal-title":"Bull. 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