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At the same time, oceans play a crucial role in climate change mitigation and adaptation, offering solutions such as renewable energy and carbon sequestration. Moreover, the availability of diverse ocean data sources, both remote sensing observations and in situ measurements, provides unprecedented opportunities to monitor these processes. Remote sensing data, with its extensive spatial coverage and accessibility, forms the foundation for accurately capturing changes in ocean conditions and developing data-driven solutions. This review explores the dual relationship between climate change and oceans, focusing on the impacts of climate change on oceans and ocean-based strategies to combat these challenges. From the artificial intelligence perspective, this study systematically analyzes recent advances in applying deep learning techniques to understand changes in ocean physical properties and marine ecosystems, as well as to optimize ocean-based climate solutions. By evaluating existing methodologies and identifying knowledge gaps, this review highlights the pivotal role of deep learning in advancing ocean-related climate research, outlines existing current challenges, and provides insights into potential future directions.<\/jats:p>","DOI":"10.3390\/rs17132306","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T02:53:07Z","timestamp":1751856787000},"page":"2306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Impacts of Climate Change on Oceans and Ocean-Based Solutions: A Comprehensive Review from the Deep Learning Perspective"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0753-4905","authenticated-orcid":false,"given":"Xin","family":"Qiao","sequence":"first","affiliation":[{"name":"Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John\u2019s, NL A1B 3X5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke","family":"Zhang","sequence":"additional","affiliation":[{"name":"Information Science and Technology College, Dalian Maritime University, Dalian 116026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9622-5041","authenticated-orcid":false,"given":"Weimin","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John\u2019s, NL A1B 3X5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"155709","DOI":"10.1016\/j.scitotenv.2022.155709","article-title":"Marine litter and climate change: Inextricably connected threats to the world\u2019s oceans","volume":"837","author":"Lincoln","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Garcia-Soto, C., Cheng, L., Caesar, L., Schmidtko, S., Jewett, E.B., Cheripka, A., Rigor, I., Caballero, A., Chiba, S., and B\u00e1ez, J.C. (2021). An overview of ocean climate change indicators: Sea surface temperature, ocean heat content, ocean pH, dissolved oxygen concentration, arctic sea ice extent, thickness and volume, sea level and strength of the AMOC (Atlantic Meridional Overturning Circulation). Front. Mar. Sci., 8.","DOI":"10.3389\/fmars.2021.642372"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gaines, S., Cabral, R., Free, C.M., Golbuu, Y., Arnason, R., Battista, W., Bradley, D., Cheung, W., Fabricius, K., and Hoegh-Guldberg, O. (2023). The expected impacts of climate change on the ocean economy. The Blue Compendium: From Knowledge to Action for a Sustainable Ocean Economy, Springer.","DOI":"10.1007\/978-3-031-16277-0_2"},{"key":"ref_4","first-page":"1","article-title":"Remote sensing of Ocean and coastal environment\u2013overview","volume":"1","author":"Rani","year":"2021","journal-title":"Remote Sens. Ocean Coast. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1038\/nclimate1908","article-title":"The role of satellite remote sensing in climate change studies","volume":"3","author":"Yang","year":"2013","journal-title":"Nat. Clim. Chang."},{"key":"ref_6","first-page":"s189","article-title":"The current status of the real-time in situ Global Ocean Observing System for operational oceanography","volume":"8","author":"Legler","year":"2015","journal-title":"J. Oper. Oceanogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3485128","article-title":"Tackling climate change with machine learning","volume":"55","author":"Rolnick","year":"2022","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/MGRS.2023.3343623","article-title":"DeepBlue: Advanced convolutional neural network applications for ocean remote sensing","volume":"12","author":"Wang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1093\/nsr\/nwaa047","article-title":"Deep-learning-based information mining from ocean remote-sensing imagery","volume":"7","author":"Li","year":"2020","journal-title":"Natl. Sci. Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"166820","DOI":"10.1016\/j.scitotenv.2023.166820","article-title":"The role of satellite remote sensing in mitigating and adapting to global climate change","volume":"904","author":"Zhao","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2003RG000148","article-title":"Optical oceanography: Recent advances and future directions using global remote sensing and in situ observations","volume":"44","author":"Dickey","year":"2006","journal-title":"Rev. Geophys."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Amani, M., Moghimi, A., Mirmazloumi, S.M., Ranjgar, B., Ghorbanian, A., Ojaghi, S., Ebrahimy, H., Naboureh, A., Nazari, M.E., and Mahdavi, S. (2022). Ocean remote sensing techniques and applications: A review (part I). Water, 14.","DOI":"10.3390\/w14213401"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MGRS.2022.3184951","article-title":"Remote Sensing for Subsurface and Deeper Oceans: An overview and a future outlook","volume":"10","author":"Meng","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.rse.2015.12.048","article-title":"Dependence of satellite ocean color data products on viewing angles: A comparison between SeaWiFS, MODIS, and VIIRS","volume":"175","author":"Barnes","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.rse.2018.10.034","article-title":"Validation of VIIRS and MODIS reflectance data in coastal and oceanic waters: An assessment of methods","volume":"220","author":"Barnes","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.rse.2013.05.032","article-title":"Application of the Geostationary Ocean Color Imager (GOCI) to mapping the temporal dynamics of coastal water turbidity","volume":"146","author":"Choi","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yan, Y., Huang, K., Shao, D., Xu, Y., and Gu, W. (2019). Monitoring the characteristics of the Bohai Sea ice using high-resolution geostationary ocean color imager (GOCI) data. Sustainability, 11.","DOI":"10.3390\/su11030777"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"113183","DOI":"10.1016\/j.rse.2022.113183","article-title":"Estimates of diurnal and daily net primary productivity using the Geostationary Ocean Color Imager (GOCI) data","volume":"280","author":"Wu","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_19","first-page":"1503605","article-title":"Monitoring Sargassum inundation on beaches and nearshore waters using PlanetScope\/Dove observations","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.pocean.2018.01.001","article-title":"An overview of approaches and challenges for retrieving marine inherent optical properties from ocean color remote sensing","volume":"160","author":"Werdell","year":"2018","journal-title":"Prog. Oceanogr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105701","DOI":"10.1016\/j.marenvres.2022.105701","article-title":"Ocean water quality monitoring using remote sensing techniques: A review","volume":"180","author":"Mohseni","year":"2022","journal-title":"Mar. Environ. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.rse.2013.07.039","article-title":"Challenges and opportunities for geostationary ocean colour remote sensing of regional seas: A review of recent results","volume":"146","author":"Ruddick","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2022.02.017","article-title":"Modeling and SAR imaging of the sea surface: A review of the state-of-the-art with simulations","volume":"187","author":"Rizaev","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.rse.2019.04.031","article-title":"Estimating melt onset over Arctic sea ice from time series multi-sensor Sentinel-1 and RADARSAT-2 backscatter","volume":"229","author":"Howell","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112177","DOI":"10.1016\/j.rse.2020.112177","article-title":"A note on radar signatures of hydrometeors in the melting layer as inferred from Sentinel-1 SAR data acquired over the ocean","volume":"253","author":"Alpers","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"111457","DOI":"10.1016\/j.rse.2019.111457","article-title":"Classification of the global Sentinel-1 SAR vignettes for ocean surface process studies","volume":"234","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3890","DOI":"10.1080\/01431161.2015.1051630","article-title":"TerraSAR-X\/TanDEM-X sea state measurements using the XWAVE algorithm","volume":"36","author":"Bruck","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, H., Yang, J., Mouche, A., Shao, W., Zhu, J., Ren, L., and Xie, C. (2017). GF-3 SAR ocean wind retrieval: The first view and preliminary assessment. Remote Sens., 9.","DOI":"10.3390\/rs9070694"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9106","DOI":"10.1109\/JSTARS.2023.3310363","article-title":"Synthetic aperture radar (SAR) for ocean: A review","volume":"16","author":"Asiyabi","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ardila, J., Laurila, P., Kourkouli, P., and Strong, S. (2022, January 17\u201322). Persistent monitoring and mapping of floods globally based on the ICEYE SAR imaging constellation. Proceedings of the IGARSS, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9883587"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"La, T.V., Pelich, R.M., Li, Y., Matgen, P., and Chini, M. (2024). Monitoring of spatio-temporal variations of oil slicks via the collocation of multi-source satellite images. Remote Sens., 16.","DOI":"10.3390\/rs16163110"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Rodriguez-Alvarez, N., Munoz-Martin, J.F., and Morris, M. (2023). Latest advances in the global navigation satellite system\u2014Reflectometry (GNSS-R) field. Remote Sens., 15.","DOI":"10.3390\/rs15082157"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s43020-024-00139-4","article-title":"Remote sensing and its applications using GNSS reflected signals: Advances and prospects","volume":"5","author":"Jin","year":"2024","journal-title":"Satell. Navig."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4989","DOI":"10.1109\/TGRS.2017.2699122","article-title":"Sea ice detection using UK TDS-1 GNSS-R data","volume":"55","author":"Zavorotny","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4237","DOI":"10.1109\/JSTARS.2017.2705350","article-title":"Sea target detection using spaceborne GNSS-R delay-Doppler maps: Theory and experimental proof of concept using TDS-1 data","volume":"10","author":"Park","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.1109\/TGRS.2015.2502245","article-title":"Calibration and unwrapping of the normalized scattering cross section for the cyclone global navigation satellite system","volume":"54","author":"Gleason","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Carreno-Luengo, H., Crespo, J.A., Akbar, R., Bringer, A., Warnock, A., Morris, M., and Ruf, C. (2021). The CYGNSS mission: On-going science team investigations. Remote Sens., 13.","DOI":"10.3390\/rs13091814"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2871","DOI":"10.1109\/TGRS.2017.2655567","article-title":"Baltic sea ice concentration estimation using SENTINEL-1 SAR and AMSR2 microwave radiometer data","volume":"55","author":"Karvonen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.rse.2015.04.005","article-title":"Evaluation of sea surface temperature and wind speed observed by GCOM-W1\/AMSR2 using in situ data and global products","volume":"164","author":"Hihara","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.rse.2018.05.032","article-title":"Remote sensing of optical characteristics and particle distributions of the upper ocean using shipboard lidar","volume":"215","author":"Collister","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"29107","DOI":"10.1364\/OE.498053","article-title":"Lidar attenuation coefficient in the global oceans: Insights from ICESat-2 mission","volume":"31","author":"Lu","year":"2023","journal-title":"Opt. Express"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Huang, W., Liu, X., and Gill, E.W. (2017). Ocean wind and wave measurements using X-band marine radar: A comprehensive review. Remote Sens., 9.","DOI":"10.3390\/rs9121261"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Huang, W., and Gill, E.W. (2015). Ocean remote sensing using X-band shipborne nautical radar\u2014Applications in eastern Canada. Coastal Ocean Observing Systems, Elsevier.","DOI":"10.1016\/B978-0-12-802022-7.00014-6"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Rubio, A., Mader, J., Corgnati, L., Mantovani, C., Griffa, A., Novellino, A., Quentin, C., Wyatt, L., Schulz-Stellenfleth, J., and Horstmann, J. (2017). HF radar activity in European coastal seas: Next steps toward a pan-European HF radar network. Front. Mar. Sci., 4.","DOI":"10.3389\/fmars.2017.00008"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Pijanowski, B.C., and Brown, C.J. (2022). Grand challenges in acoustic remote sensing: Discoveries to support a better understanding of our changing planet. Front. Remote Sens., 2.","DOI":"10.3389\/frsen.2021.824848"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1175\/JTECH-D-22-0055.1","article-title":"Acoustic sensing of ocean mixed layer depth and temperature from uplooking ADCPs","volume":"40","author":"Brenner","year":"2023","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.jglr.2013.02.003","article-title":"A review of low cost underwater acoustic remote sensing for large freshwater systems","volume":"39","author":"Meadows","year":"2013","journal-title":"J. Gt. Lakes Res."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/B978-0-12-391851-2.00003-9","article-title":"In situ ocean observations: A brief history, present status, and future directions","volume":"103","author":"Gould","year":"2013","journal-title":"Int. Geophys."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kwon, K., Choi, B.J., Kim, S.D., Lee, S.H., and Park, K.A. (2020). Assessment and improvement of global gridded sea surface temperature datasets in the Yellow Sea using in situ ocean buoy and research vessel observations. Remote Sens., 12.","DOI":"10.3390\/rs12050759"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/JOE.2018.2794095","article-title":"Ocean Floor Observation and Bathymetry System (OFOBS): A new towed camera\/sonar system for deep-sea habitat surveys","volume":"44","author":"Purser","year":"2018","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Van Vranken, C., Jakoboski, J., Carroll, J.W., Cusack, C., Gorringe, P., Hirose, N., Manning, J., Martinelli, M., Penna, P., and Pickering, M. (2023). Towards a global Fishing Vessel Ocean Observing Network (FVON): State of the art and future directions. Front. Mar. Sci., 10.","DOI":"10.3389\/fmars.2023.1176814"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3439","DOI":"10.5194\/bg-17-3439-2020","article-title":"Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections","volume":"17","author":"Kwiatkowski","year":"2020","journal-title":"Biogeosciences"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yao, R., Shao, W., Hao, M., Zuo, J., and Hu, S. (2023). The Respondence of wave on sea surface temperature in the context of global change. Remote Sens., 15.","DOI":"10.3390\/rs15071948"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1416","DOI":"10.1126\/science.adi2336","article-title":"Learning skillful medium-range global weather forecasting","volume":"382","author":"Lam","year":"2023","journal-title":"Science"},{"key":"ref_55","first-page":"794","article-title":"Current status of global ocean reanalysis datasets","volume":"33","author":"Shihong","year":"2018","journal-title":"Adv. Earth Sci."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Storto, A., Alvera-Azc\u00e1rate, A., Balmaseda, M.A., Barth, A., Chevallier, M., Counillon, F., Domingues, C.M., Drevillon, M., Drillet, Y., and Forget, G. (2019). Ocean reanalyses: Recent advances and unsolved challenges. Front. Mar. Sci., 6.","DOI":"10.3389\/fmars.2019.00418"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"107486","DOI":"10.1016\/j.oceaneng.2020.107486","article-title":"Wind-generated wave climate variability in the Indian Ocean using ERA-5 dataset","volume":"209","author":"Sreelakshmi","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"4781","DOI":"10.1016\/j.egyr.2023.11.035","article-title":"Evaluating ERA5 reanalysis predictions of low wind speed events around the UK","volume":"10","author":"Potisomporn","year":"2023","journal-title":"Energy Rep."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"C09006","DOI":"10.1029\/2004JC002817","article-title":"Sea level rise and the warming of the oceans in the Simple Ocean Data Assimilation (SODA) ocean reanalysis","volume":"110","author":"Carton","year":"2005","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_61","unstructured":"Kim, J., Teng, Y.C., Vernieres, G., Sluka, T., Paturi, S., Hao, Y., Worthen, D., Li, B., Wang, J., and Zhu, J. (2021). NOAA-NCEP Next Generation Global Ocean Data Assimilation System (NG-GODAS). Research Activities in Earth System Modelling, Working Group on Numerical Experimentation. Report."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1038\/s43247-024-01870-1","article-title":"On the spatial double peak of the 2023\u20132024 El Ni\u00f1o event","volume":"5","author":"Geng","year":"2024","journal-title":"Commun. Earth Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1501705","DOI":"10.1109\/LGRS.2024.3362474","article-title":"Assessment of GHRSST and OISST Datasets in Identification of Marine Heat-Waves and Heat-Spikes","volume":"21","author":"Gupta","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"163396","DOI":"10.1016\/j.scitotenv.2023.163396","article-title":"A quantitative analysis of marine heatwaves in response to rising sea surface temperature","volume":"881","author":"Cheng","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1080\/00288330.2020.1713179","article-title":"Evaluation of four global ocean reanalysis products for New Zealand waters\u2013A guide for regional ocean modelling","volume":"55","author":"Couto","year":"2021","journal-title":"N. Z. J. Mar. Freshw. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"120145","DOI":"10.1016\/j.oceaneng.2024.120145","article-title":"Missing values imputation in ocean buoy time series data","volume":"318","author":"Chakraborty","year":"2025","journal-title":"Ocean Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.5194\/os-20-1309-2024","article-title":"MAESSTRO: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion","volume":"20","author":"Goh","year":"2024","journal-title":"Ocean Sci."},{"key":"ref_68","first-page":"5610517","article-title":"Enhanced spatiotemporal fusion via MODIS-like images","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","first-page":"4207215","article-title":"GNSS-R Global Sea Surface Wind Speed Retrieval Based on Deep Learning","volume":"61","author":"Liu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"5000814","DOI":"10.1109\/TGRS.2021.3094117","article-title":"Optically enhanced super-resolution of sea surface temperature using deep learning","volume":"60","author":"Lloyd","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1109\/JSTARS.2020.3042242","article-title":"Applications of deep learning-based super-resolution for sea surface temperature reconstruction","volume":"14","author":"Ping","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4921","DOI":"10.1109\/JSTARS.2025.3532219","article-title":"STEPNet: A Spatial and Temporal Encoding Pipeline to handle Temporal Heterogeneity in Climate Modeling using AI: A Use Case of Sea Ice Forecasting","volume":"18","author":"Wang","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"E242","DOI":"10.1175\/BAMS-D-24-0072.1","article-title":"Investigation of a calibration change in the ocean surface wind measurements from the TAO buoy array","volume":"106","author":"Ricciardulli","year":"2025","journal-title":"Bull. Am. Meteor. Soc."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"112439","DOI":"10.1016\/j.rse.2021.112439","article-title":"Cross-calibration of MODIS and VIIRS long near infrared bands for ocean color science and applications","volume":"260","author":"Barnes","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Yang, L., Driscol, J., Sarigai, S., Wu, Q., Chen, H., and Lippitt, C.D. (2022). Google Earth Engine and artificial intelligence (AI): A comprehensive review. Remote Sens., 14.","DOI":"10.3390\/rs14143253"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2243671","DOI":"10.1080\/15481603.2023.2243671","article-title":"Coastline extraction using remote sensing: A review","volume":"60","author":"Sun","year":"2023","journal-title":"GISci. Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"106314","DOI":"10.1016\/j.oceaneng.2019.106314","article-title":"Combining random forests and physics-based models to forecast the electricity generated by ocean waves: A case study of the Mutriku wave farm","volume":"189","author":"Serras","year":"2019","journal-title":"Ocean Eng."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"102339","DOI":"10.1016\/j.apor.2020.102339","article-title":"Using Random forest and Gradient boosting trees to improve wave forecast at a specific location","volume":"104","author":"Callens","year":"2020","journal-title":"Appl. Ocean Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_80","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_81","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.accre.2022.06.001","article-title":"Contributions of internal climate variability in driving global and ocean temperature variations using multi-layer perceptron neural network","volume":"13","author":"Xiao","year":"2022","journal-title":"Adv. Clim. Chang. Res."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1007\/s00773-020-00785-8","article-title":"Sea state estimation using monitoring data by convolutional neural network (CNN)","volume":"26","author":"Kawai","year":"2021","journal-title":"J. Mar. Sci. Technol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"0196","DOI":"10.34133\/remotesensing.0196","article-title":"Expanding Horizons: U-Net Enhancements for Semantic Segmentation, Forecasting, and Super-Resolution in Ocean Remote Sensing","volume":"4","author":"Wang","year":"2024","journal-title":"J. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Zhao, Q., Peng, S., Wang, J., Li, S., Hou, Z., and Zhong, G. (2024). Applications of deep learning in physical oceanography: A comprehensive review. Front. Mar. Sci., 11.","DOI":"10.3389\/fmars.2024.1396322"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Wang, S., Li, W., Hou, S., Guan, J., and Yao, J. (2022). STA-GAN: A spatio-temporal attention generative adversarial network for missing value imputation in satellite data. Remote Sens., 15.","DOI":"10.3390\/rs15010088"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"111714","DOI":"10.1016\/j.ecolind.2024.111714","article-title":"Mangrove extraction from super-resolution images generated by deep learning models","volume":"159","author":"Hong","year":"2024","journal-title":"Ecol. Indic."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"119169","DOI":"10.1016\/j.oceaneng.2024.119169","article-title":"Deep learning approach for downscaling the significant wave height based on CBAM_CGAN","volume":"312","author":"Yu","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"100189","DOI":"10.1016\/j.egyai.2022.100189","article-title":"Ocean energy applications for coastal communities with artificial intelligencea state-of-the-art review","volume":"10","author":"Zhou","year":"2022","journal-title":"Energy AI"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Wu, S., Bao, S., Dong, W., Wang, S., Zhang, X., Shao, C., Zhu, J., and Li, X. (2024). PGTransNet: A physics-guided transformer network for 3D ocean temperature and salinity predicting in tropical Pacific. Front. Mar. Sci., 11.","DOI":"10.3389\/fmars.2024.1477710"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"106854","DOI":"10.1016\/j.engappai.2023.106854","article-title":"Spatiotemporal graph neural network for multivariate multi-step ahead time-series forecasting of sea temperature","volume":"126","author":"Kim","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/MGRS.2024.3493972","article-title":"Beyond Grid Data: Exploring graph neural networks for Earth observation","volume":"13","author":"Zhao","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"e2023EA003455","DOI":"10.1029\/2023EA003455","article-title":"Harnessing the power of graph representation in climate forecasting: Predicting global monthly mean sea surface temperatures and anomalies","volume":"11","author":"Ning","year":"2024","journal-title":"Earth Space Sci."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Wang, T., Li, Z., Geng, X., Jin, B., and Xu, L. (2022). Time series prediction of sea surface temperature based on an adaptive graph learning neural model. Future Internet, 14.","DOI":"10.3390\/fi14060171"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1503405","DOI":"10.1109\/LGRS.2024.3398709","article-title":"3D Ocean Temperature Prediction via Graph Neural Network with Optimized Attention Mechanisms","volume":"21","author":"Ou","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"102363","DOI":"10.1016\/j.ocemod.2024.102363","article-title":"A deep hybrid network for significant wave height estimation","volume":"189","author":"Iuppa","year":"2024","journal-title":"Ocean Model."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Ma, D., Gregor, L., and Gruber, N. (2023). Four decades of trends and drivers of global surface ocean acidification. Glob. Biogeochem. Cycle, 37.","DOI":"10.1029\/2023GB007765"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.5194\/gmd-13-5103-2020","article-title":"Retrieving monthly and interannual total-scale pH (pHT) on the East China Sea shelf using an artificial neural network: ANN-pHT-v1","volume":"13","author":"Li","year":"2020","journal-title":"Geosci. Model Dev."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Song, Z., Bai, Y., He, X., Yu, S., Zhang, S., and Gong, F. (2022). Remote sensing of global sea surface pH based on massive underway data and machine learning. Remote Sens., 14.","DOI":"10.3390\/rs14102366"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Wang, J., Yao, P., Liu, J., Wang, X., Mao, J., Xu, J., and Wang, J. (2023). Reconstruction of Surface Seawater pH in the North Pacific. Sustainability, 15.","DOI":"10.3390\/su15075796"},{"key":"ref_102","first-page":"719","article-title":"A global monthly field of seawater pH over 3 decades: A machine learning approach","volume":"2024","author":"Zhong","year":"2024","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Osborne, E., Xu, Y.Y., Soden, M., McWhorter, J., Barbero, L., and Wanninkhof, R. (2024). A neural network algorithm for quantifying seawater pH using Biogeochemical-Argo floats in the open Gulf of Mexico. Front. Mar. Sci., 11.","DOI":"10.3389\/fmars.2024.1468909"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1038\/s43247-022-00419-4","article-title":"Regional sensitivity patterns of Arctic Ocean acidification revealed with machine learning","volume":"3","author":"Krasting","year":"2022","journal-title":"Commun. Earth Environ."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Cameselle, S., Velo, A., Doval, M.D., Broull\u00f3n, D., and P\u00e9rez, F.F. (2024). Long-term trends of pH, alkalinity, and hydrogen ion concentration in an upwelling-dominated coastal ecosystem: R\u00eda de Vigo, NW Spain. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-68694-z"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Galdies, C., and Guerra, R. (2023). High resolution estimation of ocean dissolved inorganic carbon, total alkalinity and pH based on deep learning. Water, 15.","DOI":"10.3390\/w15081454"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1503305","DOI":"10.1109\/LGRS.2024.3398048","article-title":"Global Surface Ocean Total Alkalinity Estimation: The Machine Learning Approach","volume":"21","author":"Shaik","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"100032","DOI":"10.1016\/j.scowo.2024.100032","article-title":"Predicting Total Alkalinity in Saline Water Using Machine Learning: A Case Study with RapidMiner","volume":"4","author":"Nguyen","year":"2024","journal-title":"Sustain. Chem. One World"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Li, X., Bellerby, R.G., Wallhead, P., Ge, J., Liu, J., Liu, J., and Yang, A. (2020). A neural network-based analysis of the seasonal variability of surface total alkalinity on the East China Sea Shelf. Front. Mar. Sci., 7.","DOI":"10.3389\/fmars.2020.00219"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.5194\/essd-11-1109-2019","article-title":"A global monthly climatology of total alkalinity: A neural network approach","volume":"11","author":"Velo","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1038\/s43017-022-00345-1","article-title":"Past and future ocean warming","volume":"3","author":"Cheng","year":"2022","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s44295-024-00042-3","article-title":"Deep learning for ocean temperature forecasting: A survey","volume":"2","author":"Zhao","year":"2024","journal-title":"Intell. Mar. Technol. Syst."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"6387173","DOI":"10.1155\/2020\/6387173","article-title":"A novel method for sea surface temperature prediction based on deep learning","volume":"2020","author":"Yu","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1109\/LGRS.2019.2931728","article-title":"An adaptive scale sea surface temperature predicting method based on deep learning with attention mechanism","volume":"17","author":"Xie","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_115","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. Modell. Softw"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"12514","DOI":"10.1109\/JSTARS.2021.3128577","article-title":"D2CL: A dense dilated convolutional LSTM model for sea surface temperature prediction","volume":"14","author":"Hou","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Qi, J., Xie, B., Yang, D., Li, D., and Yin, B. (2024, January 21\u201325). Deep Learning-driven Forecasting of Daily Sea Surface Temperatures in the South China Sea. Proceedings of the 2024 Photonics & Electromagnetics Research Symposium (PIERS), Chengdu, China.","DOI":"10.1109\/PIERS62282.2024.10617964"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Taylor, J., and Feng, M. (2022). A deep learning model for forecasting global monthly mean sea surface temperature anomalies. Front. Clim., 4.","DOI":"10.3389\/fclim.2022.932932"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"4280","DOI":"10.1109\/JSTARS.2024.3357191","article-title":"Long-term Prediction of Sea Surface Temperature by Temporal Embedding Transformer with Attention Distilling and Partial Stacked Connection","volume":"17","author":"Dai","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Ducournau, A., and Fablet, R. (2016, January 4). Deep learning for ocean remote sensing: An application of convolutional neural networks for super-resolution on satellite-derived SST data. Proceedings of the 9th IAPR Workshop Pattern Recogniton Remote Sensing (PRRS), Cancun, Mexico.","DOI":"10.1109\/PRRS.2016.7867019"},{"key":"ref_121","first-page":"103312","article-title":"Multi-source deep data fusion and super-resolution for downscaling sea surface temperature guided by Generative Adversarial Network-based spatiotemporal dependency learning","volume":"119","author":"Kim","year":"2023","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.neucom.2022.11.078","article-title":"ENSO analysis and prediction using deep learning: A review","volume":"520","author":"Wang","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1007\/s12145-024-01295-6","article-title":"ENSO dataset & comparison of deep learning models for ENSO forecasting","volume":"17","author":"Mir","year":"2024","journal-title":"Earth Sci. Inform."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"e2022MS003132","DOI":"10.1029\/2022MS003132","article-title":"ENSO-GTC: ENSO Deep Learning Forecast Model With a Global Spatial-Temporal Teleconnection Coupler","volume":"14","author":"Mu","year":"2022","journal-title":"J. Adv. Model. Earth Syst."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"100350","DOI":"10.1016\/j.aosl.2023.100350","article-title":"Prediction of ENSO using multivariable deep learning","volume":"16","author":"Chen","year":"2023","journal-title":"Atmos. Ocean. Sci. Lett."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"e2021GL093531","DOI":"10.1029\/2021GL093531","article-title":"Deep residual convolutional neural network combining dropout and transfer learning for ENSO forecasting","volume":"48","author":"Hu","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s00376-023-3001-1","article-title":"Superiority of a convolutional neural network Model over Dynamical models in Predicting Central Pacific ENSO","volume":"41","author":"Wang","year":"2024","journal-title":"Adv. Atmos. Sci."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1038\/s41586-019-1559-7","article-title":"Deep learning for multi-year ENSO forecasts","volume":"573","author":"Ham","year":"2019","journal-title":"Nature"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"5271","DOI":"10.1007\/s00382-023-06854-z","article-title":"Tendency-and-attention-informed deep learning for ENSO forecasts","volume":"61","author":"Qiao","year":"2023","journal-title":"Clim. Dyn."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1029\/2018EA000423","article-title":"Analyzing El Ni\u00f1o\u2013Southern Oscillation Predictability Using Long-Short-Term-Memory Models","volume":"6","author":"Huang","year":"2019","journal-title":"Earth Space Sci."},{"key":"ref_131","first-page":"1501205","article-title":"Prediction of ENSO beyond spring predictability barrier using deep convolutional LSTM networks","volume":"19","author":"Gupta","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Geng, H., and Wang, T. (2021). Spatiotemporal model based on deep learning for ENSO forecasts. Atmosphere, 12.","DOI":"10.3390\/atmos12070810"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"He, D., Lin, P., Liu, H., Ding, L., and Jiang, J. (2019, January 26\u201330). DLENSO: A deep learning ENSO forecasting model. Proceedings of the PRICAI 2019: Trends in Artificial Intelligence, Cuvu, Yanuca Island, Fiji.","DOI":"10.1007\/978-3-030-29911-8_2"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"124023","DOI":"10.1088\/1748-9326\/ad8be1","article-title":"A probabilistic forecast for multi-year ENSO using Bayesian convolutional neural network","volume":"19","author":"Sreeraj","year":"2024","journal-title":"Environ. Res. Lett."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"124492","DOI":"10.1016\/j.eswa.2024.124492","article-title":"Adaptive Graph Spatial-Temporal Attention Networks for long lead ENSO prediction","volume":"255","author":"Liang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_136","unstructured":"Cachay, S.R., Erickson, E., Bucker, A.F.C., Pokropek, E., Potosnak, W., Osei, S., and L\u00fctjens, B. (2020). Graph Neural Networks for Improved El Ni\u00f1o Forecasting. arXiv."},{"key":"ref_137","first-page":"1003305","article-title":"Transformer for El ni\u00f1o-southern oscillation prediction","volume":"19","author":"Ye","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"17066","DOI":"10.1109\/JSTARS.2024.3447356","article-title":"ENSO Forecasts With Spatiotemporal Fusion Transformer Network","volume":"17","author":"Zhao","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Song, D., Su, X., Li, W., Sun, Z., Ren, T., Liu, W., and Liu, A.A. (2023). Spatial-temporal transformer network for multi-year ENSO prediction. Front. Mar. Sci., 10.","DOI":"10.3389\/fmars.2023.1143499"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1146\/annurev-marine-032720-095144","article-title":"Marine heatwaves","volume":"13","author":"Oliver","year":"2021","journal-title":"Annu. Rev. Mar. Sci."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1146\/annurev-marine-032122-121437","article-title":"Biological impacts of marine heatwaves","volume":"15","author":"Smith","year":"2023","journal-title":"Annu. Rev. Mar. Sci."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/s43247-024-01215-y","article-title":"Arctic marine heatwaves forced by greenhouse gases and triggered by abrupt sea-ice melt","volume":"5","author":"Barkhordarian","year":"2024","journal-title":"Commun. Earth Environ."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1038\/s43247-024-01553-x","article-title":"Marine heatwaves in a shifting Southern Ocean induce dynamical changes in primary production","volume":"5","author":"Belyaev","year":"2024","journal-title":"Commun. Earth Environ."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"1903","DOI":"10.1038\/s41467-020-15820-w","article-title":"Physical drivers of the summer 2019 North Pacific marine heatwave","volume":"11","author":"Amaya","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1038\/s41586-022-04573-9","article-title":"Global seasonal forecasts of marine heatwaves","volume":"604","author":"Jacox","year":"2022","journal-title":"Nature"},{"key":"ref_146","unstructured":"Ning, D., Vetrova, V., Bryan, K., and Delaux, S. (2021, January 23). Deep learning for spatiotemporal anomaly forecasting: A case study of marine heatwaves. Proceedings of the ICML 2021 Workshop on Tackling Climate Change with Machine Learning, Virtual."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"e2024GL110651","DOI":"10.1029\/2024GL110651","article-title":"Predictability limit of the 2021 Pacific Northwest heatwave from deep-learning sensitivity analysis","volume":"51","author":"Vonich","year":"2024","journal-title":"Geophys. Res. Lett."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Jacques-Dumas, V., Ragone, F., Borgnat, P., Abry, P., and Bouchet, F. (2022). Deep learning-based extreme heatwave forecast. Front. Clim., 4.","DOI":"10.3389\/fclim.2022.789641"},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Xie, B., Qi, J., Yang, S., Sun, G., Feng, Z., Yin, B., and Wang, W. (2024). Sea Surface Temperature and Marine Heat Wave Predictions in the South China Sea: A 3D U-Net Deep Learning Model Integrating Multi-Source Data. Atmosphere, 15.","DOI":"10.3390\/atmos15010086"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"064035","DOI":"10.1088\/1748-9326\/ad4616","article-title":"Deep learning improves sub-seasonal marine heatwave forecast","volume":"19","author":"Sun","year":"2024","journal-title":"Environ. Res. Lett."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"He, Q., Zhu, Z., Zhao, D., Song, W., and Huang, D. (2024). An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns. Appl. Sci., 14.","DOI":"10.3390\/app14020601"},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Sun, W., Zhou, S., Yang, J., Gao, X., Ji, J., and Dong, C. (2023). Artificial intelligence forecasting of marine heatwaves in the South China Sea using a combined U-Net and ConvLSTM system. Remote Sens., 15.","DOI":"10.3390\/rs15164068"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"107718","DOI":"10.1016\/j.engappai.2023.107718","article-title":"Two-pathway spatiotemporal representation learning for extreme water temperature prediction","volume":"131","author":"Kim","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"e2023GL106584","DOI":"10.1029\/2023GL106584","article-title":"Quantifying the relative contributions of the global oceans to ENSO predictability with deep learning","volume":"51","author":"Li","year":"2024","journal-title":"Geophys. Res. Lett."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"5500","DOI":"10.1109\/JSTARS.2024.3354912","article-title":"A Multi-scale Dual Attention Network for the Automatic Classification of Polar Sea Ice and Open Water Based on Sentinel-1 SAR Images","volume":"17","author":"Zhang","year":"2024","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Ren, Y., Li, X., Yang, X., and Xu, H. (2023). Sea Ice Detection from SAR Images Based on Deep Fully Convolutional Networks. Artificial Intelligence Oceanography, Springer Nature.","DOI":"10.1007\/978-981-19-6375-9_12"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"114073","DOI":"10.1016\/j.rse.2024.114073","article-title":"Sea ice detection using concurrent multispectral and synthetic aperture radar imagery","volume":"305","author":"Rogers","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_158","first-page":"1","article-title":"MMSeaIce: Multi-task mapping of sea ice parameters from AI4Arctic Sea Ice Challenge Dataset","volume":"2023","author":"Chen","year":"2023","journal-title":"EGUsphere"},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"5648216","DOI":"10.1109\/TGRS.2024.3493121","article-title":"SeaIceNet: Sea Ice Recognition via Global-Local Transformer in Optical Remote Sensing Images","volume":"62","author":"Hong","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Guo, B., Zhao, H., Jiang, Y., and Sun, Y. (2024). SICFormer: A 3D-Swin Transformer for Sea Ice Concentration Prediction. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12081424"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"1261","DOI":"10.1109\/JOE.2021.3073931","article-title":"Deep learning for imputation and forecasting tidal level","volume":"46","author":"Yang","year":"2021","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"104504","DOI":"10.1016\/j.coastaleng.2024.104504","article-title":"A deep-learning model for rapid spatiotemporal prediction of coastal water levels","volume":"190","author":"Shahabi","year":"2024","journal-title":"Coast. Eng."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Mulia, I.E., Ueda, N., Miyoshi, T., Iwamoto, T., and Heidarzadeh, M. (2023). A novel deep learning approach for typhoon-induced storm surge modeling through efficient emulation of wind and pressure fields. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-35093-9"},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Nieves, V., Radin, C., and Camps-Valls, G. (2021). Predicting regional coastal sea level changes with machine learning. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-87460-z"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Raj, N., Murali, J., Singh-Peterson, L., and Downs, N. (2024). Prediction of Sea Level Using Double Data Decomposition and Hybrid Deep Learning Model for Northern Territory, Australia. Mathematics, 12.","DOI":"10.3390\/math12152376"},{"key":"ref_166","doi-asserted-by":"crossref","unstructured":"Sabililah, R.N., and Adytia, D. (2023, January 7\u20139). Time Series Forecasting of Sea Level by Using Transformer Approach, with a Case Study in Pangandaran, Indonesia. Proceedings of the 2023 IEEE 8th International Conference for Convergence in Technology (I2CT), Lonavla, India.","DOI":"10.1109\/I2CT57861.2023.10126216"},{"key":"ref_167","first-page":"10","article-title":"The ocean and cryosphere in a changing climate","volume":"1155","author":"Roberts","year":"2019","journal-title":"IPCC Spec. Rep. Ocean Cryosphere A Chang. Clim."},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Yan, Q., and Huang, W. (2019). Sea ice remote sensing using GNSS-R: A review. Remote Sens., 11.","DOI":"10.3390\/rs11212565"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"2723","DOI":"10.5194\/essd-13-2723-2021","article-title":"Arctic sea ice cover data from spaceborne SAR by deep learning","volume":"13","author":"Wang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/JSTARS.2020.2966880","article-title":"Sea ice thickness measurement using spaceborne GNSS-R: First results with TechDemoSat-1 data","volume":"13","author":"Yan","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1109\/JSTARS.2019.2907008","article-title":"Detecting sea ice from TechDemoSat-1 data using support vector machines with feature selection","volume":"12","author":"Yan","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"2001405","DOI":"10.1109\/LGRS.2022.3198189","article-title":"Monitoring sea ice thickness using GNSS-interferometric reflectometry","volume":"19","author":"Regmi","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.1109\/LGRS.2018.2852143","article-title":"Sea ice sensing from GNSS-R data using convolutional neural networks","volume":"15","author":"Yan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Hu, Y., Hua, X., Liu, W., and Wickert, J. (2023). Sea Ice Detection from GNSS-R Data Based on Residual Network. Remote Sens., 15.","DOI":"10.3390\/rs15184477"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"8776","DOI":"10.1109\/JSTARS.2022.3213192","article-title":"MeltPondNet: A Swin Transformer U-Net for Detection of Melt Ponds on Arctic Sea Ice","volume":"15","author":"Sudakow","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Lyu, H., Huang, W., and Mahdianpari, M. (2022). Eastern Arctic sea ice sensing: First results from the RADARSAT constellation mission data. Remote Sens., 14.","DOI":"10.3390\/rs14051165"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"3471","DOI":"10.5194\/tc-18-3471-2024","article-title":"The AutoICE Challenge","volume":"18","author":"Stokholm","year":"2024","journal-title":"Cryosphere"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"18898","DOI":"10.1109\/JSTARS.2024.3464691","article-title":"CNN and Transformer Fusion Network for Sea Ice Classification Using GaoFen-3 Polarimetric SAR Images","volume":"17","author":"Zhang","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"4301315","DOI":"10.1109\/TGRS.2023.3279089","article-title":"Predicting the daily sea ice concentration on a subseasonal scale of the pan-arctic during the melting season by a deep learning model","volume":"61","author":"Ren","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s00376-023-3259-3","article-title":"Assessments of data-driven deep learning models on one-month predictions of pan-arctic sea ice thickness","volume":"41","author":"Song","year":"2024","journal-title":"Adv. Atmos. Sci."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Chi, J., Bae, J., and Kwon, Y.J. (2021). Two-stream convolutional long-and short-term memory model using perceptual loss for sequence-to-sequence Arctic sea ice prediction. Remote Sens., 13.","DOI":"10.3390\/rs13173413"},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"5124","DOI":"10.1038\/s41467-021-25257-4","article-title":"Seasonal Arctic sea ice forecasting with probabilistic deep learning","volume":"12","author":"Andersson","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_183","first-page":"4300522","article-title":"A Spatio-Temporal Multiscale Deep Learning Model for Subseasonal Prediction of Arctic Sea Ice","volume":"62","author":"Zheng","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"4045","DOI":"10.1007\/s11831-023-09934-9","article-title":"Predicting sea level rise using artificial intelligence: A review","volume":"30","author":"Bahari","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_185","doi-asserted-by":"crossref","unstructured":"Vicens-Miquel, M., Tissot, P.E., and Medrano, F.A. (2024). Exploring Deep Learning Methods for Short-Term Tide Gauge Water Level Predictions. Water, 16.","DOI":"10.3390\/w16202886"},{"key":"ref_186","doi-asserted-by":"crossref","unstructured":"Tiggeloven, T., Couasnon, A., van Straaten, C., Muis, S., and Ward, P.J. (2021). Exploring deep learning capabilities for surge predictions in coastal areas. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-96674-0"},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"102179","DOI":"10.1016\/j.ocemod.2023.102179","article-title":"Developing a deep learning-based storm surge forecasting model","volume":"182","author":"Xie","year":"2023","journal-title":"Ocean Model."},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"104024","DOI":"10.1016\/j.coastaleng.2021.104024","article-title":"Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning","volume":"170","author":"Lee","year":"2021","journal-title":"Coast. Eng."},{"key":"ref_189","doi-asserted-by":"crossref","first-page":"18971","DOI":"10.1007\/s00521-023-08719-2","article-title":"An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions","volume":"35","author":"Adeli","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_190","first-page":"1501605","article-title":"Sea surface height prediction with deep learning based on attention mechanism","volume":"19","author":"Liu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_191","unstructured":"Krishnamurthy, V.N.D., Degadwala, S., and Vyas, D. (2023, January 19\u201321). Forecasting Future Sea Level Rise: A Data-driven Approach using Climate Analysis. Proceedings of the 2023 2nd International Conference on Edge Computing and Applications (ICECAA), Namakkal, India."},{"key":"ref_192","doi-asserted-by":"crossref","unstructured":"Yang, L., Jin, T., Gao, X., Wen, H., Sch\u00f6ne, T., Xiao, M., and Huang, H. (2021). Sea level fusion of satellite altimetry and tide gauge data by deep learning in the Mediterranean Sea. Remote Sens., 13.","DOI":"10.3390\/rs13050908"},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"13400","DOI":"10.1109\/JSTARS.2024.3430899","article-title":"A novel coral reef classification method combining radiative transfer model with deep learning","volume":"17","author":"Ai","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"113584","DOI":"10.1016\/j.rse.2023.113584","article-title":"A deep learning model for measuring coral reef halos globally from multispectral satellite imagery","volume":"292","author":"Franceschini","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"6085","DOI":"10.1109\/JSTARS.2024.3396374","article-title":"ICESat-2 and Multi-spectral Images Based Coral Reefs Geomorphic Zone Mapping Using a Deep Learning Approach","volume":"17","author":"Zhong","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1007\/s00338-020-02005-6","article-title":"A global coral reef probability map generated using convolutional neural networks","volume":"39","author":"Li","year":"2020","journal-title":"Coral Reefs"},{"key":"ref_197","first-page":"5003805","article-title":"GCU-Net: Remote Sensing Classification Method for Coral Reef Geomorphology Integrating Geospatial Cognition","volume":"21","author":"Zhang","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"4211415","DOI":"10.1109\/TGRS.2024.3468380","article-title":"Benthic Mapping of Coral Reef Areas at Varied Water Depths using Integrated Active and Passive Remote Sensing Data and Novel Visual Transformer Models","volume":"62","author":"Zhou","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"e4241","DOI":"10.1002\/aqc.4241","article-title":"Deep Learning for Multilabel Classification of Coral Reef Conditions in the Indo-Pacific Using Underwater Photo Transect Method","volume":"34","author":"Shao","year":"2024","journal-title":"Aquat. Conserv. Mar. Freshw. Ecosyst."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1080\/10095020.2024.2343323","article-title":"A survey on underwater coral image segmentation based on deep learning","volume":"28","author":"Li","year":"2024","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_201","doi-asserted-by":"crossref","unstructured":"King, A., Bhandarkar, S.M., and Hopkinson, B.M. (2018, January 18\u201322). A comparison of deep learning methods for semantic segmentation of coral reef survey images. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00188"},{"key":"ref_202","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, M., Zhong, J., and Qin, J. (2024, January 3\u20138). CNet: A Novel Seabed Coral Reef Image Segmentation Approach Based on Deep Learning. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACVW60836.2024.00090"},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1111\/2041-210X.14307","article-title":"Scalable semantic 3D mapping of coral reefs with deep learning","volume":"15","author":"Sauder","year":"2024","journal-title":"Methods Ecol. Evol."},{"key":"ref_204","doi-asserted-by":"crossref","unstructured":"Jamil, S., Rahman, M., and Haider, A. (2021). Bag of features (BoF) based deep learning framework for bleached corals detection. Big Data Cogn. Comput., 5.","DOI":"10.3390\/bdcc5040053"},{"key":"ref_205","doi-asserted-by":"crossref","unstructured":"Giles, A.B., Ren, K., Davies, J.E., Abrego, D., and Kelaher, B. (2023). Combining drones and deep learning to automate coral reef assessment with RGB imagery. Remote Sens., 15.","DOI":"10.3390\/rs15092238"},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"162113","DOI":"10.1016\/j.scitotenv.2023.162113","article-title":"Oceanic differences in coral-bleaching responses to marine heatwaves","volume":"871","author":"Shlesinger","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1002\/aqc.3878","article-title":"Identifying key factors for coral survival in reef restoration projects using deep learning","volume":"32","author":"Morand","year":"2022","journal-title":"Aquat. Conserv. Mar. Freshw. Ecosyst."},{"key":"ref_208","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhao, J., Wang, F., Chen, Y., and Lee, Z. (2021). Detection of coral reef bleaching based on Sentinel-2 multi-temporal imagery: Simulation and case study. Front. Mar. Sci., 8.","DOI":"10.3389\/fmars.2021.584263"},{"key":"ref_209","doi-asserted-by":"crossref","unstructured":"Boonnam, N., Udomchaipitak, T., Puttinaovarat, S., Chaichana, T., Boonjing, V., and Muangprathub, J. (2022). Coral reef bleaching under climate change: Prediction modeling and machine learning. Sustainability, 14.","DOI":"10.3390\/su14106161"},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Burns, C., Bollard, B., and Narayanan, A. (2022). Machine-learning for mapping and monitoring shallow coral reef habitats. Remote Sens., 14.","DOI":"10.3390\/rs14112666"},{"key":"ref_211","doi-asserted-by":"crossref","unstructured":"Zhu, H., Lu, Z., Zhang, C., Yang, Y., Zhu, G., Zhang, Y., and Liu, H. (2023). Remote Sensing Classification of Offshore Seaweed Aquaculture Farms on Sample Dataset Amplification and Semantic Segmentation Model. Remote Sens., 15.","DOI":"10.3390\/rs15184423"},{"key":"ref_212","doi-asserted-by":"crossref","unstructured":"Gerlo, J., Kooijman, D.G., Wieling, I.W., Heirmans, R., and Vanlanduit, S. (2023). Seaweed growth monitoring with a low-cost vision-based system. Sensors, 23.","DOI":"10.3390\/s23229197"},{"key":"ref_213","doi-asserted-by":"crossref","unstructured":"Marquez, L., Fragkopoulou, E., Cavanaugh, K., Houskeeper, H., and Assis, J. (2022). Artificial intelligence convolutional neural networks map giant kelp forests from satellite imagery. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-26439-w"},{"key":"ref_214","doi-asserted-by":"crossref","unstructured":"Bell, T.W., Nidzieko, N.J., Siegel, D.A., Miller, R.J., Cavanaugh, K.C., Nelson, N.B., Reed, D.C., Fedorov, D., Moran, C., and Snyder, J.N. (2020). The utility of satellites and autonomous remote sensing platforms for monitoring offshore aquaculture farms: A case study for canopy forming kelps. Front. Mar. Sci., 7.","DOI":"10.3389\/fmars.2020.520223"},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"8734","DOI":"10.1109\/JSTARS.2023.3312820","article-title":"Crowdsourcing experiment and fully convolutional neural networks for coastal remote sensing of seagrass and macro-algae","volume":"16","author":"Hobley","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_216","first-page":"4200213","article-title":"Deep-Learning-Based Cloud Masking on Multispectral Ocean Color Imagery for Floating Macroalgae Monitoring","volume":"62","author":"Liu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_217","doi-asserted-by":"crossref","first-page":"113515","DOI":"10.1016\/j.rse.2023.113515","article-title":"Mapping and quantifying pelagic Sargassum in the Atlantic Ocean using multi-band medium-resolution satellite data and deep learning","volume":"289","author":"Hu","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"4210914","DOI":"10.1109\/TGRS.2022.3215895","article-title":"A deep learning model for green algae detection on SAR images","volume":"60","author":"Guo","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"6002705","DOI":"10.1109\/LGRS.2024.3351551","article-title":"Biscale Convolutional Self-Attention Network for Hyperspectral Coastal Wetlands Classification","volume":"21","author":"Luo","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_220","doi-asserted-by":"crossref","unstructured":"Zheng, J.Y., Hao, Y.Y., Wang, Y.C., Zhou, S.Q., Wu, W.B., Yuan, Q., Gao, Y., Guo, H.Q., Cai, X.X., and Zhao, B. (2022). Coastal wetland vegetation classification using pixel-based, object-based and deep learning methods based on RGB-UAV. Land, 11.","DOI":"10.3390\/land11112039"},{"key":"ref_221","doi-asserted-by":"crossref","unstructured":"Jamali, A., and Mahdianpari, M. (2022). Swin transformer and deep convolutional neural networks for coastal wetland classification using sentinel-1, sentinel-2, and LiDAR data. Remote Sens., 14.","DOI":"10.3390\/rs14020359"},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"106381","DOI":"10.1016\/j.ocecoaman.2022.106381","article-title":"Deep semantic segmentation of mangroves in Brazil combining spatial, temporal, and polarization data from Sentinel-1 time series","volume":"231","author":"Andrade","year":"2023","journal-title":"Ocean Coast. Manag."},{"key":"ref_223","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Ahmadi, S.A., Ghorbanian, A., and Amani, M. (2024). Land Cover Mapping in a Mangrove Ecosystem Using Hybrid Selective Kernel-Based Convolutional Neural Networks and Multi-Temporal Sentinel-2 Imagery. Remote Sens., 16.","DOI":"10.3390\/rs16152849"},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1007\/s13131-024-2356-1","article-title":"Mangrove monitoring and extraction based on multi-source remote sensing data: A deep learning method based on SAR and optical image fusion","volume":"43","author":"Xie","year":"2024","journal-title":"Acta Oceanol. Sin."},{"key":"ref_225","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2021.07.011","article-title":"Mapping salt marsh along coastal South Carolina using U-Net","volume":"179","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_226","doi-asserted-by":"crossref","unstructured":"Liu, K., Liu, S., Tan, K., Yin, M., and Tao, P. (2024). ANN-Based Filtering of Drone LiDAR in Coastal Salt Marshes Using Spatial\u2013Spectral Features. Remote Sens., 16.","DOI":"10.3390\/rs16183373"},{"key":"ref_227","doi-asserted-by":"crossref","first-page":"1894","DOI":"10.1038\/s41467-023-37385-0","article-title":"The value of ecosystem services in global marine kelp forests","volume":"14","author":"Eger","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"e03031","DOI":"10.1002\/ecy.3031","article-title":"Remote sensing: Generation of long-term kelp bed data sets for evaluation of impacts of climatic variation","volume":"101","author":"Hamilton","year":"2020","journal-title":"Ecology"},{"key":"ref_229","unstructured":"UN Environment Programme (2023). Into the Blue: Securing a Sustainable Future for Kelp Forests, UN Environment Programme."},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"112243","DOI":"10.1016\/j.rse.2020.112243","article-title":"Mapping bull kelp canopy in northern California using Landsat to enable long-term monitoring","volume":"254","author":"Finger","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_231","doi-asserted-by":"crossref","unstructured":"Cavanaugh, K.C., Bell, T., Costa, M., Eddy, N.E., Gendall, L., Gleason, M.G., Hessing-Lewis, M., Martone, R., McPherson, M., and Pontier, O. (2021). A review of the opportunities and challenges for using remote sensing for management of surface-canopy forming kelps. Front. Mar. Sci., 8.","DOI":"10.3389\/fmars.2021.753531"},{"key":"ref_232","first-page":"e00683","article-title":"Passive remote sensing technology for mapping bull kelp (Nereocystis luetkeana): A review of techniques and regional case study","volume":"19","author":"Schroeder","year":"2019","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_233","doi-asserted-by":"crossref","unstructured":"Bell, T.W., Cavanaugh, K.C., Saccomanno, V.R., Cavanaugh, K.C., Houskeeper, H.F., Eddy, N., Schuetzenmeister, F., Rindlaub, N., and Gleason, M. (2023). Kelpwatch: A new visualization and analysis tool to explore kelp canopy dynamics reveals variable response to and recovery from marine heatwaves. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0271477"},{"key":"ref_234","doi-asserted-by":"crossref","unstructured":"Gendall, L., Schroeder, S.B., Wills, P., Hessing-Lewis, M., and Costa, M. (2023). A multi-satellite mapping framework for floating kelp forests. Remote Sens., 15.","DOI":"10.3390\/rs15051276"},{"key":"ref_235","doi-asserted-by":"crossref","unstructured":"Mahmood, A., Ospina, A.G., Bennamoun, M., An, S., Sohel, F., Boussaid, F., Hovey, R., Fisher, R.B., and Kendrick, G.A. (2020). Automatic hierarchical classification of kelps using deep residual features. Sensors, 20.","DOI":"10.3390\/s20020447"},{"key":"ref_236","doi-asserted-by":"crossref","unstructured":"Laval, M., Belmouhcine, A., Courtrai, L., Descloitres, J., Salazar-Garibay, A., Schamberger, L., Minghelli, A., Thibaut, T., Dorville, R., and Mazoyer, C. (2023). Detection of sargassum from sentinel satellite sensors using deep learning approach. Remote Sens., 15.","DOI":"10.3390\/rs15041104"},{"key":"ref_237","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 Observ. Remote Sens."},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1080\/01431161.2020.1842543","article-title":"Semantic segmentation of major macroalgae in coastal environments using high-resolution ground imagery and deep learning","volume":"42","author":"Balado","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_239","doi-asserted-by":"crossref","first-page":"112631","DOI":"10.1016\/j.rse.2021.112631","article-title":"Satellite remote sensing of pelagic Sargassum macroalgae: The power of high resolution and deep learning","volume":"264","author":"Wang","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"105239","DOI":"10.1016\/j.marenvres.2020.105239","article-title":"Evaluating multiple stressor research in coastal wetlands: A systematic review","volume":"164","author":"Ostrowski","year":"2021","journal-title":"Mar. Environ. Res."},{"key":"ref_241","doi-asserted-by":"crossref","unstructured":"Gonzalez-Perez, A., Abd-Elrahman, A., Wilkinson, B., Johnson, D.J., and Carthy, R.R. (2022). Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets. Remote Sens., 14.","DOI":"10.3390\/rs14163937"},{"key":"ref_242","doi-asserted-by":"crossref","unstructured":"Ke, L., Lu, Y., Tan, Q., Zhao, Y., and Wang, Q. (2024). Precise mapping of coastal wetlands using time-series remote sensing images and deep learning model. Front. For. Glob. Chang., 7.","DOI":"10.3389\/ffgc.2024.1409985"},{"key":"ref_243","doi-asserted-by":"crossref","first-page":"878","DOI":"10.3390\/rs3050878","article-title":"Remote sensing of mangrove ecosystems: A review","volume":"3","author":"Kuenzer","year":"2011","journal-title":"Remote Sens."},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"2797","DOI":"10.1007\/s40747-021-00457-z","article-title":"Remote sensing techniques: Mapping and monitoring of mangrove ecosystem\u2014A review","volume":"7","author":"Maurya","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"5620619","DOI":"10.1109\/TGRS.2023.3314088","article-title":"CODE-MM: Convex deep mangrove mapping algorithm based on optical satellite images","volume":"61","author":"Lin","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_246","doi-asserted-by":"crossref","unstructured":"Guo, Y., Liao, J., and Shen, G. (2021). Mapping large-scale mangroves along the maritime Silk Road from 1990 to 2015 using a novel deep learning model and Landsat data. Remote Sens., 13.","DOI":"10.3390\/rs13020245"},{"key":"ref_247","doi-asserted-by":"crossref","unstructured":"Guo, M., Yu, Z., Xu, Y., Huang, Y., and Li, C. (2021). ME-Net: A deep convolutional neural network for extracting mangrove using Sentinel-2A data. Remote Sens., 13.","DOI":"10.3390\/rs13071292"},{"key":"ref_248","first-page":"100725","article-title":"Comparisons of regression and machine learning methods for estimating mangrove above-ground biomass using multiple remote sensing data in the red River Estuaries of Vietnam","volume":"26","author":"Quang","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_249","doi-asserted-by":"crossref","unstructured":"Dong, H., Gao, Y., Chen, R., and Wei, L. (2024). MangroveSeg: Deep-Supervision-Guided Feature Aggregation Network for Mangrove Detection and Segmentation in Satellite Images. Forests, 15.","DOI":"10.3390\/f15010127"},{"key":"ref_250","doi-asserted-by":"crossref","unstructured":"Ghorbanian, A., Ahmadi, S.A., Amani, M., Mohammadzadeh, A., and Jamali, S. (2022). Application of artificial neural networks for mangrove mapping using multi-temporal and multi-source remote sensing imagery. Water, 14.","DOI":"10.3390\/w14020244"},{"key":"ref_251","doi-asserted-by":"crossref","unstructured":"Morgan, G.R., Wang, C., Li, Z., Schill, S.R., and Morgan, D.R. (2022). Deep learning of high-resolution aerial imagery for coastal marsh change detection: A comparative study. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11020100"},{"key":"ref_252","doi-asserted-by":"crossref","first-page":"e2023JF007336","DOI":"10.1029\/2023JF007336","article-title":"Modeling the flow and geomorphic heterogeneity induced by salt marsh vegetation patches based on convolutional neural network UNet-Flow","volume":"129","author":"Chen","year":"2024","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_253","first-page":"104138","article-title":"Salt marsh carbon stock estimation using deep learning with Sentinel-1 SAR of the Yangtze River estuary, China","volume":"133","author":"Li","year":"2024","journal-title":"Int. J. Appl. Earth Observ. Geoinf."},{"key":"ref_254","first-page":"4303215","article-title":"Model ensemble with dropout for uncertainty estimation in sea ice segmentation using Sentinel-1 SAR","volume":"61","author":"Karimzadeh","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_255","first-page":"4300213","article-title":"Predicting sea ice concentration with uncertainty quantification using passive microwave and reanalysis data: A case study in Baffin Bay","volume":"61","author":"Chen","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_256","unstructured":"Wang, C. (2023). Calibration in deep learning: A survey of the state-of-the-art. arXiv."},{"key":"ref_257","doi-asserted-by":"crossref","unstructured":"Hoegh-Guldberg, O., Caldeira, K., Chopin, T., Gaines, S., Haugan, P., Hemer, M., Howard, J., Konar, M., Krause-Jensen, D., and Lovelock, C.E. (2023). The ocean as a solution to climate change: Five opportunities for action. The Blue Compendium: From Knowledge to Action for a Sustainable Ocean Economy, Springer.","DOI":"10.1007\/978-3-031-16277-0_17"},{"key":"ref_258","doi-asserted-by":"crossref","first-page":"4207013","DOI":"10.1109\/TGRS.2025.3574129","article-title":"Hybrid CNN-Transformer Network with a Weighted MSE Loss for Global Sea Surface Wind Speed Retrieval from GNSS-R Data","volume":"63","author":"Qiao","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_259","doi-asserted-by":"crossref","first-page":"8586","DOI":"10.1109\/JSTARS.2023.3291236","article-title":"Reduction of rain-induced errors for wind speed estimation on SAR observations using convolutional neural networks","volume":"16","author":"Colin","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_260","doi-asserted-by":"crossref","first-page":"5220","DOI":"10.1109\/JSTARS.2021.3078902","article-title":"A novel scheme for extracting sea surface wind information from rain-contaminated X-band marine radar images","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_261","doi-asserted-by":"crossref","unstructured":"Yang, Z., and Huang, W. (2024, January 15\u201318). A CNN-based Hybrid Dehazing and Regression Model for Sea Surface Wind Speed Retrieval from Rain-contaminated Marine Radar Data. Proceedings of the OCEANS 2024-Singapore, Singapore.","DOI":"10.1109\/OCEANS51537.2024.10682253"},{"key":"ref_262","doi-asserted-by":"crossref","unstructured":"Qiao, X., and Huang, W. (2024, January 7\u201312). An Attention-Aided Convolutional Neural Network for Global Sea Surface Wind Speed Estimation from GNSS-R Data. Proceedings of the IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece.","DOI":"10.1109\/IGARSS53475.2024.10642642"},{"key":"ref_263","doi-asserted-by":"crossref","first-page":"113629","DOI":"10.1016\/j.rse.2023.113629","article-title":"DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation","volume":"294","author":"Zhao","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_264","doi-asserted-by":"crossref","first-page":"114375","DOI":"10.1016\/j.rse.2024.114375","article-title":"Deep residual fully connected network for GNSS-R wind speed retrieval and its interpretation","volume":"313","author":"Du","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_265","doi-asserted-by":"crossref","first-page":"112801","DOI":"10.1016\/j.rse.2021.112801","article-title":"GNSS reflectometry global ocean wind speed using deep learning: Development and assessment of CyGNSSnet","volume":"269","author":"Asgarimehr","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_266","doi-asserted-by":"crossref","first-page":"112934","DOI":"10.1016\/j.rse.2022.112934","article-title":"Information fusion for GNSS-R wind speed retrieval using statistically modified convolutional neural network","volume":"272","author":"Guo","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_267","doi-asserted-by":"crossref","unstructured":"Qiao, X., and Huang, W. (2024, January 23\u201326). A Hybrid CNN-Transformer Network for Global Ocean Wind Speed Retrieval from GNSS-R Data. Proceedings of the OCEANS 2024-Halifax, Halifax, NS, Canada.","DOI":"10.1109\/OCEANS55160.2024.10754265"},{"key":"ref_268","doi-asserted-by":"crossref","first-page":"5801112","DOI":"10.1109\/TGRS.2023.3276173","article-title":"Global ocean wind speed retrieval from GNSS reflectometry using CNN-LSTM network","volume":"61","author":"Lu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_269","doi-asserted-by":"crossref","unstructured":"Yu, P., Xu, W., Zhong, X., Johannessen, J.A., Yan, X.H., Geng, X., He, Y., and Lu, W. (2022). A neural network method for retrieving sea surface wind speed for C-Band SAR. Remote Sens., 14.","DOI":"10.3390\/rs14092269"},{"key":"ref_270","doi-asserted-by":"crossref","first-page":"3868","DOI":"10.1109\/JSTARS.2022.3173001","article-title":"Sea surface wind direction retrieval based on convolution neural network and wavelet analysis","volume":"15","author":"Guo","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_271","doi-asserted-by":"crossref","first-page":"4207513","DOI":"10.1109\/TGRS.2022.3174824","article-title":"The fusion of physical, textural, and morphological information in SAR imagery for hurricane wind speed retrieval based on deep learning","volume":"60","author":"Mu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_272","doi-asserted-by":"crossref","first-page":"112178","DOI":"10.1016\/j.rse.2020.112178","article-title":"Wind direction retrieval from Sentinel-1 SAR images using ResNet","volume":"253","author":"Zanchetta","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_273","doi-asserted-by":"crossref","unstructured":"Qiao, X., and Huang, W. (2024, January 15\u201318). Global Significant Wave Height Retrieval from Spaceborne GNSS-R Using Transformers. Proceedings of the OCEANS 2024-Singapore, Singapore.","DOI":"10.1109\/OCEANS51537.2024.10706273"},{"key":"ref_274","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1109\/TGRS.2020.3003839","article-title":"Deep learning for predicting significant wave height from synthetic aperture radar","volume":"59","author":"Quach","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_275","doi-asserted-by":"crossref","first-page":"5929","DOI":"10.1109\/JSEN.2022.3149852","article-title":"Mitigation of rain effect on wave height measurement using X-band radar sensor","volume":"22","author":"Yang","year":"2022","journal-title":"IEEE Sensors J."},{"key":"ref_276","doi-asserted-by":"crossref","first-page":"1505405","DOI":"10.1109\/LGRS.2022.3195675","article-title":"Wave height estimation from X-band radar data using variational mode decomposition","volume":"19","author":"Yang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_277","doi-asserted-by":"crossref","first-page":"5207","DOI":"10.1109\/JSTARS.2021.3076693","article-title":"Evaluation and mitigation of rain effect on wave direction and period estimation from X-band marine radar images","volume":"14","author":"Yang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_278","first-page":"4201711","article-title":"Spatial\u2013temporal convolutional gated recurrent unit network for significant wave height estimation from shipborne marine radar data","volume":"60","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_279","doi-asserted-by":"crossref","first-page":"11395","DOI":"10.1109\/JSTARS.2021.3124969","article-title":"Wave height estimation from X-band nautical radar images using temporal convolutional network","volume":"14","author":"Huang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_280","first-page":"5104213","article-title":"SWHFormer: A Vision Transformer for Significant Wave Height Estimation From Nautical Radar Images","volume":"62","author":"Yang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_281","doi-asserted-by":"crossref","first-page":"4208011","DOI":"10.1109\/TGRS.2024.3433397","article-title":"WaveTransNet: A Transformer-Based Network for Global Significant Wave Height Retrieval From Spaceborne GNSS-R Data","volume":"62","author":"Qiao","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_282","doi-asserted-by":"crossref","first-page":"100041","DOI":"10.1016\/j.cles.2022.100041","article-title":"Tidal energy-path towards sustainable energy: A technical review","volume":"3","author":"Khare","year":"2022","journal-title":"Clean. Energy Syst."},{"key":"ref_283","doi-asserted-by":"crossref","first-page":"e2019EA000958","DOI":"10.1029\/2019EA000958","article-title":"Deriving tidal structure from satellite image time series","volume":"7","author":"Geyman","year":"2020","journal-title":"Earth Space Sci."},{"key":"ref_284","doi-asserted-by":"crossref","first-page":"105953","DOI":"10.1016\/j.marpol.2023.105953","article-title":"IMO 2023 strategy-Where are we and what is next?","volume":"160","author":"Bilgili","year":"2024","journal-title":"Mar. Pol."},{"key":"ref_285","doi-asserted-by":"crossref","first-page":"102489","DOI":"10.1016\/j.tre.2021.102489","article-title":"Data analytics for fuel consumption management in maritime transportation: Status and perspectives","volume":"155","author":"Yan","year":"2021","journal-title":"Transp. Res. E Logist. Transp. Rev."},{"key":"ref_286","doi-asserted-by":"crossref","first-page":"102389","DOI":"10.1016\/j.trd.2020.102389","article-title":"Machine learning approach to ship fuel consumption: A case of container vessel","volume":"84","year":"2020","journal-title":"Transp. Res. D Transp. Environ."},{"key":"ref_287","doi-asserted-by":"crossref","unstructured":"Kim, Y.R., Jung, M., and Park, J.B. (2021). Development of a fuel consumption prediction model based on machine learning using ship in-service data. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9020137"},{"key":"ref_288","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1080\/03088839.2024.2303120","article-title":"Fuel consumption cost prediction model for ro-ro carriers: A machine learning-based application","volume":"52","author":"Su","year":"2024","journal-title":"Marit. Policy Manag."},{"key":"ref_289","doi-asserted-by":"crossref","first-page":"117398","DOI":"10.1016\/j.oceaneng.2024.117398","article-title":"Short-term forecasting for ship fuel consumption based on deep learning","volume":"301","author":"Chen","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_290","doi-asserted-by":"crossref","first-page":"107604","DOI":"10.1016\/j.oceaneng.2020.107604","article-title":"A multi-task analysis and modelling paradigm using LSTM for multi-source monitoring data of inland vessels","volume":"213","author":"Yuan","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_291","doi-asserted-by":"crossref","first-page":"107425","DOI":"10.1016\/j.engappai.2023.107425","article-title":"A deep learning method for the prediction of ship fuel consumption in real operational conditions","volume":"130","author":"Zhang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_292","doi-asserted-by":"crossref","first-page":"132576","DOI":"10.1109\/ACCESS.2023.3335905","article-title":"A Multitask Learning Framework for Predicting Ship Fuel Oil Consumption","volume":"11","author":"Ilias","year":"2023","journal-title":"IEEE Access"},{"key":"ref_293","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, K., Lu, Y., Zhang, Y., Li, Z., Ma, R., and Huang, L. (2024). A ship energy consumption prediction method based on TGMA model and feature selection. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12071098"},{"key":"ref_294","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ajsl.2020.04.003","article-title":"Utilization of a deep learning-based fuel consumption model in choosing a liner shipping route for container ships in Asia","volume":"37","year":"2021","journal-title":"Asian J. Shipp. Logist."},{"key":"ref_295","doi-asserted-by":"crossref","first-page":"106887","DOI":"10.1016\/j.oceaneng.2019.106887","article-title":"A novel, data-driven heuristic framework for vessel weather routing","volume":"197","author":"Gkerekos","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_296","doi-asserted-by":"crossref","first-page":"111882","DOI":"10.1016\/j.oceaneng.2022.111882","article-title":"Marine route optimization using reinforcement learning approach to reduce fuel consumption and consequently minimize CO2 emissions","volume":"259","author":"Moradi","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_297","doi-asserted-by":"crossref","first-page":"4056","DOI":"10.1038\/s41467-022-31560-5","article-title":"A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage","volume":"13","author":"Zemskova","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_298","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.1109\/JSTARS.2021.3066552","article-title":"Carbon sinks and variations of pCO2 in the Southern Ocean from 1998 to 2018 based on a deep learning approach","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_299","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.5194\/os-20-1149-2024","article-title":"Predicting particle catchment areas of deep-ocean sediment traps using machine learning","volume":"20","author":"Picard","year":"2024","journal-title":"Ocean Sci."},{"key":"ref_300","doi-asserted-by":"crossref","first-page":"146927","DOI":"10.1016\/j.scitotenv.2021.146927","article-title":"From local to regional compound flood mapping with deep learning and data fusion techniques","volume":"782","author":"Moftakhari","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_301","doi-asserted-by":"crossref","first-page":"9101","DOI":"10.1029\/2019JC015577","article-title":"Coastal inundation mapping from bitemporal and dual-polarization SAR imagery based on deep convolutional neural networks","volume":"124","author":"Liu","year":"2019","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_302","doi-asserted-by":"crossref","first-page":"100098","DOI":"10.1016\/j.cacint.2022.100098","article-title":"Deep learning of sea-level variability and flood for coastal city resilience","volume":"17","author":"Sorkhabi","year":"2023","journal-title":"City Environ. Interact."},{"key":"ref_303","doi-asserted-by":"crossref","first-page":"094052","DOI":"10.1088\/1748-9326\/aba5b3","article-title":"Prediction of coastal flooding risk under climate change impacts in South Korea using machine learning algorithms","volume":"15","author":"Park","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_304","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1007\/s11069-024-06846-0","article-title":"Rapid forecasting of compound flooding for a coastal area based on data-driven approach","volume":"121","author":"Xu","year":"2024","journal-title":"Nat. Hazards"},{"key":"ref_305","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1016\/j.jhydrol.2019.05.087","article-title":"Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method","volume":"575","author":"Hu","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_306","doi-asserted-by":"crossref","first-page":"101495","DOI":"10.1016\/j.ecoinf.2021.101495","article-title":"Identification and measurement of tropical tuna species in purse seiner catches using computer vision and deep learning","volume":"67","author":"Lekunberri","year":"2022","journal-title":"Ecol. Informat."},{"key":"ref_307","doi-asserted-by":"crossref","unstructured":"Shedrawi, G., Magron, F., Vigga, B., Bosserelle, P., Gislard, S., Halford, A.R., Tiitii, S., Fepuleai, F., Molai, C., and Rota, M. (2024). Leveraging deep learning and computer vision technologies to enhance management of coastal fisheries in the Pacific region. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-71763-y"},{"key":"ref_308","unstructured":"Marques, T.P., Cote, M., Rezvanifar, A., Albu, A.B., Ersahin, K., Mudge, T., and Gauthier, S. (2021, January 19\u201325). Instance segmentation-based identification of pelagic species in acoustic backscatter data. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA."},{"key":"ref_309","doi-asserted-by":"crossref","first-page":"1236","DOI":"10.1109\/JOE.2023.3272393","article-title":"Classification of Herring, Salmon, and Bubbles in Multifrequency Echograms Using U-Net Neural Networks","volume":"48","author":"Slonimer","year":"2023","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_310","doi-asserted-by":"crossref","first-page":"115158","DOI":"10.1016\/j.marpolbul.2023.115158","article-title":"Construction of chub mackerel (Scomber japonicus) fishing ground prediction model in the northwestern Pacific Ocean based on deep learning and marine environmental variables","volume":"193","author":"Han","year":"2023","journal-title":"Mar. Pollut. Bull."},{"key":"ref_311","doi-asserted-by":"crossref","unstructured":"Xie, M., Liu, B., Chen, X., Yu, W., and Wang, J. (2024). Deep learning-based fishing ground prediction using asymmetric spatiotemporal scales: A case study of Ommastrephes bartramii. Fishes, 9.","DOI":"10.3390\/fishes9020064"},{"key":"ref_312","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1038\/s43017-022-00381-x","article-title":"Trends and variability in the ocean carbon sink","volume":"4","author":"Gruber","year":"2023","journal-title":"Nat. Rev. Earth Environ."},{"key":"ref_313","doi-asserted-by":"crossref","first-page":"8429","DOI":"10.1038\/s41467-024-52641-7","article-title":"Constraining the trend in the ocean CO2 sink during 2000\u20132022","volume":"15","author":"Mayot","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_314","doi-asserted-by":"crossref","unstructured":"Gattuso, J.P., Williamson, P., Duarte, C.M., and Magnan, A.K. (2021). The potential for ocean-based climate action: Negative emissions technologies and beyond. Front. Clim., 2.","DOI":"10.3389\/fclim.2020.575716"},{"key":"ref_315","doi-asserted-by":"crossref","first-page":"7251","DOI":"10.5194\/bg-12-7251-2015","article-title":"Data-based estimates of the ocean carbon sink variability\u2013first results of the Surface Ocean pCO2 Mapping intercomparison (SOCOM)","volume":"12","author":"Bakker","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_316","doi-asserted-by":"crossref","first-page":"7793","DOI":"10.5194\/bg-10-7793-2013","article-title":"A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink","volume":"10","author":"Gruber","year":"2013","journal-title":"Biogeosciences"},{"key":"ref_317","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1038\/s43247-024-01257-2","article-title":"Machine learning reveals regime shifts in future ocean carbon dioxide fluxes inter-annual variability","volume":"5","author":"Couespel","year":"2024","journal-title":"Commun. Earth Environ."},{"key":"ref_318","doi-asserted-by":"crossref","first-page":"e2217909120","DOI":"10.1073\/pnas.2217909120","article-title":"Biogenic carbon pool production maintains the Southern Ocean carbon sink","volume":"120","author":"Huang","year":"2023","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_319","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1038\/s43247-024-01566-6","article-title":"The Southern Ocean carbon sink has been overestimated in the past three decades","volume":"5","author":"Zhong","year":"2024","journal-title":"Commun. Earth Environ."},{"key":"ref_320","doi-asserted-by":"crossref","unstructured":"Li, C., Wu, H., Yang, C., Cui, L., Ma, Z., and Wang, L. (2024). Advanced Machine Learning Models for Estimating the Distribution of Sea-Surface Particulate Organic Carbon (POC) Concentrations Using Satellite Remote Sensing Data: The Mediterranean as an Example. Sensors, 24.","DOI":"10.3390\/s24175669"},{"key":"ref_321","doi-asserted-by":"crossref","first-page":"112316","DOI":"10.1016\/j.rse.2021.112316","article-title":"Improving satellite retrieval of oceanic particulate organic carbon concentrations using machine learning methods","volume":"256","author":"Liu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_322","doi-asserted-by":"crossref","unstructured":"Wu, H., Cui, L., Wang, L., Sun, R., and Zheng, Z. (2023). A method for estimating particulate organic carbon at the sea surface based on geodetector and machine learning. Front. Mar. Sci., 10.","DOI":"10.3389\/fmars.2023.1295874"},{"key":"ref_323","doi-asserted-by":"crossref","unstructured":"Cosby, A., Lebakula, V., Smith, C., Wanik, D., Bergene, K., Rose, A., Swanson, D., and Bloom, D. (2024). Accelerating growth of human coastal populations at the global and continent levels: 2000\u20132018. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-73287-x"},{"key":"ref_324","doi-asserted-by":"crossref","first-page":"103611","DOI":"10.1016\/j.coastaleng.2019.103611","article-title":"Addressing the challenges of climate change risks and adaptation in coastal areas: A review","volume":"156","author":"Toimil","year":"2020","journal-title":"Coast. Eng."},{"key":"ref_325","doi-asserted-by":"crossref","first-page":"105059","DOI":"10.1016\/j.ocecoaman.2019.105059","article-title":"Assessment of selected climate change adaptation measures for coastal areas","volume":"185","author":"Baills","year":"2020","journal-title":"Ocean Coast. Manage."},{"key":"ref_326","doi-asserted-by":"crossref","unstructured":"Bongarts Lebbe, T., Rey-Valette, H., Chaumillon, \u00c9., Camus, G., Almar, R., Cazenave, A., Claudet, J., Rocle, N., Meur-Ferec, C., and Viard, F. (2021). Designing coastal adaptation strategies to tackle sea level rise. Front. Mar. Sci., 8.","DOI":"10.3389\/fmars.2021.740602"},{"key":"ref_327","doi-asserted-by":"crossref","first-page":"132271","DOI":"10.1016\/j.jhydrol.2024.132271","article-title":"Coastal urban flood risk management: Challenges and opportunities- A systematic review","volume":"645","author":"Aziz","year":"2024","journal-title":"J. Hydrol."},{"key":"ref_328","doi-asserted-by":"crossref","first-page":"101716","DOI":"10.1016\/j.uclim.2023.101716","article-title":"Rapid spatio-temporal prediction of coastal urban floods based on deep learning approaches","volume":"52","author":"Zhang","year":"2023","journal-title":"Urban Clim."},{"key":"ref_329","doi-asserted-by":"crossref","unstructured":"Lehodey, P., Bertrand, A., Hobday, A.J., Kiyofuji, H., McClatchie, S., Menk\u00e8s, C.E., Pilling, G., Polovina, J., and Tommasi, D. (2020). ENSO impact on marine fisheries and ecosystems. El Ni\u00f1o Southern Oscillation in a Changing Climate, American Geophysical Union.","DOI":"10.1002\/9781119548164.ch19"},{"key":"ref_330","doi-asserted-by":"crossref","unstructured":"Cheung, W.W., and Fr\u00f6licher, T.L. (2020). Marine heatwaves exacerbate climate change impacts for fisheries in the northeast Pacific. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-63650-z"},{"key":"ref_331","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1111\/faf.12595","article-title":"Climate change adaptation in fisheries","volume":"23","author":"Galappaththi","year":"2022","journal-title":"Fish Fish."},{"key":"ref_332","doi-asserted-by":"crossref","unstructured":"Atlas, W.I., Ma, S., Chou, Y.C., Connors, K., Scurfield, D., Nam, B., Ma, X., Cleveland, M., Doire, J., and Moore, J.W. (2023). Wild salmon enumeration and monitoring using deep learning empowered detection and tracking. Front. Mar. Sci., 10.","DOI":"10.3389\/fmars.2023.1200408"},{"key":"ref_333","doi-asserted-by":"crossref","first-page":"106166","DOI":"10.1016\/j.fishres.2021.106166","article-title":"Automatic, operational, high-resolution monitoring of fish length and catch numbers from landings using deep learning","volume":"246","author":"Palmer","year":"2022","journal-title":"Fish. Res."},{"key":"ref_334","doi-asserted-by":"crossref","first-page":"107815","DOI":"10.1016\/j.ecss.2022.107815","article-title":"Coastal fisheries resource monitoring through A deep learning-based underwater video analysis","volume":"269","author":"Zhang","year":"2022","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_335","doi-asserted-by":"crossref","first-page":"106790","DOI":"10.1016\/j.fishres.2023.106790","article-title":"Acoustic fish species identification using deep learning and machine learning algorithms: A systematic review","volume":"266","author":"Yassir","year":"2023","journal-title":"Fish. Res."},{"key":"ref_336","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1093\/icesjms\/fsz235","article-title":"Acoustic classification in multifrequency echosounder data using deep convolutional neural networks","volume":"77","author":"Brautaset","year":"2020","journal-title":"ICES J. Mar. Sci."},{"key":"ref_337","doi-asserted-by":"crossref","first-page":"102906","DOI":"10.1016\/j.ecoinf.2024.102906","article-title":"A deep learning model for detecting and classifying multiple marine mammal species from passive acoustic data","volume":"84","author":"Hamard","year":"2024","journal-title":"Ecol. Inform."},{"key":"ref_338","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1007\/s11802-023-5525-5","article-title":"Effects of Different Spatial Resolutions on Prediction Accuracy of Thunnus alalunga Fishing Ground in Waters Near the Cook Islands Based on Long Short-Term Memory (LSTM) Neural Network Model","volume":"22","author":"Xu","year":"2023","journal-title":"J. Ocean Univ."},{"key":"ref_339","doi-asserted-by":"crossref","first-page":"102537","DOI":"10.1016\/j.ocemod.2025.102537","article-title":"Attention-enhanced deep learning model for reconstruction and downscaling of thermocline depth in the tropical Indian Ocean","volume":"196","author":"Feng","year":"2025","journal-title":"Ocean Model."},{"key":"ref_340","doi-asserted-by":"crossref","unstructured":"Kellogg, K., Hoffman, P., Standley, S., Shaffer, S., Rosen, P., Edelstein, W., Dunn, C., Baker, C., Barela, P., and Shen, Y. (2020, January 7\u201314). NASA-ISRO synthetic aperture radar (NISAR) mission. Proceedings of the 2020 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO47225.2020.9172638"},{"key":"ref_341","doi-asserted-by":"crossref","first-page":"6987","DOI":"10.1109\/JSTARS.2021.3089550","article-title":"An introduction to the HydroGNSS GNSS reflectometry remote sensing mission","volume":"14","author":"Unwin","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_342","doi-asserted-by":"crossref","first-page":"3464","DOI":"10.1021\/acs.est.3c01106","article-title":"Risks and benefits of large language models for the environment","volume":"57","author":"Rillig","year":"2023","journal-title":"Environ. Sci. Technol."},{"key":"ref_343","doi-asserted-by":"crossref","unstructured":"Ho, K.T.M., Chen, K.C., Lee, L., Burt, F., Yu, S., and Lee, P.H. (2024, January 15\u201320). Quantum computing for climate resilience and sustainability challenges. Proceedings of the 2024 IEEE International Conference on Quantum Computing and Engineering (QCE), Montreal, QC, Canada.","DOI":"10.1109\/QCE60285.2024.10289"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/17\/13\/2306\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:04:56Z","timestamp":1760033096000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/17\/13\/2306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,4]]},"references-count":343,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["rs17132306"],"URL":"https:\/\/doi.org\/10.3390\/rs17132306","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,4]]}}}