{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T13:54:05Z","timestamp":1769694845471,"version":"3.49.0"},"reference-count":76,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,14]],"date-time":"2023-05-14T00:00:00Z","timestamp":1684022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004410","name":"Scientific and Technological Research Council of Turkey","doi-asserted-by":"publisher","award":["121Y366"],"award-info":[{"award-number":["121Y366"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellite-derived bathymetry (SDB) is the process of estimating water depth in shallow coastal and inland waters using satellite imagery. Recent advances in technology and data processing have led to improvements in the accuracy and availability of SDB. The increased availability of free optical satellite sensors, such as Landsat missions and Sentinel 2 satellites, has increased the quantity and frequency of SDB research and mapping efforts. In addition, machine learning (ML)- and deep learning (DL)-based algorithms, which can learn to identify features that are indicative of water depth, such as color or texture variations, have started to be used for extracting bathymetry information from satellite imagery. This study aims to produce an initial optical image-based SBD map of Horseshoe Island\u2019s shallow coasts and to perform a comprehensive and comparative evaluation with Landsat 8 and Sentinel 2 satellite images. Our research considers the performance of empirical SDB models (classical, ML-based, and DL-based) and the effects of the atmospheric correction methods ACOLITE, iCOR, and ATCOR. For all band combinations and depth intervals, the ML-based random forest and XGBoost models delivered the highest performance and best fitting ability by achieving the lowest error with MAEs smaller than 1 m up to 10 m depth and a maximum correlation of R2 around 0.80. These models are followed by the DL-based ANN and CNN models. Nonetheless, the non-linearity of the reflectance\u2013depth connection was significantly reduced by the ML-based models. Furthermore, Landsat 8 showed better performance for 10\u201320 m depth intervals and in the entire range of (0\u201320 m), while Sentinel 2 was slightly better up to 10 m depth intervals. Lastly, ACOLITE, iCOR, and ATCOR provided reliable and consistent results for SDB, where ACOLITE provided the highest automation.<\/jats:p>","DOI":"10.3390\/rs15102568","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T02:02:11Z","timestamp":1684116131000},"page":"2568","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Satellite-Derived Bathymetry Mapping on Horseshoe Island, Antarctic Peninsula, with Open-Source Satellite Images: Evaluation of Atmospheric Correction Methods and Empirical Models"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6548-6561","authenticated-orcid":false,"given":"Emre","family":"G\u00fclher","sequence":"first","affiliation":[{"name":"Geomatics Engineering Program, Graduate School, Istanbul Technical University, 34469 Istanbul, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5693-3614","authenticated-orcid":false,"given":"Ugur","family":"Alganci","sequence":"additional","affiliation":[{"name":"Geomatics Engineering Department, Civil Engineering Faculty, Istanbul Technical University, 34469 Istanbul, Turkey"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jawak, S.D., and Luis, A.J. (2016, January 7). High-resolution multispectral satellite imagery for extracting bathymetric information of Antarctic shallow lakes. Proceedings of the SPIE 9878, Remote Sensing of the Oceans and Inland Waters: Techniques, Applications, and Challenges, New Delhi, India.","DOI":"10.1117\/12.2222769"},{"key":"ref_2","unstructured":"Makowski, C., and Finkl, C.W. (2016). Seafloor Mapping along Continental Shelves: Research and Techniques for Visualizing Benthic Environments, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3178","DOI":"10.1111\/gcb.15083","article-title":"The 2019\/2020 summer of Antarctic heatwaves","volume":"26","author":"Robinson","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_4","first-page":"1","article-title":"Climate warming amplified the 2020 record-breaking heatwave in the Antarctic peninsula","volume":"3","author":"Barriopedro","year":"2022","journal-title":"Commun. Earth Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2021GL097108","article-title":"An extreme high temperature event in coastal East Antarctica associated with an atmospheric river and record summer downslope winds","volume":"49","author":"Turner","year":"2022","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.joes.2021.02.006","article-title":"Review of near-shore satellite derived bathymetry: Classification and account of five decades of coastal bathymetry research","volume":"6","author":"Ashphaq","year":"2021","journal-title":"J. Ocean Eng. Sci."},{"key":"ref_7","first-page":"102678","article-title":"Analysis of univariate linear, robust-linear, and non-linear machine learning algorithms for satellite-derived bathymetry in complex coastal terrain","volume":"56","author":"Ashphaq","year":"2022","journal-title":"Reg. Stud. Mar. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Vrdoljak, L., and Kili\u0107 Pamukovi\u0107, J. (2022). Assessment of Atmospheric Correction Processors and Spectral Bands for Satellite-Derived Bathymetry Using Sentinel-2 Data in the Middle Adriatic. Hydrology, 9.","DOI":"10.3390\/hydrology9120215"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3238","DOI":"10.1364\/OE.444557","article-title":"Satellite-derived bathymetry using Landsat-8 and Sentinel-2A images: Assessment of atmospheric correction algorithms and depth derivation models in shallow waters","volume":"30","author":"Duan","year":"2022","journal-title":"Opt. Express"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.isprsjprs.2018.06.015","article-title":"Satellite derived photogrammetric bathymetry","volume":"142","author":"Bird","year":"2018","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yunus, A.P., Dou, J., Song, X., and Avtar, R. (2019). Improved bathymetric mapping of coastal and lake environments using Sentinel-2 and Landsat-8 images. Sensors, 19.","DOI":"10.3390\/s19122788"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2855","DOI":"10.1080\/01431161.2018.1533660","article-title":"Assessment of empirical algorithms for bathymetry extraction using Sentinel-2 data","volume":"40","author":"Casal","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8745","DOI":"10.1109\/TGRS.2019.2922724","article-title":"Technical Framework for Shallow-Water Bathymetry with High Reliability and No Missing Data Based on Time-Series Sentinel-2 Images","volume":"57","author":"Chu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1080\/01431168508948428","article-title":"Shallow-water bathymetry using combined lidar and passive multispectral scanner data","volume":"6","author":"Lyzenga","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"547","DOI":"10.4319\/lo.2003.48.1_part_2.0547","article-title":"Determination of water depth with high-resolution satellite imagery over variable bottom types","volume":"48","author":"Stumpf","year":"2003","journal-title":"Limnol. Oceanogr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.ecss.2010.05.015","article-title":"Remote sensing of water depths in shallow waters via artificial neural networks","volume":"89","author":"Ceyhun","year":"2010","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4431","DOI":"10.1080\/01431161.2017.1421796","article-title":"Shallow Water Bathymetry Mapping Using Support Vector Machine (SVM) Technique and Multispectral Imagery","volume":"39","author":"Misra","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","first-page":"117","article-title":"Satellite-Derived Bathymetry Using Random Forest Algorithm and Worldview-2 Imagery","volume":"3","author":"Manessa","year":"2016","journal-title":"Geoplanning"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sagawa, T., Yamashita, Y., Okumura, T., and Yamanokuchi, T. (2019). Satellite derived bathymetry using machine learning and multi-temporal satellite images. Remote Sens., 11.","DOI":"10.3390\/rs11101155"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1143","DOI":"10.1080\/15481603.2022.2100597","article-title":"Satellite-derived bathymetry using machine learning and optimal Sentinel-2 imagery in South-West Florida coastal waters","volume":"59","author":"Mudiyanselage","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Gafoor, F.A., Al-Shehhi, M.R., Cho, C.-S., and Ghedira, H. (2022). Gradient Boosting and Linear Regression for Estimating Coastal Bathymetry Based on Sentinel-2 Images. Remote Sens., 14.","DOI":"10.3390\/rs14195037"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1080\/01490419.2022.2064572","article-title":"Satellite Derived Bathymetry with Sentinel-2 Imagery: Comparing Traditional Techniques with Advanced Methods and Machine Learning Ensemble Models","volume":"45","author":"Susa","year":"2022","journal-title":"Mar. Geod."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s12524-011-0142-y","article-title":"Artificial neural network (ann) based inversion of benthic substrate bottom type and bathymetry in optically shallow waters\u2014Initial model results","volume":"40","author":"Nagamani","year":"2012","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5334","DOI":"10.1109\/TGRS.2018.2814012","article-title":"Deriving bathymetry from optical images with a localized neural network algorithm","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote"},{"key":"ref_25","first-page":"4","article-title":"Machine Learning of Derived Bathymetry and Coastline Detection","volume":"2","author":"Dickens","year":"2019","journal-title":"SMU Data Sci. Rev."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2888","DOI":"10.1109\/JSTARS.2020.2993731","article-title":"Convolutional Neural Network to Retrieve Water Depth in Marine Shallow Water Area from Remote Sensing Images","volume":"13","author":"Ai","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1007\/s12524-020-01255-9","article-title":"Shallow Water Bathymetry Mapping of Xinji Island Based on Multispectral Satellite Image using Deep Learning","volume":"49","author":"Wan","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhou, W., Tang, Y., Jing, W., Li, Y., Yang, J., Deng, Y., and Zhang, Y. (2023). A Comparison of Machine Learning and Empirical Approaches for Deriving Bathymetry from Multispectral Imagery. Remote Sens., 15.","DOI":"10.3390\/rs15020393"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hernandez, W.J., and Armstrong, R.A. (2016). Deriving bathymetry from multispectral remote sensing data. J. Mar. Sci. Eng., 4.","DOI":"10.3390\/jmse4010008"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11742","DOI":"10.1364\/OE.390316","article-title":"Atmospheric correction for satellite-derived bathymetry in the Caribbean waters: From a single image to multi-temporal approaches using Sentinel-2A\/B","volume":"28","author":"Caballero","year":"2020","journal-title":"Opt. Express"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"151","DOI":"10.3846\/gac.2020.11426","article-title":"Evaluating ACOMP, FLAASH and QUAC on Worldview-3 for satellite derived bathymetry (SDB) in shallow water","volume":"46","author":"Basith","year":"2020","journal-title":"Geod. Cartogr."},{"key":"ref_32","unstructured":"(2023, January 05). Antarctic Wheather, Available online: https:\/\/www.antarctica.gov.au\/about-antarctica\/weather-and-climate\/weather\/."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1038\/s41598-021-04231-6","article-title":"Intense Ocean freshening from melting glacier around the Antarctica during early twenty-first century","volume":"12","author":"Pan","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1038\/s41561-020-0616-z","article-title":"Interannual variations in meltwater input to the Southern Ocean from Antarctic ice shelves","volume":"13","author":"Adusumilli","year":"2020","journal-title":"Nat. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"Q10009","DOI":"10.1029\/2007GC001694","article-title":"Bathymetry of the Amundsen Sea continental shelf: Implications for geology, oceanography, and glaciology","volume":"8","author":"Nitsche","year":"2007","journal-title":"Geochem. Geophys. Geosyst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"95","DOI":"10.5194\/tc-5-95-2011","article-title":"An improved bathymetry compilation for the Bellingshausen Sea, Antarctica, to inform ice-sheet and ocean models","volume":"5","author":"Graham","year":"2011","journal-title":"Cryosphere"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1017\/S0954102021000298","article-title":"A bathymetric compilation of the cape Darnley region, East Antarctica","volume":"33","author":"Smith","year":"2021","journal-title":"Antarctic Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e2021GL096215","DOI":"10.1029\/2021GL096215","article-title":"Bathymetry beneath the amery ice shelf, East Antarctica, revealed by airborne gravity","volume":"48","author":"Yang","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1038\/s41597-022-01366-7","article-title":"The International Bathymetric Chart of the Southern Ocean Version 2","volume":"9","author":"Dorschel","year":"2022","journal-title":"Sci. Data"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Alganci, U. (2019). Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030139"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2014.12.004","article-title":"Retrieval of nearshore bathymetry from Landsat 8 images: A tool for coastal monitoring in shallow waters","volume":"159","author":"Pacheco","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_43","unstructured":"(2022, January 01). Earth Explorer; 2000; FS; 083-00; Geological Survey (U.S.), Available online: https:\/\/earthexplorer.usgs.gov\/."},{"key":"ref_44","unstructured":"Copernicus Open Access Hub (2022, January 01). Copernicus, ESA. Available online: https:\/\/scihub.copernicus.eu\/dhus."},{"key":"ref_45","first-page":"281","article-title":"Bathymetric analysis of Lystad Bay, Horseshoe Island by Using High Resolution Multibeam Echosounder Data","volume":"18","author":"Kaya","year":"2022","journal-title":"J. Nav. Sci. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"D06204","DOI":"10.1029\/2008JD011257","article-title":"Jourdin Maritime Aerosol Network as a component of Aerosol Robotic Network","volume":"114","author":"Smirnov","year":"2009","journal-title":"J. Geophys. Res."},{"key":"ref_47","unstructured":"(2022, January 16). Nasa Aeronet Maritime Aerosol Network (MAN)\u2014Version 2, Available online: https:\/\/aeronet.gsfc.nasa.gov\/new_web\/maritime_aerosol_network.html."},{"key":"ref_48","unstructured":"IOCCG (2022, January 01). Atmospheric Correction for Remotely-Sensed Ocean-Colour Products. Available online: http:\/\/www.ioccg.org\/reports\/report10.pdf."},{"key":"ref_49","unstructured":"Babin, M., Arrigo, K., B\u00e9langer, S., and Forget, M.-H. (2015). Reports of the International Ocean-Colour Coordinating Group, No. 16, International Ocean-Colour Coordinating Group."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.rse.2014.06.017","article-title":"SIMilarity Environment Correction (SIMEC) applied to MERIS data over inland and coastal waters","volume":"157","author":"Sterckx","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_51","unstructured":"Richter, R., and Schl\u00e4pfer, D. (2017). Atmospheric\/Topographic Correction for Satellite Imagery: ATCOR-2\/3 User Guide, ResearchGate. DLR IB 565-01\/17."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"22","DOI":"10.3389\/feart.2019.00022","article-title":"Application of Sentinel-2 MSI in Arctic Research: Evaluating the Performance of Atmospheric Correction Approaches Over Arctic Sea Ice","volume":"7","author":"Hieronymi","year":"2019","journal-title":"Front. Earth Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.rse.2019.03.010","article-title":"Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives","volume":"225","author":"Vanhellemont","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1080\/22797254.2018.1457937","article-title":"Atmospheric correction of landsat-8\/OLI and sentinel-2\/MSI data using iCOR algorithm: Validation for coastal and inland waters","volume":"51","author":"Sterckx","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2014.01.009","article-title":"Turbid wakes associated with offshore wind turbines observed with Landsat 8","volume":"145","author":"Vanhellemont","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.rse.2018.07.015","article-title":"Atmospheric correction of meter-scale optical satellite data for inland and coastal water applications","volume":"216","author":"Vanhellemont","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_57","unstructured":"Guanter, L. (2006). New Algorithms for Atmospheric Correction and Retrieval of Biophysical Parameters in Earth Observation. Application to ENVISAT\/MERIS Data. [Ph.D. Thesis, Universitat de Val\u00e9ncia, Departament de F\u00edsica de la Terra i Termodin\u00e0mica]."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Berk, A., Anderson, G., Acharya, P., Bernstein, L., Muratov, L., Lee, J., Fox, M., Adler-Golden, S., Chetwynd, J., and Hoke, M. (2006, January 17). MODTRANTM5: 2006 update. Proceedings of the SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, Orlando, FL, USA.","DOI":"10.1117\/12.665077"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"F1","DOI":"10.1364\/AO.47.0000F1","article-title":"Influence of atmospheric and sea-surface corrections on retrieval of bottom depth and reflectance using a semi-analytical model: A case study in Kaneohe Bay, Hawaii","volume":"47","author":"Goodman","year":"2008","journal-title":"Appl. Opt."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1080\/01431160500034086","article-title":"Technical note: Simple and robust removal of sun glint for mapping shallow-water benthos","volume":"26","author":"Hedley","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1364\/AO.17.000379","article-title":"Passive remote sensing techniques for mapping water depth and bottom features","volume":"17","author":"Lyzenga","year":"1978","journal-title":"Appl. Opt."},{"key":"ref_62","unstructured":"Green, E., Mumby, P., Edwards, A., and Clark, C. (2000). Remote Sensing: Handbook for Tropical Coastal Management, United Nations Educational Scientific and Cultural Organization (UNESCO)."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2251","DOI":"10.1109\/TGRS.2006.872909","article-title":"Multispectral bathymetry using a simple physically based algorithm","volume":"44","author":"Lyzenga","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5130","DOI":"10.1109\/JSTARS.2016.2598152","article-title":"Nonparametric empirical depth regression for bathymetric mapping in coastal waters","volume":"9","author":"Kibele","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1109\/LGRS.2018.2884347","article-title":"Retrieval of Near-Shore Bathymetry from Multispectral Satellite Images Using Generalized Additive Models","volume":"16","author":"Shen","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.2166\/hydro.2013.234","article-title":"A machine learning approach for estimation of shallow water depths from optical satellite images and sonar measurements","volume":"15","author":"Vojinovic","year":"2013","journal-title":"J. Hydroinform."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/BF00195855","article-title":"Networks and the best approximation property","volume":"63","author":"Girosi","year":"1990","journal-title":"Biol. Cybern."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s12524-015-0523-8","article-title":"Bathymetric mapping of Bhopal City Lower Kake using IRS-P6: LISS-4 imagery and artificial neural network technique","volume":"44","author":"Patel","year":"2016","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_72","unstructured":"Lumban-Gaol, Y.A., Ohori, K.A., and Peters, R.Y. (2021, January 5\u20139). Satellite-derived bathymetry using convolutional neural networks and multispectral sentinel-2 images. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Proceedings of the XXIV ISPRS Congress, Nice, France."},{"key":"ref_73","first-page":"1107","article-title":"Satellite derived bathymetry using deep learning","volume":"112","author":"Thoumyre","year":"2021","journal-title":"Mach. Learn."},{"key":"ref_74","unstructured":"International Hydrographic Organization (IHO) (2022, December 01). S-67 Mariners\u2019 Guide to Accuracy of Depth Information in Electronic Navigational Charts (ENC) (Edition 1.0.0, September 2020). Available online: https:\/\/iho.int\/en\/standards-and-specifications."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1139\/geomat-2019-0022","article-title":"The impact of sensors for satellite derived bathymetry within the Canadian arctic","volume":"74","author":"Ahola","year":"2020","journal-title":"Geomatica"},{"key":"ref_76","first-page":"310","article-title":"Evaluation and performance of satellite-derived bathymetry algorithms in turbid coastal water: A case study of Vengurla rocks","volume":"51","author":"Ashphaq","year":"2022","journal-title":"Indian J. Mar. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2568\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:34:49Z","timestamp":1760124889000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/10\/2568"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,14]]},"references-count":76,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15102568"],"URL":"https:\/\/doi.org\/10.3390\/rs15102568","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,14]]}}}