{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T12:06:21Z","timestamp":1788869181896,"version":"build-2803163510"},"reference-count":46,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T00:00:00Z","timestamp":1561075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The bathymetry of nearshore coastal environments and lakes is constantly reworking because of the change in the patterns of energy dispersal and related sediment transport pathways. Therefore, updated and accurate bathymetric models are a crucial component in providing necessary information for scientific, managerial, and geographical studies. Recent advances in satellite technology revolutionized the acquisition of bathymetric profiles, offering new vistas in mapping. This contribution analyzed the suitability of Sentinel-2 and Landsat-8 images for bathymetric mapping of coastal and lake environments. The bathymetric algorithm was developed using an empirical approach and a random forest (RF) model based on the available high-resolution LiDAR bathymetric data for Mobile Bay, Tampa Bay, and Lake Huron regions obtained from the National Oceanic and Atmospheric Administration (NOAA) National Geophysical Data Center (NGDC). Our results demonstrate that the satellite-derived bathymetry is efficient for retrieving depths up to 10 m for coastal regions and up to 30 m for the lake environment. While using the empirical approach, the root-mean-square error (RMSE) varied between 1.99 m and 4.74 m for the three regions. The RF model, on the other hand, provided an improved bathymetric model with RMSE between 1.13 m and 1.95 m. The comparative assessment suggests that Sentinel-2 has a slight edge over Landsat-8 images while employing the empirical approach. On the other hand, the RF model shows that Landsat-8 retrieves a better bathymetric model than Sentinel-2. Our work demonstrated that the freely available Sentinel-2 and Landsat-8 imageries proved to be reliable data for acquiring updated bathymetric information for large areas in a short period.<\/jats:p>","DOI":"10.3390\/s19122788","type":"journal-article","created":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T11:54:31Z","timestamp":1561118071000},"page":"2788","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":94,"title":["Improved Bathymetric Mapping of Coastal and Lake Environments Using Sentinel-2 and Landsat-8 Images"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6852-145X","authenticated-orcid":false,"given":"Ali P.","family":"Yunus","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP), Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Dou","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata 940-2188, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Song","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science, The University of Tokyo, Chiba 277-8568, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3653-5771","authenticated-orcid":false,"given":"Ram","family":"Avtar","sequence":"additional","affiliation":[{"name":"Faculty of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1130\/G20377.1","article-title":"Natural bathymetric change as a control on century-scale shoreline behavior","volume":"32","author":"Cooper","year":"2004","journal-title":"Geology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11896","DOI":"10.1038\/ncomms11896","article-title":"First wide-angle view of channelized turbidity currents links migrating cyclic steps to flow characteristics","volume":"7","author":"Clarke","year":"2016","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Simons, D., and Richardson, E. (1966). Resistance to Flow in Alluvial Channels.","DOI":"10.3133\/pp422J"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1130\/B25510.1","article-title":"Semiannual patterns of erosion and deposition in upper Monterey Canyon from serial multibeam bathymetry","volume":"117","author":"Smith","year":"2005","journal-title":"Gsa Bull."},{"key":"ref_5","first-page":"191","article-title":"Assessment of depth and turbidity with airborne Lidar bathymetry and multiband satellite imagery in shallow water bodies of the Alaskan North Slope","volume":"58","author":"Saylam","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1016\/j.rse.2008.12.003","article-title":"A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data","volume":"113","author":"Brando","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.marpol.2015.03.014","article-title":"Dynamic ocean management: Defining and conceptualizing real-time management of the ocean","volume":"58","author":"Maxwell","year":"2015","journal-title":"Mar. Policy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ecolind.2013.07.003","article-title":"The application of remote sensing for marine protected area management","volume":"36","author":"Kachelriess","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"13782","DOI":"10.3390\/rs71013782","article-title":"Spatial prediction of coastal bathymetry based on multispectral satellite imagery and multibeam data","volume":"7","author":"Monteys","year":"2015","journal-title":"Remote Sens."},{"key":"ref_11","first-page":"49","article-title":"A proposed international long-term project to systematically map the world\u2019s ocean floors from beach to trench: GOMaP (Global Ocean Mapping Program)","volume":"2","author":"Carron","year":"2001","journal-title":"Int. Hydrogr. Rev."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","first-page":"159","DOI":"10.5194\/isprsarchives-XXXIX-B8-159-2012","article-title":"Shallow-water bathymetry over variable bottom types using multispectral Worldview-2 image","volume":"39","author":"Doxani","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3539","DOI":"10.1109\/TGRS.2014.2377300","article-title":"High-resolution maps of bathymetry and benthic habitats in shallow-water environments using multispectral remote sensing imagery","volume":"53","author":"Eugenio","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"397","DOI":"10.5194\/adgeo-45-397-2019","article-title":"Bathymetric maps from multi-temporal analysis of Sentinel-2 data: The case study of Limassol, Cyprus","volume":"45","author":"Evagorou","year":"2019","journal-title":"Adv. Geosci."},{"key":"ref_17","first-page":"4","article-title":"Application of Machine Learning in Satellite Derived Bathymetry and Coastline Detection","volume":"2","author":"Dickens","year":"2019","journal-title":"SMU Data Sci. Rev."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1016\/j.crte.2006.05.014","article-title":"Bathymetry from space: Rationale and requirements for a new, high-resolution altimetric mission","volume":"338","author":"Sandwell","year":"2006","journal-title":"Comptes Rendus Geosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1029\/JC088iC03p01563","article-title":"Bathymetric prediction from Seasat altimeter data","volume":"88","author":"Dixon","year":"1983","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2651","DOI":"10.1080\/01431160110109642","article-title":"Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research","volume":"23","author":"Lu","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","first-page":"1","article-title":"Prediction of bathymetry from satellite altimeter based gravity in the Arabian Sea: Mapping of two unnamed deep seamounts","volume":"16","author":"Jena","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","first-page":"253","article-title":"Airborne lidar bathymetry","volume":"2","author":"Guenther","year":"2007","journal-title":"Digit. Elev. Model Technol. Appl. Dem Users Man."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.rse.2006.11.006","article-title":"MISR-based passive optical bathymetry from orbit with few-cm level of accuracy on the Salar de Uyuni, Bolivia","volume":"107","author":"Bills","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"407","DOI":"10.3390\/rs6010407","article-title":"Remote sensing-derived bathymetry of lake Poop\u00f3","volume":"6","author":"Arsen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1177\/0309133309105657","article-title":"Bathymetric mapping by means of remote sensing: Methods, accuracy and limitations","volume":"33","author":"Gao","year":"2009","journal-title":"Prog. Phys. Geogr."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.geomorph.2012.05.029","article-title":"An improved bathymetric model for the modern and palaeo Lake Eyre","volume":"173","author":"Leon","year":"2012","journal-title":"Geomorphology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4036","DOI":"10.1364\/AO.26.4036_1","article-title":"Bathymetry calculations with Landsat 4 TM imagery under a generalized ratio assumption","volume":"26","author":"Clark","year":"1987","journal-title":"Appl. Opt."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1016\/j.jenvman.2007.08.021","article-title":"Predictive models of turbidity and water depth in the Do\u00f1ana marshes using Landsat TM and ETM+ images","volume":"90","author":"Bustamante","year":"2009","journal-title":"J. Environ. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"42","DOI":"10.3390\/rs3010042","article-title":"Integrating Quickbird multi-spectral satellite and field data: Mapping bathymetry, seagrass cover, seagrass species and change in Moreton Bay, Australia in 2004 and 2007","volume":"3","author":"Lyons","year":"2011","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"295","DOI":"10.7763\/IJESD.2016.V7.787","article-title":"Bathymetry determination from high resolution satellite imagery using ensemble learning algorithms in Shallow Lakes: Case study El-Burullus Lake","volume":"7","author":"Mohamed","year":"2016","journal-title":"Int. J. Environ. Sci. Dev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1007\/s00338-007-0271-5","article-title":"Bathymetry, water optical properties, and benthic classification of coral reefs using hyperspectral remote sensing imagery","volume":"26","author":"Lesser","year":"2007","journal-title":"Coral Reefs"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1002\/esp.3794","article-title":"Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments","volume":"41","author":"Legleiter","year":"2016","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1080\/2150704X.2015.1034885","article-title":"Performance of two neural network models in bathymetry","volume":"6","author":"Liu","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1080\/15481603.2018.1538620","article-title":"Bathymetry retrieval from optical images with spatially distributed support vector machines","volume":"56","author":"Wang","year":"2019","journal-title":"Gisci. Remote Sens."},{"key":"ref_36","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_37","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_38","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.scitotenv.2019.01.221","article-title":"Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan","volume":"662","author":"Dou","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_39","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_40","doi-asserted-by":"crossref","first-page":"15","DOI":"10.5194\/tc-10-15-2016","article-title":"Estimating supraglacial lake depth in West Greenland using Landsat 8 and comparison with other multispectral methods","volume":"10","author":"Pope","year":"2016","journal-title":"Cryosphere"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA data mining software: An update","volume":"11","author":"Hall","year":"2009","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1191\/0309133306pp492ra","article-title":"Causes and consequences of error in digital elevation models","volume":"30","author":"Fisher","year":"2006","journal-title":"Prog. Phys. Geogr."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1016\/j.proeng.2017.02.486","article-title":"Performance assessment of ANN in estimating remotely sensed extracted bathymetry. Case study: Eastern harbor of alexandria","volume":"181","author":"Makboul","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1007\/s11069-015-1799-2","article-title":"An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan","volume":"78","author":"Dou","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"4318","DOI":"10.3390\/rs70404318","article-title":"Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm","volume":"7","author":"Dou","year":"2015","journal-title":"Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2788\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:00:13Z","timestamp":1760187613000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2788"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,21]]},"references-count":46,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["s19122788"],"URL":"https:\/\/doi.org\/10.3390\/s19122788","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,21]]}}}