{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T17:32:12Z","timestamp":1777483932421,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,8,6]],"date-time":"2019-08-06T00:00:00Z","timestamp":1565049600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010681","name":"H2020 Environment","doi-asserted-by":"publisher","award":["641762"],"award-info":[{"award-number":["641762"]}],"id":[{"id":"10.13039\/100010681","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Deutsches Zentrum f\u00fcr Luft- und Raumfahrt \/ Deutscher Akademischer Austauschdienst","award":["57186656"],"award-info":[{"award-number":["57186656"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The global coastal seascape offers a multitude of ecosystem functions and services to the natural and human-induced ecosystems. However, the current anthropogenic global warming above pre-industrial levels is inducing the degradation of seascape health with adverse impacts on biodiversity, economy, and societies. Bathymetric knowledge empowers our scientific, financial, and ecological understanding of the associated benefits, processes, and pressures to the coastal seascape. Here we leverage two commercial high-resolution multispectral satellite images of the Pleiades and two multibeam survey datasets to measure bathymetry in two zones (0\u201310 m and 10\u201330 m) in the tropical Anguilla and British Virgin Islands, northeast Caribbean. A methodological framework featuring a combination of an empirical linear transformation, cloud masking, sun-glint correction, and pseudo-invariant features allows spatially independent calibration and test of our satellite-derived bathymetry approach. The best R2 and RMSE for training and validation vary between 0.44\u20130.56 and 1.39\u20131.76 m, respectively, while minimum vertical errors are less than 1 m in the depth ranges of 7.8\u201310 and 11.6\u201318.4 m for the two explored zones. Given available field data, the present methodology could provide simple, time-efficient, and accurate spatio-temporal satellite-derived bathymetry intelligence in scientific and commercial tasks i.e., navigation, coastal habitat mapping and resource management, and reducing natural hazards.<\/jats:p>","DOI":"10.3390\/rs11151830","type":"journal-article","created":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T03:09:08Z","timestamp":1565147348000},"page":"1830","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Leveraging Commercial High-Resolution Multispectral Satellite and Multibeam Sonar Data to Estimate Bathymetry: The Case Study of the Caribbean Sea"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5629-5157","authenticated-orcid":false,"given":"Samuel","family":"Pike","sequence":"first","affiliation":[{"name":"Environment Systems Ltd., Aberystwyth SY23 3AH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0766-7986","authenticated-orcid":false,"given":"Dimosthenis","family":"Traganos","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, Rutherfordstra\u00dfe 2, 12489 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3228-280X","authenticated-orcid":false,"given":"Dimitris","family":"Poursanidis","sequence":"additional","affiliation":[{"name":"Foundation for Research and Technology\u2014Hellas (FORTH), Institute of Applied and Computational Mathematics, N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jamie","family":"Williams","sequence":"additional","affiliation":[{"name":"Environment Systems Ltd., Aberystwyth SY23 3AH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katie","family":"Medcalf","sequence":"additional","affiliation":[{"name":"Environment Systems Ltd., Aberystwyth SY23 3AH, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-1475","authenticated-orcid":false,"given":"Peter","family":"Reinartz","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Earth Observation Center (EOC), 82234 We\u00dfling, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5208-626X","authenticated-orcid":false,"given":"Nektarios","family":"Chrysoulakis","sequence":"additional","affiliation":[{"name":"Foundation for Research and Technology\u2014Hellas (FORTH), Institute of Applied and Computational Mathematics, N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,6]]},"reference":[{"key":"ref_1","unstructured":"(2019, June 12). Coastal and Marine Ecosystems\u2014Marine Jurisdictions: Coastline Length. Available online: https:\/\/web.archive.org\/web\/20120419075053\/http:\/\/earthtrends.wri.org\/text\/coastal-marine\/variable-61.html."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"OECD (2016). An overview of the ocean economy: Assessments and recommendations. The Ocean Economy in 2030, OECD Publishing. Available online: https:\/\/doi.org\/10.1787\/9789264251724-4-en.","DOI":"10.1787\/9789264251724-4-en"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nature14258","article-title":"Defining the Anthropocene","volume":"519","author":"Lewis","year":"2015","journal-title":"Nature"},{"key":"ref_4","unstructured":"Collins, A. (2019). The Global Risks Report 2019, World Economic Forum. [14th ed.]. Available online: http:\/\/www3.weforum.org\/docs\/WEF_Global_Risks_Report_2019.pdf."},{"key":"ref_5","unstructured":"(2019, June 12). IPBES Global Assessment Preview. Available online: https:\/\/www.ipbes.net\/news\/ipbes-global-assessment-preview."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"283","DOI":"10.3389\/fmars.2019.00283","article-title":"Seafloor Mapping\u2014The Challenge of a Truly Global Ocean Bathymetry","volume":"6","author":"Snaith","year":"2019","journal-title":"Front. Mar. Sci."},{"key":"ref_7","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_8","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_9","doi-asserted-by":"crossref","first-page":"6329","DOI":"10.1364\/AO.37.006329","article-title":"Hyperspectral Remote Sensing for Shallow Waters. 1. A Semianalytical Model","volume":"37","author":"Lee","year":"1998","journal-title":"Appl. Opt."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1364\/AO.38.003831","article-title":"Hyperspectral Remote Sensing for Shallow Waters. 2. A Semianalytical Model","volume":"38","author":"Lee","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.isprsjprs.2018.06.015","article-title":"Satellite derived photogrammetric bathymetry","volume":"142","author":"Hodul","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ch\u00e9nier, R., Faucher, M.-A., Ahola, R., Shelat, Y., and Sagram, M. (2018). Bathymetric Photogrammetry to Update CHS Charts: Comparing Conventional 3D Manual and Automatic Approaches. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100395"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.rse.2018.03.024","article-title":"An algorithm for optically-deriving water depth from multispectral imagery in coral reef landscapes in the absence of ground-truth data","volume":"2010","author":"Kerr","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.marpolbul.2017.06.075","article-title":"Mapping Mediterranean seagrasses with Sentinel-2 imagery","volume":"134","author":"Traganos","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9428","DOI":"10.1080\/01431161.2018.1519289","article-title":"Machine learning-based retrieval of benthic reflectance and Posidonia oceanica seagrass extent using a semi-analytical inversion of Sentinel-2 satellite data","volume":"39","author":"Traganos","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","first-page":"58","article-title":"On the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band","volume":"80","author":"Poursanidis","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"56","DOI":"10.2112\/SI76-006","article-title":"Identifying bathymetric differences over Alaska\u2019s North Slope using a satellite-derived bathymetry multi-temporal approach","volume":"76","author":"Madore","year":"2016","journal-title":"J. Coast. Res."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Traganos, D., Poursanidis, D., Aggarwal, B., Chrysoulakis, N., and Reinartz, P. (2018). Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2. Remote Sens., 10.","DOI":"10.3390\/rs10060859"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Poursanidis, D., Traganos, D., Chrysoulakis, N., and Reinartz, P. (2019). Cubesats Allow High Spatiotemporal Estimates of Satellite-Derived Bathymetry. Remote Sens., 11.","DOI":"10.3390\/rs11111299"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1007\/s13280-011-0192-y","article-title":"The Use of Bathymetric Data in Society and Science: A Review from the Baltic Sea","volume":"41","author":"Hell","year":"2012","journal-title":"Ambio"},{"key":"ref_22","unstructured":"International Hydrographic Organization (IHO) (2014). S-57 Supplement No. 3\u2014Supplementary Information for the Encoding of S-57 Edition 3.1 ENC Data, International Hydrographic Organization. Available online: https:\/\/www.iho.int\/iho_pubs\/standard\/S-57Ed3.1\/S-57_e3.1_Supp3_Jun14_EN.pdf."},{"key":"ref_23","unstructured":"(2019, January 23). DPLUS0045 Anguilla Seabed Classification from MBES data. Available online: http:\/\/data.cefas.co.uk\/#\/View\/19316."},{"key":"ref_24","unstructured":"(2019, January 23). DPLUS026 British Virgin Islands Seabed Classification Map. Available online: http:\/\/data.cefas.co.uk\/#\/View\/18174."},{"key":"ref_25","unstructured":"(2019, January 23). DPLUS0045 Anguilla MBES Bathymetry 2m. Available online: http:\/\/data.cefas.co.uk\/#\/View\/19312."},{"key":"ref_26","unstructured":"(2019, January 23). British Virgin Islands multibeam bathymetry data. Available online: http:\/\/data.cefas.co.uk\/#\/View\/3511."},{"key":"ref_27","unstructured":"Astrium GEO-Information Services (2019, June 17). Pl\u00e9iades Imagery\u2014User Guide. Available online: http:\/\/satimagingcorp.s3.amazonaws.com\/site\/pdf\/User_Guide_Pleiades.pdf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1080\/01431169308904363","article-title":"Remote sensing of submerged vegetation canopies for biomass estimation","volume":"14","author":"Armstrong","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Robinson, N.P., Allred, B.W., Jones, M.O., Moreno, A., Kimball, J.S., Naugle, D.E., Erickson, T.A., and Richardson, A.D. (2017). A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States. Remote Sens., 9.","DOI":"10.3390\/rs9080863"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0034-4257(88)90116-2","article-title":"Radiometric scene normalization using pseudo-invariant features","volume":"26","author":"Schott","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.rse.2018.02.015","article-title":"High resolution topobathymetry using a Pleiades-1 triplet: Moorea Island in 3D","volume":"208","author":"Collin","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1080\/01490419.2016.1245227","article-title":"Satellite-Derived Bathymetry using Adaptive Geographically Weighted Regression Model","volume":"39","author":"Vinayaraj","year":"2016","journal-title":"Mar. Geod."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, J., Schill, S.R., Knapp, D.E., and Asner, G.P. (2019). Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites. Remote Sens., 11.","DOI":"10.3390\/rs11121445"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_36","unstructured":"(2019, June 14). Allen Coral Atlas. Available online: http:\/\/www.allencoralatlas.com."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1029\/2018EA000506","article-title":"The GEDI Simulator: A Large-Footprint Waveform LidarSimulator for Calibration and Validationof Spaceborne Missions","volume":"6","author":"Hancock","year":"2019","journal-title":"Earth Space Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Forfinski-Sarkozi, N.A., and Parrish, C.E. (2016). Analysis of MABEL Bathymetry in Keweenaw Bay and Implications for ICESat-2 ATLAS. Remote Sens., 8.","DOI":"10.3390\/rs8090772"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Y., Gao, H., Jasinski, M., Zhang, S., and Stoll, J. (2019). Deriving High-Resolution Reservoir Bathymetry From ICESat-2 Prototype Photon-Counting Lidar and Landsat Imagery. IEEE Trans. Geosci. Remote Sens., in press.","DOI":"10.1109\/TGRS.2019.2917012"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Guanter, L., Kaufmann, H., Segl, K., Foerster, S., Rogass, C., Chabrillat, S., Kuester, T., Hollstein, A., Rossner, G., and Chlebek, C. (2017). The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation. Remote Sens., 7.","DOI":"10.1109\/IGARSS.2016.7729059"},{"key":"ref_41","unstructured":"Turpie, K., Ackelson, S., Bell, T., Dierssen, H., Goodman, J., Green, O.R., Guild, L., Hochberg, E., Klemas, V.V., and Lavender, S. (2019, June 18). Global Observations of Coastal and Inland Aquatic Habitats, Available online: https:\/\/hyspiri.jpl.nasa.gov\/downloads\/RFI2_HyspIRI_related_160517\/RFI2_final_coastalpp_TurpieKevinR.pdf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_43","unstructured":"(2019, June 14). Microsoft Azure. Available online: https:\/\/azure.microsoft.com\/en-us\/."},{"key":"ref_44","unstructured":"(2019, June 14). Amazon AWS. Available online: https:\/\/aws.amazon.com\/."},{"key":"ref_45","unstructured":"(2019, June 14). Copernicus DIAS. Available online: https:\/\/www.copernicus.eu\/en\/access-data\/dias."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1830\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:08:54Z","timestamp":1760188134000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/15\/1830"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,6]]},"references-count":45,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2019,8]]}},"alternative-id":["rs11151830"],"URL":"https:\/\/doi.org\/10.3390\/rs11151830","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,6]]}}}