{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:50:34Z","timestamp":1781484634156,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,1]],"date-time":"2018-06-01T00:00:00Z","timestamp":1527811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["641762"],"award-info":[{"award-number":["641762"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DLR-DAAD","award":["57186656"],"award-info":[{"award-number":["57186656"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Bathymetry mapping forms the basis of understanding physical, economic, and ecological processes in the vastly biodiverse coastal fringes of our planet which are subjected to constant anthropogenic pressure. Here, we pair recent advances in cloud computing using the geospatial platform of the Google Earth Engine (GEE) with optical remote sensing technology using the open Sentinel-2 archive, obtaining low-cost in situ collected data to develop an empirical preprocessing workflow for estimating satellite-derived bathymetry (SDB). The workflow implements widely used and well-established algorithms, including cloud, atmospheric, and sun glint corrections, image composition and radiometric normalisation to address intra- and inter-image interferences before training, and validation of four SDB algorithms in three sites of the Aegean Sea in the Eastern Mediterranean. Best accuracy values for training and validation were R2 = 0.79, RMSE = 1.39 m, and R2 = 0.9, RMSE = 1.67 m, respectively. The increased accuracy highlights the importance of the radiometric normalisation given spatially independent calibration and validation datasets. Spatial error maps reveal over-prediction over low-reflectance and very shallow seabeds, and under-prediction over high-reflectance (&lt;6 m) and optically deep bottoms (&gt;17 m). We provide access to the developed code, allowing users to map bathymetry by customising the time range based on the field data acquisition dates and the optical conditions of their study area.<\/jats:p>","DOI":"10.3390\/rs10060859","type":"journal-article","created":{"date-parts":[[2018,6,1]],"date-time":"2018-06-01T04:37:46Z","timestamp":1527827866000},"page":"859","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":210,"title":["Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0766-7986","authenticated-orcid":false,"given":"Dimosthenis","family":"Traganos","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, Rutherfordstra\u00dfe 2, 12489 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bharat","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, Rutherfordstra\u00dfe 2, 12489 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"201","DOI":"10.3354\/meps0279","article-title":"(Eds.) Biodiversity, ecosystems and coastal zone management: Linking science and policy. Theme Section","volume":"434","author":"Paterson","year":"2011","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_2","unstructured":"Robertson, E. (2018, April 20). Crowd-Sourced Bathymetry Data via Electronic Charting Systems. Available online: http:\/\/proceedings.esri.com\/library\/userconf\/oceans16\/papers\/oceans_12.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01490410151079891","article-title":"Spatial Modeling and Analysis for Shoreline Change Detection and Coastal Erosion Monitoring","volume":"24","author":"Li","year":"2010","journal-title":"Mar. Geod."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.5194\/nhess-13-1779-2013","article-title":"Performance of coastal sea-defense infrastructure at El Jadida (Morocco) against tsunami threat: Lessons learned from the Japanese 11 March 2011 tsunami","volume":"13","author":"Omira","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.rse.2018.02.005","article-title":"Coral reef habitat mapping: A combination of object-based image analysis and ecological modelling","volume":"208","author":"Roelfsema","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.scitotenv.2017.11.224","article-title":"Effects of sea level rise, land subsidence, bathymetric change and typhoon tracks on storm flooding in the coastal areas of Shanghai","volume":"621","author":"Wang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10039","DOI":"10.1029\/96JB03223","article-title":"Marine gravity anomaly from Geosat and ERS 1 satellite altimetry","volume":"102","author":"Sandwell","year":"1997","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_8","unstructured":"Olson, C.J., Becker, J.J., and Sandwell, D.T. (2014, January 15\u201319). A new global bathymetry map at 15 arcsecond resolution for resolving seafloor fabric: SRTM15_PLUS. Proceedings of the AGU Fall Meeting Abstracts, San Francisco, CA, USA."},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Misra, A., Vojinovic, Z., Ramakrishnan, B., Luijendijk, A., and Ranasinghe, R. (2018). Shallow water bathymetry mapping using Support Vector Machine (SVM) technique and multispectral imagery. Int. J. Remote Sens.","DOI":"10.1080\/01431161.2017.1421796"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"D\u00f6rnh\u00f6fer, K., G\u00f6ritz, A., Gege, P., Pflug, B., and Oppelt, N. (2016). Water Constituents and Water Depth Retrieval from Sentinel-2A\u2014A First Evaluation in an Oligotrophic Lake. Remote Sens., 8.","DOI":"10.3390\/rs8110941"},{"key":"ref_13","first-page":"15","article-title":"Mapping South Baltic Near-Shore Bathymetry Using Sentinel-2 Observations","volume":"24","author":"Chybicki","year":"2017","journal-title":"Pol. Mar. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01490419.2017.1373173","article-title":"Three-Dimensional Geographically Weighted Inverse Regression (3GWR) Model for Satellite Derived Bathymetry Using Sentinel-2 Observations","volume":"41","author":"Chybicki","year":"2017","journal-title":"Mar. Geod."},{"key":"ref_15","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_16","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_17","doi-asserted-by":"crossref","unstructured":"Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K., and Gorelick, N. (2017). Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine. Remote Sens., 9.","DOI":"10.3390\/rs9101065"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Parastatidis, D., Mitraka, Z., Chrysoulakis, N., and Abrams, M. (2017). Online Global Land Surface Temperature Estimation from Landsat. Remote Sens., 9.","DOI":"10.3390\/rs9121208"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chrysoulakis, N., Mitraka, Z., and Gorelick, N. (2018). Exploiting satellite observations for global surface albedo trends monitoring. Theor. Appl. Climatol., accepted.","DOI":"10.1007\/s00704-018-2663-6"},{"key":"ref_20","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_21","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_22","doi-asserted-by":"crossref","unstructured":"Poursanidis, D., Topouzelis, K., and Chrysoulakis, N. (2018). Mapping coastal marine habitats and delineating the deep limits of the Neptune\u2019s seagrass meadows using Very High Resolution Earth Observation data. Int. J. Remote Sens., accepted.","DOI":"10.1080\/01431161.2018.1490974"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"789","DOI":"10.5194\/os-9-789-2013","article-title":"The Mediterranean Sea system: A review and an introduction to the special issue","volume":"9","author":"Tanhua","year":"2013","journal-title":"Ocean Sci."},{"key":"ref_24","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., and Stone, C.J. (1984). Classification and Regression Trees, Chapman & Hall\/CRC."},{"key":"ref_25","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_26","unstructured":"European Space Agency (ESA) (2015). SENTINEL-2 User Handbook, ESA."},{"key":"ref_27","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_28","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_29","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s00338-004-0418-6","article-title":"Satellite observation of Keppel Islands (Great Barrier Reef) 2002 coral bleaching using IKONOS data","volume":"23","author":"Elvidge","year":"2004","journal-title":"Coral Reefs"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Traganos, D., and Reinartz, P. (2017). Mapping Mediterranean seagrasses with Sentinel-2. Mar. Pollut. Bull.","DOI":"10.1016\/j.marpolbul.2017.06.075"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"444","DOI":"10.4319\/lo.2003.48.1_part_2.0444","article-title":"Ocean color remote sensing of seagrass and bathymetry in the Bahamas Banks by high- resolution airborne imagery","volume":"48","author":"Dierssen","year":"2003","journal-title":"Limnol. Oceanogr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"16257","DOI":"10.3390\/rs71215829","article-title":"Derivation of High-Resolution Bathymetry from Multispectral Satellite Imagery: A Comparison of Empirical and Optimisation Methods through Geographical Error Analysis","volume":"7","author":"Hamylton","year":"2015","journal-title":"Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Traganos, D., and Reinartz, P. (2018). Interannual Change Detection of Mediterranean Seagrasses Using RapidEye Image Time Series. Front. Plant Sci., 9.","DOI":"10.3389\/fpls.2018.00096"},{"key":"ref_35","unstructured":"(1970, January 01). TeamSurv, 2018. Available online: https:\/\/www.teamsurv.com\/."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hedley, J.D., Roelfsema, C.M., Chollett, I., Harborne, A.R., Heron, S.F., Weeks, S., Skirving, W.J., Strong, A.E., Eakin, C.M., and Christensen, T.R.L. (2016). Remote Sensing of Coral Reefs for Monitoring and Management: A Review. Remote Sens., 8.","DOI":"10.3390\/rs8020118"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1080\/01431161.2018.1430916","article-title":"Airborne lidar bathymetry: Assessing quality assurance and quality control methods with Leica Chiroptera examples","volume":"39","author":"Saylam","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s11001-017-9338-z","article-title":"Combining pixel and object based image analysis of ultra-high resolution multibeam bathymetry and backscatter for habitat mapping in shallow marine waters","volume":"39","author":"Ierodiaconou","year":"2018","journal-title":"Mar. Geophys. Res."},{"key":"ref_39","unstructured":"International Hydrographic Organization (2018, April 20). Available online: https:\/\/www.iho.int\/iho_pubs\/draft_pubs\/CSB-Guidance_Document-Ed1.0.0.pdf."},{"key":"ref_40","unstructured":"(1970, January 01). BioBase, 2018. Available online: https:\/\/www.cibiobase.com\/."},{"key":"ref_41","unstructured":"(2018, May 02). Nippon Foundation-GEBCO, 2018. Available online: https:\/\/seabed2030.gebco.net\/."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1080\/07038992.2014.945827","article-title":"Pixel-based image compositing for large-area dense time series applications and science","volume":"40","author":"White","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3831","DOI":"10.1364\/AO.38.003831","article-title":"Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization","volume":"38","author":"Lee","year":"1999","journal-title":"Appl. Opt."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Danilo, C., and Melgani, F. (2016). Wave period and coastal bathymetry using wave propagation on optical images. IEEE Trans. Geosci. Remote Sens., 54.","DOI":"10.1109\/TGRS.2016.2579266"},{"key":"ref_45","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_46","first-page":"25","article-title":"Remote Sensing, natural hazards and the contribution of ESA Sentinels missions","volume":"6","author":"Poursanidis","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/859\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:06:50Z","timestamp":1760195210000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/859"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,1]]},"references-count":46,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["rs10060859"],"URL":"https:\/\/doi.org\/10.3390\/rs10060859","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,1]]}}}