{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:33:24Z","timestamp":1762868004506,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,22]],"date-time":"2019-11-22T00:00:00Z","timestamp":1574380800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The article presents a new hybrid bio-optical transformation (HBT) method for the rapid modelling of bathymetry in coastal areas. The proposed approach exploits free-of-charge multispectral images and their processing by applying limited manpower and resources. The testbed area is a strait between two Greek Islands in the Aegean Sea with many small islets and complex seabed relief. The HBT methodology implements semi-analytical and empirical steps to model sea-water inherent optical properties (IOPs) and apparent optical properties (AOPs) observed by the Sentinel-2A multispectral satellite. The relationships of the calculated IOPs and AOPs are investigated and utilized to classify the study area into sub-regions with similar water optical characteristics, where no environmental observations have previously been collected. The bathymetry model is configured using very few field data (training depths) chosen from existing official nautical charts. The assessment of the HBT indicates the potential for obtaining satellite derived bathymetry with a satisfactory accuracy for depths down to 30 m.<\/jats:p>","DOI":"10.3390\/rs11232746","type":"journal-article","created":{"date-parts":[[2019,11,22]],"date-time":"2019-11-22T09:02:52Z","timestamp":1574413372000},"page":"2746","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery"],"prefix":"10.3390","volume":"11","author":[{"given":"Athanasios K.","family":"Mavraeidopoulos","sequence":"first","affiliation":[{"name":"Remote Sensing Laboratory, National and Kapodistrian University of Athens, ZC 157 84 Athens, Greece"}]},{"given":"Emmanouil","family":"Oikonomou","sequence":"additional","affiliation":[{"name":"University of West Attica, Department of Surveying and Geo-informatics Engineering, ZC 122 43 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0411-8287","authenticated-orcid":false,"given":"Athanasios","family":"Palikaris","sequence":"additional","affiliation":[{"name":"Hellenic Naval Academy, Navigation and Sea Sciences Laboratory, ZC 185 39 Piraeus, Greece"}]},{"given":"Serafeim","family":"Poulos","sequence":"additional","affiliation":[{"name":"Department of Geology and Geoenvironment, Section of Geography and Climatology, Laboratory of Physical Geography, National and Kapodistrian University of Athens, ZC 157 84 Athens, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/0198-0149(89)90046-0","article-title":"Remote Sensing of Oceanic Primary Production: Computations using a Spectral Model","volume":"36","author":"Sathyendranath","year":"1989","journal-title":"Deep-Sea Res."},{"key":"ref_2","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. 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