{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:02:56Z","timestamp":1760234576525,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T00:00:00Z","timestamp":1621468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"French National Space Agency","award":["TOSCA-HYPCOLAC"],"award-info":[{"award-number":["TOSCA-HYPCOLAC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The relevant benefits of hyperspectral sensors for water column determination and seabed features mapping compared to multispectral data, especially in coastal areas, have been demonstrated in recent studies. In this study, we used hyperspectral satellite data in the accurate mapping of the bathymetry and the composition of water habitats for inland water. Particularly, the identification of the bottom diversity for a shallow lagoon (less than 2 m in depth) was examined. Hyperspectral satellite data were simulated based on aerial hyperspectral imagery acquired above a lagoon, namely the Vaccar\u00e8s lagoon (France), considering the spatial and spectral resolutions, and the signal-to-noise ratio of a satellite sensor, BIODIVERSITY, that is under study by the French space agency (CNES). Various sources of uncertainties such as inter-band calibration errors and atmospheric correction were considered to make the dataset realistic. The results were compared with a recently launched hyperspectral sensor, namely the DESIS sensor (DLR, Germany). The analysis of BIODIVERSITY-like sensor simulated data demonstrated the feasibility to satisfactorily estimate the bathymetry with a root-mean-square error of 0.28 m and a relative error of 14% between 0 and 2 m. In comparison to open coastal waters, the retrieval of bathymetry is a more challenging task for inland waters because the latter usually shows a high abundance of hydrosols (phytoplankton, SPM, and CDOM). The retrieval performance of seabed abundance was estimated through a comparison of the bottom composition with in situ data that were acquired by a recently developed imaging camera (SILIOS Technologies SA., France). Regression coefficients for the retrieval of the fractional species abundances from the theoretical inversion and measurements were obtained to be 0.77 (underwater imaging camera) and 0.80 (in situ macrophytes data), revealing the potential of the sensor characteristics. By contrast, the comparison of the in situ bathymetry and macrophyte data with the DESIS inverted data showed that depth was estimated with an RSME of 0.38 m and a relative error of 17%, and the fractional species abundance was estimated to have a regression coefficient of 0.68.<\/jats:p>","DOI":"10.3390\/rs13101999","type":"journal-article","created":{"date-parts":[[2021,5,20]],"date-time":"2021-05-20T06:13:45Z","timestamp":1621491225000},"page":"1999","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2290-6383","authenticated-orcid":false,"given":"Audrey","family":"Minghelli","sequence":"first","affiliation":[{"name":"CNRS, SeaTech, LIS Laboratory, Universit\u00e9 de Toulon, UMR 7296, F-83041 Toulon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0576-9471","authenticated-orcid":false,"given":"Sayoob","family":"Vadakke-Chanat","sequence":"additional","affiliation":[{"name":"CNRS, SeaTech, LIS Laboratory, Universit\u00e9 de Toulon, UMR 7296, F-83041 Toulon, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7815-0533","authenticated-orcid":false,"given":"Malik","family":"Chami","sequence":"additional","affiliation":[{"name":"CNRS-INSU, Laboratoire Atmosph\u00e8res Milieux Observations Spatiales (LATMOS), Boulevard de l\u2019Observatoire, Sorbonne Universit\u00e9, CS 34229, F-06304 Nice, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mireille","family":"Guillaume","sequence":"additional","affiliation":[{"name":"CNRS, Centrale Marseille, Institut Fresnel, Aix Marseille Universit\u00e9, F-13013 Marseille, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanuelle","family":"Migne","sequence":"additional","affiliation":[{"name":"Soci\u00e9t\u00e9 Nationale de Protection de la Nature (S.N.P.N.), R\u00e9serve Naturelle Nationale de Camargue, Centre La Capeli\u00e8re C134 route de Fi\u00e9louse, F-13200 Arles, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick","family":"Grillas","sequence":"additional","affiliation":[{"name":"Tour du Valat Research Institute, F-13200 Arles, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6495-7563","authenticated-orcid":false,"given":"Olivier","family":"Boutron","sequence":"additional","affiliation":[{"name":"Tour du Valat Research Institute, F-13200 Arles, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,20]]},"reference":[{"key":"ref_1","first-page":"016504","article-title":"How much benthic information can be retrieved with hyperspectral sensor from the optically complex coastal waters?","volume":"14","author":"Paavel","year":"2020","journal-title":"J. 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