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Article

Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach

1
CNRS, SeaTech, LIS Laboratory, Université de Toulon, UMR 7296, F-83041 Toulon, France
2
CNRS-INSU, Laboratoire Atmosphères Milieux Observations Spatiales (LATMOS), Boulevard de l’Observatoire, Sorbonne Université, CS 34229, F-06304 Nice, France
3
CNRS, Centrale Marseille, Institut Fresnel, Aix Marseille Université, F-13013 Marseille, France
4
Société Nationale de Protection de la Nature (S.N.P.N.), Réserve Naturelle Nationale de Camargue, Centre La Capelière C134 route de Fiélouse, F-13200 Arles, France
5
Tour du Valat Research Institute, F-13200 Arles, France
*
Author to whom correspondence should be addressed.
Academic Editors: Xavier Monteys, John D. Hedley and Ele Vahtmäe
Remote Sens. 2021, 13(10), 1999; https://doi.org/10.3390/rs13101999
Received: 24 March 2021 / Revised: 12 May 2021 / Accepted: 14 May 2021 / Published: 20 May 2021
(This article belongs to the Special Issue Satellite Mapping and Monitoring of the Coastal Zone)
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 Vaccarès 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. View Full-Text
Keywords: hyperspectral; benthic habitats; bathymetry hyperspectral; benthic habitats; bathymetry
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MDPI and ACS Style

Minghelli, A.; Vadakke-Chanat, S.; Chami, M.; Guillaume, M.; Migne, E.; Grillas, P.; Boutron, O. Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach. Remote Sens. 2021, 13, 1999. https://doi.org/10.3390/rs13101999

AMA Style

Minghelli A, Vadakke-Chanat S, Chami M, Guillaume M, Migne E, Grillas P, Boutron O. Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach. Remote Sensing. 2021; 13(10):1999. https://doi.org/10.3390/rs13101999

Chicago/Turabian Style

Minghelli, Audrey, Sayoob Vadakke-Chanat, Malik Chami, Mireille Guillaume, Emmanuelle Migne, Patrick Grillas, and Olivier Boutron. 2021. "Estimation of Bathymetry and Benthic Habitat Composition from Hyperspectral Remote Sensing Data (BIODIVERSITY) Using a Semi-Analytical Approach" Remote Sensing 13, no. 10: 1999. https://doi.org/10.3390/rs13101999

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