Benefit of the Potential Future Hyperspectral Satellite Sensor (BIODIVERSITY) for Improving the Determination of Water Column and Seabed Features in Coastal Zones

Abstract

Most of the studies dealing with seabed mapping from hyperspectral images have been carried out using airborne data although hyperspectral satellite sensors have already been or are planned to be launched for the near future (HICO ENMAP or BIODIVERSITY). The objective of this study is to evaluate the benefit of a BIODIVERSITY-like sensor to determine the biooptical properties of the water column, namely the Chlorophyll-a concentration, the Suspended Particulate Matter concentration, the absorption coefficient of the Colored Dissolved Organic Matter, the bathymetry and the composition of the seabed, according to its spatial resolution and spectral resolution and its Signal to Noise Ratio (SNR). For this purpose, radiative transfer simulations are analyzed together with remote sensing hyperspectral airborne data (HYSPEX) acquired above the Porquerolles Island (France). The retrieval performance of all inwater and seabed parameters derived from the inversion of BIODIVERSITY-like data is compared with the performance obtained using ENMAP and HICO spatial and radiometric specifications. It is shown that a BIODIVERSITY-like sensor significantly improves the estimation performance of the water column parameters. Furthermore, BIODIVERSITY-like sensor is highly appropriate for seabed mapping when bottom pixels are composed of pure material (e.g., Sand or Posidonia) in shallow waters when seabed depth is less than 10 m. Conversely, the performance of the inversion deteriorates when seabed pixels are composed of mixed materials (e.g., Sand mixed with Posidonia). It is also shown that the concentration of chlorophyll, SPM and CDOM absorption are less sensitive to noise level than depth and seabed abundance.

Determination of Bayesian Cramér–Rao Bounds for Estimating Uncertainties in the Bio-Optical Properties of the Water Column, the Seabed Depth and Composition in a Coastal Environment

“…The mean square error between the modeled reflectance and the measured reflectance is optimized for each pixel N to retrieve the vector parameter Λ i . The spectral signatures used here are extracted from the same spectral library than those used in [59], which were collected in 2018 in the nearby area of Porquerolle island (France). Posidonia, brown algae and colonised Posidonia reflectance are the same than those used in [59], the sand spectrum magnitude is higher here.…”

“…Many different statistical and physical methods have been developed for remote sensing of optically shallow waters [17,18,[51][52][53][54][55][56][57][58]. The uncertainties established for a BIODIVERSITY-like sensor have been predicted using simulated images that matches the sensor features [40,59]. In [60,61], the authors present a method to obtain uncertainty estimates for inverted IOP by propagating the errors through the semianalytical model used for inversion.…”

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

“…The water reflectance derived from the satellite simulated data could then be used for deriving the water and bottom parameters. The reader is referred to [15] for details about the methodological aspects of the simulated satellite data. As the calibration error can also lead to an error in the estimation of the water parameters, depth and abundances, a calibration error was added to the simulation processing chain.…”

“…Hyperspectral remotely sensed data are relevant to study such complex and dynamic ecosystems [14][15][16]. Research on the estimation of water depth and benthic habitat composition is an emerging sector in remote sensing that has received extensive attention in the last decade [1,2,[17][18][19][20][21].…”

“…In contrast to a recently published work about a BIODIVERSITY-like hyperspectral sensor that focused on clear coastal waters [15], the current work focused on an inland water area; more specifically, the retrieval of bottom characteristics was examined for shallow (<2 m) inland lagoons where waters can be considered as moderately turbid. In addition, the estimations of bio-optical parameters, namely bathymetry and bottom composition, obtained with simulated BIODIVERSITY data were compared with real satellite hyperspectral data (namely DESIS data) rather than simulations, as carried out for the last published work [15]. The DESIS is an imaging spectrometer that has been onboard the International Space Station (ISS) since 2018.…”