Mapping the surface properties of the Asal-Ghoubbet rift by massive inversion of the Hapke model on Pleiades multiangular images

A massive inversion of the Hapke model is carried out over the Asal-Ghoubbet rift (Republic of Djibouti) using high-resolution multiangular Pleiades images. This is the first time that such an inversion is performed on Earth over an entire image, previous studies having focused on planetary surfaces. This work addresses challenges such as atmospheric and geometrical corrections of these images to produce parameter maps. The use of fast Bayesian inversion significantly reduces computation times thanks to efficient exploration of the parameter space and leads to improved prediction. The parameters of the Hapke model are also interpreted in terms of surface physical properties, thanks to field measurements. Single scattering albedo is the parameter extracted with the greatest reliability, although its validation is still difficult due to the absence of a simple formula linking it to surface reflectance. Our study reveals a close relationship between photometric roughness and single scattering albedo, indicating that accurate extraction of the former is highly dependent on values of the latter, which must be below 0.8 for reliable estimation. Finally, the correlation between phase function parameters and grain properties depends on surface type and material properties. • Bidirectional soil reflectance is extracted from multiangular Pleiades images. • Fast Bayesian inversion of the Hapke model is applied to an entire region. • Retrieved parameters are compared with ground measurements. • The parameter maps produced are interpreted in terms of surface properties.

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Publication Details

Journal
Remote Sensing of Environment
Published
2025-03-07
DOI
https://doi.org/10.1016/j.rse.2025.114691
Citations
3
Primary Topic
Remote Sensing and LiDAR Applications
Type
article
Field-Weighted Citation Impact
2.40
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article

Mapping the surface properties of the Asal-Ghoubbet rift by massive inversion of the Hapke model on Pleiades multiangular images

C. Ferrari, Dan T. Nguyen, Aimé Meygret, Sophie Coustance et al.
3 citations
Remote Sensing of Environment
Remote Sensing and LiDAR Applications
2.40
article

Mapping the surface properties of the Asal-Ghoubbet rift by massive inversion of the Hapke model on Pleiades multiangular images

C. Ferrari, Dan T. Nguyen, Aimé Meygret, Sophie Coustance, Antoine Łucas, Sébastien Marcq, S. Douté, Stéphane Jacquemoud
article en
3 citations

Abstract

A massive inversion of the Hapke model is carried out over the Asal-Ghoubbet rift (Republic of Djibouti) using high-resolution multiangular Pleiades images. This is the first time that such an inversion is performed on Earth over an entire image, previous studies having focused on planetary surfaces. This work addresses challenges such as atmospheric and geometrical corrections of these images to produce parameter maps. The use of fast Bayesian inversion significantly reduces computation times thanks to efficient exploration of the parameter space and leads to improved prediction. The parameters of the Hapke model are also interpreted in terms of surface physical properties, thanks to field measurements. Single scattering albedo is the parameter extracted with the greatest reliability, although its validation is still difficult due to the absence of a simple formula linking it to surface reflectance. Our study reveals a close relationship between photometric roughness and single scattering albedo, indicating that accurate extraction of the former is highly dependent on values of the latter, which must be below 0.8 for reliable estimation. Finally, the correlation between phase function parameters and grain properties depends on surface type and material properties. • Bidirectional soil reflectance is extracted from multiangular Pleiades images. • Fast Bayesian inversion of the Hapke model is applied to an entire region. • Retrieved parameters are compared with ground measurements. • The parameter maps produced are interpreted in terms of surface properties.

Remote Sensing of EnvironmentVol. 322
Centre National de la Recherche Scientifique (FR), Institut de physique du globe de Paris (FR), Université Paris Cité (FR), Centre National d'Études Spatiales (FR), Institut de Planétologie et d'Astrophysique de Grenoble (FR)
Openalex Percentile: Top 14%
Remote Sensing and LiDAR Applications
2.40
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