Skip to Main content Skip to Navigation
New interface
Journal articles

Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth

Abstract : Surface roughness can be defined as the mean slope angle integrated over all scales from the grain size to the local topography. It controls the energy balance of bare soils, in particular the angular distribution of scattered and emitted radiation. This provides clues to understand the intimate structure and evolution of planetary surfaces over ages. In this article we investigate the capacity of the Hapke photometric model, the most widely used in planetary science, to retrieve surface roughness from multiangular reflectance data. Its performance is still a question at issue and we lack validation experiments comparing model retrievals with ground measurements. To address this issue and to show the potentials and limits of the Hapke model, we compare the mean slope angle determined from very high resolution digital elevation models of volcanic and sedimentary terrains sampled in the Asal-Ghoubbet rift (Republic of Djibouti), to the photometric roughness estimated by model inversion on multiangular reflectance data measured on the ground (Chamelon field goniometer) and from space (Pleiades images). The agreement is good on moderately rough surfaces, in the domain of validity of the Hapke model, and poor on others.
Document type :
Journal articles
Complete list of metadata
Contributor : Accord Elsevier CCSD Connect in order to contact the contributor
Submitted on : Friday, October 22, 2021 - 11:50:12 AM
Last modification on : Friday, October 21, 2022 - 3:34:06 PM
Long-term archiving on: : Sunday, January 23, 2022 - 7:29:45 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution - NonCommercial 4.0 International License



S. Labarre, S. Jacquemoud, C. Ferrari, A. Delorme, A. Derrien, et al.. Retrieving soil surface roughness with the Hapke photometric model: Confrontation with the ground truth. Remote Sensing of Environment, 2019, 225, pp.1--15. ⟨10.1016/j.rse.2019.02.014⟩. ⟨hal-02293285⟩



Record views


Files downloads