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Photometric characterization of planetary surfaces: Can surface roughness of the Moon be estimated by inversion of the Hapke model?

Presentation #207.03 in the session Moon & Earth II (Oral Presentation)

Published onOct 23, 2023
Photometric characterization of planetary surfaces: Can surface roughness of the Moon be estimated by inversion of the Hapke model?

Surface roughness is a physical characteristic that can help to understand the formation and evolution processes of a planetary body, as well as the properties of the materials that make up the surface. It also provides constraints for future planetary exploration missions, such as landing safety and rover trafficability. This work aims to retrieve lunar surface roughness by inversion of a radiative transfer model on multi-angular remote sensing data. Remote sensing techniques for estimating roughness are generally based on laser scanning systems or stereo images acquired from a spacecraft, drone or handheld camera. Surface roughness has also been estimated by inversion of the Hapke model, a semi-empirical radiative transfer model widely used in planetology. This approach has been validated on Earth using field measurements (Labarre et al., 2019). Here, we attempt to retrieve the Moon’s surface roughness from photometry using the bidirectional reflectance distribution function (BRDF) extracted from multi-angular data acquired by the Pleiades satellites, as part of the in-flight sensor calibration carried out by the Centre national d’études spatiales (CNES). Taking advantage of its pointing capability, Pleiades regularly targets the Moon. The images encompassing the entire near side of the Moon were acquired in four spectral bands in the VIS/NIR, with pixel size of 1500 m. The data covers a wide range of phase angles, from 0° to 110°. Next, we used a fast Bayesian inversion method developed by Kugler et al. (2022), which can significantly reduce the cost of individual inversions (the number of drawn samples), enabling the algorithm to be applied to a large number of observations. This framework has been tested for individual pixels and small scenes of ~100 pixels with promising results. Therefore, we planned to extend it to the Moon’s entire surface (near side, ~1 million pixels).


Kugler, B., Forbes, F., and Douté, S. (2022). Fast Bayesian Inversion for high dimensional inverse problems. Statistics and Computing, 32(31).

Labarre, S., Jacquemoud, S., Ferrari, C., Delorme, A., Derrien, A., Grandin, R., Jalludin, M., Lemaître, F., Métois, M., Pierrot-Deseilligny, M., Rupnik, E., and Tanguy, B. (2019). Retrieving soil surface roughness with the hapke photometric model: Confrontation with the ground truth. Remote Sensing of Environment, 225:1–15.

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