VENμS (Vegetation and Environment New micro (μ) Satellite) is a micro satellite launched in 2017 by the Israeli Space Agency (ISA) and the French Centre National d’Etudes Spatiales (CNES). VENμS is a research satellite containing two very different devices: an electric Hall effect thruster and a multispectral optical camera. This paper focuses on the multispectral camera. The camera provides images at a resolution of 5 m, with a field of view of 27 km, and the orbit of the satellite was chosen to allow us to revisit of each observed site with constant angles every second day. In November 2020, VENμS ended the first phase of its mission. This phase, called VM01, allowed us to provide about 150 accurate time series over selected scientific sites over almost three years. Extensive work was conducted to calibrate the camera and assess the quality of the products. Not everything worked as planned before launch and a large amount of work was necessary to correct some defects of the camera or to improve the geometric registration of images. This article establishes the image quality VM01 final assessment including the presentation of radiometric and geometric calibration methods, the estimation of instrument performances and their associated temporal stabilities and the monitoring activities. In addition, it highlights the whole mechanism of data programming, reception and production. The end of VM01 phase is not the end of the VENμS mission, and a new phase started on a one-day repeat orbit.

The correction of atmospheric effects on optical remote sensing products is an essential component of Analysis Ready Data (ARD) production lines. The MAJA processor aims at providing accurate time series of surface reflectances over land for satellite missions, such as Sentinel-2, Venμs, and Landsat 8. The Centre d’Études Spatiales de la Biosphère (CESBIO) and the Centre National d’Études Spatiales (CNES) share a common effort to maintain, validate, and improve the MAJA processor, using state-of-the-art ground measurement sites, and participating in processor inter-comparisons, such as the Atmospheric Correction Intercomparison Exercise (ACIX). While contributing to the second ACIX-II Land validation exercise, it was found that the candidate MAJA dataset could not adequately be compared to the main reference dataset. MAJA reflectances were corrected for adjacency and topography effects while the reference dataset was not, excluding MAJA from a part of the performance metrics of the exercise. The first part of the following study aims at providing complementary performance assessment to ACIX-II by reprocessing MAJA surface reflectances without adjacency nor topographic correction, allowing for an un-biased full resolution comparison with the reference Sentinel-2 dataset. The second part of the study consists of validating MAJA against surface reflectance measurements time series of up to five years acquired at three automated stations. Both approaches provide extensive insights on the quality of MAJA Sentinel-2 Level 2 products.

Understanding the physical characteristics of terrestrial and planetary surfaces is imperative for unraveling the complexity of landscape formation and evolution, and to develop strategies for future planetary rover missions. Photometry is one of the most widely used methods for studying these characteristics. The light scattered by a surface is quantified by the bidirectional reflectance distribution function (BRDF), providing a uniquely detailed optical measurement for each target observed. Hapke model inversion, an approach widely used over the past decades, reveals complex surface attributes, including roughness, porosity, grain size and shape, micro-texture, mineral composition, and more. Although the challenges of restrictive data conditions and limited computational capabilities impeded the inversion of the Hapke model for large-scale surface analysis, we’ve addressed these issues with appropriate data and a comprehensive framework. Extracting multiangular surface data requires optical sensors with pointing capabilities and, by extension, images captured from different illumination directions. Earth observation satellites such as the Pleiades constellation managed by the Centre National d’Études Spatiales (CNES), have demonstrated their agility in extracting large-scale BRDF data on the Moon for optical sensor calibration. The processing chain involves geometric correction using digital elevation models supplied by NASA, and inversion of the Hapke model on each pixel, which is facilitated by a fast Bayesian inversion framework (Kugler et al., 2022). Inversion of the Hapke model on the BRDF extracted from each pixel generates maps of the six model parameters for the areas studied on the near side of the lunar surface, primarily the Apollo landing sites. The BRDFs extracted from Pleiades images over the Apollo 17 landing site are consistent with prior knowledge of the photometric behavior of the Moon's surface. The quality of these BRDFs prompted us to extend our analysis to a 10° x 10° region around the mentioned site. Given the 1.5 km ground sampling distance of Pleiades images, the map size is 200 x 200 pixels (approximately 300 x 300 km). The distribution of the parameter values reflects the topography of the region, with a notable contrast between flat and steeply sloping areas. Optimal fits with an acceptable level of error are obtained on flat terrain, while the algorithm encounters difficulties in steeply sloping areas due to the complexity of the terrain within the large ground sampling distance. In the current state, the application of the framework is extending to cover the near side of the Moon. The parameters obtained for each terrain unit will be compared with previous works (Souchon et al., 2013; Sato et al., 2014; Gimar et al., 2022; Marshal et al., 2023; Nagori et al., 2023) and correlated with a geological map (Fortezzo et al., 2020).

Numerous research projects have successfully exploited remote sensing data to analyze Earth and planetary surfaces. The use of radiative transfer models simulating the interaction between electromagnetic radiation and bare soils, such as the Hapke model, is becoming increasingly widespread. However, the in- version of these models is relatively uncommon due to a number of difficulties. To address these issues, our team has collected relevant field and satellite data from the Asal-Ghoubbet rift (Republic of Djibouti) and developed a comprehensive framework for analyzing these data. This site was chosen for the diversity of its terrains, characterized by varied albedo and surface roughness, which are well preserved due to the desert climate. It also has the advantage of being easily accessible (Labarre et al., 2019).To carry out our study, we used images from the Pleiades-1B satellite captured in video mode over the Asal-Ghoubbet rift on January 26, 2013, during the in-flight commissioning of the satellite. This unique four-minute flyby produced 21 images at viewing angles ranging from from -56.7° to +52.6°. The images were corrected for atmospheric effects, which modify the photometric response of surfaces. To achieve this, experts from CNES applied a variant of the MACCS ATCOR Joint Algorithm (MAJA), using auxiliary data to take into account the water vapor content and aerosol optical thickness (Hagolle et al., 2015). In addition, to validate the results of the Hapke model inversion, a field experiment was conducted in February 2016 in the Asal-Ghoubbet rift to collect soil samples and acquire data (ground truth).To meet the challenge of limited geometric observation configurations, essential for constraining model parameters, our global approach tackled the known coupling effect between parameters. We also had to take into account the prohibitive computation time required to process millions of pixels over multiple spectral bands, which was a major obstacle to generating the Hapke parameter map. We applied the fast Bayesian inversion method developed by Kugler et al. (2022), which offers an efficient solution to overcome this problem. In parallel, a geometrical correction was applied using a previously constructed digital elevation model (DEM) of the rift that used the same data set. In the end, with each spectral band, we obtained four maps of Hapke model parameters corresponding to the single scattering albedo w, the photometric roughness θ, the asymmetry b and backscattering c parameters of the phase function. The areas of low reconstruction error (less than 1.5%) represents 70% of the entire region. The remainder can be attributed to areas with extremely steep slopes and heterogeneous terrains along slopes such as mass wasting deposits, or areas hindered by clouds and their associated shadows on the ground. The correlation between the parameters and the geological map, the analysis of the soil samples of each terrain units will be presented and discussed.