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.

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).

With rates locally exceeding one centimeter of denudation per year [1,2], i.e., more than 100 t/ha/year, the Durance basin in the French Alps is one of the world’s most heavily eroding areas [3]. A combination of favorable conditions explains this phenomenon, including a very steep topography, sparse vegetation and a particular susceptibility of the Jurassic black marls, also called Terres Noires, to seasonal climatic forcing [4]. The Draix-Bléone observatory uses hydro-sedimentary stations to instrument several of these small, non-anthropized catchments, where hydrological responses to seasonal storms are rapid and intense [5]. The work presented combines the use of LiDAR time series from airborne, UAV and ground measurements, with sediment flux chronicles recorded at the outlet of the Laval catchment, a small steep watershed (0.86 m2), to address questions of upstream/downstream transport modalities. An assessment of the potential of remote sensing methods for attributing the contributions of each critical zone compartment (channel, gullies, landslides, etc.) to the erosive dynamics of this basin and its connectivity is carried out.References :[1] N. Mathys, S. Brochot, M. Meunier and D. Richard (2003). Erosion quantification in the small marly experimental catchments of Draix (Alpes de Haute Provence, France). Calibration of the ETC rainfall-runoff-erosion model. CATENA. 50(2–4):527–548. DOI : 10.1016/S0341-8162(02)00122-4.[2] A. Carriere, C. Le Bouteiller, G. E. Tucker, S. Klotz, and M. Naaim (2020). Impact of vegetation on erosion: Insights from the calibration and test of a landscape evolution model in alpine badland catchments. Earth Surf. Process. Landf., 45(5):1085–1099. DOI : 10.1002/esp.4741.[3] D. E. Walling (1998). Measuring sediment yield from river basins. in Soil Erosion Research Methods (R. Lal, Ed.). Soil and Water Conservation Society, Iowa, USA, pp 39–73.[4] L. Descroix and N. Mathys (2003). Processes, spatio-temporal factors and measurements of current erosion in the French Southern Alps: A review. Earth Surf. Process. Landf. 28( 9): 993–1011. DOI : 10.1002/esp.514.[5] Draix-Bleone Observatory. (2015). Observatoire hydrosédimentaire de montagne Draix-Bléone [Data set]. Irstea. DOI : 10.17180/obs.draix

Abstract. With denudation rates locally exceeding one centimetre of fresh marl per year, i.e., more than 250 T.ha−1.yr−1, the badlands of the Durance basin in the French Alps makes it one of the world’s most heavily eroding areas. Since 1983, the Draix-Bléone Observatory has been using hydro-sedimentary stations to instrument several of these small, unmanaged badland catchments, where the hydrological response to seasonal storms is rapid and intense. We combine such chronicles at the outlet of the Laval basin (86 ha) with a six-year diachronic analysis of airborne and UAV LiDAR data and a bulk density modelling to map mass movements and constrain a catchment-scale mass balance. We find out that landslides and crests failures represents very active areas, accounting for at least 15 % of the sediment budget of the watershed, while affecting only 1 % of the bare surfaces. They contribute to making the low drainage areas the places with highest erosion rates, reaching as much as two centimetres of fresh marl per year, 3.5 times more than the average value on denuded slopes. Despite some methodological constraints, our approach seams very promising at quantifying and localising the erosion hotspots as well as assessing sediment transport through critical zone compartments, and could be adapted to time series for monitoring the dynamics of badland catchments in a changing climate.

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.

With denudation rates locally exceeding one centimetre of weathered marl per year, i.e., more than 200 T ha−1 yr−1, the badlands of the Durance basin in the French Alps are one of the most eroding areas in the world. Since 1983, the Draix-Bléone Observatory has been using hydro-sedimentary stations to monitor several of these small, unmanaged badland catchments, where the hydrological response to seasonal storms is rapid and intense. In order to fingerprint soil loss in the 86 ha Laval basin, we combine outlet records with an analysis of airborne and UAV LiDAR data taken over a 6-year period, alongside a bulk density model to account for porosity variations with depth and drainage network reconstruction. This allows us to map mass movements and determine a sediment budget at catchment scale. We find that landslides and crest failures represent very active areas, accounting for at least 15 % of the watershed's sediment budget throughout the period under study, despite affecting only 1 % of the bare surfaces. They contribute to the high erosion rates observed in low-drainage areas, with up to two centimetres of fresh marl lost per year, 3.5 times the average value on the rest of the bare slopes. Despite certain methodological constraints, our approach seems very promising at identifying local erosion hotspots, quantifying their contribution to the sediment budget and assessing sediment transport across geomorphological units. It could also be adapted to time series and more detailed identification of geomorphic processes in order to monitor the dynamics of badland catchments in a changing climate.

CNES is currently carrying out a Phase A study to assess the feasibility of a future hyperspectral imaging sensor (10 m spatial resolution) combined with a panchromatic camera (2.5 m spatial resolution). This mission focuses on both high spatial and spectral resolution requirements, as inherited from previous French studies such as HYPEX, HYPXIM, and BIODIVERSITY. To meet user requirements, cost, and instrument compactness constraints, CNES asked the French hyperspectral Mission Advisory Group (MAG), representing a broad French scientific community, to provide recommendations on spectral sampling, particularly in the Short Wave InfraRed (SWIR) for various applications. This paper presents the tests carried out with the aim of defining the optimal spectral sampling and spectral resolution in the SWIR domain for quantitative estimation of physical variables and classification purposes. The targeted applications are geosciences (mineralogy, soil moisture content), forestry (tree species classification, leaf functional traits), coastal and inland waters (bathymetry, water column, bottom classification in shallow water, coastal habitat classification), urban areas (land cover), industrial plumes (aerosols, methane and carbon dioxide), cryosphere (specific surface area, equivalent black carbon concentration), and atmosphere (water vapor, carbon dioxide and aerosols). All the products simulated in this exercise used the same CNES end-to-end processing chain, with realistic instrument parameters, enabling easy comparison between applications. 648 simulations were carried out with different spectral strategies, radiometric calibration performances and signal-to-noise Ratios (SNR): 24 instrument configurations × 25 datasets (22 images + 3 spectral libraries). The results show that spectral sampling up to 20 nm in the SWIR range is sufficient for most applications. However, 10 nm spectral sampling is recommended for applications based on specific absorption bands such as mineralogy, industrial plumes or atmospheric gases. In addition, a slight performance loss is generally observed when radiometric calibration accuracy decreases, with a few exceptions in bathymetry and in the cryosphere for which the observed performance is severely degraded. Finally, most applications can be achieved with a realistic SNR, with the exception of bathymetry, shallow water classification, as well as carbon dioxide and methane estimation, which require the optimistic SNR level tested. On the basis of these results, CNES is currently evaluating the best compromise for designing the future hyperspectral sensor to meet the objectives of priority applications.

Many plant species have dorsiventral leaves that have significant differences in optical properties from one side to the other. Several studies have revealed that ignoring this asymmetry induces significant errors in plant canopy reflectance, and current leaf models simulating leaf dorsiventrality are limited to the 0.4–2.5 μm wavelength range. This article, partly based on two recently collected datasets in the 2.5–14 μm wavelength range, demonstrates that ignoring leaf dorsiventrality induces significant errors in brightness temperature and effective emissivity at the canopy scale. The PROLIB model, which inherits from the PROSPECT-VISIR and LIBERTY models, is the first radiative transfer model to simulate the reflectance and transmittance of both leaf sides from 0.4 to 5.7 μm. The palisade and spongy mesophylls are represented as plate and sphere layers, respectively, to account for the structural asymmetry of leaf cells. The sieve effect that explains the differences in transmittance between the adaxial and abaxial sides of the leaf is successfully incorporated into PROLIB. Evaluation of the model on several leaf datasets shows that: (1) It reproduces well the adaxial and abaxial optical properties of the leaves, with a root mean square error (RMSE) of 0.0109 for reflectance and transmittance. (2) It can be inversed to retrieve leaf traits, with RMSE values for leaf chlorophyll, carotenoid, anthocyanin, water, and dry matter content of 5.519 μg/cm2, 2.344 μg/cm2, 4.219 μg/cm2, 0.0022 g/cm2, and 0.0017 g/cm2, respectively (corresponding normalized RMSE values of 22.0%, 34.0%, 49.4%, 19.6%, and 24.7%). However, better and more complete leaf datasets are needed for leaf dorsiventrality analysis and model calibration.

This study introduces the development of Spatially Upscaled Soil Spectral Libraries (SUSSL) approach to assess spectral disturbances caused by variations in surface conditions in remote sensing-based soil property prediction. The SUSSL incorporates realistic cropland reflectance scenarios using spectral modelling and aggregation techniques. By convoluting the spectral database to multispectral and hyperspectral satellite sensors, the sensitivity of spectral indices in retrieving undisturbed surface reflectance is evaluated. Preliminary findings indicate that the spectral disturbance effects significantly impact the accuracy of soil organic carbon (SOC) estimations, resulting in a noticeable loss compared to bare soil spectra. However, strict filtering criteria using spectral indices exhibit promise in enhancing SOC modelling performance, particularly for multispectral sensors. Hyperspectral sensors demonstrate higher baseline accuracies even in disturbed soil cases. This research highlights the importance of accounting for surface condition variations for reliable soil property mapping. Future work involves leveraging machine learning techniques on SUSSL data to improve prediction accuracy and spatial coverage of soil properties using Earth Observation data.

Soils are at the crossroads of many existential issues that humanity is currently facing. Soils are a finite resource that is under threat, mainly due to human pressure. There is an urgent need to map and monitor them at field, regional, and global scales in order to improve their management and prevent their degradation. This remains a challenge due to the high and often complex spatial variability inherent to soils. Over the last four decades, major research efforts in the field of pedometrics have led to the development of methods allowing to capture the complex nature of soils. As a result, digital soil mapping (DSM) approaches have been developed for quantifying soils in space and time. DSM and monitoring have become operational thanks to the harmonization of soil databases, advances in spatial modeling and machine learning, and the increasing availability of spatiotemporal covariates, including the exponential increase in freely available remote sensing (RS) data. The latter boosted research in DSM, allowing the mapping of soils at high resolution and assessing the changes through time. We present a review of the main contributions and developments of French (inter)national research, which has a long history in both RS and DSM. Thanks to the French SPOT satellite constellation that started in the early 1980s, the French RS and soil research communities have pioneered DSM using remote sensing. This review describes the data, tools, and methods using RS imagery to support the spatial predictions of a wide range of soil properties and discusses their pros and cons. The review demonstrates that RS data are frequently used in soil mapping (i) by considering them as a substitute for analytical measurements, or (ii) by considering them as covariates related to the controlling factors of soil formation and evolution. It further highlights the great potential of RS imagery to improve DSM, and provides an overview of the main challenges and prospects related to digital soil mapping and future sensors. This opens up broad prospects for the use of RS for DSM and natural resource monitoring.

Leaf mass per area (LMA) is an important leaf trait but challenging to be accurately estimated. This article proposes a simple leaf radiative transfer model called ISPECT. It explains the difference in optical properties observed on the adaxial (upper) and abaxial (lower) sides of leaves, i.e., their dorsiventrality, with a limited number of structural parameters. The performance of ISPECT in estimating LMA is compared to that of five other leaf radiative transfer models (FASPECT, DLM, PROSPECT-D, PROSPECT-5B, and Leaf-SIP). We tested six experimental datasets with 962 leaf samples and two spectral ranges: the solar domain (0.4–2.5 µm) and the shortwave infrared (1.7–2.4 µm). Results show that PROSPECT-D and PROSPECT-5B accurately estimate LMA using the shortwave infrared spectra, while ISPECT and FASPECT perform well in both spectral ranges. Further analysis demonstrates that leaf dorsiventrality is likely to be an influential factor for LMA estimation: thus ISPECT can accurately estimate LMA in the solar and shortwave infrared domains, with NRMSE of 26.0% and 28.8%, respectively. This motivates further studies on LMA mapping from spaceborne imaging spectrometers (e.g., PRISMA, GaoFen-5, EnMAP) by coupling ISPECT with canopy radiative transfer models.

This paper presents MARMIT-2, a radiative transfer model that predicts the spectral reflectance of soils in the solar domain (0.4-2.5 mu m) as a function of their surface moisture. This is an improved version of MARMIT (multilayer radiative transfer model of soil reflectance) that represents a wet soil as a dry soil topped with a thin layer of liquid water. The changes brought in this article concern the mixing of the spectral reflectance of the dry and wet soil areas, the transmission of diffuse light in the water layer, and the inclusion of soil particles in the water layer. Wet soil reflectance is now expressed in terms of dry soil reflectance and three free parameters: the thickness of the water layer, the surface fraction of the wet soil, and a new parameter, the volume fraction of soil particles in the water layer. With more accurate physical modeling, MARMIT-2 simulates soil spectral reflectance with better accuracy than MARMIT. In particular, the fit of the soil reflectance spectra is much better for high water contents, both in the visible range (0.4-0.7 mu m) and in the water absorption bands around 1.45 mu m and 1.95 mu m. The average root mean square error between measured and predicted reflectance obtained on a set of 225 soil samples is about 0.8% with MARMIT-2 versus 1.8% with MARMIT.

Abstract Titan's rich and dense atmosphere, composed mainly of methane and nitrogen, maintains a methane cycle that shapes its surface, like the water cycle on Earth. Methane precipitations erodes Titan's surface and forms complex river networks observed at all latitudes by the Cassini-Huygens mission. However, precipitation rates are poorly constrained and, in the absence of in situ measurements, understanding Titan’s global climate and meteorology is done primarily through climate models. Here, we apply a physics-based theory of river morphogenesis to provide new constraints on methane precipitation rates. In particular, we estimate the river discharges and precipitation rates required to shape two methane rivers located at the equator and south pole, which incise the surface of Titan. Our results show that the use of river morphology is relevant for inferring methane precipitation rates at various latitudes. Our estimates reduce the uncertainties from the climate models. In addition, our results reflect the climate variability between the equator and the south pole. Finally, this work sheds the light to unknown river on Earth: the giant gravel-bed rivers.

In preparation of the micro-bolometer-based MIcro Satellite for Thermal Infrared GRound surface Imaging (MISTIGRI) mission, we study the error budget of the Temperature-Emissivity Separation (TES) method using several spectral configurations that differ in channel numbers, locations, and widths. The error budget quantifies the contribution of 1) the TES underlying assumption about emissivity spectral contrast, 2) the errors on atmospheric corrections, and 3) the instrumental noise. When dealing with atmospheric corrections, we consider errors in atmospheric temperature, water vapor content, and concentrations of CO 2 and O 3 . To that end, we design an end-to-end simulator of MISTIGRI measurements in order to simulate the radiative and biophysical quantities involved in the data processing. We conduct numerous simulations over a wide range of realistic setups that include cavity effect, i.e., radiance trapping within vegetation canopy. In the case of micro-bolometer-based sensing, the current study highlights that atmospheric and instrumental noises have similar impacts on the TES retrievals, with resulting errors twice as large as those due to the TES intrinsic assumption about spectral contrast, where the latter contributes to the TES error budget within the [0.005–0.009] interval for emissivity, and within the [0.3–0.4 K] interval for land surface temperature (LST). Also, we show that retrieval performance of surface temperature is very similar across all considered MISTIGRI spectral configurations, with RMSE variation within 0.2 K. Eventually, our study permits us to select a 4-channels spectral configuration as the most suited for the MISTIGRI instrument, notably because it enables a moderately better capture of the emissivity contrast than a 3-channels one.

An improved version of the MARMIT model is proposed. Wet soil is represented as a dry soil layer surmounted by a layer of a mixture of water and soil particles.

Identification of materials based on spectral reflectance is confounded by variations in reflectance magnitude that are independent of the spectral shape. Local variations such as viewing/illumination angles, multiscale soil surface roughness that causes shadows and redistributes light, and soil moisture content, all drive changes in magnitude that are distinct from the spectral variations, and complicate identification and modeling of targets based on spectral features. Normalization metrics that remove magnitude variations can greatly clarify the nature of spectral differences, simplifying interpretation of reflectance features in spectral imagery. Normalized difference measures are particularly useful because of the simplicity of the computation, the convenient scaling, and the ease with which the normalized difference procedure can be extended to multiple dimensions. The twodimensional normalized difference space described here allows for improved discrimination among bare soils and emergent vegetation when there are multiple soil types in the scene. A 2-D model of soil-specific change in vegetation density is presented. An application of the vector index to mineral identification and mapping is also presented, with an emphasis on band selection.