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International audience; The use of remote sensing data methods is affordable for the mapping of soil properties of the plowed layer over croplands. Carried out in the framework of the ongoing STEROPES project of the European Joint H2020 Program SOIL, this work is focused on the feasibility of Sentinel-2 based approaches for the high resolution mapping of topsoil clay and organic carbon (SOC) contents at the within-farm or within-field scales, for cropland sites of contrasted climates and soil types across the Northern hemisphere. Four pixelwise temporal mosaicking methods, using a two years-Sentinel-2 time series and several spectral indices (NDVI, NBR2, BSI, S2WI), were developed and compared for i) pure bare soil condition (maxBSI), ii) driest soil condition (minS2WI), iii) average bare soil condition (Median) and iv) dry soil conditions excluding extreme reflectance values (R90). Three spectral modeling approaches, using the Sentinel-2 bands of the output temporal mosaics as covariates, were tested and compared: (i) Quantile Regression Forest (QRF) algorithm; (ii) QRF adding longitude and latitude as covariates (QRFxy); (iii) a hybrid approach, Linear Mixed Effect Model (LMEM), that includes spatial autocorrelation of the soil properties. We tested pairs of mosaic and spectral approaches on ten sites in Türkiye, Italy, Lithuania, and USA where soil samples were collected and SOC and clay content were measured in the lab. The average RPIQ of the best performances among the test sites was 2.50 both for SOC (RMSE = 0.15%) and clay (RMSE = 3.3%). Both accuracy level and uncertainty were mainly influenced by site characteristics of cloud frequency, soil types and management. Generally, the models including a spatial component (QRFxy and LMEM) were the best performing, while the best spatial mosaicking approaches mostly were Median and R90. The most frequent optimal combination of mosaicking and model type was Median or R90 and QRFxy for SOC, and R90 and LMEM for clay estimation.

Imaging spectroscopy has demonstrated its interest in characterizing the biochemical, biophysical and structural properties of vegetation, natural and agricultural soils, as well as artificial surfaces. Following the Hyperion mission, new space missions have emerged (PRISMA, EnMap), or are under study (CHIME, SBG). However, one of their main limitations lies in their spatial resolution that induces a large number of mixed pixels reducing their potential for discrimination for very heterogeneous areas. The BIODIVERSITY mission aims to complement these space missions with better GSD acquisitions (typically 8-10 m) with a 5-day revisit on targeted reference sites with identified and well-located characteristics. It will thus make it possible, in particular, to answer two scientific issues that will design the instrument. The first issue concerns the spatial and temporal distribution of vegetation traits in species assemblages; these traits are associated with the resilience of terrestrial ecosystems, anthropogenic influences, and the biodiversity of ecosystems in terms of species composition and assemblages. The second issue relates to improving our knowledge of coastal areas and inland waters in terms of biodiversity, water quality and bathymetry, in order to assess the impact of human activity on their ecosytems. The scientific challenges as well as the user requirements for such a mission are presented for each application., L’imagerie hyperspectrale a démontré son intérêt pour la caractérisation des propriétés biochimiques, biophysiques et structurelles de la végétation, des sols naturels et agricoles ainsi que des surfaces artificialisées. A la suite de la mission Hyperion, de nouvelles missions spatiales ont vu le jour (PRISMA, EnMAP), ou sont en phase d’étude (SBG, CHIME). Ces spectro-imageurs ont une résolution spatiale au sol de l’ordre de 30 m, un large champ de vue et peuvent couvrir de vastes zones du globe terrestre afin de caractériser les écosystèmes terrestres et océaniques avec un temps de revisite variant de 4 à 16 jours. Néanmoins, leur résolution spatiale est limitée ce qui induit un nombre important de pixels mixtes réduisant leur potentiel de discrimination pour des zones hétérogènes. La mission BIODIVERSITY a pour objectif de compléter ces missions par des acquisitions de meilleure résolution spatiale (typiquement 8-10 m) avec une revisite de l’ordre de 5 jours sur des sites de référence ciblés possédant des caractéristiques identifiées et bien localisées. Elle permettra ainsi de répondre à deux problématiques scientifiques qui vont dimensionner l’instrument. La première problématique porte sur la distribution spatiale et temporelle des traits de la végétation dans les assemblages d’espèces ; ces traits sont associés à la résilience des écosystèmes terrestres, aux influences anthropiques et à la biodiversité des écosystèmes en termes de composition et d’assemblages en espèces. La seconde problématique porte sur l’amélioration de nos connaissances des zones côtières et des eaux continentales en termes de biodiversité, de qualité des eaux et de bathymétrie, pour ensuite évaluer l’impact de l’activité anthropique sur leurs écosystèmes. Enfin, ces deux applications qui déterminent les spécifications de l’instrument seront complétées par l’étude, à fine résolution spatiale, de l’impact des pratiques de gestion des sols dans un processus environnemental tels que le stockage du carbone dans les sols, l’infiltration et la rétention d’eau en surface ou l’érosion des sols. Elles ouvrent également de nouvelles voies pour évaluer comment les matériaux urbains influencent notre environnement proche et pour caractériser les pollutions urbaine et industrielle. Les défis scientifiques ainsi que les exigences-utilisateurs pour une telle mission sont présentés pour chaque application.

International audience; pas de résumé

International audience; In agronomy, soil organic carbon (SOC) content is important for the development and growth of crops. From an environmental monitoring viewpoint, SOC sequestration is essential for mitigating the emission of greenhouse gases into the atmosphere. SOC dynamics in cropland soils should be further studied through various approaches including remote sensing. In order to predict SOC content over croplands in southwestern France (area of 22,177 km²), this study addresses (i) the influence of the dates on which Sentinel-2 (S2) images were acquired in the springs of 2017–2018 as well as the influence of the soil sampling period of a set of samples collected between 2005 and 2018, (ii) the use of soil moisture products (SMPs) derived from Sentinel-1/2 satellites to analyze the influence of surface soil moisture on model performance when included as a covariate, and (iii) whether the spatial distribution of SOC as mapped using S2 is related to terrain-derived attributes. The influences of S2 image dates and soil sampling periods were analyzed for bare topsoil. The dates of the S2 images with the best performance (RPD ≥ 1.7) were 6 April and 26 May 2017, using soil samples collected between 2016 and 2018. The soil sampling dates were also analyzed using SMP values. Soil moisture values were extracted for each sample and integrated into partial least squares regression (PLSR) models. The use of soil moisture as a covariate had no effect on the prediction performance of the models; however, SMP values were used to select the driest dates, effectively mapping topsoil organic carbon. S2 was able to predict high SOC contents in the specific soil types located on the old terraces (mesas) shaped by rivers flowing from the southwestern Pyrénées.

International audience; Increasing attention has been placed on the agroecological impact of applying exogenous organic matter (EOM) amendments, such as green waste compost (GWC) and livestock manure, to agricultural landscapes. However, monitoring the frequency and locality of this practice poses a major challenge, as these events are typically unreported. The purpose of this study is to evaluate the utility of Sentinel-2 imagery for the detection of EOM amendments. Specifically, we investigated the spectral shift resulting from GWC and manure application at two spatial scales, satellite and proximal. At the satellite scale, multispectral Sentinel-2 image pairs were analyzed before and after EOM application to six cultivated fields in the Versailles Plain, France. At the proximal scale, multi-temporal spectral field measurements were taken of experimental plots consisting of 14 total treatments: EOM variety, amendment quantity (15, 30 and 60 t.ha−1) and tillage. The Sentinel-2 images showed significant spectral differences before and after EOM application. Exogenous Organic Matter Indices (EOMI) were developed and analyzed for separative performance. The best performing index was EOMI2, using the B4 and B12 Sentinel-2 spectral bands. At the proximal scale, simulated Sentinel-2 reflectance spectra, which were created using field measurements, successfully monitored all EOM treatments for three days, except for the buried green waste compost at a rate of 15 t.ha−1.

International audience

In order to reduce chemical fertilizer in agricultural use and to value the urban organic matter in substitution, a precise knowledge of soil properties is mandatory. Carbon is a good indicator of soil fertility and having at our disposal a precise mappingof its content is useful. In this study we aim at spatially estimate the soil carbon content (SOC) in the Versailles plain and the Alluets plateau, a 220 km2 agricultural area. The novel Bayesian inference approach called Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation (INLA-SPDE) allows us to ensure consistency between the various available sources of information (soil samples and satellite image) and to produce in a short time a posteriori estimations of the parameters and the SOC field, considered as a latent field. Several models were evaluated and compared using the elevation covariate stemming from a Digital Elevation Model (DEM), including or not the data from the satellite image. Adding the image slightly improves the prediction quality in terms of RMSE (Root Mean Square Error RMSE) since the median goes from 3.17 g.kg−1 to 3.15 g.kg −1. Overall the carbon prediction map from the joint model represents more realistically the spatial structure of the carbon field.; La réduction des intrants chimiques d’usage agricole et la valorisatition des mati`eres organiques d’origine urbaine en substitution nécessitent une connaissance fine des propriétés des sols agricoles. Le carbone étant un bon indicateur de la fertilité des sols, il s’avère nécessaire de disposer d’une cartographie précise des teneurs. Dans cette étude, on cherche à produire une cartographie des teneurs en carbone dans la plaine de Versailles et le plateau des Alluets, région agricole de 220 km2. La nouvelle approche d’inférence Bayesienne Integrated Nested Laplace Approximation et Stochastic Partial Differential Equation (INLA-SPDE) nous permet de mettre en cohérence les différentes sources d’information disponibles (prélèvements au sol et image satellite) et d’obtenir en peu de temps des estimations a posteriori des paramètres et du champ de carbone considéré comme un champ latent. Nous avons évalué et comparé via une procédure bootstrap les performances plusieurs modèles utilisant comme covariable l’altitude issue d’un modèle numérique de terrain incluant ou non les données des images satellites.L’intégration de l’image améliore légèrement la qualité de prédiction en terme de RMSE (Root Mean Square Error) dont la médiane passe de 3.17 g.kg−1 `a 3.15 g.kg−1. Mais surtout la carte de prédiction du carbone issue de la modélisation jointe présente de façon beaucoup plus réaliste la structure spatiale du carbone.

For adequate crop and soil management, rapid and accurate techniques for monitoring soil properties are particularly important when a farmer starts up his activities and needs a diagnosis of his cultivated fields. This study aimed to evaluate the potential of fluorescence measured directly on 146 whole soil solid samples, for predicting key soil properties at the scale of a 6 ha Mediterranean wine estate with contrasting soils. UV-Vis fluorescence measurements were carried out in conjunction with reflectance measurements in the Vis-NIR-SWIR range. Combining PLSR predictions from Vis-NIR-SWIR reflectance spectra and from a set of fluorescence signals enabled us to improve the power of prediction of a number of key agronomic soil properties including SOC, Ntot, CaCO3, iron, fine particle-sizes (clay, fine silt, fine sand), CEC, pH and exchangeable Ca2+ with cross-validation RPD ≥ 2 and R² ≥ 0.75, while exchangeable K+, Na+, Mg2+, coarse silt and coarse sand contents were fairly predicted (1.42 ≤ RPD < 2 and 0.54 ≤ R² < 0.75). Predictions of SOC, Ntot, CaCO3, iron contents, and pH were still good (RPD ≥ 1.8, R² ≥ 0.68) when using a single fluorescence signal or index such as SFR_R or FERARI, highlighting the unexpected importance of red excitations and indices derived from plant studies. The predictive ability of single fluorescence indices or original signals was very significant for topsoil: this is very important for a farmer who wishes to update information on soil nutrient for the purpose of fertility diagnosis and particularly nitrogen fertilization. These results open encouraging perspectives for using miniaturized fluorescence devices enabling red excitation coupled with red or far-red fluorescence emissions directly in the field.

The potential of UAS imagery for soil mapping at the agricultural plot scale. EGU 2017, European Geophysical Union General Assembly 2017

Potential of SENTINEL-2 images for predicting common topsoil properties over Temperate and Mediterranean agroecosystems. EGU 2017, European Geophysical Union General Assembly 2017

Retracing recurring vine mortality patterns over a long duration: case study of a Mediterranean viticultural estate. EGU 2017, European Geophysical Union General Assembly 2017

Soil and culture viewed through the prism of viticultural terroir. EGU 2017, European Geophysical Union General Assembly 2017

International audience

De la mise en place d’indicateurs pour la planification urbaine à la mesure des stocks de carbone, en passant par les instruments juridiques et économiques pour la protection des sols, cet ouvrage, issu du programme Gessol, synthétise les dernières connaissances biotechniques et sociétales sur le sujet. Il souligne l’importance d’une gestion durable des sols dans les enjeux globaux et identifie les leviers d’actions possibles.

oral presentation, abstract; This study was carried out in the framework of the Prostock-Gessol3 and the BASC-SOCSENSIT projects, dedicated to the spatial monitoring of the effects of exogenous organic matter land application on soil organic carbon storage. It aims at identifying the potential of airborne hyperspectral AISA-Eagle data for predicting the topsoil organic carbon (SOC) content of bare cultivated soils over a large peri-urban area (221 km2) with both contrasted soils and SOC contents, located in the western region of Paris, France. Soils comprise hortic or glossic luvisols, calcaric, rendzic cambisols and colluvic cambisols. Airborne AISA-Eagle data (400-1000 nm, 126 bands) with 1 m-resolution were acquired on 17 April 2013 over 13 tracks which were georeferenced. Tracks were atmospherically corrected using a set of 22 synchronous field spectra of both bare soils, black and white targets and impervious surfaces. Atmospherically corrected track tiles were mosaicked at a 2 m-resolution resulting in a 66 Gb image. A SPOT4 satellite image was acquired the same day in the framework of the SPOT4-Take Five program of the French Space Agency (CNES) which provided it with atmospheric correction. The land use identification system layer (RPG) of 2012 was used to mask non-agricultural areas, then NDVI calculation and thresholding enabled to map agricultural fields with bare soil. All 18 sampled sites known to be bare at this very date were correctly included in this map. A total of 85 sites sampled in 2013 or in the 3 previous years were identified as bare by means of this map. Predictions were made from the mosaic spectra which were related to topsoil SOC contents by means of partial least squares regression (PLSR). Regression robustness was evaluated through a series of 1000 bootstrap data sets of calibration-validation samples. The use of the total sample including 27 sites under cloud shadows led to non-significant results. Considering 43 sites outside cloud shadows only, median validation root-mean-square errors (RMSE) were abour 4-4.5 g. kg-1. An additional set of 15 samples with bare soils led to similar RMSE values. Such results are only slightly better than those resulting from an earlier study with multispectral satellite images (Vaudour et al., 2013). The influence of soil surface condition and particularly soil roughness is discussed

The design of sustainable vineyard management is needed at varied scales and particularly at farm-scale. More and more winegrowers wish to adopt environmental-friendly practices while better controlling harvest composition. This leads to question complex issues with regard to sustainability of winegrowing agroecosystem and the adoption of new soil and vineyard management practices that are likely to favour a long-term preservation of quality production together with soil ecosystem functions. This study aims at elaborating a multivariate approach framework for vineyard soil fertility assessment over a 6 ha-farm planted with rainfed black Grenache and Syrah varieties in the Southern Rhone Valley. In a previous study carried out at the regional scale, soil landscape and potential terroir units had been characterized. A new field survey comprising _20 soil pits, physico-chemical analyses for all soil profile horizons, and a series of additional soil surface samples analyzed for several parameters including soil organic carbon, calcium carbonate, copper and the major mineral nutrients, is here carried out. Along with soil parameters and soil surface condition, vine biological parameters including vigour, presence of diseases, stock-unearthing are collected. Very high resolution multispectral satellite data and resistivity EMI data are acquired and processed in order to characterize spatial variations in both physiological responses, soil surface conditions, soil depth and/or the presence of coarse elements. Multi-temporal historical aerial photographs are used in order to complement farmer’s surveys regarding past management practices. The farm is characterized by a diversity of soils including Red Mediterranean soils (chromic luvisols), colluvic calcisols, arenosols, fluvisols, and regosols, which develop from top to slope then bottom of a Neogene molassic and conglomeratic plateau. Soil management past practices are marked by the absence of chemical/organic manuring in the last decades, resulting in erosional features and weakened vines. Relationships between vine spatial variability, soil spatial variability and the impact of past practices are analyzed and result in formalizing decision rules. Several restoration scenarios are then proposed, that focus either on chemical fertilizer and/or organic amendment and/or interrow cover and/or agroforestry practices.

Toujours perdante dès que survient un conflit d’usage, la gestion durable des sols est-elle une gageure ? Cette question est souvent renforcée par une sensation de menace majeure : que faire pour que l’urbanisation n’envahisse pas tout ? Plus largement, comment mieux prendre en compte les sols dans les situations de conflits d’usage d’un territoire ? Comment mieux les placer dans la vision socio- économique d’ensemble ? Pour quelle décision ? Un usage définitif ou bien faut-il le conditionner à un suivi vérifiant la stabilité des qualités initiales ?

For adequate crop and soil management, rapid and accurate techniques for monitoring soil properties are particularly important when a farmer starts up his activities and needs a diagnosis of his cultivated fields. This study aimed to evaluate the potential of fluorescence measured directly on 146 whole soil solid samples, for predicting key soil properties at the scale of a 6 ha Mediterranean wine estate with contrasting soils. UV-Vis fluorescence measurements were carried out in conjunction with reflectance measurements in the Vis-NIR-SWIR range. Combining PLSR predictions from Vis-NIR-SWIR reflectance spectra and from a set of fluorescence signals enabled us to improve the power of prediction of a number of key agronomic soil properties including SOC, Ntot, CaCO3, iron, fine particle-sizes (clay, fine silt, fine sand), CEC, pH and exchangeable Ca2+ with cross-validation RPD ≥ 2 and R² ≥ 0.75, while exchangeable K+, Na+, Mg2+, coarse silt and coarse sand contents were fairly predicted (1.42 ≤ RPD < 2 and 0.54 ≤ R² < 0.75). Predictions of SOC, Ntot, CaCO3, iron contents, and pH were still good (RPD ≥ 1.8, R² ≥ 0.68) when using a single fluorescence signal or index such as SFR_R or FERARI, highlighting the unexpected importance of red excitations and indices derived from plant studies. The predictive ability of single fluorescence indices or original signals was very significant for topsoil: this is very important for a farmer who wishes to update information on soil nutrient for the purpose of fertility diagnosis and particularly nitrogen fertilization. These results open encouraging perspectives for using miniaturized fluorescence devices enabling red excitation coupled with red or far-red fluorescence emissions directly in the field.

For any wine estate, there is a need to demarcate homogeneous within-vineyard zones (‘terroirs’) so as to manage grape production, which depends on vine biological condition. Until now, the studies performing digital zoning of terroirs have relied on recent spatial data and scant attention has been paid to ancient geoinformation likely to retrace past biological condition of vines and especially occurrence of vine mortality. Is vine mortality characterized by recurrent and specific patterns and if so, are these patterns related to terroir units and/or past landuse? This study aimed at performing a historical and spatial tracing of vine mortality patterns using a long time-series of aerial survey images (1947–2010), in combination with recent data: soil apparent electrical conductivity EM38 measurements, very high resolution Pleiades satellite images, and a detailed field survey. Within a 6 ha-estate in the Southern Rhone Valley, landuse and planting history were retraced and the map of missing vines frequency was constructed from the whole time series including a 2015-Pleiades panchromatic band. Within-field terroir units were obtained from a support vector machine classifier computed on the spectral bands and NDVI of Pleiades images, EM38 data and morphometric data. Repeated spatial patterns of missing vines were highlighted throughout several plantings, uprootings, and vine replacements, and appeared to match some within-field terroir units, being explained by their specific soil characteristics, vine/soil management choices and the past landuse of the 1940s. Missing vines frequency was spatially correlated with topsoil CaCO3 content, and negatively correlated with topsoil iron, clay, total N, organic C contents and NDVI. A retrospective spatio-temporal assessment of terroir therefore brings a renewed focus on some key parameters for maintaining a sustainable grape production.