Service innovant unique au monde, Farmstar valorise les images satellites et des modèles agronomiques pour aider les agriculteurs dans leurs décisions d’apport d’azote en cours de campagne. Son ...succès, avec plus 16 000 agriculteurs abonnés exploitant plus de 700 000 hectares, s’explique par sa grande précision sur un élément minéral qui, d’une part, est, en France, avant l’eau, le principal facteur limitant la production agricole tant en quantité qu’en qualité, et qui, d’autre part, peut être responsable d’une baisse de la qualité des eaux (augmentation de la teneur en nitrates des nappes phréatiques). Les bénéfices générés concernent la rentabilité économique (gains de rendement, réduction des doses d’intrants, récolte de grains de meilleure qualité) et les enjeux environnementaux et sociétaux (en évitant tout type d’excès d’azote, en traçant et en justifiant les interventions des agriculteurs). Par ailleurs, grâce aux cartes de zonage livrées aux agriculteurs, chacun d’eux peut faire varier les doses nécessaires en fonction des besoins des plantes en différents points d’une même parcelle.Cette réussite enviée par beaucoup résulte de l’agrégation de compétences complémentaires entre les agronomes d’ARVALIS et les experts en télédétection d’Airbus.
An assessment of the organic carbon stock present in living or dead vegetation and in the soil on the 450 km² of the future Nam Theun 2 hydroelectric reservoir in Lao People's Democratic Republic was ...made. Nine land cover types were defined on the studied area: dense, medium, light, degraded, and riparian forests; agricultural soil; swamps; water; and others (roads, construction sites, and so on). Their geographical distribution was assessed by remote sensing using two 2008 SPOT 5 images. The area is mainly covered by dense and light forests (59%), while agricultural soil and swamps account for 11% and 2%, respectively. For each of these cover types, except water, organic carbon density was measured in the five pools defined by the Intergovernmental Panel on Climate Change: aboveground biomass, litter, deadwood, belowground biomass, and soil organic carbon. The area-weighted mean carbon densities for these pools were estimated at 45.4, 2.0, 2.2, 3.4, and 62.2 tC/ha, respectively, i.e., a total of about 115 ± 15 tC/ha for a soil thickness of 30 cm, corresponding to a total flooded organic carbon stock of 5.1 ± 0.7 MtC. This value is much lower than the carbon density for some South American reservoirs for example where total organic carbon stocks range from 251 to 326 tC/ha. It can be mainly explained by (1) the higher biomass density of South American tropical primary rainforest than of forests in this study and (2) the high proportion of areas with low carbon density, such as agricultural or slash-and-burn zones, in the studied area.
An index-based insurance solution was developed to estimate and monitor near real-time forage production using the indicator Forage Production Index (FPI) as a surrogate of the grassland production. ...The FPI corresponds to the integral of the fraction of green vegetation cover derived from moderate spatial resolution time series images and was calculated at the 6 km × 6 km scale. An upscaled approach based on direct validation was used that compared FPI with field-collected biomass data and high spatial resolution (HR) time series images. The experimental site was located in the Lot and Aveyron departments of southwestern France. Data collected included biomass ground measurements from grassland plots at 28 farms for the years 2012, 2013 and 2014 and HR images covering the Lot department in 2013 (n = 26) and 2014 (n = 22). Direct comparison with ground-measured yield led to good accuracy (R2 = 0.71 and RMSE = 14.5%). With indirect comparison, the relationship was still strong (R2 ranging from 0.78 to 0.93) and informative. These results highlight the effect of disaggregation, the grassland sampling rate, and irregularity of image acquisition in the HR time series. In advance of Sentinel-2, this study provides valuable information on the strengths and weaknesses of a potential index-based insurance product from HR time series images.
Ten years after the introduction of zone-based management to take into account within-field phenomena in agronomic practices, several methodological developments have progressed to the operational ...level. However, this raises a new scientific question: how can the relevance of this type of management be evaluated? This paper adapts the concept of a technical opportunity index to zone-specific management. Based on the characteristics of machinery, zoning opportunity is introduced through a new index (
ZOI
) adapted specifically to zone-based management. This index takes into account the operational conditions in which zoning is applied, together with its associated risks. The results obtained on simulated and real field data highlight the relevance of this index.
An index-based insurance is being developed to estimate and monitor forage production in France in near real-time based on a forage production index (FPI) derived from the fraction of green ...vegetation cover (fCover) integral, obtained from medium spatial resolution time series. This article presents the first step of the scientific validation implemented. The grassland parcels, the field protocol established to collect biomass production data, and the method used to get the fCover are described. Local ground measurements of biomass production are compared with FPI values obtained from high-resolution space-based images. Discrepancies between the two variables are quantified by the coefficient of determination, the mean square error and the normalised root mean square error. First, fCover derived from the four sensors are coherent demonstrating the ability of the algorithm used to provide a consistent way of calculating fCover. Second, for the whole data set, the scatter plot between FPI and biomass shows an acceptable correlation (R ² = 0.75) improved when only taking into account data recorded up until the production maximum (R ² = 0.81). Third, the analysis carried out on the scale of the parcels, grass species, period of mowing or climatic conditions reveals variability on the regression coefficients indicating that other explanatory variables should be integrated to better compute the FPI.
Site-specific management (SSM) is a common way to manage within-field variability. This concept divides fields into site-specific management zones (SSMZ) according to one or several soil or crop ...characteristics. This paper proposes an original methodology for SSMZ delineation which is able to manage different kinds of crop and/or soil images using a powerful segmentation tool: the watershed algorithm. This image analysis algorithm was adapted to the specific constraints of precision agriculture. The algorithm was tested on high-resolution bio-physical images of a set of fields in France.
In an attempt to extract information relevant for agriculture in remotely sensed wheat crops, MIVIS hyperspectral images are analyzed in the visible and near-infrared domains. Through the selection, ...by means of a principal component analysis (PCA), of two endmembers of wheat, related respectively to well-developed and stressed plants, a water deficiency is detected among the spectral population of wheat. The image is then modeled by a spectral mixture analysis (unmixing) of these two wheat endmembers, soil, and shade. Resulting fraction images are interpreted in terms of crop vitality (level of green biomass) in relation to stress presence and compared to field knowledge. In addition, these images allow mapping the leaf area index (LAI) over the whole scene, with an empirical relationship based on 12 ground measurements of this variable. This work shows the interest of the approach combining PCA and unmixing for stress detection and mapping of agronomic variables, with a good accuracy compared to spectral ratio analysis. It provides relevant support for crop monitoring and precision agriculture, by means of numerical cartographic products obtained by hyper- (super-) spectral remote sensing. It demonstrates the need for improved methodologies derived from hyperspectral data analysis, and reveals that, through such methods, one can, however, retrieve a significant amount of information with limited number of spectral channels (10–20), highlighting the potential of superspectral observations.
This paper describes the methodology to produce seasonal meter-scale satellite image mosaics enabling detailed mapping projects, land cover and land use studies or ecosystem monitoring in remote ...locations where in situ measurements are difficult to achieve. In areas with strong seasonal changes in vegetation coverage or extent of water surfaces, it is useful to produce seasonal mosaics to depict these different conditions. Such a mosaic can be realized at a global scale thanks to access to an archive of 400,000 SPOT 6/7 images from 2013 to 2020, acquired at any date of the year, and the design of a specific methodology.
Experts have identified the leaf area index (LAI) as an important parameter for assessment of a range of eco-physiological processes within a vegetation canopy. Findings are reported from tests ...investigating the estimation of LAI spatial and temporal variation based on multi-temporal remote sensing observations processed using a simple semi-mechanistic canopy structure dynamic model (CSDM) integrated with a radiative transfer model. The CSDM is capable of defining the temporal evolution of the LAI as a function of the accumulated daily air temperature as determined from conventional ground meteorological stations. Two datasets were used to evaluate the retrieval performance of the proposed analytical methodology. Benefits included smoothing of residual errors associated with individual observations.
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