Estimation of Biomass and CO 2 Fluxes Of Sunflower by Assimilating Hstr Data in a Simple Crop Model

International audienceAlthough croplands are affected by climate change and contribute to it, their carbon budgets remain little known because of the lack of tools allowing estimating it at a fine resolution. In this study a modeling approach is proposed, assimilating high spatial and temporal resolution (HSTR) remote sensing optical products in a semi-physical crop model called SAFY-CO 2 to estimate crop production and the components of the carbon budget. The model correctly reproduces the green area index (GAI) (R 2 = 0.98, rRMSE = 8%), the biomass (R 2 = 0.95, rRMSE = 16%) and the components of the carbon budget (GPP: R 2 = 0.90; RMSE = 1.61gC.m -2 .d -1 ; R eco : R 2 = 0.87; RMSE = 0.81 gC.m -2 .d -1 gC.m -2 .d -1 ; NEE: R 2 = 0.86; RMSE = 0.85 gC.m -2 .d -1 ). This approach requires a few input parameters and no information about management practices which makes it suitable for large scale application and monitoring, reporting and verification (MRV) approaches

Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks

International audienceThe yield forecasting of corn constitutes a key issue in agricultural management, particularly in the context of demographic pressure and climate change. This study presents two methods to estimate yields using artificial neural networks: a diagnostic approach based on all the satellite data acquired throughout the agricultural season, and a real-time approach, where estimates are updated after each image was acquired in the microwave and optical domains (Formosat-2, Spot-4/5, TerraSAR-X, and Radarsat-2) throughout the crop cycle. The results are based on the Multispectral Crop Monitoring experimental campaign conducted by the CESBIO (Centre d'Études de la BIOsphère) laboratory in 2010 over an agricultural region in southwestern France. Among the tested sensor configurations (multi-frequency, multi-polarization or multi-source data), the best yield estimation performance (using the diagnostic approach) is obtained with reflectance acquired in the red wavelength region, with a coefficient of determination of 0.77 and an RMSE of 6.6 q ha-1. In the real-time approach the combination of red reflectance and CHH backscattering coefficients provides the best compromise between the accuracy and earliness of the yield estimate (more than 3 months before the harvest), with an R2 of 0.69 and an RMSE of 7.0 q ha-1 during the development of the central stem. The two best yield estimates are similar in most cases (for more than 80% of the monitored fields), and the differences are related to discrepancies in the crop growth cycle and/or the consequences of pests

Contribution of multispectral (optical and radar) satellite images to the classification of agricultural surfaces

International audienceThe monitoring of different crops (cultivated plots) and types of surface (bare soils, etc.) is a crucial economic and environmental issue for the management of resources and human activity. In this context, the objective of this study is to evaluate the contribution of multispectral satellite imagery (optical and radar) to land use and land cover classification. Object-oriented supervised classifications, based on a Random Forest algorithm, and majority zoning post-processing are used. This study emerges from the experiment on multi-sensor crop monitoring (MCM'10, Baup et al., 2012) conducted in 2010 on a mixed farming area in the southwest of France, near Toulouse. This experiment enabled the regular and quasi-synchronous collection of multi-sensor satellite data and in situ observations, which are used in this study. 211 plots with contrasting characteristics (different slopes, soil types, aspects, farming practices, shapes and surface areas) were monitored to represent the variability of the study area. They can be grouped into four classes of land cover: 39 grassland areas, 100 plots of wheat, 13 plots of barley, 20 plots of rapeseed, and 2 classes of bare soil: 23 plots of small roughness and 16 plots of medium roughness. Satellite radar images in the X-, C- and L-bands (HH polarization) were acquired between 14 and 18 April 2010. Optical images delivered by Formosat-2 and corresponding field data were acquired on 14 April 2010. The results show that combining images acquired in the L-band (Alos) and the optical range (Formosat-2) improves the classification performance (overall accuracy = 0.85, kappa = 0.81) compared to the use of radar or optical data alone. The results obtained for the various types of land cover show performance levels and confusions related to the phenological stage of the species studied, with the geometry of the cover, the roughness states of the surfaces, etc. Performance is also related to the wavelength and penetration depth of the signal providing the images. Thus, the results show that the quality of the classification often increases with increasing wavelength of the images used

Combined use of optical and radar satellite data for the monitoring of irrigation and soil moisture of wheat crops

The objective of this study is to get a better understanding of radar signal over irrigated wheat fields and to assess the potentialities of radar observations for the monitoring of soil moisture. Emphasis is put on the use of high spatial and temporal resolution satellite data (Envisat/ASAR and Formosat-2). Time series of images were collected over the Yaqui irrigated area (Mexico) throughout one agricultural season from December 2007 to May 2008, together with measurements of soil and vegetation characteristics and agricultural practices. The comprehensive analysis of these data indicates that the sensitivity of the radar signal to vegetation is masked by the variability of soil conditions. On-going irrigated areas can be detected all over the wheat growing season. The empirical algorithm developed for the retrieval of topsoil moisture from Envisat/ASAR images takes advantage of the Formosat-2 instrument capabilities to monitor the seasonality of wheat canopies. This monitoring is performed using dense time series of images acquired by Formosat-2 to set up the SAFY vegetation model. Topsoil moisture estimates are not reliable at the timing of plant emergence and during plant senescence. Estimates are accurate from tillering to grain filling stages with an absolute error about 9% (0.09 m(3) m(-3), 35% in relative value). This result is attractive since topsoil moisture is estimated at a high spatial resolution (i.e. over subfields of about 5 ha) for a large range of biomass water content (from 5 and 65 t ha(-1)) independently from the viewing angle of ASAR acquisition (incidence angles IS1 to IS6)

Impact of sowing date on yield and water use efficiency of wheat analyzed through spatial modeling and FORMOSAT-2 images

Regional analysis of water use efficiency (WUE) is a relevant method for diagnosing the performance of irrigation systems in water-limited environments. In this study, we investigated the potential of FORMOSAT-2 images to provide spatial estimates of WUE over irrigated wheat crops cultivated within the semi-arid Yaqui Valley, in the northwest of Mexico. FORMOSAT-2 provided us with a unique dataset of 36 images at a high resolution (8 m) encompassing the wheat growing season from November 2007 to May 2008. Time series of green leaf area index were derived from these satellite images and used to calibrate a simple crop/water balance model. The method was applied over an 8 x 8 km(2) irrigated area on up to 530 wheat fields. It allowed us to accurately reproduce the time courses of Leaf Area Index and dry aboveground biomass, as well as evapotranspiration and soil moisture. In a second step, we analyzed the variations of WUE as the ratio of accumulated dry aboveground biomass to seasonal evapotranspiration. Despite the study area being rather small and homogeneous (soil, climate), we observed a large range in wheat biomass production, from 5 to 15 t center dot ha(-1), which was primarily related to the timing of plant emergence. In contrast, the seasonal evapotranspiration only varied from 350 to 450 mm, with no evident link with sowing practices. A significant gain in crop water productivity was found for the fields sown the earliest (maximal WUE around 3.5 kg center dot m(-3)) compared to those sown the latest (minimal WUE around 1.5 kg center dot m(-3)). These results demonstrated the value of the FORMOSAT-2 images to provide spatial estimates of crop production and water consumption. The detailed information provided by such high space and time resolution imaging systems is highly valuable to identify agricultural practices that could enlarge crop water productivity

CAN INTERCROPPED TREES MITIGATE HEAT AND DROUGHT EFFECTS ON GRAPEVINES?

International audienc

Combined use of optical and radar satellite data for the monitoring of irrigation and soil moisture of wheat crops

The objective of this study is to get a better understanding of radar signal over irrigated wheat fields and to assess the potentialities of radar observations for the monitoring of soil moisture. Emphasis is put on the use of high spatial and temporal resolution satellite data (Envisat/ASAR and Formosat-2). Time series of images were collected over the Yaqui irrigated area (Mexico) throughout one agricultural season from December 2007 to May 2008, together with measurements of soil and vegetation characteristics and agricultural practices. The comprehensive analysis of these data indicates that the sensitivity of the radar signal to vegetation is masked by the variability of soil conditions. On-going irrigated areas can be detected all over the wheat growing season. The empirical algorithm developed for the retrieval of topsoil moisture from Envisat/ASAR images takes advantage of the Formosat-2 instrument capabilities to monitor the seasonality of wheat canopies. This monitoring is performed using dense time series of images acquired by Formosat-2 to set up the SAFY vegetation model. Topsoil moisture estimates are not reliable at the timing of plant emergence and during plant senescence. Estimates are accurate from tillering to grain filling stages with an absolute error about 9% (0.09 m<sup>3</sup> m<sup>−3</sup>, 35% in relative value). This result is attractive since topsoil moisture is estimated at a high spatial resolution (i.e. over subfields of about 5 ha) for a large range of biomass water content (from 5 and 65 t ha<sup>−1</sup> independently from the viewing angle of ASAR acquisition (incidence angles IS1 to IS6)

Quantification of the impact of cover crops on Net Ecosystem Exchange using AgriCarbon-EOv0.1

International audienceDetermination of agricultural fields carbon budget is needed to devise the best sustainable agriculture strategies, and quantify future subsidies in the agricultural sector. One major sustainable practice is cover crops that are planted in between main crops cycles, enabling storage of carbon and reduction of reflected heat. Assimilation of Earth Observation data is crucial for such exercise due to the sheer heterogeneity and complexity of crop modeling in the real environment. In this paper, we present the impact of cover crops on Net Ecosystem Exchange (NEE) and Biomass over a large area in the south-west of France using the AgriCarbon-EO. This tool is an end-to-end solution that enables a Bayesian assimilation of multi-temporal and entire tiles (100x100 km) Sentinel-2 reflectance data into a radiative model and a crop model. Our results show an increase of NEE of about due to cover crops over maize fields