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Saint-Pierre-du-Chemin, France

Morel J.,UR SCA | Lebourgeois V.,UMR TETIS | Martine J.-F.,UR SCA | Todoroff P.,UR SCA | And 2 more authors.
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2013

Coupling remotely sensed data with crop model is known to improve the estimation of crop variables by the model. The recalibration coupling approach tends to reduce the differences between observation and simulation by optimizing the value of one of the model's parameter. In this study, we used this approach with a sugarcane model and Crop Water Stress Index calculated using remotely sensed thermal infrared data in order to optimize the value of the root depth parameter thanks to measured and simulated AET/MET ratio. The effect of the root depth recalibration has also been assessed on the yield estimation, which showed good trends with a significant enhancement of the estimated yield. © 2013 IEEE. Source

Morel J.,UR SCA | Martine J.F.,UR SCA | Begue A.,CIRAD - Agricultural Research for Development | Todoroff P.,UR SCA | Petit M.,IRD Montpellier
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2012

Coupling remote sensing data with crop model has been shown to improve accuracy of the model yield estimation. MOSICAS model simulates sugarcane yield in controlled conditions plot, based on different variables, including the interception efficiency index (εi). In this paper, we assessed the use of remote sensing data to sugarcane growth modeling by 1) comparing the sugarcane yield simulated with and without satellite data integration in the model, and 2) comparing two approaches of satellite data forcing. The forcing variable is the interception efficiency index (εi). The yield simulations are evaluated on a data set of cane biomass measured on four on-farm fields, over three years, in Reunion Island. Satellite data are derived from a SPOT 10 m resolution time series acquired during the same period. Three types of simulations have been made: a raw simulation (where the only input data are daily precipitations, daily temperatures and daily global radiations), a partial forcing coupling method (where MOSICAS computed values of εi have been replaced by NDVI computed εi for each available satellite image), and complete forcing method (where all MOSICAS simulated εi have been replaced by NDVI computed εi). Results showed significant improvements of the yield's estimation with complete forcing approach (with an estimation of the yield 8.3 % superior to the observed yield), but minimal differences between the yields computed with raw simulations and those computed with partial forcing approach (with a mean overestimation of respectively 34.7 and 35.4 %). Several enhancements can be made, especially by optimizing MOSICAS parameters, or by using other remote sensing index, like NDWI. © 2012 SPIE. Source

Valade A.,CEA Saclay Nuclear Research Center | Vuichard N.,CEA Saclay Nuclear Research Center | Ciais P.,CEA Saclay Nuclear Research Center | Ruget F.,French National Institute for Agricultural Research | And 4 more authors.
GCB Bioenergy | Year: 2014

Agro-Land Surface Models (agro-LSM) combine detailed crop models and large-scale vegetation models (DGVMs) to model the spatial and temporal distribution of energy, water, and carbon fluxes within the soil-vegetation-atmosphere continuum worldwide. In this study, we identify and optimize parameters controlling leaf area index (LAI) in the agro-LSM ORCHIDEE-STICS developed for sugarcane. Using the Morris method to identify the key parameters impacting LAI, at eight different sugarcane field trial sites, in Australia and La Reunion island, we determined that the three most important parameters for simulating LAI are (i) the maximum predefined rate of LAI increase during the early crop development phase, a parameter that defines a plant density threshold below which individual plants do not compete for growing their LAI, and a parameter defining a threshold for nitrogen stress on LAI. A multisite calibration of these three parameters is performed using three different scoring functions. The impact of the choice of a particular scoring function on the optimized parameter values is investigated by testing scoring functions defined from the model-data RMSE, the figure of merit and a Bayesian quadratic model-data misfit function. The robustness of the calibration is evaluated for each of the three scoring functions with a systematic cross-validation method to find the most satisfactory one. Our results show that the figure of merit scoring function is the most robust metric for establishing the best parameter values controlling the LAI. The multisite average figure of merit scoring function is improved from 67% of agreement to 79%. The residual error in LAI simulation after the calibration is discussed. © 2013 Blackwell Publishing Ltd. Source

Valade A.,CEA Saclay Nuclear Research Center | Ciais P.,CEA Saclay Nuclear Research Center | Vuichard N.,CEA Saclay Nuclear Research Center | Viovy N.,CEA Saclay Nuclear Research Center | And 4 more authors.
Geoscientific Model Development | Year: 2014

Agro-land surface models (agro-LSM) have been developed from the integration of specific crop processes into large-scale generic land surface models that allow calculating the spatial distribution and variability of energy, water and carbon fluxes within the soil-vegetation-atmosphere continuum. When developing agro-LSM models, particular attention must be given to the effects of crop phenology and management on the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty of agro-LSM models is related to their usually large number of parameters. In this study, we quantify the parameter-values uncertainty in the simulation of sugarcane biomass production with the agro-LSM ORCHIDEE-STICS, using a multi-regional approach with data from sites in Australia, La Réunion and Brazil. In ORCHIDEE-STICS, two models are chained: STICS, an agronomy model that calculates phenology and management, and ORCHIDEE, a land surface model that calculates biomass and other ecosystem variables forced by STICS phenology. First, the parameters that dominate the uncertainty of simulated biomass at harvest date are determined through a screening of 67 different parameters of both STICS and ORCHIDEE on a multi-site basis. Secondly, the uncertainty of harvested biomass attributable to those most sensitive parameters is quantified and specifically attributed to either STICS (phenology, management) or to ORCHIDEE (other ecosystem variables including biomass) through distinct Monte Carlo runs. The uncertainty on parameter values is constrained using observations by calibrating the model independently at seven sites. In a third step, a sensitivity analysis is carried out by varying the most sensitive parameters to investigate their effects at continental scale. A Monte Carlo sampling method associated with the calculation of partial ranked correlation coefficients is used to quantify the sensitivity of harvested biomass to input parameters on a continental scale across the large regions of intensive sugarcane cultivation in Australia and Brazil. The ten parameters driving most of the uncertainty in the ORCHIDEE-STICS modeled biomass at the 7 sites are identified by the screening procedure. We found that the 10 most sensitive parameters control phenology (maximum rate of increase of LAI) and root uptake of water and nitrogen (root profile and root growth rate, nitrogen stress threshold) in STICS, and photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients) in ORCHIDEE. We find that the optimal carboxylation rate and photosynthesis temperature parameters contribute most to the uncertainty in harvested biomass simulations at site scale. The spatial variation of the ranked correlation between input parameters and modeled biomass at harvest is well explained by rain and temperature drivers, suggesting different climate-mediated sensitivities of modeled sugarcane yield to the model parameters, for Australia and Brazil. This study reveals the spatial and temporal patterns of uncertainty variability for a highly parameterized agro-LSM and calls for more systematic uncertainty analyses of such models. © Author(s) 2014. CC Attribution 3.0 License. Source

Rathbauer J.,BLT Wieselburg | Sonnleitner A.,Bioenergy 2020+ GmbH | Pirot R.,UR SCA | Zeller R.,BLT Wieselburg | Bacovsky D.,Bioenergy 2020+ GmbH
Biomass and Bioenergy | Year: 2012

This publication deals with the characterisation of Jatropha curcas seeds and the oil obtained hereof. The analyzed seeds have been harvested from hedges and plantations in the regions of Teriya Bugu and Bla in Mali in the years 2009 and 2010. The oil is obtained through solvent extraction. Parameters analyzed are those which are relevant for processing of the oil into fatty acid methyl ester (FAME, biodiesel), and include acid value, fatty acid profile and contents of S, P, K, Na, Ca and Mg. All oil samples are suitable for processing into biodiesel, but some of them require pre-treatment because of high contents of free fatty acids and phosphorous. The margin of deviation of acid value and element contents throughout the oil samples depends on the way of cultivation, harvest and storage of the Jatropha curcas plants and seeds. Despite high acid values, all oil samples show high oxidation stability. © 2012 Elsevier Ltd. Source

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