• Energy Research

Abstract. The heterogeneity of Agroecosystems, in terms of hydric conditions, crop types and states, and meteorological forcing, is difficult to characterize precisely at the field scale over an agricultural landscape. This study aims to perform a sensitivity study with respect to the uncertain model inputs of two classical approaches used to map the evapotranspiration of agroecosystems: (1) a surface energy balance (SEB) model, the Two-Source Energy Balance (TSEB) model, forced with thermal infrared (TIR) data as a proxy for the crop hydric conditions, and (2) a soil–vegetation–atmosphere transfer (SVAT) model, the SEtHyS model, where hydric conditions are computed from a soil water budget. To this end, the models' skill was compared using a large and unique in situ database covering different crops and climate conditions, which was acquired over three experimental sites in southern France and Morocco. On average, the models provide 30 min estimations of latent heat flux (LE) with a RMSE of around 55 W m−2 for TSEB and 47 W m−2 for SEtHyS, and estimations of sensible heat flux (H) with a RMSE of around 29 W m−2 for TSEB and 38 W m−2 for SEtHyS. A sensitivity analysis based on realistic errors aimed to estimate the potential decrease in performance induced by the spatialization process. For the SVAT model, the multi-objective calibration iterative procedure (MCIP) is used to determine and test different sets of parameters. TSEB is run with only one set of parameters and provides acceptable performance for all crop stages apart from the early growing season (LAI < 0.2 m2 m−2) and when hydric stress occurs. An in-depth study on the Priestley–Taylor key parameter highlights its marked diurnal cycle and the need to adjust its value to improve flux partitioning between the sensible and latent heat fluxes (1.5 and 1.25 for France and Morocco, respectively). Optimal values of 1.8–2 were highlighted under cloudy conditions, which is of particular interest due to the emergence of low-altitude drone acquisition. Under developed vegetation (LAI > 0.8 m2 m−2) and unstressed conditions, using sets of parameters that only differentiate crop types is a valuable trade-off for SEtHyS. This study provides some scientific elements regarding the joint use of both approaches and TIR imagery, via the development of new data assimilation and calibration strategies.

International audience

International audience

International audience

Dataset contains the outputs of the AgriCarbon-EO Agronomical modeling tool for the carbon and water flux estimates by bayesian assimilation of S2 and LandSat8 remote sensing data into the Prosail radiative transfer model and the SAFYE-CO2 crop model. ----------------------- -for TILE : T31TCJ -for year: 2017 -for Winter wheat crops - at 10 m resolution Maps: -file: "GLA_statmap.tif" Description: A raster with 4 bands containing respectivly: *The R2 of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01 *The RMSE of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01 *The Bias of the GLAI observed by satellite and simulations from 2016/11/01 until 2017/08/01 *The number of images that are assimilated into SAFYE-CO2 from 2016/11/01 until 2017/08/01 -file: "emerg_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of emerg retrieved by the SAFYE-CO2 inversion in days of simulation (the simulation begins the 01/01/2016). *The standard deviation of emerg retrieved by the SAFYE-CO2 inversion. -file: "LUEa_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ. *The standard deviation of LUEa retrieved by the SAFYE-CO2 inversion in g/MJ. -file: "SENa_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of Sena retrieved by the SAFYE-CO2 inversion in °C. *The standard deviation of Sena retrieved by the SAFYE-CO2 inversion in °C. -file: "SENb_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of SENb retrieved by the SAFYE-CO2 inversion. *The standard deviation of SENb retrieved by the SAFYE-CO2 inversion. -file: "PRT_La_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of DAM retrieved by the SAFYE-CO2 inversion. *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion. -file: "DAM_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of DAM retrieved by the SAFYE-CO2 inversion in g/m2. *The standard deviation of DAM retrieved by the SAFYE-CO2 inversion in g/m2. -file: "NEP_wheat_2017.tif" Description: A raster with 2 bands containing respectivly: *The mean value of NEP retrieved by the SAFYE-CO2 inversion in g/m2. *The standard deviation of NEP retrieved by the SAFYE-CO2 inversion in g/m2. Shapefiles a GIS: -file: "S2_TILE_T31TCJ.shp" Description: shape file of the contour of the T231 TCJ sentinel2 tile -file: "FR_AUR.shp" Description: shape file of the contour of AURADE experimental field -file: "FR_AUR_TOWER.shp" Description: shape file of the location of the AURADE eddy covariance flux tower -file: "POI_2017.shp" Description: shape file of the location of points of interest that illustrate the ... paper -file: "ESU_DAM.shp" Description: shape file of the contour of the plots where dry biomass samples where taken. -file: "ESU_DAM_points.shp" Description: shape file of the location of the points where dry biomass samples where taken. -file: "mapT31TCJ_spamaps.qgz" QGIS project file for the visualisation of the NEP maps.

Plants acquire carbon through photosynthesis to sustain biomass production, autotrophic respiration and production of non-structural compounds for multiple purposes. The fraction of photosynthetic production used for biomass production, the biomass production efficiency, is a key determinant of the conversion of solar energy to biomass. In forest ecosystems, biomass production efficiency was suggested to be related to site fertility. Here we present a database of biomass production efficiency from 131 sites compiled from individual studies using harvest, biometric, eddy covariance, or process-based model estimates of production. The database is global, but dominated by data from Europe and North America. We show that instead of site fertility, ecosystem management is the key factor that controls biomass production efficiency in terrestrial ecosystems. In addition, in natural forests, grasslands, tundra, boreal peatlands and marshes, biomass production efficiency is independent of vegetation, environmental and climatic drivers. This similarity of biomass production efficiency across natural ecosystem types suggests that the ratio of biomass production to gross primary productivity is constant across natural ecosystems. We suggest that plant adaptation results in similar growth efficiency in high- and low-fertility natural systems, but that nutrient influxes under managed conditions favour a shift to carbon investment from the belowground flux of non-structural compounds to aboveground biomass.