Estimation des rendements du Maïs (Zea mays) et du Sorgho (Sorghum bicolor) à l’échelle de la parcelle et de la région agricole par assimilation de données de télédétection dans le modèle AquaCrop

In a context of climate change that directly affects agricultural production, yield estimation is of great importance regarding economic, geopolitical and food security issues. Traditionally carried out through agricultural costly and time-consuming surveys, yield estimation can be improved with crop growth models combined with Earth observation data. This research focuses on the implementation of an operational system to assimilate biophysical variables obtained from high spatial and temporal resolution satellite data into the AquaCrop model to estimate total biomass for maize and sorghum. First, the biophysical variable of interest, canopy cover, derived from satellite data was validated at the field scale with measured data obtained from digital hemispherical photography. The canopy cover parameters derived from the satellite time series – maximum canopy cover and emergence date – were assimilated into the AquaCrop model for better calibration and validation toward a limited number of maize fields in Belgium. The performance of the method allowed, in a second step, to estimate the total biomass of all the maize fields in the country using a crop mask and plot-specific data. To make the results available at the scale of the agricultural region, data aggregation was performed. This approach resulted in a satisfactory estimate of yields at the scale of the agricultural region. The approach was also tested in a semi-arid context and under weedy crop management in Niger for sorghum. It also provided interesting results both with reference data (measured) and with open access data.
This research provides a framework for operationalizing yield estimation at the agricultural region scale using plot-specific information and the AquaCrop model.