Remote Sensing Data Assimilation by Forcing method in Simulation of Silage maize Yield using AquaCrop Model

Estimation and forecast of crop yield using crop growth models is imperative to plan agricultural operations and manage crop yield. To this end, the AquaCrop model parameters were estimated and the model was calibrated with measuring and sampling different requied information of model in the crop growing stages and prior to cultivation over agricultural silage maize fields at the regional scale. Field sampling of soil (prior to cultivation) and crop (during the growth season), digital hemispherical photography (DHP) and destructive method for comparison purposes were carried out for silage maize in Qhale-Nou county, South Tehran, in the summer of 2019. Remote sensing data assimilation based on forcing method, by biophysical variable of fCover extracted of remote sensing data was incorporated into the AquaCrop model. Then, the most sensitive model parameters which identified through sensitivity analysis were estimated and the obtained results were then compared with the case where assimilated data were not incorporated. As the results suggest, the output yield for the model with data assimilation was estimated with R2 values of 0.89 and 0.88 for calibration and evaluation, respectively. The superiority of RS data assimilation into the model as opposed to not its incorporating was also verified by improving the accuracy with Relative RMSE (RRMSE) values of 4.12 and 5.17 percent and RMSE of 2.5 and 2.4 ton/ha for calibration and evaluation, respectively. The overall findings allude to the advantages of incorporating remote sensing data assimilation by the forcing method as a relatively efficient tool for simulating silage maize yield under variable environmental conditions.