Integration of calibration and forcing methods for predicting timely crop states by using AquaCrop-OS model

This paper presents a framework for predicting canopy states in real time by adopting a recent MATLAB based crop model: AquaCrop-OS. The historical observations are firstly used to estimate the crop sensitive parameters in Bayesian approach. Secondly, the model states will be replaced by updating remotely sensed observations in a sequential way. The final predicted states will be in comparison with the groundtruth and the RMSE of these two are 39.4155 g/ 𝒎𝟐 (calibration method) and 19.3679 g/𝒎𝟐
(calibration with forcing method) concluding that the system is capable of predicting the crop status timely and improve the performance of calibration strategy.