Tianxiang Zhang, Jinya Su, Cunjia Liu, Wen‐Hua Chen
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引用次数: 1
Abstract
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.
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