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

Tianxiang Zhang, Jinya Su, Cunjia Liu, Wen-Hua Chen

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

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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 ground truth 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.
Original languageEnglish
Title of host publication2 nd UK-RAS ROBOTICS AND AUTONOMOUS SYSTEMS CONFERENCE, Loughborough, 2019
Publication statusPublished - 2019

Keywords

  • data assimilation
  • Bayesian calibration
  • sequential forcing method
  • crop model
  • remote sensing
  • states prediction

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