State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


Crop models play a paramount role in providing quantitative information on crop growth and field management. However, its prediction performance degrades significantly in the presence of unknown, uncertain parameters and noisy measurements. Consequently, simultaneous state and parameter estimation (SSPE) for crop model is required to maximize its potentials. This work aims to develop an integrated dynamic SSPE framework for the AquaCrop model by leveraging constrained particle filter, crop sensitivity analysis and UAV remote sensing. Both Monte Carlo simulation and one winter wheat experimental case study are performed to validate the proposed framework. It is shown that: (i) the proposed framework with state/parameter bound and parameter sensitivity information outperforms conventional particle filter and constrained particle filter in both state and parameter estimation in Monte Carlo simulations; (ii) in real-world experiment, the proposed approach achieves the smallest root mean squared error for canopy cover estimation among the three algorithms by using day forward-chaining validation method.

Original languageEnglish
Article number105909
Number of pages11
JournalComputers and Electronics in Agriculture
Early online date23 Dec 2020
Publication statusPublished - 1 Jan 2021

Bibliographical note

Funding Information:
This work was supported by Science and Technology Facilities Council (STFC) under Newton fund with Grant No. ST/N006852/1.


  • Machine learning
  • Multispectral image
  • Particle filter
  • Sensitivity analysis
  • Unmanned Aerial Vehicle


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