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Data assimilation to reduce uncertainty of crop model prediction with Convolution Particle Filtering

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  • Chen, Yuting
  • Cournède, Paul-Henry

Abstract

A three-step data assimilation approach is proposed in this paper to enhance crop model predictive capacity in various environmental conditions. The most influential parameters are first selected by global sensitivity analysis and then estimated in a Bayesian framework. The posterior distribution of the estimation step is then considered as prior information for data assimilation. In this last step, a filtering method is sequentially applied to update state and parameter estimates, with the purpose of improving model prediction and assessing the prediction uncertainty.

Suggested Citation

  • Chen, Yuting & Cournède, Paul-Henry, 2014. "Data assimilation to reduce uncertainty of crop model prediction with Convolution Particle Filtering," Ecological Modelling, Elsevier, vol. 290(C), pages 165-177.
    • Handle: RePEc:eee:ecomod:v:290:y:2014:i:c:p:165-177
    DOI: 10.1016/j.ecolmodel.2014.01.030
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    References listed on IDEAS

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    1. Wu, Qiong-Li & Cournède, Paul-Henry & Mathieu, Amélie, 2012. "An efficient computational method for global sensitivity analysis and its application to tree growth modelling," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 35-43.
    2. Wernsdörfer, H. & Rossi, V. & Cornu, G. & Oddou-Muratorio, S. & Gourlet-Fleury, S., 2008. "Impact of uncertainty in tree mortality on the predictions of a tropical forest dynamics model," Ecological Modelling, Elsevier, vol. 218(3), pages 290-306.
    3. Naud, Cédric & Makowski, David & Jeuffroy, Marie-Hélène, 2007. "Application of an interacting particle filter to improve nitrogen nutrition index predictions for winter wheat," Ecological Modelling, Elsevier, vol. 207(2), pages 251-263.
    4. Campillo, Fabien & Rakotozafy, Rivo & Rossi, Vivien, 2009. "Parallel and interacting Markov chain Monte Carlo algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(12), pages 3424-3433.
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    Cited by:

    1. Linker, Raphael & Kisekka, Isaya, 2022. "Concurrent data assimilation and model-based optimization of irrigation scheduling," Agricultural Water Management, Elsevier, vol. 274(C).
    2. Alaa Jamal & Raphael Linker, 2020. "Genetic Operator-Based Particle Filter Combined with Markov Chain Monte Carlo for Data Assimilation in a Crop Growth Model," Agriculture, MDPI, vol. 10(12), pages 1-22, December.
    3. Yuting Chen & Samis Trevezas & Paul-Henry Cournède, 2015. "A Regularized Particle Filter EM Algorithm Based on Gaussian Randomization with an Application to Plant Growth Modeling," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 847-870, December.
    4. Wang, Weishu & Rong, Yao & Zhang, Chenglong & Wang, Chaozi & Huo, Zailin, 2024. "Data assimilation of soil moisture and leaf area index effectively improves the simulation accuracy of water and carbon fluxes in coupled farmland hydrological model," Agricultural Water Management, Elsevier, vol. 291(C).

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