Bayesian methods for predicting LAI and soil water content

Majdi Mansouri, Benjamin Dumont, Vincent Leemans, Marie France Destain

Research output: Contribution to journalArticle

5 Citations (Scopus)


LAI of winter wheat (Triticum aestivum L.) and soil water content of the topsoil (200 mm) and of the subsoil (500 mm) were considered as state variables of a dynamic soil-crop system. This system was assumed to progress according to a Bayesian probabilistic state space model, in which real values of LAI and soil water content were daily introduced in order to correct the model trajectory and reach better future evolution. The chosen crop model was mini STICS which can reduce the computing and execution times while ensuring the robustness of data processing and estimation. To predict simultaneously state variables and model parameters in this non-linear environment, three techniques were used: extended Kalman filtering (EKF), particle filtering (PF), and variational filtering (VF). The significantly improved performance of the VF method when compared to EKF and PF is demonstrated. The variational filter has a low computational complexity and the convergence speed of states and parameters estimation can be adjusted independently. Detailed case studies demonstrated that the root mean square error of the three estimated states (LAI and soil water content of two soil layers) was smaller and that the convergence of all considered parameters was ensured when using VF. Assimilating measurements in a crop model allows accurate prediction of LAI and soil water content at a local scale. As these biophysical properties are key parameters in the crop-plant system characterization, the system has the potential to be used in precision farming to aid farmers and decision makers in developing strategies for site-specific management of inputs, such as fertilizers and water irrigation.

Original languageEnglish
Pages (from-to)184-201
Number of pages18
JournalPrecision Agriculture
Issue number2
Publication statusPublished - 1 Apr 2014



  • Bayes
  • Crop model
  • Data assimilation
  • Extended Kalman filtering
  • Particle filtering
  • Variational filtering

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)

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