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

Yuting Chen and Paul-Henry Cournède

Ecological Modelling, 2014, vol. 290, issue C, 165-177

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.

Keywords: Parameter estimation; Data assimilation; Dynamic crop model; Convolution Particle Filtering; Uncertainty analysis; Sugar beet; Winter wheat; LNAS; STICS; Yield prediction (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:290:y:2014:i:c:p:165-177

DOI: 10.1016/j.ecolmodel.2014.01.030

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