Assimilation of canopy cover and biomass measurements in the crop model AquaCrop. (October 2017)

• Record Type: Journal Article
• Authors: Linker, Raphael; Ioslovich, Ilya
• Is Part Of: Biosystems engineering. Volume 162(2017)
• Page Start: 57
• Page End: 66
• Publication Date: 2017-10
• Subjects: Crop modelling -- Data assimilation -- Extended Kalman Filter -- Potato -- Cotton -- FIGARO; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2017.08.003
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Measurements indicative of crop development, such as leaf area index, canopy cover or biomass are typically performed only a few times throughout the season at irregular time intervals. Furthermore, due to the inherent spatial variability that exists in the field, combining measurements taken at different locations in the field usually leads to large uncertainty around the mean value. These factors, together with the fact that crop-soil models are strongly non-linear, render assimilation of measurements in crop-soil models non-trivial. This work presents procedures for performing such data assimilation, using the crop model AquaCrop as specific example. The procedures are based on Extended Kalman Filter, with some heuristic adjustments, and enable re-initialisation of state variables and/or adjustments of selected parameters of the model. The uncertainties of the measurements are taken into account explicitly in the proposed assimilation scheme. The procedures were tested with data obtained from experiments conducted with potato in Denmark and cotton in Greece. In both cases the data available consisted of canopy cover and biomass (average and standard deviation on 5–10 days), and a locally-calibrated AquaCrop model was used as starting point for the assimilation process. The results demonstrate the soundness of the approach but also emphasise the inherent limitations associated with data assimilation. In particular, assimilation of easy-to-obtain canopy cover measurements did not always improve the predictions of biomass. Highlights: A general framework for data assimilation is presented. The approach is based on Extended Kalman Filter. Both re-initialisation of state variables and parameters adjustments are considered. The model AquaCrop is used to test the data assimilation procedures. Two case studies with canopy cover and biomass measurements are presented.