This paper introduces a method for modelling the deterministic component of eddy covariance CO2 flux time series in order to supplement missing data in these important data sets. The method is based on combining multidimensional semi-parametric spline interpolation with an assumed but unstated dependence of net CO2 flux on light, temperature and time. We test the model using a range of synthetic canopy data sets generated using several canopy simulation models realized for different micrometeorological and vegetation conditions. The method appears promising for filling large systematic gaps providing the associated missing data do not overerode critical information content in the conditioning data used for the model optimization.
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