Reconstruction of leaf area time series using data assimilation on the GreenLab plant growth model and remote sensing
Abstract
The GreenLab plant growth model is a powerful and complex tool able to simulate plant growth evolution. The calibration of GreenLab model for a specific plant usually requires field experiments with destructive sampling periodically done on individual plants in order to characterize growth and organogenesis. However when one is merely interested on particular aspects of plant growth, such as leaf area evolution or fruit harvest, we demonstrate in our work that a substantially simplified GreenLab model, associated with data assimilation methods, is able to provide satisfactory performances. Based on these techniques, we have successfully applied GreenLab to remote-sensing area where the observation data is highly limited. Experimental results show that qualitative and quantitative criteria on leaf area reconstructed time series are of good quality.
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