Assimilation of sensor-based observations on photosynthesis rates into crop models

Crop science

Assimilation of observational data into crop models is known to improve predictive capacities of models. Modern, non-invasive sensor techniques provide information on leaf area development, greenness of leaves and instant rates of photosynthesis in high spatial and temporal resolution at the plot level. The thesis should explore the potential of non-invasive measurements of photosynthetic activity of selected crops to improve crop model performance through data assimilation

To investigate the potential of data assimilation from non-invasive measuremements of photosynthetic activity to improve the performance of a field-scale crop model

The study will measure photosynthetic activity on Campus Klein-Altendorf with non-invasive sensors and apply existing assimilation techniques to include the measurements in a field scale crop model

Comparison of performance of a crop model with and without data assimilation which could finally issue into a peer-reviewed paper

6 to 8 Month

Preferably English, German is also possible

Interest in crop phenotyping and sensor techniques, good quantitative and analytical skills (eg good marks in math and statistics classes), preferable basic knowledge in the use of R and/or XML scripting

Thomas Gaiser (Universität Bonn) and Uwe Rascher (FZ Jülich)

tgaiser@uni-bonn.de, u.rascher@fz-juelich.de