Aggregated and scaled up estimation of net primary production in a seasonal ecosystem

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We propose to develop and compare two methods for predicting net-primary productivity (NPP) in a seasonal environment. The first approach will scale up from leaf-level measurements of photosynthesis and transpiration to canopy and stand scale NPP estimates. The second approach will rely on multi- and hyper-spectral remote sensing technologies to generate NPP estimates. We will develop the leaf level approach by developing models that identify the timing of leaf deployment that optimise (maximise) the plants carbon balance. We will develop the remote sensing approach by establishing links between leaf-level physiology and remotely sensed spectral indices. The workplan will include the collection of leaf-level gas exchange measurements and spectral measurements conducted at the leaf, canopy and stand scale. Empirical methods will be used to calibrate remote sensing based estimates of leaf area index, absorbed photosynthetically active radiation and the efficiency of photosynthesis. This project will improve the physiological basis of remote sensing based estimates of NPP and will provide an alternative, leaf-level, method for predicting NPP.