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

我们建议开发和比较两种方法预测净初级生产力（NPP）在季节性环境。第一种方法将从光合作用和蒸腾作用的叶水平测量扩大到冠层和林分尺度的NPP估计。第二种方法将依靠多光谱和超光谱遥感技术来估算净初级生产力。我们将通过开发模型来确定叶片部署的时间，从而优化（最大化）植物碳平衡，从而开发叶片水平方法。我们将通过建立叶级生理和遥感光谱指数之间的联系来开发遥感方法。工作计划将包括收集叶片一级的气体交换测量数据和在叶片、树冠和林分尺度上进行的光谱测量数据。将使用经验方法校准基于遥感的叶面积指数、吸收的光合有效辐射和光合作用效率的估计数。该项目将改进基于遥感的净初级生产力估算的生理基础，并将提供一种替代的叶级净初级生产力预测方法。

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.