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Coupling Imaging Spectroscopy and Ecosystem Process Modelling - The Importance of Spatially Distributed Foliar Biochemical Concentration Estimates for Modelling NPP of Grassland Habitats

机译:耦合成像光谱与生态系统过程建模 - 用于建模草地栖息地模拟空间分布叶面生物化学浓度估计的重要性

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Information on canopy chemical concentrations is of great importance for the study of nutrient cycling, productivity and for input to ecosystem process models. In particular, foliar Carbon to Nitrogen ratio (C:N) drives terrestrial biogeochemical processes such as decomposition and mineralization, and thus strongly influences soil organic matter concentrations and turnover rates. This study evaluated the effects of using spatial estimates of foliar C:N derived from hyperspectral remote sensing for simulating NPP by means of the ecosystem process model Biome-BGC. The main objectives of this study were to calibrate spatial statistical models for the prediction of foliar C:N for grassland habitats at the regional scale, using airborne HyMap hyperspectral data, to use the foliar C:N predictions as input to the ecosystem process model Biome-BGC and derive NPP estimates and finally to compare these results to NPP estimates derived using C:N value reported in literature and derived from field measurements. Results from this research indicate that NPP estimates using the HyMap predicted C:N differed significantly from those when C:N values from "global" or "regional" measurements were used. Extending the current research to broader spatial scales can help to initialise, validate and adjust better ecological process models.
机译:关于冠层化学浓度的信息对于营养循环,生产力和输入到生态系统流程模型的研究具有重要意义。特别地,叶状碳与氮比(C:n)驱动陆地生物地球化学方法,如分解和矿化,因此强烈影响土壤有机质浓度和周转率。该研究评估了使用从高光谱遥感的Feariar C:N的空间估计来借助于生态系统过程模型Biome-BGC模拟NPP的效果。本研究的主要目标是校准用于在区域规模的草地栖息地预测的空间统计模型,利用空降性Hymap高光谱数据,使用FORIAR C:N预测作为生态系统过程模型生物群系的输入-BGC和派生NPP估计,最后将这些结果与在文献中报告的C:N值衍生的NPP估计进行比较,并从现场测量结果进行。该研究的结果表明,使用Hymap预测的C:n的NPP估计与来自“全局”或“区域”测量的C:N值的数量有显着不同。将目前的研究扩展到更广泛的空间尺度可以帮助初始化,验证和调整更好的生态过程模型。

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