Grassland Productivity Simulation: Integrating Remote Sensing and an Ecosystem Process Model
The heterogeneous nature of semi-arid grasslands in Canada creates significant challenges in monitoring grassland conditions, especially in light of increasing human activities and rapid environmental changes. It is thus imperative to develop a spatially-explicit tool to monitor and predict grassland productivity and to examine its responses to land-use and environmental change processes. In response to this need, we use a spatial BIOME-BGC model to estimate spatially distributed net primary productivity (NPP) for a mixed semi-arid grassland in Canada. Given the importance of the foliar C:N ratio in modelling terrestrial biochemical cycles and the ability of remote sensing in deriving spatially distributed data, a C:N ratio map is first produced from MODIS data which is then used to drive the spatial BIOME-BGC model. The simulated NPP driven by the fixed foliar C: N (i.e., C:N = 24.0) has an average of 112.53 g C m−2 years−1, while simulated NPP driven by MODIS-derived spatial foliar C:N has an average of 107.36 g C m−2 years−1. The latter better reflects the actual NPP on the ground which is 98.29 g C m−2 years−1. The results demonstrate that spatial foliar C:N can produce a more accurate simulation of grassland biogeochemical cycles thus improving NPP simulation accuracy.
KeywordsGrassland ecosystems Productivity modelling MODIS derived C:N ratio map
This research was supported by ISTPCanada C4507 to Dr. Xulin Guo and NSERC to Dr. Yuhong He. Grateful thanks to Kristina Trusilova for Max-Planck-Institut für Biogeochemie, Germany for generously sharing the codes for the terrestrial ecosystem model GBIME-BGCv1.0.
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