Chapter 8
Grassland Productivity Simulation:
Integrating Remote Sensing
and an Ecosystem Process Model
Yuhong He, Zhangbao Ma, and Xulin Guo
Abstract 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 ¼ 24.0) has an average of 112.53 g C m
À2 years
À1 , while simulated
NPP driven by MODIS-derived spatial foliar C:N has an average of 107.36 g C m
À2
years
À1 . The latter better reflects the actual NPP on the ground which is 98.29 g C
m
À2 years
À1 . The results demonstrate that spatial foliar C:N can produce a more
accurate simulation of grassland biogeochemical cycles thus improving NPP
simulation accuracy.
Keywords Grassland ecosystems • Productivity modelling • MODIS derived C:N
ratio map
Y. He (*)
Department of Geography, University of Toronto Mississauga,
3359 Mississauga Road, Mississauga, ON L5L1C6, Canada
e-mail: yuhong.he@utoronto.ca
Z. Ma • X. Guo
Department of Geography and Planning, University of Saskatchewan,
117 Science Place, Saskatoon, SK S7N5C8, Canada
e-mail: zhangbao.ma@usask.ca; xulin.guo@usask.ca
© Springer Science+Business Media Dordrecht 2015
J. Li, X. Yang (eds.), Monitoring and Modeling of Global Changes:
A Geomatics Perspective, Springer Remote Sensing/Photogrammetry,
DOI 10.1007/978-94-017-9813-6_8
155
Grassland Productivity Simulation:
Integrating Remote Sensing
and an Ecosystem Process Model
Yuhong He, Zhangbao Ma, and Xulin Guo
Abstract 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 ¼ 24.0) has an average of 112.53 g C m
À2 years
À1 , while simulated
NPP driven by MODIS-derived spatial foliar C:N has an average of 107.36 g C m
À2
years
À1 . The latter better reflects the actual NPP on the ground which is 98.29 g C
m
À2 years
À1 . The results demonstrate that spatial foliar C:N can produce a more
accurate simulation of grassland biogeochemical cycles thus improving NPP
simulation accuracy.
Keywords Grassland ecosystems • Productivity modelling • MODIS derived C:N
ratio map
Y. He (*)
Department of Geography, University of Toronto Mississauga,
3359 Mississauga Road, Mississauga, ON L5L1C6, Canada
e-mail: yuhong.he@utoronto.ca
Z. Ma • X. Guo
Department of Geography and Planning, University of Saskatchewan,
117 Science Place, Saskatoon, SK S7N5C8, Canada
e-mail: zhangbao.ma@usask.ca; xulin.guo@usask.ca
© Springer Science+Business Media Dordrecht 2015
J. Li, X. Yang (eds.), Monitoring and Modeling of Global Changes:
A Geomatics Perspective, Springer Remote Sensing/Photogrammetry,
DOI 10.1007/978-94-017-9813-6_8
155