Chapter 5
Gross Primary Production of Terrestrial
Vegetation
Xiangming Xiao, Cui Jin and Jinwei Dong
Abstract Gross primary production (GPP) of vegetation is the first and important
flux of the terrestrial carbon cycle, and there is no direct measurement technique
for GPP at ecosystem and landscape scales. A number of satellite-based Light Use
Efficiency (LUE) models or Production Efficiency Models (PEM) have been
developed to estimate GPP in the past few decades, and they are driven by satellite
images with or without climate data. This chapter provides a brief review on some
of these LUE models, and illustrates the Vegetation Photosynthesis Model (VPM)
through its simulations of C3 crop (soybean) and C4 crop (maize) at the CO2 eddy
flux tower site in Nebraska, USA.
5.1 Introduction
Gross primary production (GPP) and net primary production (NPP) are two
important components of the carbon cycle. GPP is defined as the rate at which
vegetation captures and stores carbon dioxide in a given length of time via a
photosynthetic process. NPP is defined as the difference between GPP and autotrophic respiration (R a ), and is often measured as net production or accumulation
of dry matter in vegetation during a year (Roxburgh et al. 2005). Plant photosynthesis at the chloroplast and leaf levels can be accurately measured through
various instruments (Taiz and Zeiger 2002). However, GPP at the canopy, ecosystem, and landscape scales cannot be directly measured through instruments, yet.
Therefore, how to scale up the estimates of GPP from individual leaves to the
canopy, ecosystem and landscape scales is still a challenging scientific question.
Currently, three research approaches are widely applied to estimate GPP at
X. Xiao (&) Á C. Jin Á J. Dong
Department of Microbiology and Plant Biology, Center for Spatial Analysis,
University of Oklahoma, Norman, OK, USA
e-mail: xiangming.xiao@ou.edu
J. M. Hanes (ed.), Biophysical Applications of Satellite Remote Sensing,
Springer Remote Sensing/Photogrammetry, DOI: 10.1007/978-3-642-25047-7_5,
Ó Springer-Verlag Berlin Heidelberg 2014
127
Gross Primary Production of Terrestrial
Vegetation
Xiangming Xiao, Cui Jin and Jinwei Dong
Abstract Gross primary production (GPP) of vegetation is the first and important
flux of the terrestrial carbon cycle, and there is no direct measurement technique
for GPP at ecosystem and landscape scales. A number of satellite-based Light Use
Efficiency (LUE) models or Production Efficiency Models (PEM) have been
developed to estimate GPP in the past few decades, and they are driven by satellite
images with or without climate data. This chapter provides a brief review on some
of these LUE models, and illustrates the Vegetation Photosynthesis Model (VPM)
through its simulations of C3 crop (soybean) and C4 crop (maize) at the CO2 eddy
flux tower site in Nebraska, USA.
5.1 Introduction
Gross primary production (GPP) and net primary production (NPP) are two
important components of the carbon cycle. GPP is defined as the rate at which
vegetation captures and stores carbon dioxide in a given length of time via a
photosynthetic process. NPP is defined as the difference between GPP and autotrophic respiration (R a ), and is often measured as net production or accumulation
of dry matter in vegetation during a year (Roxburgh et al. 2005). Plant photosynthesis at the chloroplast and leaf levels can be accurately measured through
various instruments (Taiz and Zeiger 2002). However, GPP at the canopy, ecosystem, and landscape scales cannot be directly measured through instruments, yet.
Therefore, how to scale up the estimates of GPP from individual leaves to the
canopy, ecosystem and landscape scales is still a challenging scientific question.
Currently, three research approaches are widely applied to estimate GPP at
X. Xiao (&) Á C. Jin Á J. Dong
Department of Microbiology and Plant Biology, Center for Spatial Analysis,
University of Oklahoma, Norman, OK, USA
e-mail: xiangming.xiao@ou.edu
J. M. Hanes (ed.), Biophysical Applications of Satellite Remote Sensing,
Springer Remote Sensing/Photogrammetry, DOI: 10.1007/978-3-642-25047-7_5,
Ó Springer-Verlag Berlin Heidelberg 2014
127