Advantages of the RI
This study introduces a new index based on radiation balance to estimate
GPP of terrestrial ecosystems of diverse vegetation types. By
implementing a simple linear regression equation, RI is a universal
index that provides satisfactory estimations of GPP, especially at the
annual scale, regardless of the ecosystem types. This approach can be an
alternative to the expensive conventional EC method, especially in
places where implementation of EC system is not possible. The index also
has great potential to be used for GPP upscaling and prediction at the
global scale, which needs further evaluations.
The GPP estimation using process-based and empirical models requires
complex inputs from meteorological and vegetation observations in
addition to remote-sensing data (Huete et al., 2008; Harris & Dash,
2011; Ma et al., 2014; Zhang et al., 2016; Sims et al., 2006, 2008;
Jiang & Ryu, 2016; Wonsick et al., 2014). The required data in these
models were often not available at a sufficient temporal or spatial
scale (Sims et al., 2008; Zhang & Zhao, 2014). For example, inputs of
the vegetation photosynthesis model (VPM) include both leaf chlorophyll
content and chlorophyll-level fraction of absorbed photosynthetically
active radiation (FPARchl) across various terrestrial biomes over time.
However, obtaining extensive field measurements of chlorophyll content
at different levels is a challenging task (Zhang et al., 2016). Any
uncertainty associated with the observations (e.g. sample size) will be
propagated throughout the model simulation and will affect the final
estimation. By contrast, the RI approach requires only radiation
variables as inputs, which can be reliably measured in situ using
a net radiometer (Prince, 1991; Gower et al., 1999; Xiao et al., 2005;
Tang et al., 2015) or acquired from satellite-based remote-sensing
products (Zhou et al., 2007; Bonan, 2008; Jackson et al., 2008; Kosugi
et al., 2008; Pitman et al., 2011). Therefore, the simplicity of RI
makes it an approach with a great potential for wide implementation.