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.