## 4 Discussion
### Impact of Meteorological Data on MODIS GPP  
There are three meteorological factors (PAR, T\({}_{min}\) and VPD\({}_{avg}\)) involved in the MOD17A2H GPP algorithm, which suggests that meteorology reanalysis data could be the main source of error in the GPP estimates [(44)]. For the previous MOD17A2 GPP product, the meteorological input data were selected from the DAO meteorology dataset (1.00\({}^{\circ}\)\(\times\) 1.25\({}^{\circ}\) spatial resolution). In contrast, the latest MOD17A2H product uses an updated version of the GMAO meteorology dataset (0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\) spatial resolution). However, the spatial resolution of GMAO meteorology data is still greatly inferior to that of the MODIS GPP product (500 m). Although a strict interpolation was completed in the data pre-processing stage, the errors caused by the original low resolution could not be eliminated. Moreover, the interpolation process may have resulted in additional uncertainties. Analytic comparisons between the GMAO meteorology reanalysis dataset and the site-based meteorological observations indicated that GMAO data were effective for estimating on-site temperature elements and moisture conditions (Figure 4a-c). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Biome (Site)** & \begin{tabular}{c} **MOD17A2H \(\epsilon_{max}\)** \\ **(gC-m\({}^{-2}\)-d\({}^{-1}\).MJ\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **Inferred \(\epsilon_{max}\)** \\ **(gC-m\({}^{-2}\)-d\({}^{-1}\).MJ\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Bias** \\ **(gC-m\({}^{-2}\)-d\({}^{-1}\).MJ\({}^{-1}\))** \\ \end{tabular} \\ \hline \hline
**ENF** & 0.962 & 1.256 & \(-\)0.294 \\ (AR-Vir) & 0.962 & 1.521 & \(-\)0.559 \\ (AU-ASM) & 0.962 & 1.225 & \(-\)0.263 \\ (IT-Ren) & 0.962 & 1.183 & \(-\)0.221 \\ \hline
**EBF** & 1.268 & 1.539 & \(-\)0.271 \\ (AU-Whr) & 1.268 & 1.175 & 0.093 \\ (BR-Sa3) & 1.268 & 1.695 & \(-\)0.427 \\ (FR-Pue) & 1.268 & 1.418 & \(-\)0.150 \\ (GH-Ank) & 1.268 & 1.496 & \(-\)0.228 \\ \hline
**DBF** & 1.165 & 2.008 & \(-\)0.843 \\ (DE-Hai) & 1.165 & 2.037 & \(-\)0.872 \\ (DK-Sor) & 1.165 & 1.988 & \(-\)0.823 \\ \hline
**MF** & 1.051 & 1.692 & \(-\)0.641 \\ (BE-Vie) & 1.051 & 1.968 & \(-\)0.917 \\ (CA-Gro) & 1.051 & 1.371 & \(-\)0.320 \\ \hline
**GRA** & 0.860 & 1.125 & \(-\)0.265 \\ (AU-Rig) & 0.860 & 1.292 & \(-\)0.432 \\ (CZ-BK2) & 0.860 & 1.118 & \(-\)0.258 \\ (PA-SPs) & 0.860 & 1.141 & \(-\)0.281 \\ (US-Wkg) & 0.860 & 0.869 & \(-\)0.009 \\ \hline
**CRO** & 1.044 & 1.303 & \(-\)0.259 \\ (BE-Lon) & 1.044 & 1.655 & \(-\)0.611 \\ (FR-Gri) & 1.044 & 1.086 & \(-\)0.042 \\ (US-CRT) & 1.044 & 1.234 & \(-\)0.190 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the inferred \(\epsilon_{max}\) and MOD17A2H \(\epsilon_{max}\) for various biomes. ENF: evergreen needleleaf forests; EBF: evergreen broadleaf forests; DBF: deciduous broadleaf forests; MF: mixed forests; GRA: grasslands; CRO: croplands. Compared with the previous research results about MOD17A2 meteorology data, the GMAO data adopted in the current MOD17A2H product achieved substantial progress in estimating actual values of meteorological elements [45; 46]. This demonstrated the considerable effectiveness of the efforts that MODIS GPP has taken in the version upgrading process to reduce the error derived from meteorological data. Note that as the source of original energy for GPP, PAR plays an extremely important role among the four meteorological elements involved in the MOD17A2H algorithm. It can be seen from this work that the effectiveness of GMAO data in estimating PAR was less than that of GMAO data in estimating the temperature and moisture elements (Figure 4d), which is mainly because the GMAO data largely overestimated the site-measured PAR. In this study, we replaced the GMAO meteorology reanalysis dataset with the site-based meteorological observations to recalculate the MOD17A2H GPP algorithm and gained new GPP estimates (MOD_Tower GPP).