## 5 Conclusions  
Satellite remote sensing observes the land surface continuously, providing a spatial and temporal database for simulating terrestrial GPP from national to global scales. However, there are significant differences in the performance of different remotely-derived GPP products. Spatially, both MODIS and BESS GPP showed an increasing trend from northwest to southeast, which is closely related to the spatial distribution of vegetation cover across China. Temporally, all these GPP products, including BESS, MODIS, and VPM data, exhibited distinct seasonal patterns for an 8-day interval and reached peak values in the summertime. However, the three GPP products performed differently in monitoring the interannual dynamics of vegetation GPP over the eight climate zones across China throughout the years from 2001 to 2015. Particularly, the MODIS GPP products were generally lower than the BESS GPP data due to the influence of C3/C4 plants, except in the humid tropical region (VIII). Further analysis based on the VPM GPP product found that BESS GPP had better consistency than MODIS GPP at the annual and seasonal time scales. In addition, the spatial correlation analysis was applied to investigate the impacts of climate factors (precipitation and temperature) on the variability in GPP over these climate zones. The rising trend of GPP in Northern China can be explained by more precipitation in recent years because natural rainfall was the dominant driver of GPP changes in these areas. However, the uptrend of GPP in Southern China and the Tibet Plateau resulted partially from higher temperatures in these regions as temperature exhibited a positive correlation with GPP. Therefore, our analyses have important implications for evaluating the performance of these satellite-based GPP products across different climate regions. Meanwhile, a multimodel comparison revealed the uncertainties in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **Range** & \
\begin{tabular}{c} **Proportion of Area** \\ **(GPP–Temperature)** \\ \end{tabular} &
\begin{tabular}{c} **Proportion of Area** \\ **(GPP–Precipitation)** \\ \end{tabular} \\ \hline Highly negative correlation & \(<\)-0.6 & 0.84\% & 1.60\% \\ Moderate negative correlation & \(-\)0.6\(\sim\)\(-\)0.3 & 9.77\% & 13.64\% \\ Low negative correlation & \(-\)0.3\(-\)0 & 30.28\% & 28.92\% \\ Low positive correlation & 0\(-\)0.3 & 37.37\% & 30.66\% \\ Moderate positive correlation & 0.3\(-\)0.6 & 19.47\% & 19.99\% \\ Highly positive correlation & \(>\)0.6 & 2.27\% & 5.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Area statistic of the correlations between annual mean BESS GPP and the corresponding temperature and precipitation in China from 2001 to 2015.  
algorithms and subsequent improvements. With the development of ChinaFLUX and the data sharing policy, it will be feasible to quantitatively assess the reliability of these remotely-derived GPP products in future.  
Conceptualization, Y.C. and X.T.; methodology, Y.C. and X.T.; validation, H.G., M.W. and Q.G.; formal analysis, M.W., Z.D.; data curation, H.G. and M.M.; writing--review and editing, Y.C and X.T.  
This research was funded by the National Key R&D Program of China (2017 YFB0503903-3), Chongqing Basic and Frontier Research Program (cstc2018 jcyjAX0056), and the Fundamental Research Funds for the Central Universities in China (SWU117035).  
The meteorological dataset is provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC). We thank the scientists who have made the BESS, MODIS and VPM GPP products available.  
The authors declare no conflict of interest.