## 4 Discussion  
### Comparison of Multi-Source Data  
Considering the inconsistencies in the values of current GPP products among different regions, seasons and vegetation types [10], multiple sets of GPP products were selected to further corroborate our results. It should be mentioned that our study merely focuses on consistencies and discrepancies in GPP variation, trends and predication, rather the intrinsic difference between the GPP products.  
Figure 10 shows the temporal and spatial comparison of the monthly mean values of different GPP products. It is observed that five sets of GPP products are consistent in seasonality, i.e., the value is smaller in winter while the value reaches the peak in summer. Spatially, the coincident patterns occur in the SRB, the HRB and the YRB regions. Remarkable differences are found in the northwest inland region represented by the CB and the southeast coastal region represented by the SEB. The differences in the CB may be related to the lesser vegetation coverage and the inconsistencies of the GPP anomalies among different products. As for the SEB, the discrepancy in GPP values can be explained by the fact that different products are discordant in the quantification of the impact of the hydrothermal environment on GPP.  
The trends of GPP in each basin obtained by different products are shown in Figure 11. The overall trends calculated by each set of products are consistent, which demonstrates the growing trend of GPP in China. However, there are certain differences among basins. Such differences mainly appear in the SEB and the PRB where the GPPNIRv product shows a positive trend (0.112 g C/m\({}^{2}\)/year and 0.447 g C/m\({}^{2}\)/year), and the GLASS product a negative one (\(-\)0.338 g C/m\({}^{2}\)/year and \(-\)0.028 g C/m\({}^{2}\)/year). The LUEopt product shows a negative trend in the SEB (\(-\)0.186 g C/m\({}^{2}\)/year), and a positive trend in the PRB (1.051 g C/m\({}^{2}\)/year). Compared with the above products, the results generated by  
Figure 9: Uncertainty analysis of the ARIMA model regarding (**a,b**) data value range and (**c,d**) data availability, assessed with RMSE and MAE. The shaded areas represent one standard deviation around the mean RMSE or MAE.  
Figure 12 shows the accuracy of the predicted GPP using ARIMA obtained by the four products. The prediction results of each set of products have good accuracy. It is observed that the FluxCom product showed better accuracy, which may be related to its low GPP value. The GPPNIRv product had the most variable accuracy. Consistent with the above conclusions that the performance of the ARIMA model is affected by the GPP values, the predicted value of the model had high uncertainty in areas with high GPP values, such as the PRB (the averaged RMSE and MAE of the four sets of products were 0.36 g C/m\({}^{2}\)/d and 0.289 g C/m\({}^{2}\)/d, respectively). In contrast, in areas with low GPP values such as the  
Figure 11: The trend of GPP in each basin obtained by different products from 1982 to 2015. The error bars reflect the standard deviation.  
Figure 10: Comparison of monthly mean values from different GPP products.  
CB, the predicted value of the model had better accuracy (the averaged RMSE and MAE of the four sets of products were 0.03 g C/m\({}^{2}\)/d and 0.025 g C/m\({}^{2}\)/d, respectively).