## 4 Discussion  
### Regional Differences in the GPP Products  
All the three GPP products use the MODIS land cover type product (MCD12 QI) as input data to depict the different underlying surfaces of terrestrial ecosystems. The MODIS land cover data identify fourteen classes in total, including ten natural vegetation classes, two human-altered classes, and two nonvegetated classes [54]. In the ecologically-fragile Northwestern China region, the nonvegetated classes, which included perennial salt, inland fresh water, perennial snow and ice, and gobi with sparse vegetation, are widely distributed. This caused large-area GPP values to be lost for both MODIS and BESS products in Figure 2. However, both GPP products exhibited high coefficients of determination (up to 0.997) with VPM GPP data, even though the GPP values varied substantially (Figure 6). [PERSON] et al. [16] mentioned that the LUE should take into account spatial differences in the application of the MODIS GPP algorithm. Because the land surface is heterogeneous, most of the pixels are mixed in the medium and low resolution GPP remote sensing products [55], which will increase the MODIS GPP estimation error when computed using a constant \(\mathrm{LUE_{max}}\). In the humid tropical and subtropical regions, [PERSON] et al. [56] have shown that LUE has a strong negative correlation with water vapor pressure. [PERSON] et al. [57] proposed that the LUE is higher in the case of a small amount of clouds than with no clouds, which was helpful to the vegetation photosynthetic activity. Consistent with previous findings, MODIS GPP in the tropical region was notably higher than BESS GPP products,  
Figure 6: Interannual dynamics of the average MODIS GPP, BESS GPP, and VPM GPP over the eight climatic zones from 2001 to 2015 with linear trend \(k\) and significance levels (\(p<0.01\) is highly significant, and \(p<0.05\) is significant); \(r\) and \(r^{\prime}\) represented the correlations between the auxiliary VPM GPP with the MODIS and BESS GPP, respectively.  
which can be partly ascribed to the effects of cloud cover and water vapor pressure [58]. Furthermore, through analyzing the interannual variations of the three GPP products over varied climate zones (Figure 6), the study found that the MODIS GPP in the subtropical humid region (VII), the tropical humid region (VIII), and for all of China decreased sharply in 2015, which contrasted with the positive directions obtained from the BESS and VPM GPP products. Interestingly, few studies have ever used the MODIS GPP products from 2015 for analysis. Figure 7 revealed that in the terrestrial ecosystems of Southern China displaying large annual carbon sequestration, the MODIS GPP products had the zero values in 2015 more than in 2014.. Overall, the annual MODIS GPP trends still retained large uncertainties across climate zones, whereas multiple GPP simulations from different models can help to make the long-term changes clearer. It is normal that these satellite-based GPP products exhibited contrasting performances across the different climate zones. On the one hand, terrestrial ecosystems are strongly affected by the local climate characteristics. On the other hand, these GPP models, including MODIS, BESS, and VPM, have different algorithms when estimating the variability in GPP. Generally, C\({}_{4}\) plant have higher \(\text{LUE}_{\text{max}}\) than C\({}_{3}\) plants due to different physiological pathways [59; 60; 61]. The MODIS algorithm simulated GPP across complex surface vegetation types with the same \(\text{LUE}_{\text{max}}\) regardless of C\({}_{3}\)/C\({}_{4}\) plant [17], but BESS and VPM models considered this photosynthetic pathway difference. The use of different assumptions and input values is main reason which led to MODIS GPP values that were apparently lower than those obtained with BESS or VPM GPP products in the northern area of China.