## 3 Results
### Evaluation of MODIS GPP Products and MOD17 Algorithm in the Arid Region
#### 3.1.3 Site-Specific Evaluation of MODIS GPP Products and MOD17 Algorithms  
The flux tower-observed GPP were compared with the original MOD17A2H GPP and GPP estimated from the MOD17 model with in situ meteorology forcing data (GPP_Institu), LUE optimized (GPP_LUEopt) and five optimized parameters (GPP_Fiveopt) (Figure 7 and Table 2). Figure 6 illustrates the scatter plots between EC GPP and simulated GPP at the eight-day time scale at all sites. From the slope of linear regression for the scatter plot in Figure 6, most of the slope values were less than 1.0, which revealed the MOD17A2H GPP in most of the sites were obviously underestimated, as compared with the flux tower-observed GPP (Figure 6), except for the three desert grassland sites, where MODIS GPP was close to the observed GPP in most cases. However, relatively large biases existed in the desert sites. While all sites of MODIS GPP were underestimated except the desert sites, a good correlation between MOD17A2H GPP and tower-observed GPP was shown in grassland and cropland sites (coefficients of determination were greater than 0.7), followed by forest and desert ecosystems. After modelling GPP using in situ climate data, a better correlation between modeled GPP and observed GPP occurred in most sites. However, there were still apparently underestimations in most  
Figure 5: Comparison between eight-day GPP of MOD17A2H products and GPP simulations by MOD17 model with the flux tower GPP for the major ecosystems including: (**a**) Grassland; (**b**) cropland; (**c**) desert steppe; and (**d**) forest. The unit of RMSE is gC/m\({}^{2}\)/day. The blue points represent original MOD17A2H products; green points represent in situ meteorology forcing data; pink points represent only LUE optimized results; and red points represent all parameters optimized results.  
sites, which means the forcing data were not the main reason of the underestimation of GPP. Instead of the forcing data, the inappropriate BPLUT parameters were the main source of the uncertainty of GPP simulation. After the optimization of LUE and other parameters in the MOD17 model, GPP in most sites was improved significantly. Meanwhile, the performance of optimization of five parameters was better than that of only optimization of the LUE parameter. As shown in Table 2 and Figure 6, good performance of GPP simulation was observed in DMZ, ARZ, and SDZ (R\({}^{2}\) were greater than 0.9). However, the MODIS GPP showed a moderate performance in capturing the corresponding GPP simulation of desert ecosystems. Overall, the current MODIS GPP model correctly simulated the dynamics of GPP at most sites in the arid region. After the parameter optimization, the coefficients of determination were improved apparently, and the RMSE of most sites was less than 1 gC/m\({}^{2}\)/day.  
Figure 6: Time series of eight-day MODIS GPP and GPP simulations derived from the MOD17 model with the tower-estimated GPP. The full name for each site is listed in Table 1. The blue points represent original MOD17A2H products; green points represent in situ meteorology forcing data; pink points represent only maximum LUE optimized results; and red points represent all parameters optimized results.