## 3 Results  
### Evaluation of MODIS GPP Products and MOD17 Algorithm in the Arid Region  
#### 3.1.1 Site-Specific Evaluation of MODIS GPP Products and MOD17 Algorithm  
The eight-day EC flux tower GPP (GPP_obs) was compared with the results of MOD17A2H GPP (GPP_MODIS), GPP simulated with the in situ meteorology forcing data (GPP_Insitu), and GPP simulated with optimized maximum LUE parameter (GPP_LUEopt) and with optimized all five parameters (GPP_Fiveopt). As illustrated in Figure 2(a), the overall eight-day MOD17A2H GPP products were significantly underestimated when compared with the EC-observed GPP. The RMSE between MOD17 products and in situ flux observations of all sites was 1.80 gC/m\({}^{2}\)/day, while R\({}^{2}\) was 0.71 and the slope of the model was 0.49, which means the model could only contribute 71% of the tower-observed GPP. As shown in Figure 2(b), when we used the in situ meteorology data to simulate the MOD17 model, a better correlation between simulation and observation was found. The model could explain 79% of the observation (the slope was 0.43, R\({}^{2}\) was 0.79), with a large biases close to that of MOD17A2H products. However, the modeled GPP still underestimate as compared to the observed GPP, which means that the meteorology forcing data were not the main reasons for the underestimation of GPP. By contrast, when we optimized the maximum LUE parameter (Figure 2(c)), a significant improvement of model performance for all sites was seen, with R\({}^{2}\) = 0.86, RMSE = 1.01 gC/m\({}^{2}\)/day, rRMSE = 6.99%, and the slope of the regression lines was closer to the 1:1 line, which signifies the importance of the LUE parameter in GPP modelling. Furthermore, as we optimized all parameters, a better performance of the model occurred. Almost all the points were close to the 1:1 line (Figure 2(d)), with R\({}^{2}\) = 0.91, RMSE = 0.81 gC/m\({}^{2}\)/day, and rRMSE = 5.59%, which indicates the best performance in these simulations.  
As we accumulated the observed and simulated GPP at a yearly timescale for every site, a significant underestimation of MOD17A2H GPP products also existed (Figure 4), which were similar to the results of the eight-day time scale. On an annual time scale, the simulated GPP showed a generally good agreement with the tower-observed GPP across all sites with R\({}^{2}\) = 0.69, RMSE = 347.31 gC/m\({}^{2}\)/y and rRMSE = 60.48% (Figure 4a) A better relationship was found between the modeled GPP and tower-observed GPP (R\({}^{2}\) = 0.73). The model was improved by using in situ climate forcing data. However, the modeled GPP was still underestimated as compared with observation. Moreover, the modeled GPP was significantly improved as we optimized the model parameters (Figure 4c,d). The modeled GPP was closer to the observed GPP (almost all points close to the 1:1 line), and all five parameters optimization results were better than for LUE\({}_{\text{max}}\) parameter optimized only with R\({}^{2}\) of 0.87 and 0.92, respectively. The rRMSE was 23.93% and 19.55%, respectively, which signifies the importance of optimizing the temperature and water-constrained factors in arid regions.  
Figure 3: Comparisons of eight-day gross primary productivity (GPP) of MOD17A2H products and GPP simulations by MOD17 model with the flux tower GPP for all sites. Eight-day GPP scatter plots of the EC-observed GPP and (**a**) the original MOD17A2H products; (**b**) in situ meteorology forcing data; (**c**) only LUE optimized results; and (**d**) all parameters optimized results. The units of RMSE and rRMSE are gC/m\({}^{2}\)/day and %, respectively.