## 4 Discussion
### Uncertainty of Input Data in MODIS GPP Estimation in Diversity Ecosystems in the Arid Region  
The accuracy of GPP estimation highly depends on the precision of all input data of the MOD17A2H GPP algorithm. Therefore, uncertainties of GPP products arise mainly from the climate drivers, parameter variability, and land cover classification [20]. There are three meteorological data types (PAR, T\({}_{\text{min}}\) and VPD), as well as FPAR and land cover classification data involved in the MOD17A2H GPP algorithm, which could be the main source of error in the GPP estimates. The MOD17A2H products used GMAO Reanalysis data for direct meteorological inputs, which is an hourly time-step data set with about a half-degree spatial resolution (0.5 latitude degree by 0.67 longitude degree) generated by the Goddard Earth Observing System Model, Version 5 (GEOS-5) data assimilation system [15]. In this study, we replaced the GMAO dataset with in situ meteorological data and recalculated the MOD17 algorithm with default parameters in comparison (GPP_Insitu). Our study revealed that using the in situ forcing data can improve the relationship between modeled GPP and tower-observed GPP compared to the original MOD17A2H products both at eight-day and annual timescales (Figures 3 and 4); the determination coefficients (R\({}^{2}\)) of these sites were slightly higher than that of the original MOD17 products (R\({}^{2}\) ranging from 0.71 to 0.79 for eight-day step and 0.69 to 0.73 for annual step). However, larger biases still exist between GPP_Insitu and GPP_tower. Using in situ meteorological data did not result in obvious improvements of the GPP estimation performances; on the contrary, some sites were not even as accurate as those calculated with the GMAO datasets (ARZ, DSL, DMZ, and SDZ in Table 2), which is similar to the other results from validation of the MOD17 GPP products [21; 27; 32]. This is caused by some missing values in the original MOD17A2H GPP products making a shorter length of model evaluations, thus reducing the model errors of the GPP_MODIS. The other implication of the results is that an improvement in meteorological data did not have a significant effect on the MODIS GPP estimation, which means the meteorological data is not the main source of uncertainty in GPP simulation in the arid region. An accurate land cover classification map is vital to MOD17 GPP simulation [18]. Misclassification of the land cover directly determines the value of maximum light use efficiency and the other MOD17 BPLUT parameters, thus further influencing the inaccuracies of GPP calculation [20]. We validated the MOD12Q1 vegetation maps with our site observations and found the MODIS data misclassified almost all sites of forest and cropland types in the downstream HRB (Figure 7). Study suggested that the accuracies of MOD12Q1 vegetation maps are within 65-80%, and most inaccuracies are in between similar classes [55]. Since large desert-natural oasis ecosystems are distributed in the downstream HRB and most of the vegetation cover was less than 30%, the 500 m unit of MODIS landcover classification could pose a risk at such a coarse resolution. Mixed pixels, composed of varied ecosystem types, may occur in the sparsely vegetated region, thus making it difficult to describe the biophysical parameters properly. This incorrect classification of land cover types will therefore lead to an inaccurate GPP calculation. In addition, FPAR is also an important input physiology variable in the MOD17 model, which directly modulates the essential energy input to photosynthetic processes [8; 9]. In our study, we compared the MOD15A2H FPAR products with the observations in the study area, and found it significantly overestimated the ecosystems with low productivity (such as the desert ecosystems) and underestimated the ecosystems with high productivity (such as the crop ecosystems) in MODIS FPAR products in the HRB (Figure 8). This will greatly impact the energy redistribution in photosynthetic systems, and thus the GPP estimations in arid regions.