*4.1. Evaluations of the MOD17A2H Products over Diversity Ecosystems in the Arid Region*

The MODIS Collection 6 GPP products improved the spatial resolution of GPP estimation, which means the estimated GPP is more comparable with the footprint in the areas with heterogeneous landscapes in the desert–oasis–alpine ecosystems in the arid region. Meanwhile, the MOD17A2H products updated the meteorological forcing data, FPAR data and land cover data, which highlight the better spatial resolution of 500 m. However, compared to the flux tower-based GPP data, the MOD17A2H GPP products still illustrate some limitations in the simulations of magnitude and spatial-temporal variation of GPP in the desert–oasis–alpine ecosystems in the arid region. From the slope of linear regression for the scatter plot in Figures 3a and 4a, the slope values were only 0.49, which revealed significant underestimation of the GPP in the study region. When compared to the site level of flux tower-based GPP (Figure 6), the slope values in most sites of the study regions were also less than 1.0, except for some desert ecosystem sites. This showed that the MOD17 product underestimated GPP in most high productivity sites of cropland, grassland, and forest ecosystems in the arid region, but overestimated GPP at some low productivity sites of desert ecosystems compared with tower-based GPP, consistent with the results of Reference [12] and Reference [27].

#### *4.2. 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, Tmin 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 (R2) of these sites were slightly higher than that of the original MOD17 products (R2 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 land cover 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. Research revealed that FPAR often produces misleading signals in GPP estimations due to contamination by atmospheric characteristics [19]. The overestimation of FPAR data is caused by sparse vegetation cover and the effects of large desert cover that impacts the signals of vegetation detection in arid regions. To improve the FPAR estimation in the arid region, we can use the improved FPAR retrieval products with the multi-angle vegetation index information in the future [56].
