## 4 Discussion
### Uncertainty of Input Data in MODIS GPP Estimation in Diversity Ecosystems in the Arid Region
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]. Uncertainty and Variability of Biophysical Parameters in Modelling GPP over Diversity Ecosystems in Arid Regions  
To estimate GPP across varied worldwide ecosystems, MOD17 algorithms use the biophysical variability of parameters generated from look up tables, which include five biome-specific physiological parameters in the model, i.e., \(\epsilon_{\text{max}}\), \(\text{T}_{\text{min\_min}}\), \(\text{T}_{\text{min\_max}}\), \(\text{VPD}_{\text{min}}\), and \(\text{VPD}_{\text{max}}\)[56]. In this study, we optimized the maximum LUE, one of the most important parameters for GPP estimation, with the tower-based GPP. An obvious improved performance of GPP simulation can be found in Figures 3c and 4c, which show good agreements between the observed GPP and the simulated GPP with the parameter optimized (GPP_LUE). The R\({}^{2}\) increased from 0.71 to 0.86, and rRMSE decreased from 12.45% to 6.99%, at an eight-day timescale. Those improved performances were also seen at an annual timescale, with R\({}^{2}\) increasing from 0.69 to 0.87, and rRMSE decreasing from 60.48% to 23.97%. Table 2 reveals the performance of the GPP_LUE model and flux GPP observation were better than the results of only using the ground forcing data; the determination coefficients increased significantly when using optimized \(\epsilon_{\text{max}}\) parameter, with smaller RMSE, which revealed greater improvements after performing LUE optimization. Calibration of the maximum LUE parameter improved the performance of MODIS GPP estimation was in accordance with other studies [24; 27; 32]. In addition, we also investigated the potential impacts of uncertainty of the other model parameters (e.g., VPD-limited factors) on GPP monitoring, which has been overlooked by other researchers. In fact, water stress is one of the most important limiting factors controlling terrestrial primary production, especially in arid regions. Previous studies showed that the MOD17 products underestimate water stress, and thus overestimate GPP in some extremely arid regions lacking in water [57]. In our study, we found that optimizing the VPD-limited factors can further improve the performance of the GPP estimations. From the eight-day time step of the overall performances, the R\({}^{2}\) increased from 0.86 for the results of only optimizing the LUE parameter to 0.91 for the results of optimizing all parameters, and rRMSE decreased from 6.99% to 5.59% (Figure 3). These results were mainly distributed in some sites in the extremely arid region (i.e., SDQ, HHL, HYL etc. ), which revealed the important role of the parameters of water-constrained factors in GPP simulation in arid regions. Uncertainty of GPP Modelling of the Desert Ecosystems and Its Implications for GPP Simulation in Arid Regions  
The current MOD17 model can effectively simulate GPP of main ecosystems in the arid region, however, there are still some difficulties in simulating GPP more accurately in the desert ecosystems. Model analyses indicate the importance of arid regions in the global carbon cycle, while the models suffer from a lack of data in water-limited regions [3; 4]. The large errors of GPP simulation in desert ecosystems is caused by the uncertainty of remote sensing vegetation products in regions with large heterogeneity of landscape and low vegetation cover. Moreover, the uncertainty of flux tower observation in desert ecosystems makes it is difficult to estimate a relative 'true' value of GPP [58]. To improve GPP estimation in arid regions, several directions can be explored further in the future.