## 4 Discussion
### Uncertainty Analysis and Directions for Further Research  
There are several areas of uncertainty in the results. (1) Uncertainty in MODIS NPP: The MODIS product used to calculate vegetation autotrophic respiration (R\({}_{\text{A}}\)) has a high-temperature sensitivity parameter, which can lead to a decrease in the NPP with increasing temperature [53]. The MODIS product overestimates the value of the product in low-productivity areas and underestimates the value of the product in high-productivity areas [99]. As a result, the value of the NPP is inaccurate in some areas, leading to errors in the NEP results. (2) Uncertainty in heterotrophic respiration (R\({}_{\text{H}}\)): This study adopted an empirical equation based on temperature and precipitation [43], and although this equation has been used in many studies [8; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53], the homogeneous nature of the equation makes it difficult to account for the differences caused by differences in soil and vegetation. The soil structure, number of freeze-thaw alternations, and soil biological properties also affect the magnitude of soil respiration under seasonal freezing and thawing, which in turn affects the carbon sink. The improvement of this equation requires the continued monitoring of field data, a step that could not be pursued due to the limitations of this study. (3) Uncertainty in MODIS ETa: more clouds reduce the MODIS surface albedo and may increase the uncertainty in the reanalyzed data, deteriorating ETa accuracy under an overcast sky [100]. (4) The problem of mixed pixels: Although the land cover data in this study has high spatial resolution, pixel problems are unavoidable. For instance, there is no obvious demarcation line for each vegetation type, which leads to errors when calculating the NEP of each vegetation type separately. Moreover, because of the ecological restoration project in the MRYR, the land cover types change to a certain extent every year, which may lead to an essential change in the land use type in a certain area in a year.  
Through this analysis and discussion of the results, we believe that increasing the spatial and temporal scale of the NPP data will play a decisive role in measuring the NEP more accurately. For example, the parametric model CASA could be used [8]. It is capable of inputting higher-resolution remote sensing data, improving the spatial resolution of the NPP, and realizing the integration of monthly and seasonal NPP data. Finally, three representative climate factors were selected to calculate the correlations with climate variables. In addition, three more, i.e., the CO\({}_{2}\) concentration, altitude, and solar radiation, can profoundly affect the NEP, WUE, and CUE. Furthermore, we calculated the correlations using a linear correlation approach. The relationship between climate variables and productivity is often very complex, and the relationship is likely to be nonlinear. Therefore, in subsequent studies, more factors should be selected, and better mathematical fitting methods should be adopted to assess the water-carbon cycle in a region. These are all elements that deserve further research.