## 4 Discussion
[PERSON] and [PERSON] (2014) also directly evaluated MODIS products against the FLUXNET 2015 dataset and found that croplands exhibited the largest GPP RMSE (4.80 g m\({}^{-2}\) d\({}^{-1}\)) and slight underestimation in ET (0.88 mm d\({}^{-1}\)). Then, by setting different parameters for C\({}_{3}\) and C\({}_{4}\) crops, the new Breathing Earth System Simulator (BESS) model achieved better GPP results than MODIS at different temporal and spatial resolutions. Figure 6: Comparisons of seasonal dynamics in gross primary production and evapotranspiration between flux tower measurements (GPP\({}_{\text{EC}}\), ET\({}_{\text{EC}}\)) and MODIS estimates (GPP\({}_{\text{MOD}}\), ET\({}_{\text{MOD}}\)) at the Arou grassland site and Daman cropland site. Figure 7: The scatterplots of eight-day average GPP and ET between flux tower measurements and MODIS products at the two flux sites during 2013 and 2014. (**a**,**b**) represent the Arou grassland site and the Daman cropland site, respectively. 92 periods of data are used in total. The black dashed line and red line represent 1:1 line and linear fit, respectively. The mismatch between the actual flux tower footprint with a 1 km\({}^{2}\) approximation used in the comparisons to MODIS GPP and ET products is also an issue. Although the EC-based flux measurement has a larger footprint compared to the field measurements, it remains uncertain owing to random measurement errors, gap-filling errors, the energy balance disclosure issue and the variation in the footprint with wind speed and direction [61; 62]. Spatially-averaged MODIS values from a \(3\times 3\) window over each tower location were used to reduce the effects of spatial heterogeneity, but may also introduce additional uncertainties through regional smoothing of MODIS retrievals relative to tower observations, especially over spatially-complicated vegetation and terrain [47; 63]. Additionally, remote sensing data and flux data have different observation frequencies, so there are unavoidable errors in estimating daily/eight-day GPP and ET from both transient remote sensing data and half-hourly eddy flux data [64]. [PERSON] et al. [59] revealed that true ET values were usually underestimated by the eddy covariance measurements to a certain degree. Essentially, the GPP values were partitioned from EC-based NEE data relying on empirical models, which also included plenty of uncertainties [31; 65]. All of these factors contributed to the uncertainty in estimated WUE at the eight-day time scale. In this study, the proposed GR model solely based on time-series MODIS EVI data exhibited great potential in capturing the seasonal variations in ecosystem WUE for both grassland and cropland in the dry areas of Northwest China (Figure 5). Even so, we have to recognize that because of the diversity in community composition, climate conditions and external disturbances [47; 66; 67], it remains a challenge to propose a robust and general model for monitoring ecosystem WUE variability over large scales. In addition, [PERSON] et al. [68] indicated that at least 5-10 years of flux observations are required for optimal model development. Given the significant role of vegetation in the water-limited environment, this study will help us to understand the coupling relationship between carbon-water interactions in this region.