## 4 Discussion
### Error Sources of Remotely-Derived Products  
This study found that both the MODIS and BESS GPP performed with good accuracy for grasslands, which was consistent with previous studies [29; 45], but larger uncertainties existed in other ecosystems. Regardless of the algorithm error, the precision of the model parameters used for the estimation plays an indispensable role in the accuracy of GPP estimation. For the input parameters of the MODIS GPP algorithm, we can take knowledge from the MOD17 user guide [38], while for the model parameters of the BESS GPP, the description is very clear in the research of [PERSON] and [PERSON] [29]. The BESS model contains the six key modules algorithm [17], and most of its input parameters come from the MODIS products whose resolution difference is large enough to make uncertainty inevitable in the process of the resolution consistency. For the GOSIF GPP, the GOSIF data set, the key part of the input data was affected by MERRA-2 with coarse spatial resolution [41]. Therefore, these products had their own errors. In this study, we found that these BESS, MODIS, and GOSIF GPP products largely underestimated the GPP during the growing period, except the S_Ar site. This can be partly explained by the open-path EC-based flux measurements. Such systems would inevitably lead to the overestimation of the GPP of terrestrial ecosystems because of the limitation of technical means. However, the GOSIF GPP had obvious advantages in seasonal and interannual dynamics, which can be ascribed to the universal GOSIF-GPP relationship and negligible impact of land covers [41]. Through a thorough analysis of the performances of the MODIS and BESS GPP, we found that both the BESS and MODIS GPP underestimated the GPP data by more than half at S_Hbl (Figures 3 and 6). The reason can be largely explained by the fact that both the BESS and MODIS GPP products used the same land cover product (MCD12Q1) [29; 46], which had significant classification uncertainty. Different judgments of land cover resulted in inaccurate LUEmax and affected the estimation results. Figure 7 showed that the S_Hbl and S_Sdq sites were located on grasslands surface, but not on Populus euphratica and Tamarix spp. For the MODIS GPP product, LUEmax, Tmin, and VPD of grasslands are much lower than those of forest land, which is another important reason for underestimation. [PERSON] [47] found that the Tmin and VPD from the GMAO were generally underestimated. Furthermore, the LUEmax value in MODIS GPP depends on the simple look-up table approach. Among the same vegetation types, the LUEmax value was also generally underestimated [48]. After [PERSON] [28] optimized the LUEmax using the site-observed GPP, the performance of the MODIS GPP had obvious improvement. The footprints of these flux sites can reach several hundred meters around the tower. However, owing to the spatial heterogeneity, its effect on the remote sensing-based GPP products cannot be neglected, which would lead to inconsistency between the site-level measurement and satellite-based product. In addition, soil moisture has been indicated as a major factor limiting the GPP in the water-constrained ecosystems [15; 49]. However, the BESS GPP assumed that the water stress is embedded in the variability of leaf area index, which lacked an accurate proxy for soil moisture dynamics [50]. The BESS and MODIS GPP applied different types of models, which made it hard to compare from the model structure. However, the most important difference is that the BESS model distinguished the difference between C\({}_{3}\) and C\({}_{4}\) plants while MODIS GPP did not. The cropland ecosystems mainly represent rice at MCD12Q1 land cover products. [PERSON] [51] also found that the GPP of maize was obviously higher than the other C\({}_{3}\) crops. Rice at the S_Dm is a C\({}_{3}\) plant different from C\({}_{4}\) plants (maize), which is the main reason for the underestimation of MODIS GPP.