## 3 Results
### Meteorology Variables
As shown in Table 5, \(\text{GPP}_{\text{MODIS-GPP}}\) tracked \(\text{GPP}_{\text{EC}}\) well at the US-Ton site, with the lowest RMSE (1.14 gC/m\({}^{2}\)/d) and the second-lowest bias (\(-\)0.30 gC/m\({}^{2}\)/d). Similarly, \(\text{GPP}_{\text{EC-LUE}}\) tracked \(\text{GPP}_{\text{EC}}\) well at the US-Rws site, with the lowest RMSE (1.01 gC/m\({}^{2}\)/d) and bias (\(-\)0.72 gC/m\({}^{2}\)/d). However, there were still substantial underestimations and overestimations in some sites among \(\text{GPP}_{\text{MODIS-GPP}}\), \(\text{GPP}_{\text{EC-LUE}}\) and \(\text{GPP}_{\text{EC}}\). For example, for the MODIS-GPP algorithm, at the US-Bi2 site, \(\text{GPP}_{\text{MODIS-GPP}}\) were underestimated (RMSE = 7.70 gC/m\({}^{2}\)/d), bias = \(-\)4.53 gC/m\({}^{2}\)/d). For the EC-LUE model, GPP fluxes were underestimated at several sites, such as the US-Bi2 site (RMSE = 7.80 gC/m\({}^{2}\)/d, bias = \(-\)4.15 gC/m\({}^{2}\)/d) and the US-GLE site (RMSE = 2.31 gC/m\({}^{2}\)/d, bias = \(-\)1.39 gC/m\({}^{2}\)/d), but \(\text{GPP}_{\text{EC-LUE}}\) values at the US-PFA site (RMSE = 3.23 gC/m\({}^{2}\)/d, bias = 2.07 gC/m\({}^{2}\)/d) and the US-WCr site (RMSE = 4.05 gC/m\({}^{2}\)/d, bias = 1.71 gC/m\({}^{2}\)/d) were overestimated. Notably, \(\text{GPP}_{\text{MODIS-GPP}}\) at the US-WCr site and \(\text{GPP}_{\text{EC-LUE}}\) at the US-Ton site obtained a low bias but high RMSE. It was because the underestimation of GPP offset part of the deviation during the months of 2017, as well as the overestimation of compensation in 2018 or GPP underestimation during the growing season, alongside overestimation during the nongrowing season. Agreement between \(\text{GPP}_{\text{GR}}\), \(\text{GPP}_{\text{VI}}\) and \(\text{GPP}_{\text{EC}}\)  
The scatter plots between the \(\text{GPP}_{\text{GR}}\), \(\text{GPP}_{\text{VI}}\), and \(\text{GPP}_{\text{EC}}\) for every site are displayed in Figures 5 and 6. The performance of the two models is different at various sites. Therefore, the two models' applicability depends on the biome type. For example, insignificant correlations between the GR and VI models were obtained at the US-Rws site, US-Ton site and US-RIs site. Even for the same biome type, their performance was different. For instance, the GR model had a better performance than the VI model at the US-GLE site. Additionally, strong relationships between \(\text{GPP}_{\text{GR}}\), \(\text{GPP}_{\text{VI}}\) and \(\text{GPP}_{\text{EC}}\) were established at the US-WCr site, US-Bi2 site, US-PFA site, and US-Var site. In particular, the GR and VI models both obtained the best performance at the US-WCr site, with R\({}^{2}\) = 0.84 and 0.81, respectively. Figure 4: Temporal dynamics of GPP\({}_{\text{MODB-GPP}}\), GPP\({}_{\text{EC-LUE}}\), and GPP\({}_{\text{EC}}\) for all sites in 2017–2018. (**a**): evergreen needleleaf forest site, (**b**) deciduous broadleaf forest site, (**c**) mixed forest site, (**d**) closed shrubland site, (**e**) open shrubland site, (**f**) woody savannas site, (**g**) grassland site, (**h**) cropland site. Figure 5: Scatter plots between GPP\({}_{\text{CR}}\) and GPP\({}_{\text{EC}}\) for all sites in 2017–2018. (**a–h**) represent the site the same as Figure 4.  
The time series of \(\text{GPP}_{\text{GR}}\), \(\text{GPP}_{\text{VI}}\) and \(\text{GPP}_{\text{EC}}\) in 2017-2018 are presented in Figure 7. Overall, the temporal dynamics of \(\text{GPP}_{\text{GR}}\) and \(\text{GPP}_{\text{VI}}\) are roughly the same. The \(\text{GPP}_{\text{GR}}\) and \(\text{GPP}_{\text{VI}}\) tracked the \(\text{GPP}_{\text{EC}}\) well at the US-Bi2 site, US-Pra site, US-Var site, and US-WCr site. As shown in Table 6, \(\text{GPP}_{\text{GR}}\) and \(\text{GPP}_{\text{VI}}\) at the US-GLE site were overestimated, with bias = 3.18 gC/m\({}^{2}\)/d and 1.16 gC/m\({}^{2}\)/d, respectively. Additionally, \(\text{GPP}_{\text{GR}}\) and \(\text{GPP}_{\text{VI}}\) were underestimated at the US-Ton site, with bias = \(-\)0.75 gC/m\({}^{2}\)/d and \(-\)1.48 gC/m\({}^{2}\)/d, respectively.