## 3 Results  
### Meteorology Variables  
In this study, the MERRA2 meteorology reanalysis data were taken as site meteorological data for four GPP estimation models. For determining the meteorological data effects on GPP estimation, we conducted a direct comparison of daily MERRA2 meteorology reanalysis data and site meteorological data (Figure 2). The two datasets have a strong correlation with T2M_MIN and T2M_MEAN (Figure 2a,b), suggesting that the MERRA2 meteorology reanalysis data can be reliably applied to estimate the on-site temperature conditions. The IPAR (Figure 2c) at the eight sites had a high correlation with the measured data. The R2 was above 0.8, and the scatter distribution was very close to the 1:1 line, which indicated that the on-site IPAR could be reliably calculated from the MERRA2 meteorology reanalysis data. For VPD (Figure 2d), R2 varied at different sites, and the range of R2 was 0.51-0.95. Among them, VPD at the US-WCr (DBF) and US-Bi2 (CRO) obtained a relatively low accuracy, with R2 = 0.54 and 0.62, respectively. The VPD across shrublands obtained the highest accuracy (R2 = 0.95). Agreement between GPP\({}_{\text{MODIS-GPP}}\), GPP\({}_{\text{EC-LUE}}\), GPP\({}_{\text{MOD17}}\) and GPP\({}_{\text{EC}}\)  
We first compared the performance of GPP\({}_{\text{MODIS-GPP}}\) and GPP\({}_{\text{EC-LUE}}\) against that of the GPP\({}_{\text{EC}}\) at all sites for 2017 and 2018 (Figure 3). For the MODIS-GPP algorithm, the results showed that the US-Ton site obtained the best performance (R\({}^{2}\) = 0.73), followed by US-GLE with R\({}^{2}\) = 0.72, US-Bi2 with R\({}^{2}\) = 0.68, US-WCr with R\({}^{2}\) = 0.67, US-PFA with R\({}^{2}\) = 0.66, and US-Rls with R\({}^{2}\) = 0.63 (Table 5). The R\({}^{2}\) values between the GPP\({}_{\text{MODIS-GPP}}\) and GPP\({}_{\text{EC}}\) at the US-Var site and US-Rws site were lower than 0.5. In terms of the RMSE, the US-Bi2 site produced the maximum error (RMSE = 7.70 gC/m\({}^{2}\)/d) and the US-Ton site displayed the lowest error with RMSE = 1.15 gC/m\({}^{2}\)/d. For the EC-LUE model, the results showed that the US-Ton site obtained the best performance (R\({}^{2}\) = 0.74), followed by the US-Var with R\({}^{2}\) = 0.67, US-WCr with R\({}^{2}\) = 0.58, US-Rws with R\({}^{2}\) = 0.57, US-PFA with R\({}^{2}\) = 0.56, and US-Bi2 with R\({}^{2}\) = 0.53. However, insignificant correlations were obtained at the US-GLE site, and this situation was mainly influenced by the ENF when using EC-LUE model [(32; 75)]. For MODIS-GPP products, the US-GLE site exhibited the best performance, with R\({}^{2}\) = 0.92. At the US-Rws site and US-Var site, GPP\({}_{\text{MOD17}}\) performed relatively poorly, with R\({}^{2}\) = 0.48 and 0.46, respectively. These results were consistent with GPP\({}_{\text{MODIS-GPP}}\). It is worth noting that the performance of GPP\({}_{\text{MOD17}}\) was worse than GPP\({}_{\text{MODIS-GPP}}\) for the US-Ton, US-Var, and US-Bi2 site and worse than GPP\({}_{\text{EC-LUE}}\) for the US-Rws, US-Ton, and US-Var site. Figure 2: Scatter plots of daily Modern-Era Retrospective analysis for Research and Applications (MERRA2) meteorology reanalysis data against daily site meteorology data: (**a**) T2M\({}_{\text{MIN}}\); (**b**) T2M\({}_{\text{MEAN}}\); (**c**) IPAR; and (**d**) VPD from different biome types. Figure 3: Scatter plots between \(\text{GPP}_{\text{MODISGPP}}\), \(\text{GP}_{\text{PEC-LUE}}\), \(\text{GP}_{\text{MOD17}}\), and \(\text{GPP}_{\text{EC}}\) for all sites in 2017–2018. The left column refers to the MODIS-GPP model, the middle column represents the EC-LUE model, and the right column represents the MODIS-GPP products. Figure 4 illustrates the temporal variation of \(\text{GPP}_{\text{MODIS-GPP}}\), \(\text{GPP}_{\text{EC-LUE}}\) and \(\text{GPP}_{\text{EC}}\) for all sites. Overall, \(\text{GPP}_{\text{MODIS-GPP}}\) and \(\text{GPP}_{\text{EC-LUE}}\) effectively matched \(\text{GPP}_{\text{EC}}\) and generally captured the seasonal variations aligned with the \(\text{GPP}_{\text{EC}}\).