## 3 Results  
### Validation against Site GPP  
We established linear correlations between the SIF-estimated GPP, MODIS GPP, and GLASS GPP (GPP\({}_{\text{SIF}}\), GPP\({}_{\text{MOD}}\), and GPP\({}_{\text{GLASS}}\), respectively) and the flux tower GPP observations (GPP\({}_{\text{EC}}\)) for each vegetation type from 2001 to 2014, as shown in Table 1. For different vegetation types, GPP\({}_{\text{SIF}}\) (R\({}^{2}\) = 0.16-0.78, RMSE = 1.50-4.72 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), bias = -1.77-1.43 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)) performed well compared to GPP\({}_{\text{MODIS}}\) (R\({}^{2}\) = 0.10-0.75, RMSE = 1.12-5.21 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), bias = -2.14-0.54 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)) and GPP\({}_{\text{GLASS}}\) (R\({}^{2}\) = 0.11-0.72, RMSE = 1.31-4.92 gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\), bias = -1.14-2.23 g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)). The poor performances of the different vegetation types may have been because of the small number of sites, or because of insufficient accuracy within the GPP products (e.g., the quality of the global land cover product). It is worth noting that the R\({}^{2}\), RMSE, and the bias of wetlands (WET) did not perform well, meaning that more work is needed before the GPP of this vegetation type can be estimated using GOSIF datasets. The coefficients of determination of the three remote sensing products and GPP\({}_{\text{EC}}\) were small for evergreen broadleaf forests (EBF), savanna (SAV), and woody savannas (WSA), but were slightly higher for deciduous broadleaf forests (DBF), grasslands (GRA), and open shrublands (OSH). This indicates that the vegetation type had a great impact on the GPP estimations of remote sensing products. Referring to Table 1, the RMSE of the GPP\({}_{\text{MODIS}}\)-GPP\({}_{\text{EC}}\) model was the largest among the ten vegetation types. Overall, GPP\({}_{\text{SIF}}\), GPP\({}_{\text{MOD}}\), and GPP\({}_{\text{GLASS}}\) underestimated the GPP for croplands (CRO), DBF, evergreen needleleaf forests (ENF), mixed forests (MF), SAV, and WSA, whereas they overestimated GPP for OSH. More than half of the GPP values were underestimated by GPP\({}_{\text{MOD}}\). MODIS GPP underestimated vegetation GPP, which has also been shown consistently in other studies [85; 86]. For all sites, the correlations of GPP\({}_{\text{SIF}}\)-GPP\({}_{\text{EC}}\), GPP\({}_{\text{MODIS}}\)-GPP\({}_{\text{EC}}\), and GPP\({}_{\text{GLASS}}\)-GPP\({}_{\text{EC}}\) were similar, but GPP\({}_{\text{SIF}}\)-GPP\({}_{\text{EC}}\) had the lowest RMSE and bias. To some extent, GOSIF demonstrated the ability to estimate GPP, and SIF outperformed the LUE model with regards to estimating GPP. The poor performances of GPP\({}_{\text{MODIS}}\) and GPP\({}_{\text{GLASS}}\) may have been because MODIS and GLASS used the LUE model to calculate GPP. \(\varepsilon_{p}\) is a key parameter within the LUE model, but it exhibits great variability within a single biological community. In the LUE model, \(\varepsilon_{p}\) is often set as a fixed parameter according to a specific vegetation type, leading to great uncertainty in the estimation of GPP. Moreover, the vegetation index is not sensitive to changes in plant photosynthetic status when vegetation is under stress (e.g., due to temperature extremes or limited water). This could also lead to inaccuracies in the model. In contrast, SIF is very sensitive to plant growth status.