## 3 Results
### Relationships of SIF757, GOSIF, and MYD with GPP\({}_{\rm EC}\) for Different Vegetation Types  
We used SIF757-GPP\({}_{\rm EC}\), GOSIF-GPP\({}_{\rm EC}\), and MYD-GPP\({}_{\rm EC}\) models to estimate the GPP (GPP\({}_{\rm SIF}\), GPP\({}_{\rm GOSIF}\), and GPP\({}_{\rm MYD}\), respectively) for each vegetation type and then established linear correlation between GPP\({}_{\rm SIF}\), GPP\({}_{\rm GOSIF}\), GPP\({}_{\rm MYD}\), and GPP\({}_{\rm EC}\), as shown in Figure 2. On the whole, GPP\({}_{\rm GOSIF}\) (R = 0.62-0.95, _p_! 0.001) was most strongly correlated with GPP\({}_{\rm EC}\), compared with GPP\({}_{\rm MYD}\) (R = 0.67-0.92, _p_! 0.001) and GPP\({}_{\rm SIF}\) (R = 0.28-0.86, _p_! 0.001). The amount of SIF757 is small, which directly leads to a poor performance of the GPP\({}_{\rm SIF}\)-GPP\({}_{\rm EC}\) model, especially for (g) open shrulands (OSH), for which only seven observations are available. Moreover, the insufficient accuracy of a small portion of OCO-2 SIF757 products is also responsible for the performance of the GPP\({}_{\rm SIF}\)-GPP\({}_{\rm EC}\) model. Referring to Table 3, an interesting phenomenon is worth noting, in that GPP\({}_{\rm MYD}\) exhibits a poor correlation with GPP\({}_{\rm EC}\) in terms of a single flux tower. However, a good correlation between GPP\({}_{\rm MYD}\) and GPP\({}_{\rm EC}\) was witnessed in terms of the vegetation type, as shown in Figure 3. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Site ID**} & \multicolumn{3}{c}{**R\({}^{2}\)**} \\ \cline{2-4}  & **MYD-GPP\({}_{\rm EC}\)** & **GOSIF-GPP\({}_{\rm EC}\)** & **SIF757-GPP\({}_{\rm EC}\)** \\ \hline \multicolumn{4}{c}{**Mixed Forest**} \\ US-PFa & 0.57 & 0.79 & 0.99 \\ US-Syv & 0.62 & 0.86 & 0.67 \\ \multicolumn{4}{c}{**Permanent Wetlands**} \\ US-Los & 0.58 & 0.88 & 0.66 \\ US-PHM & 0.62 & 0.81 & 0.70 \\ US-Tw1 & 0.37 & 0.70 & 0.11 \\ US-Tw4 & 0.45 & 0.71 & 0.08 \\ US-ALQ & – & 0.13 & 0.41 \\ \multicolumn{4}{c}{**Evergreen Needleleaf Forests**} \\ CA-SCC & 0.74 & 0.87 & 0.74 \\ CA-TP1 & 0.57 & 0.57 & 0.08 \\ CA-TP3 & 0.64 & 0.64 & 0.56 \\ CA-TP4 & 0.76 & 0.78 & 0.76 \\ US-GLE & – & 0.82 & 0.52 \\ \multicolumn{4}{c}{**Croplands**} \\ US-Twt & 0.31 & 0.52 & 0.26 \\ US-Ro2 & 0.22 & 0.15 & 0.43 \\ US-Tw3 & – & 0.24 & 0.18 \\ \multicolumn{4}{c}{**Deciduous Broadleaf Forests**} \\ CA-TPD & 0.79 & 0.88 & 0.49 \\ US-UMd & 0.42 & 0.76 & 0.52 \\ US-WCr & 0.62 & 0.82 & 0.53 \\ \multicolumn{4}{c}{**Grasslands**} \\ US-KFS & 0.30 & 0.37 & 0.51 \\ US-KLS & 0.15 & 0.16 & 0.56 \\ US-Ro4 & 0.62 & 0.85 & 0.91 \\ \multicolumn{4}{c}{**OSH**} \\ US-EML & 0.69 & 0.70 & 0.69 \\ \multicolumn{4}{c}{**CSH**} \\ US-RIs & 0.70 & 0.73 & 0.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of correlation coefficients between the MODIS, GOSIF, SIF757, and EC gross primary production (GPP) at a single tower (parameter: R\({}^{2}\)). Figure 3: Annual average GPP of different vegetation types from 2015 to 2017. The vegetation type is represented by the x-coordinate, and the annual average GPP is represented by the y-coordinate. Green represents \(\text{GPP}_{\text{EC}}\), yellow represents \(\text{GPP}_{\text{MYD}}\), and orange represents \(\text{GPP}_{\text{GOSIF}}\). Figure 2: Validation of the \(\text{SIF-GPP}_{\text{EC}}\), \(\text{GOSIF-GPP}_{\text{EC}}\), and \(\text{MYD-GPP}_{\text{EC}}\) models. Orange squares indicate the \(\text{GPP}_{\text{MYD}}\)-\(\text{GPP}_{\text{EC}}\) model data points, green squares indicate \(\text{GPP}_{\text{GOSIF}}\)-\(\text{GPP}_{\text{EC}}\) model data points, and red triangles indicate \(\text{GPP}_{\text{SIF}}\)-\(\text{GPP}_{\text{EC}}\) model data points. Figure 3 shows the annual average \(\mathrm{GPP_{EC}}\), \(\mathrm{GPP_{MYD}}\), and \(\mathrm{GPP_{GOSIF}}\) for different vegetation types. It can be concluded that the annual average GPP of evergreen needleleaf forests (ENF) is the highest, and that of OSH is the lowest.