## 3 Results
### Comparisons of Seasonal Cycles of Different GPP Estimates  
We compared the seasonal trends of \(\mathrm{GPP_{EC}}\) and that of SIF757 for eight vegetation types, as shown in Figure 4. Overall, SIF757 demonstrates a single peak state within one year, which is consistent with the growth pattern of vegetation. However, Figure 4 also shows that the amount of SIF757 data for each vegetation type from 2015 to 2017 is small. This is attributed to the fact that the OCO-2 footprint is sparse in space and does not match well with the flux tower. Therefore, it would be difficult to estimate the global distribution of GPP with a high accuracy using SIF757. Due to inadequate amounts of data, it is impossible to judge whether the seasonal cycle of the eight vegetation types is advanced or delayed. In Figure 4a, croplands (CRO), and in Figure 4d, ENF, SIF757 do not demonstrate a clear single peak. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & \(\mathrm{GPP_{GOSIF}}\) & \(\mathrm{GPP_{SIF}}\) & \(\mathrm{GPP_{MYD}}\) \\ \hline CRO & 3.121 & 4.578 & 3.326 \\ GRA & 3.154 & 3.402 & 3.253 \\ MF & 2.160 & 2.437 & 1.856 \\ ENF & 3.695 & 3.873 & 3.884 \\ DBF & 2.566 & 3.262 & 2.585 \\ CSH & 1.187 & 1.938 & 1.157 \\ OSH & 2.346 & 2.609 & 2.138 \\ WET & 3.981 & 2.399 & 1.245 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical comparisons of the \(\mathrm{GPP_{GOSIF}}\)-\(\mathrm{GPP_{EC}}\), \(\mathrm{GPP_{SIF}}\)-\(\mathrm{GPP_{EC}}\), and \(\mathrm{GPP_{MYD}}\)-\(\mathrm{GPP_{EC}}\) models from 2015 to 2017 for different vegetation types. Parameters: RMSE. The units are \(\mathrm{gCm^{-2}day^{-1}}\). The seasonal cycle of \(\text{GPP}_{\text{EC}}\) and MODIS GPP (MYD17A2H) for different vegetation types is shown in Figure 5. On the whole, the trends of MODIS GPP and \(\text{GPP}_{\text{EC}}\) are similar for eight vegetation types. However, MODIS GPP significantly underestimated GPP for (a) CRO, (b) CSH, (c) DBF, (d) ENF, (e) GRA, (f) MF, and (h) WET. This is because the \(\text{LUE}_{\text{P}}\) of these eight vegetation types is set too low in the LUE model, resulting in the low MODIS GPP value of the final estimate. Only for (g) OSH was the MODIS GPP value close to \(\text{GPP}_{\text{EC}}\) in 2016 and 2017. It can also be seen in Figure 5 that the MODIS GPP fluctuates greatly after reaching its peak in summer, especially for (c) CSH, (f) MF, and (g) OSH. MODIS GPP reaches its peak in February 2015 for (e) GRA and (h) WET, suggesting erroneous seasonal cycles of MODIS GPP products. However, MODIS GPP has a longer time series than OCO-2 SIF. MYD17A2H can thus be used to monitor changes in the vegetation cover. Figure 4: The seasonal trends of tower GPP and SIF757 from January 1, 2015 to January 1, 2017, for different vegetation types: (**a**) CRO, (**b**) CSH, (**c**) DBF, (**d**) ENF, (**e**) GRA, (**f**) MF, (**g**) OSH, and (**h**) WET. The dot indicates flux tower GPP measurements, and the triangle indicates OCO-2 SIF757 data. We also evaluated seasonal cycles of GPP\({}_{\text{EC}}\) and GOSIF data for each vegetation type from 2015 to 2017, as shown in Figure 6. The seasonal cycle of GOSIF was the most consistent with GPP\({}_{\text{EC}}\) in all three datasets, verifying the conclusion that the linear correlation between GOSIF and GPP\({}_{\text{EC}}\) is the highest. Especially for (c) DBF, (f) MF, and (g) OSH, the seasonal trend of GOSIF is exactly consistent with that of GPP\({}_{\text{EC}}\). However, the advanced seasonal cycle of (e) GRA and (h) WET and lagged seasonal cycle of (b) CSH suggest significant errors in GOSIF data for those vegetation types. However, there may be errors in the GPP\({}_{\text{EC}}\) data of (a) CRO in 2017, which led to the low coincidence of seasonal cycles of GOSIF. Figure 5: The seasonal trends of tower GPP and MODIS GPP (MYD17A2H) from January 1, 2015 to January 1, 2017, for different vegetation types: (**a**) CRO, (**b**) CSH, (**c**) DBF, (**d**) ENF, (**e**) GRA, (**f**) MF, (**g**) OSH, and (**h**) WET.