## 4 Discussion  
There is a significant linear relationship between SIF and GPP. We demonstrated that SIF757 is better than SIF771 in estimating GPP, which is consistent with many previous research results [12; 23]. This is because 771 nm falls farther away from peak emission on the SIF spectrum and OCO-2 SIF757 is considered to have a higher level of retrieval precision than SIF771 [12]. Our results demonstrate that the linear relationship between SIF in nadir mode and GPP is slightly better than that for SIF in glint and target mode. We believe that SIF data in nadir mode has the highest spatial resolution. In this study, we can conclude that the ability of GOSIF and SIF757 to evaluate the GPP of a single tower is not significantly different. However, for different vegetation types, GOSIF was better at estimating GPP than SIF757 [19]. One of the main reasons for this is that the footprint of the OCO-2 is sparse. OCO-2 has a fine spatial resolution, which provides us with an unprecedented opportunity to explore the relationship between OCO-2 SIF and GPP of different vegetation types at an ecosystem scale. However, the amount of SIF data of OCO-2 around each flux tower is small. This causes the following problems. First, at present, the main research on SIF aims to estimate GPP by SIF. Many researches have demonstrated that there is a strong linear relationship between SIF and GPP compared with the traditional vegetation index [40; 41]. There is a clear and simple mechanism for estimating GPP using SIF [42]. However, SIF estimates of the GPP have been limited by the satellite spatial resolution. The GOME-2 data covers the globe and has a long time series, but the noise of the data leads to a poor GOME-2 SIF data quality [43]. OCO-2 data has a high spatial resolution, but the data volume is small and the time series is short. Currently, there are no satellites dedicated to SIF observation, which will limit our ability to use SIF to estimate GPP. Therefore, we also cannot accurately estimate global carbon budgets. Secondly, many researchers have shown that the significance of the linear relationship between SIF and GPP for different vegetation types is different [13; 44]. OCO-2 has a small amount of data, which affects our ability to explore SIF to estimate GPP for different vegetation types. Thirdly, in this study, the seasonal cycle of SIF757 presents a'single peak'. However, because the data volume of SIF is too small, we cannot compare whether the seasonal cycles of OCO-2 SIF and GPP\({}_{\text{EC}}\) from 2015 to 2017 are advanced or delayed. Therefore, if we use OCO-2 SIF to explore the phenology of vegetation, it will be a great challenge [24]. Moreover, most flux tower data are not publicly available. Hence, we validated SIF data by using flux tower data on a global scale, which produces limitations. Above all, scientists using OCO-2 SIF data to estimate global GPP will also face huge challenges. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **GOSIF** & **SIF757** & **MYD** \\ \hline CRO & 0.37 & 0.24 & 0.56 \\ GRA & 0.72 & 0.67 & 0.68 \\ MF & 0.87 & 0.71 & 0.82 \\ ENF & 0.74 & 0.37 & 0.71 \\ DBF & 0.91 & 0.60 & 0.87 \\ CSH & 0.69 & 0.36 & 0.58 \\ OSH & 0.68 & 0.69 & 0.39 \\ WET & 0.88 & 0.63 & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Correlation coefficients (R\({}^{2}\)) of GPP\({}_{\text{GOSIF}}\)-GPP\({}_{\text{EC}}\), GPP\({}_{\text{SIF}}\)-GPP\({}_{\text{EC}}\), and GPP\({}_{\text{MYD}}\)-GPP\({}_{\text{EC}}\) models with an extraction range of 25 km. Although OCO-2 has a high temporal and spatial resolution, the dataset still cannot match flux towers well on an ecological scale [19; 23]. On one hand, in order to get more OCO-2 SIF data in this study, we had to expand our study area to 25 km. However, the expansion of the research scope led to an increase in mixed pixels, and the increase in pixel heterogeneity may have had an impact on our research results. On the other hand, measuring GPP by EC is complicated with many factors influence the final measurement.