## 3 Results  
### Selection of Appropriate Bands and Timescales  
Figure 3 illustrates the linear relationship between instantaneous SIF of different bands and instantaneous GPP. In general, there is a strong linear relationship between SIF and GPP. For most towers, ins757 was more strongly correlated to tower-measured GPP than ins771. Therefore, the ins757 could be a better proxy to estimate instantaneous GPP than ins771. Our results are consistent with the hypothesis of a previous study [18]. A plausible reason to explain such results would be that 771 nm is farther away from the peak of SIF spectrum than 757 nm. We also speculate that the insufficient sample size would lead to an unstable and inaccurate model and that could be responsible for the exception [12].  
The linear relationships between daily SIF and daily GPP at 12 sites were shown in Figure 4. Overall, R\({}^{2}\) of linear models for daily GPP estimating are evidently higher than those for instantaneous GPP. Besides, the advantage of SIF products of 757 nm has been further strengthened over SIF products of 771 nm in terms of estimations of daily GPP. Consequently, we concluded that OCO-2 SIF products of 757 nm are better than those of 771 nm in estimating GPP, and it is better to estimate daily GPP  
Figure 3: The linear relationship between instantaneous solar-induced chlorophyll fluorescence (SIF) and instantaneous gross primary productivity (GPP) for different EC flux towers. The red circle represents the data of ins757 while the black dot represents the data of ins771. The linear relationships are presented in colorful lines for different independent variables.  
than instantaneous GPP. In order to figure out which timescale of the SIF data is more suitable for estimating GPP, we compare the coefficient of determination R\({}^{2}\) of ins757-GPP and daily757-GPP in Table 2. Except for CA-TP4, CA-SCC, US-EML and US-Los, R\({}^{2}\) of -SIF757-GPP at other sites is significantly higher than that of ins757-GPP. Therefore, we can conclude that daily SIF data is more suitable for estimating GPP.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Site** & **ins757-GPP** & **Daily757-GPP** \\ \hline US-Srr & 0.30 (\(p<0.001\)) & 0.53 (\(p<0.001\)) \\ US-Wcr & 0.67 (\(p<0.001\)) & 0.80 (\(p<0.001\)) \\ US-Svy & 0.18 (\(p<0.001\)) & 0.61 (\(p<0.001\)) \\ US-PTa & 0.60 (\(p<0.001\)) & 0.72 (\(p<0.001\)) \\ CA-TP3 & 0.40 (\(p<0.001\)) & 0.47 (\(p<0.001\)) \\ CA-TP4 & 0.47 (\(p<0.001\)) & 0.41 (\(p<0.001\)) \\ CA-SCC & 0.74 (\(p<0.001\)) & 0.68 (\(p<0.001\)) \\ US-EML & 0.81 (\(p<0.001\)) & 0.73 (\(p<0.001\)) \\ US-PHM & 0.60 (\(p<0.001\)) & 0.75 (\(p<0.001\)) \\ US-Ro4 & 0.74 (\(p<0.001\)) & 0.87 (\(p<0.001\)) \\ US-TPD & 0.36 (\(p<0.001\)) & 0.40 (\(p<0.001\)) \\ US-Los & 0.81 (\(p<0.001\)) & 0.64 (\(p<0.001\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of R\({}^{2}\) of SIF-GPP models at instantaneous and daily scales.  
Figure 4: The linear relationship between daily SIF and daily GPP for different EC flux towers. The red circle represents the data of daily757 while the black dot represents the data of daily771. The linear relationships are presented in colorful lines for different independent variables.