## 3 Results
### Model Development by Exclusive Use of SIF Data  
The study also examined the relationships between these 8-day tower-based GPPs and the associated SIF products at the two subtropical forest sites. As shown in Figure 5, the GPP at both the QYZ and DHS sites exhibited strong correlations to these satellite-derived SIF retrievals, revealing the great potential to monitor the dynamics of GPP by the remote-sensing-based SIF technique. The correlation between GPP and SIF reached up to 0.909 (\(p<0.01\)) in the evergreen needleleaf forest. Although the \(r\) value was slightly lower (\(r=0.589\)) in the evergreen broadleaf forest, it also exhibited a strong linear relationship with SIF.  
On this basis, the work proposed the models for quantifying the seasonal dynamics of GPP at the two subtropical forest ecosystems by exclusive use of satellite-based SIF data. Then, this study evaluated the model-estimated GPP performance using the in situ measured GPP through leave-one-out cross-validation method (Table 2). At the evergreen needleleaf forest, the regressions of site-level GPP observations and GPP estimates in the cross-validation implied \(R^{2}\) changed between 0.75 and 0.94, and the associated \(RMSE\) values varied from 0.71 to 1.11 g C m\({}^{-2}\) d\({}^{-1}\) for the GPP estimates by withholding years. Meanwhile, \(R^{2}\) changed between 0.28 and 0.61, and the associated \(RMSE\) values varied from 0.80 to 1.03 g C m\({}^{-2}\) d\({}^{-1}\) for the GPP estimates by withholding years at the evergreen broadleaf forest. For all site-years measurements, the regression of in situ measured GPP and the estimated GPP in the cross-validation implied that \(R^{2}=0.82\) and \(RMSE=0.93\) g C m  
Figure 4: Seasonal trajectories of solar radiation (R\({}_{\rm g}\)), air temperature (T\({}_{\rm a}\)), vapour pressure deficit (VPD) and precipitation (P) during 2003–2010 at the DHS flux site (evergreen broadleaf forest) for 8-day periods. For \(x\)-axis, the data refer to week of year (WOY) ranging from 1 to 46.  
at the QYZ site (Figure 6a), and \(R^{2}=0.35\) and \(RMSE=0.95\) g C m\({}^{-2}\) d\({}^{-1}\) at the DHS site (Figure 6b). As illustrated in Figure 6, the scatterplots between GPP observations and GPP estimates were close to the 1:1 regression line, demonstrated by the great performance of such SIF-based method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Validation Year** & \(R^{2}\) & \(RMSE\) \\ \hline \multirow{6}{*}{
\begin{tabular}{c} QYZ \\ (Evergreen \\ needleleaf forest) \\ \end{tabular} } & 2003 & 0.78 & 0.98 \\ \cline{2-4}  & 2004 & 0.94 & 0.90 \\ \cline{2-4}  & 2005 & 0.90 & 0.71 \\ \cline{2-4}  & 2006 & 0.86 & 0.88 \\ \cline{2-4}  & 2007 & 0.82 & 0.89 \\ \cline{2-4}  & 2008 & 0.87 & 0.85 \\ \cline{2-4}  & 2009 & 0.83 & 0.94 \\ \cline{2-4}  & 2010 & 0.75 & 1.11 \\ \cline{2-4}  & 2003 & 0.40 & 0.80 \\ \cline{2-4}  & 2004 & 0.61 & 0.89 \\ \cline{2-4}  & 2005 & 0.42 & 1.03 \\ \cline{2-4}  & 2006 & 0.30 & 0.98 \\ \cline{2-4}  & 2007 & 0.39 & 0.99 \\ \cline{2-4}  & 2008 & 0.45 & 0.91 \\ \cline{2-4}  & 2009 & 0.31 & 1.01 \\ \cline{2-4}  & 2010 & 0.28 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy assessment of the model-estimated GPP relying on time-series SIF data at the two subtropical forest flux sites through leave-one-out cross-validation method.  
Figure 5: Relationships between 8-day mean tower-based GPP and the associated SIF product at the two subtropical forest (QYZ, evergreen needleleaf forest; DHS, Evergreen broadleaf forest) flux sites. The red line is linear regression.