## 3 Results
### Results of Reflectance and GPP Simulations  
The simulated canopy reflectance and GPP separately contain information on two aspects: one is the leaf and canopy characteristic represented by RTMo module, and the other one is the plant physiological and photosynthesis state represented by the biochemical module. Both aspects impact the simulating accuracy of SIF. Therefore, the results of reflectance and GPP simulations should be inspected before the evaluation of SIF simulations. Figure 5 displays the results of the simulated and measured reflectance spectra and their residuals at 10:00 for every fieldwork day. In general, the simulated reflectance spectra fit the measured ones well: the residual absolute values are less than 0.03 in the full region of 650 nm to 800 nm for all ten fieldwork days. Specifically, the simulated and measured reflectance spectra are approximately the same in the NIR from 750 nm to 800 nm (the residual absolute values in this region are less than 0.012), except for 25 April 2015. However, in the red and red edge region from 650 nm to 750 nm, the residuals between the two reflectance spectra are slightly higher. Figure 6 shows the RMSE statistics between the simulated and measured reflectance spectra for 106 spectral measurements in 2015 and 2016. As illustrated, the model reproduces the reflectance well: the RMSE values for 106 spectral measurements are between 0.0041 and 0.0437, and most of (67%) the RMSE values are lower than 0.02. According to [29], the leaf and canopy parameters--including LIDFa, LAI, Cab, and Cdm--together govern the variation in the reflectance spectra from 650 nm to 800 nm. So, reflectance simulation accuracy depends on the joint accuracy of these parameters. All these results indicate that using the measured and retrieved vegetation parameters as inputs, the SCOPE model can accurately model the leaf and canopy characteristic and reproduce the reflectance spectra. Figure 5: The simulated and measured reflectance spectra and their residuals at 10:00 for every fieldwork day in 2015 and 2016. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**2015**} & \multicolumn{6}{c}{**2016**} \\ \cline{2-11}  & **April 3** & **April 13** & **April 14** & **April 24** & **April 25** & **April 8** & **April 9** & **April 18** & **May 3** & **May 4** \\ \hline LIDFa & \(-0.975\) & \(-0.875\) & \(-0.875\) & \(-0.625\) & \(-0.625\) & \(-0.875\) & \(-0.875\) & \(-0.75\) & \(-0.85\) & \(-0.85\) \\ V\({}_{\text{cmo}}\) & 50 & 80 & 80 & 110 & 110 & 45 & 55 & 65 & 95 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The retrieved daily LIDFa and V\({}_{\text{cmo}}\) values (unit: \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\)) on ten fieldwork days in 2015 and 2016. Figure 7 shows the diurnal cycles of the simulated and measured GPP on ten fieldwork days in 2015 and 2016. On one hand, the absolute intensities of the two GPP data sets (simulated and measured diurnal GPP) agree well: the RMSEs of two GPP data sets range from 1.514 umol m\({}^{-2}\) s\({}^{-1}\) to 5.627 umol m\({}^{-2}\) s\({}^{-1}\), and the corresponding relative root mean square error (RRMSE) values range from 10.31% to 29.65% on the ten fieldwork days. On the other hand, the diurnal patterns of simulated GPP agree well with measured GPP on most fieldwork days, except for 18 April 2016, when the GPP observations were likely inaccurate and inconsistent with the PAR changes (see Figure 1b). This result indicates that the simulated diurnal GPP matches the PAR changes better than the measured GPP. This is because SCOPE's biochemical module can track the weather fluctuations via the meteorological inputs and thus accurately simulate the GPP, while the NEE observations are likely to be disturbed by changing air parameters like wind speed and direction. For further illustration, Figure 8 displays the correlation and RRMSE values between simulated and measured GPP for all half-hour flux observations in 2015 and 2016.