## 3 Results
### Simulation Results by the Fluspect Model with/without the FQE Inversion Code
Reflectance and transmittance data were applied to perform the simulation, along with the fluorescence spectra of PS II and PS I as a basis for the doubling algorithm. To improve the model, an FQE II and I retrieval code was added to the model to more accurately simulate the fluorescence. With the same measured data, the simulation was performed again to obtain new, improved simulation results. From Figure 4 and Table 2, the simulation results with the inversion code are clearly better than those without, as the overestimated components of the first and second peaks were adjusted (with \(R^{2}=0.9\)-\(0.98\) and \(RMSE=0.24\times 10^{-4}\)-\(1.0\times 10^{-4}\) Wm\({}^{-2}\)um\({}^{-1}\)sr\({}^{-1}\)). This means that FQE II and I play an important role in the emission of fluorescence in the first and second peaks. After adjusting the FQE to minimize the difference between the measured and simulated fluorescence, the simulation results will be better. Moreover, we noticed that the overestimation of red SIF from [PERSON] et al. (2016) was attributed to the calibration of the emission spectrum rather than to the FQE. The normalized Pearson, \(R^{2}\), and \(RMSE\) values between the independent in situ measurements and the simulated fluorescence were investigated. The simulated fluorescence of the representative leaves was compared with the measured fluorescence for all wavelengths, with respect to the distribution of the curve in Table 2. The values in the table demonstrate a strong relationship between the simulated fluorescence and the measured data. The \(R^{2}\) and Pearson indices were much higher than the results from the model without the inversion code, and the total \(R^{2}\) was higher than 0.9. This indicates that both the simulated upward and downward fluorescence values fit well with the measured data; however, these indices correspond to the correlation for all wavelengths together (660 nm to 850 nm with 1 nm resolution). The model can simulate the fluorescence relatively well over the entire wavelength with the FQE inversion code (with \(R^{2}=0.9\)-\(0.98\) and \(RMSE=0.24\times 10^{-4}\)-\(1.0\times 10^{-4}\) Wm\({}^{-2}\)um\({}^{-1}\)sr\({}^{-1}\)). As shown in Figure 4, the model dramatically reduced the overestimation of fluorescence in the two peaks compared to that without the FQE inversion code. As shown in Figure 4, for high and low chlorophyll contents at the top and middle layers of leaves, PS II and PS I were assigned a value after adding the FQE inversion code. These values were smaller than the default values, which were assigned basic values in the model simulation. Thus, the addition of the inversion code improved the simulated curves. The simulation results in Figure 4 show a good correlation with the measured data, with most of the \(R^{2}\) values being above 0.9 for the improved simulated results; the peaks of the simulation results are much closer to those of the measured data than the original simulated results were without the FQE inversion code. The corresponding simulation parameters for the leaves with high chlorophyll content are listed in Table 3. The vegetation parameters were also retrieved and did not differ from those in Table 3. The improvement in the simulation results indicates that the FQE values were related to the fluorescence simulation. In addition to these representative leaf samples, the \(R^{2}\) for all the measured leaves is plotted in Figure 5. The total \(R^{2}\) without the FQE inversion code is above 0.7, and that with the code is above 0.95.