## 3 Results
### Evaluation of SIF Simulations
#### 3.3.1 Evaluation of SIF Simulations with Fixed FQE
In addition, the bias values for \(\mathrm{SIF_{B}}\) and \(\mathrm{SIF_{A}}\) were mostly at different levels or even opposite in sign. For example, on 24 & 25 April 2015, the \(\mathrm{SIF_{A}}\) was largely underestimated, while the \(\mathrm{SIF_{B}}\) was overestimated; on 3 May 2016, the \(\mathrm{SIF_{B}}\) was greatly overestimated while the simulated \(\mathrm{SIF_{A}}\) values were just right. It implied that the SCOPE-simulated \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) with fixed FQE was probably unreliable, which should be investigated further. Second, we evaluated the simulating accuracy of \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) to investigate whether the SCOPE model can reproduce the \(\mathrm{SIF}\) spectral shape with fixed FQE. Figure 10 displays the correlation and RRMSE values between the diurnally simulated and measured \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) with fixed FQE for all 106 spectral measurements (Figure 10a) and the corresponding daily RRMSE for each single fieldwork day (Figure 10b). As illustrated, the SCOPE-simulated \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) values were not satisfying: for all 106 spectral measurements, most scatters were located far away from the 1:1 line with an \(R^{2}\) of only 0.0286 and an RRMSE of nearly 30%, and most daily RRMSEs were larger than 25% (with a range from 23.57% to 33.29%) on ten fieldwork days. In addition, the measured \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) values changed with a range from 0.593 to 3.460, while the simulated \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) values were relatively stable with a range from 1.552 to 2.021 across wheat's growing season in 2015 and 2016. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**O\({}_{2}\)-B**} & \multicolumn{2}{c}{**O\({}_{2}\)-A**} \\ \cline{3-6}  & & **RRMSE** & \(\mathit{bias}\) & **RRMSE** & \(\mathit{bias}\) \\ \hline \multirow{4}{*}{2015} & April 3 & 25.97\% & \(-\)20.35\% & 11.14\% & \(-\)1.74\% \\  & April 13 & 12.47\% & \(-\)10.78\% & 22.64\% & \(-\)1.96\% \\  & April 14 & 12.13\% & 0.71\% & 28.30\% & 18.29\% \\  & April 24 & 13.43\% & 12.62\% & 22.30\% & \(-\)22.24\% \\  & April 25 & 24.54\% & 13.47\% & 26.43\% & \(-\)13.85\% \\ \hline \multirow{4}{*}{2016} & April 8 & 20.23\% & \(-\)13.70\% & 12.07\% & 6.93\% \\  & April 9 & 19.53\% & \(-\)5.83\% & 18.77\% & 12.43\% \\ \cline{1-1}  & April 18 & 29.54\% & 12.99\% & 21.69\% & 5.58\% \\ \cline{1-1}  & May 3 & 40.13\% & 38.16\% & 8.94\% & 6.43\% \\ \cline{1-1}  & May 4 & 38.67\% & 27.79\% & 23.51\% & 10.84\% \\ \hline \multicolumn{2}{c}{all ten days} & 24.35\% & — & 23.67\% & — \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The relative root mean square error (RRMSE) and bias of diurnal \(\mathrm{SIF}\) simulations with fixed FQE at O\({}_{2}\)-B and O\({}_{2}\)-A bands on ten fieldwork days. Table 5 concludes the quantitative assessment of systematic deviation in the \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) simulations at all growth stages. The ratio of daily \(\mathrm{SIF_{A}}\) and \(\mathrm{SIF_{B}}\) averages (\(\overline{\mathrm{SIF_{A}}}\)/\(\overline{\mathrm{SIF_{B}}}\)) was calculated to express the systematic deviation, because the diurnal variations of \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) are dominated by the geometry of incidence and observation, making it more difficult to reflect seasonal variations of systematic deviation. As reported in Table 5, the simulated \(\overline{\mathrm{SIF_{A}}}\)/\(\overline{\mathrm{SIF_{B}}}\) values were different from the measured ones at wheat's erecting and booting or flowering period, with RE's absolute value larger than 20%. Note that the RE variation in \(\mathrm{SIF_{A}}\)/\(\mathrm{SIF_{B}}\) simulations shows a consistent seasonal pattern between 2015 and 2016 that opposes the bias variation in \(\mathrm{SIF_{B}}\) simulations (as mentioned previously).