## 4 Discussion
As we focus on the evaluation the SCOPE model for SIF simulation in this study, further attempts on the more accurate estimation of V\({}_{\rm{cmo}}\) was not included. However, there are some uncertainties within the process to derive FQE. Firstly, the SIF retrieved from the spectral data was regarded as the true value for accessing the accuracy of SIF simulation. Based on this assumption, SIF simulations could be validated. However, the SIF retrieval based on 3 FLD method has some uncertainties: the accuracy is dependent on the spectral characteristics of the reflectance and irradiance spectra at the absorption band, and on the spectral resolution and SNR of the sensor used. Thus, the RRMSE of simulated SIF to measured SIF is not the real error of the SCOPE model's SIF simulation. Fortunately, the SIF observations derived from our experiments were sufficiently reliable thanks to the robust retrieval method with high spectral resolution and the SNR of the QE Pro spectrometer. According to the accuracy assessment using simulated data with the same SNR and SR of QE Pro spectrometer in [56; 57], the RRMSE for the SIF retrieved using 3 FLD methods is 13.2% at the O\({}_{2}\)-B band and 9.5% at the O\({}_{2}\)-A bands. Thus, the 3 FLD method can retrieve SIF with sufficient accuracy and provide the reference value for evaluating SIF simulations. Therefore, the quantitative accuracy assessments made in this study can reflect the SCOPE model's reliability. Secondly, the vegetation parameters and meteorological or flux variables derived from in situ observations were considered accurate in this study. They also suffer from some uncertainties due to instrumental or artificial errors in field measurements. These uncertainties affect the LIDFa and V\({}_{\rm{cmo}}\) retrieval accuracy and cause an additional error in SIF simulations. According to the error propagation presented in [28], the effects of Cdm and LIDFa or Cab and LIDFa on reflectance are opposite, implying that an overestimate (underestimate) in measured Cdm or Cab will lead to an overestimate (underestimate) in the LIDFa retrievals. Moreover, the effects of Cdm and LIDFa or Cab and LIDFa on fluorescence are also opposite. Thus, the effects of a simultaneous overestimate or underestimate can to some degree cancel out in the SIF simulation. Similarly, the effect of an overestimation or underestimate of the measured LAI can also be weakened in simulated SIF, because the effects of LAI and LIDFa on reflectance and fluorescence are both accordant. Therefore, the model calibration approach of reproducing the measured reflectance spectra can to some degree weaken the impacts of vegetation parametric uncertainties on SIF simulations. These two sources of uncertainties will propagate to our FQE estimations and then affect their variation patterns. Nevertheless, the ranges and variation patterns of our FQE values were quite consistent between two growth seasons in 2015 and 2016. Moreover, the estimated fqe2 values were close to their literature values derived from some laboratory measurements. As reported in [PERSON] et al. (2015, 2016), the fqe2 values was around 0.01 based on the laboratory measurements in [PERSON] et al. (2015). [PERSON] et al. (2016) considered three different levels of fqe2 values (0.01, 0.018, and 0.021) for the modeling of PSII photochemistry. Our estimated fqe2 values seem a little lower than the fqe2 values reported in literatures (around 0.02) ([PERSON] et al., 2016; [PERSON] et al., 2016). This discrepancy may be caused by the uncertainties of parameter determinations in this study, or be caused by the errors of laboratory measurements in following two aspects. First, the measured PSII fluorescence signal inevitably included the contamination of PSI fluorescence, which would cause overestimation in fqe2 ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016), while our estimated fqe2 values can avoid this error, as the simulated SIF for PSII and PSI was evaluated separately.