## 4 Discussion  
For the sequential system of the SIF measurement, which is widely used in field automatic measurements of SIF, the difference between the measured \(E\) and the actual \(E\) during the measurement of \(L\) is one of the primary reasons for the error in the estimation of SIF [8; 15]. In this study, we added a _PAR_ sensor to the SIF observation system to continuously measure irradiances at a frequency significantly higher than spectral measurements. Based on the coefficients of variation calculated by high-frequency _PAR_ values, we proposed a method to evaluate the illumination stability during each SIF measurement to select reliable SIF measurements. Our results show that the correlations between _PAR_ and SIF of the 3 FLD and SFM methods were significantly reduced when the illumination changed rapidly, which is consistent with the existing research [19]. The proposed method could effectively delete abnormal data caused by rapid illumination fluctuation and significantly improve the correlation between the PAR and reserved SIF values (Figures 7 and 8). [PERSON] et al. [19] proposed using the coefficients of variation of incident PAR recorded by a quantum sensor and a clearness index to classify sky conditions. The results of this study suggested that the use of high-frequency PAR data can be further used to evaluate the data quality of SIF. Figure 9: The PAR and spectral data during two measurement cases. (**a**,**b**) for the case 1 and (**c**,**d**) for the case 2. The red dots, blue dots, and green dots represent the PAR measured during the upwelling radiance measurement (_L_) and two irradiance measurements (_E_1 and \(E\)2) acquired just before and immediately after the \(L\) measurement, respectively. The blue solid line, green dotted line, and red solid line represent the spectra of \(E\)1, \(E\)2, and \(L\), respectively. The black solid line represents the reflectance (_Rapp_) of the maize canopy calculated by \(E\)1 and \(L\). When compared to the commonly used data quality indicator \(DQ_{S}\)[11], the method proposed in this study has a higher accuracy when the illumination changes rapidly. As an example, although the magnitudes of the E values before and after the L measurement in case 2 were comparable, the magnitude was significantly greater than the actual value during the L measurement (Figure 9c,d). \(DQ_{S}\) cannot identify this abnormal observation, but the method proposed in this study can. \(DQ_{S}\) has the advantage of not requiring an additional quantum sensor. However, adding a quantum sensor to a SIF observation system is simple and inexpensive. Furthermore, some SIF observation systems and quantum sensors are integrated with eddy covariance systems [9]. Therefore, the PAR observation data can be conveniently used for quality assessment. Our results show that, without data filtering, the applicability of SVD to various illumination conditions was better than that of SFM and 3 FLD (Figure 7f). The results are consistent with those of [PERSON] et al. [19]. This is because the SVD does not suffer from temporal mismatches between L and E, since it estimates E through a training dataset as opposed to using a special measure of E. However, even for the SVD, removing the error data based on high-frequency PAR could improve the estimation accuracy when the illumination was rapidly fluctuating (Figure 7f). After removing the error data, the correlation between PAR and SIF estimated by the SVD was lower than that of the SFM and 3 FLD (Figures 7 and 8). This may be due to the accuracy of the SVD algorithm itself. This could also be related to the parameter settings or the training dataset selection [28; 29]. How to select the optimal parameters and training dataset for SVD remains a challenging problem. In addition, we found that the SIFs estimated by the SVD were generally higher than those estimated by the SFM and 3 FLD (Figure 6). When the illumination changed rapidly, the phenomenon became more pronounced (Figure 6d-f). This phenomenon is consistent with previous studies [30].