For example, it has been used to provide training and test data sets for new SIF retrieval methods [21, 22]; to derive empirical relationships between the seasonal maximum carboxylation rate (V\({}_{\text{cmax}}\)) and SIF, which are used to retrieve the global photosynthetic capacity of crops [19, 23]; to evaluate the predictive power of SIF to estimate gross primary productivity (GPP); to investigate the sensitivities of both GPP and SIF and their relationship to the biochemical parameters, as well as to the environmental conditions at different spatial-temporal scales [24, 25, 26, 27]; and to assess the influence of confounding factors such as physiological and structural interferences or temporal scaling effects on SIF-GPP relationships [8]. Despite the SCOPE model-integrated existing theories of radiative transfer, energy balance, micrometeorology, and plant physiology, the model is analytical. Thus, it inevitably contains assumptions due to model abstractions for SIF representations and uncertainties in driving variables [28]. To date, only a few studies have involved the experimental validation of the SCOPE model for SIF interpretation. [PERSON] et al. provided insight into the key variables that drive the reflectance and SIF emission simulations, based on a global sensitivity analysis (GSA) of the SCOPE model [29]. The results showed that leaf composition, leaf area index, leaf inclination, irradiance, and V\({}_{\text{cmax}}\) are the most important factors affecting the SIF simulation and need to be accurately parameterized to produce unbiased SIF interpretations. By comparing the simulated SIF with corresponding field observations, [PERSON] et al. assessed the impacts of leaf pigment concentrations and canopy structures on simulated SIF, as well as the impacts of PQ and NPQ [28]. However, they focused on revealing information about the biochemical regulation of the energy pathways contained in the SIF signal, without offering the quantitative accuracy of SIF simulation, and only the simulations of far-red SIF at O\({}_{2}\)-A band (SIF\({}_{\text{A}}\)) have been investigated. Several studies have reported that the red SIF at the O\({}_{2}\)-B band (SIF\({}_{\text{B}}\)) is more closely connected to plant photosynthesis, possibly because SIF\({}_{\text{B}}\) is located near the fluorescence peak emitted by PSII [3, 30, 31]. Additionally, the ratio of SIF\({}_{\text{A}}\) to SIF\({}_{\text{B}}\) can express SIF's spectral shape, which can provide important information regarding physiological and biochemical activities in vegetation [5, 6, 32]. Therefore, both the intensity and shape of the SCOPE-simulated SIF spectra must be evaluated quantitatively. On the other hand, the fluorescence quantum efficiency in dark-adapted conditions (defined as F\({}_{0}\)-level fluorescence yield) for PSII (fqe2) and PSI (fqe1) was always set as fixed values derived from laboratory measurements, which may be unsuitable for the accurate simulation of SIF. According to [PERSON] et al. [28], the suggested values of fqe2 and the ratio of fqe1 to fqe2 (fqe1/fqe2) for SCOPE were 0.01 and 0.2, respectively. While, for early versions of the SCOPE model (before version 1.53), the fqe2 value was suggested to be 0.01 by the model developer. This priori value can be obtained from the work by [PERSON] et al., [33], but it is unknown whether the value is universal [20]. The measured fluorescence yields values at F\({}_{0}\)-level have been reported as around 0.02 [34; 35; 36]. In the study by [PERSON] et al. [37], three different levels (0.01, 0.021, and 0.018) of the fluorescence yields at F\({}_{0}\)-level were considered, and the contribution from PSI to the total fluorescence signal is reported as around 20%. As reported by [PERSON] and [PERSON] [38], the FQE values have been observed to change with different vegetation species, chlorophyll contents, and exposures of leave surfaces to the sun.