## 4 Discussion  
In this study, SCOPE's driving input parameters were derived from ten field measurements in two ways: (i) direct measurements of vegetation and meteorological parameters and (ii) a model inversion approach to retrieve LIDFa and \(\text{V}_{\text{cmo}}\) from in situ reflectance and GPP observations. These accurate input parameters are vital for persuasive evaluation results of the SCOPE model. The determination of these parameters is one way to calibrate the main modules related to SIF simulation. In the SCOPE model, canopy SIF emissions are propagated using three modules: the Fluspect and biochemical modules at the leaf level and the RTMo module for canopy radiative transfer of SIF signal. Vegetation parameters (e.g., LIDFa, Cab, and LAI) determine the leaf and canopy characteristics represented with the RTMo module, and the meteorological and leaf biochemical  
Figure 12: The correlation and relative root mean square error (RRMSE) values between simulated and measured \(\text{SIF}_{\text{A}}\) and \(\text{SIF}_{\text{B}}\) with variable FQE: (**a**) the correlation and RRMSE for 106 spectral measurements and (**b**) the RRMSE values for each fieldwork day in 2015 and 2016. Figure 13: The correlation and relative root mean square error (RRMSE) values between simulated and measured \(\text{SIF}_{\text{A}}/\text{SIF}_{\text{B}}\) with variable FQE: (**a**) the correlation and RRMSE for 106 spectral measurements and (**b**) the RRMSE values for each fieldwork day in 2015 and 2016.  
parameters (e.g., V\({}_{\rm{cmo}}\), R\({}_{\rm{in}}\), and Ta) determine the plant physiological and photosynthesis state represented with the biochemical module. The simulated canopy reflectance and GPP outputs separately contain information on these two aspects. So, the accuracy of reflectance and GPP simulations can reflect the joint accuracy of input parameters, which has been verified in this study. The V\({}_{\rm{cmo}}\) values across the growing season were inverted by fitting the SCOPE model to measured GPP data. The V\({}_{\rm{cmo}}\) is one of the key parameters in the biochemical module of SCOPE for GPP modeling [20; 43]. However, [PERSON] et al. [65; 66] pointed out that the Rubisco may not be the rate limiting factor of the rate of CO\({}_{2}\) assimilation, which means there may be some uncertainties of our method for the V\({}_{\rm{cmo}}\) estimation and new models reflecting this fact should be considered. Nevertheless, the retrieved V\({}_{\rm{cmo}}\) values match well with the literature values and show credible seasonal patterns. First, the retrieved V\({}_{\rm{cmo}}\) varied from 45 to 110 umol m\({}^{-2}\) s\({}^{-1}\), which was in good accord with those displayed in the literatures for C3 crops: V\({}_{\rm{cmo}}\) ranges from 35 to 83 umol m\({}^{-2}\) s\({}^{-1}\) for wheat in [61], V\({}_{\rm{cmo}}\) ranges from 76 to 136 umol m\({}^{-2}\) s\({}^{-1}\) for soybean in [67], and the common V\({}_{\rm{cmo}}\) value is 93 umol m\({}^{-2}\) s\({}^{-1}\) for wheat in [68; 69]. Second, the retrieved V\({}_{\rm{cmo}}\) values showed plausible seasonal change patterns, which agrees well with the seasonal changes of V\({}_{\rm{cmo}}\) retrieved from space-based SIF data in [23]. To date, the possibility of prescribing V\({}_{\rm{cmo}}\) from another information source is limited. V\({}_{\rm{cmo}}\) could be estimated using leaf nitrogen content [49; 70], but this estimation relied only on few experimental results, and the validation is still needed. [PERSON] et al. [23] retrieved the V\({}_{\rm{cmo}}\) from changes in SIF, as observed by the GOME-2 satellite, but the satellite data were aggregated over space and time with the entire growth seasons containing drought and senescence, which is different from our focus on several diurnal cycles at the canopy scale. Therefore, our model-based inversion approach for V\({}_{\rm{cmo}}\) may provide an alternative way for the estimation of V\({}_{\rm{cmo}}\) on ground or global scales.