## 2 Materials and Methods
### SCOPE (Soil Canopy Observation of Photochemistry and Energy Fluxes)
#### 2.1.2 SCOPE Model Inputs  
To simulate photosynthesis and fluorescence, the SCOPE model requires inputs related to meteorology, leaf optical properties and canopy structure, leaf biochemistry, and illumination/observation geometry (see Table 1 for details). These input parameters were derived from three sources: the field measurements, the related literatures, and the model inversion. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Definition** & **Unit** & **Value/Source** \\ \hline _Leaf biochemistry_ & & & \\ V\({}_{\text{cmo}}\) & Maximum carboxylation capacity at 25 \({}^{\circ}\)C & \(\upmu\)mol m\({}^{-2}\) s\({}^{-1}\) & Inversion \\ m & Ball-berry stomatal conductance parameter & — & 9 \\ Rdparam & Parameter for dark respiration (Rd = Rdparam \(\times\) V\({}_{\text{cmo}}\)) & — & 0 \\ \hline _Leaf optical_ & & & \\ Cab & Chlorophyll content density & \(\upmu\)g/cm\({}^{2}\) & Measurement \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values or sources of the main input parameters of the SCOPE model used in our simulation Most of the main input parameters needed in the SCOPE model were considered either known as their literature values or directly measured from the field experiments with sufficient confidence. A majority of leaf optical and canopy structural parameters--including Cab, Cw, Cdm, and LAI--were accurately measured from our field experiments. In addition, the meteorological variables were derived from our high-accuracy meteorological observations using the automatic weather station (AWS). These diurnal meteorological variables were imported into the model input files and loaded for the time series simulations with SCOPE. Meanwhile, the diurnal solar zenith angles were automatically calculated during the simulation using the inputs Julian day, time, and field site longitude and latitude. Thus, the SCOPE's parameterization can keep pace with the observed canopy's vegetation growth and environmental variation at both diurnal and seasonal time scales. On the other hand, some parameters' values were determined based on the related literatures. Following [49], the Ball-berry stomatal conductance parameter (m) should be set around 9 for well-watered C3 species, and the dark respiration parameter (Rdparam) value was set to zero regarding the output NPC as GPP. The LIDFa and LIDFb were two canopy structural parameters that determine the leaf inclination distribution function defined in [50]. LIDFa controls the average leaf scope, and LIDFb controls the distribution's bimodality. According to [29], LIDFa can largely affect both the simulated reflectance and fluorescence, and LIDFb has only a marginal impact on the simulated reflectance and fluorescence. Therefore, we consider only the variations in LIDFa in this study, and LIDFb was set to its default value of \(-0.15\). The V\({}_{\text{cmax}}\) at 25 \({}^{\circ}\)C (denoted as 'V\({}_{\text{cmo}}\)' in the SCOPE model) and LIDFa were two key variables driving the SIF simulation and were accurately retrieved from the in situ observations. According to the global sensitivity analysis of the SCOPE model in [29], for the TB12 module used in this work, the canopy-leaving SIF variability was determined mainly by four driving vegetation variables: Cab, LIDFa, LAI, and V\({}_{\text{cmo}}\). These key inputs need to be reliably confirmed to accurately interpret canopy SIF and photosynthesis. Cab and LAI were easily measured, while field measurements of leaf angle distribution and V\({}_{\text{cmo}}\) consume lots of time and effort. Therefore, LIDFa and V\({}_{\text{cmo}}\) were estimated using the model inversion method with measured reflectance spectra and GPP data. The foe2 and foe1 were two key parameters that determined the simulated SIF intensity. They were two multiplicative factors added to the probability matrices of PSII and PSI fluorescence. Thus, in the