## 2 Materials and Methods
### Estimation of fPARchl  
According to Equations (1) and (2) in Section 1, fPAR was essential for the escape probability to calculate SIFTot, which was not measured directly in the field experiment. Therefore, the statistical model based on regression analysis between the fPAR and VIs was selected as the method to estimate fPAR. Pioneering works [33; 34] have shown that fPAR (i.e., fPARcanopy or fPARchl) could be approximated with the NDVI, enhanced vegetation index (EVI), wide dynamic range vegetation index (WDRVI), and so on. Here, the SCOPE 2.0 model [35] was utilized to illustrate the relationships between different fPAR values and VIs.  
Details about the SCOPE input parameters are listed in Table 3 (i.e., different leaf chlorophyll contents (Cab) and LAI levels to cover the most common vegetation conditions of winter wheat, as well as different soil moisture contents (SMCs) and SZA to represent different drought levels and solar-view geometries, respectively). Consequently, 10,530 different samples of spectral reflectance (1-nm intervals), aPAR, and aPAR\({}_{\text{byCab}}\) were generated. Different VIs containing the Normalized Difference Vegetation Index (NDVI), Red Edge NDVI (NDVI\({}_{705}\)), WDRVI, and Optimized Soil-Adjusted Vegetation Index (OS-AVI) are calculated in Table 4, where the average values of 684-686 nm, 704-706 nm, and 749-751 nm were used for the Red, Red\({}_{\text{edge}}\), and NIR bands, respectively.  
As depicted in Figure 2, the fPARchl estimation of NDVI\({}_{705}\) outperformed the original counterparts with the highest R\({}^{2}\) (0.921). The linear fPARchl\({}_{\text{ell}}\)-NDVI\({}_{705}\) function was expressed as:  
\[\text{fPAR}_{\text{chl}}=0.872\cdot\text{NDVI}_{705}+0.119 \tag{4}\]  
The PAR above the canopy (PARabove) was directly measured using three quantum sensors (S-LIA-M003, Onset Computer Corporation, Bourne, MA, USA). Consequently, the APAR\({}_{\text{chl}}\) could be calculated as follows:  
\[\text{APAR}_{\text{chl}}=\text{PAR}\cdot\text{fPAR}_{\text{chl}} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equation** & **Reference** \\ \hline \(\text{NDVI}_{705}=\frac{\left(\text{NIR}_{\text{det}}-\text{Rededge}_{ \text{bld}}\right)}{\left(\text{NIR}_{\text{det}}+\text{Rededge}_{ \text{bld}}\right)}\) & ([PERSON], 2002) [36] \\ \(\text{WDVI}=\frac{\left(0.1\ast\text{NIR}_{\text{det}}-\text{Rededge}_{ \text{bld}}\right)}{\left(0.1\ast\text{NIR}_{\text{det}}+\text{Rededge}_{ \text{bld}}\right)}\) & ([PERSON] et al., 2014) [37] \\ \(\text{NDVI}=\frac{\left(\text{NIR}_{\text{bld}}-\text{Red}_{ \text{bld}}\right)}{\left(\text{NIR}_{\text{det}}+\text{Red}_{ \text{bld}}\right)}\) & ([PERSON], 1979) [38] \\ \(\text{OSAVI}=\frac{1.6\ast\left(\text{NIR}_{\text{det}}-\text{Red}_{ \text{bld}}\right)}{\left(\text{NIR}_{\text{det}}+\text{Red}_{ \text{det}}+0.16\right)}\) & ([PERSON] et al., 1996) [39] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mathematical formulations and references for the VIs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Values** & **Unit** & **Description** \\ \hline Cab & 20, 25, 30, 35, 40, 45, 50, & \(\text{\mu g/cm}^{2}\) & Leaf chlorophyll a + b content \\ Cdm & 0.012 & g/cm\({}^{2}\) & Dry matter content \\ Cw & 0.009 & cm & Leaf water equivalent layer \\ N & 1.4 & / & Leaf mesophyll scattering \\ SMC & 8–25 & \(\text{m}^{3}\text{/m}^{3}\) & Soil moisture content \\ LAI & 1, 2, 3, 4, 5 & \(\text{m}^{2}\text{/m}^{2}\) & Leaf area index \\ LIDFa & \(-0.35\) & / & Leaf inclination parameter \\ LIDPb & \(-0.15\) & / & Bimodality parameter \\ FQE & 0.01 & / & Fluorescence quantum yield \\ SZA & 20, 25, 30, 35, 40, 45, 50, & & \\  & 55, 60 & degree & Solar zenith angle \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main input parameters for the SCOPE simulations.