## 4 Discussion  
\(q_{\rm L}\) plays a pivotal role in linking PSII chlorophyll fluorescence emission to the actual electron transport rate from PSII to PSI [32]. The accurate estimation of \(q_{\rm L}\) facilitates a quantitative understanding of the relationship between \(\rm{ChlF_{PSII}}\) and the electron transport rate required for carbon metabolism, thereby providing a mechanistic foundation for leveraging chlorophyll fluorescence to estimate GPP [17]. In this study, we developed an advanced system capable of simultaneously measuring active and passive chlorophyll fluorescence alongside gas exchange, offering robust support for the precise quantification of \(q_{\rm L}\). Using data collected under varying temperature conditions (i.e., temperature response curves), we fitted the key parameters, namely \(m_{\rm opt}\), \(H_{\rm d}\), \(H_{\rm a}\), and \(T_{\rm opt}\), of the \(\rm{ChlF_{PSII}}\)-\(q_{\rm L}\) relationship for 52 species across seven major PFTs (Table S1). With the fitted parameters, \(q_{\rm L\_MOD}\) exhibited strong agreement with \(q_{\rm L\_PAM}\) under various light conditions (Figure 5). In addition, we found that \(f_{\rm PSII\_760}\) exhibited minimal sensitivity to environmental variations, remaining stable under most conditions. This observation is consistent with results from a process-based model [20], reinforcing confidence in the reliability of \(f_{\rm PSII\_760}\) estimates. Furthermore, \(\rm{ChlF_{PSII}}\) estimated using \(f_{\rm PSII\_760}\) closely matched \(\rm{ChlF_{PSII}}\) derived from PAM measurements (Figure 4), highlighting the consistency between active and passive chlorophyll fluorescence measurements obtained with the developed system. We also calculated the PSII contribution at 740 nm (\(f_{\rm PSII\_740}\)), as most satellite platforms retrieve SIF at this wavelength. The results show that \(f_{\rm PSII\_740}\) was higher than \(f_{\rm PSII\_760}\) due to the PSII fluorescence emission peak occurring around 740 nm. However, the PSII contribution exhibited a similar response to changes in light intensity at both 740 nm and 760 nm (Figure S2), indicating that near-infrared fluorescence at these wavelengths provides comparable physiological insights. The observed differences in the PSII contributions across the wavelengths in this region likely stem from the intrinsic properties of plant fluorescence emission rather than variations in plant physiological conditions. Therefore, selecting either 740 nm or 760 nm produces consistent results and does not influence the overall conclusions of this study. These findings provide a robust methodological foundation for estimating the broadband total SIF emitted from PSII and \(q_{\rm L}\) using SIF data from remote sensing platforms. It is important to note that the light response curves in this study were measured under a protocol in which light intensities transitioned from low to high, rather than \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **ENF** & **EBF** & **DNF** & **DBF** & **SHR** & **GRA** & **CRO** \\ \hline \(R^{2}\) & 0.93 & 0.92 & 0.86 & 0.90 & 0.85 & 0.95 & 0.96 \\ RMSE & 0.08 & 0.1 & 0.1 & 0.09 & 0.11 & 0.09 & 0.1 \\ rRMSE & 8.89 & 10.71 & 12.60 & 10.32 & 12.66 & 9.40 & 11.24 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the model (\(q_{\rm L\_MOD}\)) and measured \(q_{\rm L}\) (\(q_{\rm L\_PAM}\)) across different plant functional types. \(R^{2}\), RMSE, and rRMSE are the coefficient of determination, root-mean-square error, and relative root-mean-square error (%), respectively. ENF: evergreen needleleaf forest; EBF: evergreen broadleaf forest; DNF: deciduous needleleaf forest; DBF: deciduous broadleaf forest; SHR: shrubland; GRA: grass; CRO: cropland. the conventional protocol in which light intensities are adjusted from high to low [18]. This modification was implemented to address specific challenges associated with PAM parameters.