## 4 Discussion
### Effect of Feature Selection and Informative Bands on the Estimation of ChlF Parameters  
This study demonstrated that integrating LASSO with PLSR models led to the improved estimation of most ChlF parameters (Figure 5), emphasizing the importance of feature selection in PLSR model construction. Similarly, previous research on mango leaves found that combining PLSR with stepwise regression enhanced the prediction of NPQ, qL, \(\Phi\)P, and \(\Phi\)F under varying light conditions [19]. Furthermore, the selection of spectral bands that are rich in information has been demonstrated to enhance PLSR-based estimations of photosynthetic capacity [23], leaf nitrogen content [24], and chlorophyll content [49]. Consequently, effective feature selection emerges as a promising approach for the preliminary selection of spectral signals associated with leaf biochemical traits. This approach is also valuable for the estimation of ChlF parameters. This study further identified the relative contributions of wavelengths in the optimal PLSR model based on effective feature selection. The unique contributions of red-edge wavelengths to PSII\({}_{\text{max}}\) (702 nm), qL (707 nm), and \(\Phi\)P (752 nm) were observed in this study (Figure 7). This is consistent with the mechanistic understanding that this region is closely associated with photosynthetic pigments [50]. These findings are supported by the study of [PERSON] et al. [51] on grapevine leaves, which demonstrated that 722 and 735 nm were effective for interpreting PSII\({}_{\text{max}}\), while 676 and 688 nm played a key role in estimating \(\Phi\)P. In a similar vein, [PERSON] et al. [19] conducted a comparative analysis of different wavelength point selection methods and found that the 407-490 nm, 517-578 nm, 702-794 nm, and 1802-2052 nm regions were key spectral regions for qL estimation. This preference can be attributed to the high sensitivity of these photochemistry-related ChlF parameters to variations in photosynthetic pigments. The contribution of the SWIR region to NPQ and \(\Phi\)N is a critical factor that cannot be overlooked. Prior studies have underscored that this region is predominantly characterized by the absorption properties of nitrogen, protein, and other biochemical components, with leaf water content exhibiting a substantial impact [52]. In the context of plant heat dissipation, the role of leaf water content is particularly evident. Correlations between leaf water content and NPQ have been reported in several studies [53; 54], suggesting that reflectance information in the SWIR region is a suitable probe for NPQ estimation. It is noteworthy that the influence of 424, 438, and 463 nm on the estimation of \(\Phi\)F was found to be the most significant (Figure 7). This finding is consistent with the conclusions of a prior study, which indicated that the blue-green bands consistently contribute to the estimation of \(\Phi\)F [19]. This phenomenon can be attributed to the strong correlation between flavonoids, phenolics, and photosynthetic pigments in this region, as well as their relationship with chlorophyll fluorescence intensity [55; 56; 57]. Furthermore, this study observed contributions from bands in the SWIR and NIR regions to \(\Phi\)F estimation. Given the intricate nature of \(\Phi\)F, changes in photosynthetic pigments, leaf structural properties, and biochemical components collectively influence its variation. As a result, accurate \(\Phi\)F estimation requires integrating information from multiple spectral regions. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{**PSII\({}_{\text{max}}\)**} & \multicolumn{2}{c}{**NPQ**} & \multicolumn{2}{c}{**qL**} & \multicolumn{2}{c}{\(\Phi\)**P**} & \multicolumn{2}{c}{\(\Phi\)**N**} & \multicolumn{2}{c}{\(\Phi\)**F**} \\
**Species** & **R\({}^{\text{2}}\)** & **RPD** & **R\({}^{\text{2}}\)** & **RPD** & **R\({}^{\text{2}}\)** & **RPD** & **R\({}^{\text{2}}\)** & **RPD** & **R\({}^{\text{2}}\)** & **RPD** & **R\({}^{\text{2}}\)** & **RPD** \\ \hline _A.