## 3 Results
### Wavelengths of Optimal PLSR Models for Predicting ChIP Parameters  
The wavelengths utilized by the optimal PLSR model for ChIP parameter estimation were further analyzed and are presented in Figures 6-8. Overall, the wavelength range spanned the VIS, red-edge, NIR, and SWIR regions. Specifically, the best PLSR model for predicting PSIImax employed 49 wavelengths. Among them, 702, 411, and 737 nm exhibited the highest relative contributions, accounting for 20.71%, 13.29%, and 11.14%, respectively. The VIP scores for these three bands were all greater than 1, with values of 2.79, 1.79, and 1.50, respectively. For NPQ prediction, the optimal PLSR model utilized 74 bands, predominantly in the SWIR region, all with VIP scores exceeding 1. The ten wavelengths with the highest relative importance were 1777, 634, 598, 1001, 1801, 1672, 1742, 2236, 2364, and 2323 nm. Among these, 1777 nm had the highest relative importance of 2.48%, with a VIP score of 23.37. For qL prediction, the optimal model used 77 bands, with VIP scores ranging from 3.76 to 35.26. The top three bands, all situated in the red-edge region, were 707, 743, and 747, with the relative importance of 2.77%, 2.68%, and 2.67%, respectively.  
Figure 5: Comparison of R\({}^{2}\), RPD, NRMSE, and AICc for PLSR models based on different FOD spectra and feature selection methods using the test dataset (141 samples). Different feature selection methods are distinguished by color. Additionally, AICc is standardized (AICcscale) for easier comparison. Red circles indicate the best-performing models based on the highest R\({}^{2}\) and RPD values, and the lowest NRMSE and AICc values. For \(\Phi\)F, the selected LASSO-PLSR model using 0.8-order FOD spectra is highlighted by a red square to balance model complexity and performance.  
The optimal model for estimating \(\Phi\)P incorporated a total of 105 bands. Among them, the top ten bands in relative importance were primarily in the VIS and red-edge regions, except for 2347 nm, which ranked fifth. The top three bands, 752, 571, and 547 nm, had a relative importance of 5.65%, 4.64%, and 3.44%, with VIP scores of 31.88, 26.15, and 19.41, respectively. The PLSR model that demonstrated the greatest efficacy in estimating \(\Phi\)N was determined to be the one with 25 bands, primarily distributed in the VIS region, with a few in the SWIR region. The 582 nm band in the VIS region demonstrated the highest relative importance (11.68%) and VIP score (16.26). In the SWIR region, the band at 1704 nm had a relative importance of 4.57% and a VIP score of 6.36. The most appropriate PLSR model for estimating \(\Phi\)F incorporated a total of 73 bands. Among these, the bands with the  
Figure 6: The optimal PLSR model selected informative wavelengths for estimating ChIF parameters, with each parameter represented by a distinct color. The number of selected wavelengths is 49 for PSIImax, 74 for NPQ, 77 for qL, 105 for \(\Phi\)P, 25 for \(\Phi\)N, and 73 for \(\Phi\)F.  
Figure 7: The relative contribution of wavelengths in predicting ChIF parameters within the optimal PLSR model. Different ChIF parameters are represented by distinct colors. The figure shows the top ten wavelengths with the highest contributions.  
highest relative importance were 424, 438, and 463 nm, with a relative importance of 3.85%, 3.24%, and 3.10%, respectively. The distribution of other key bands was predominantly observed in the SWIR and NIR regions, with 2040 nm in the SWIR region (2.51% relative importance) and 1070 nm in the NIR region being the most significant.