## 4 Discussion  
### Effectiveness of the Optimal PLSR Model Constructed Based on FOD Spectra  
This study highlights the enhanced tracking capability of the optimal PLSR model constructed based on FOD spectra for estimating ChIP parameters, particularly when applied to a composite dataset that includes multiple species and spatiotemporal attributes. To date, there have been few studies that have employed PLSR models to estimate ChIP parameters, and most of these studies have relied on original reflectance. For instance, [PERSON] et al. [18] developed a PLSR model for tobacco using original reflectance within the 400-2400 nm range, achieving R\({}^{2}\) values of 0.81, 0.67, and 0.92 for PSIImax, NPQ, and \(\Phi\)P, respectively. Meanwhile, [PERSON] et al. [44] constructed a PLSR model utilizing the original reflectance from 400 to 2000 nm and reported that it explained 81% of the variation in NPQ for grapevine leaves under varying water conditions. Overall, PLSR models show potential in predicting ChIP parameters, but are limited to specific species or growth conditions, and their effectiveness is doubtful when applied to composite datasets. For example, as evaluated in this study, the PLSR models constructed using original spectra (FOD = 0) showed remarkably limited explanatory power for ChIP parameters. The best-performing model was for \(\Phi\)N, but the R\({}^{2}\) was only 0.30 (Figure 5). This finding underscores concerns about the direct and widespread applicability of PLSR models. As far as we know, this study is the first to investigate the potential of FOD-based PLSR models for retrieving ChIP parameters. Earlier research has shown the effectiveness of these models in estimating photosynthetic pigment content in apple leaves [45] and  
Figure 8: The variable importance in projection (VIP) scores of wavelengths for predicting ChIP parameters in the optimal PLSR model. Different ChIP parameters are represented by distinct colors. The black dashed line indicates a VIP score of 1, while the three wavelengths with the highest VIP scores are highlighted in red text. water content in corn leaves [46]. Moreover, a recent study has validated the reliability of PLSR models utilizing FOD spectra for tracking photosynthetic capacity parameters across diverse species [25]. These findings partially highlight the potential of FOD spectra in retrieving leaf biochemical parameters, likely due to the effectiveness of FOD conversion in eliminating instrument baseline drift and spectral overlap effects [47]. In this study, the explanatory power of the optimal PLSR model for ChlF parameters increased with fractional orders in the low-order range (order < 1) (Figure 5). Specifically, the optimal models for NPQ, \(\Phi\)P, \(\Phi\)N, and \(\Phi\)F were confirmed at 0.8-, 0.9-, 0.7-, and 0.8-order FOD spectra, respectively. These findings highlight the substantial enhancements in the estimation accuracy of ChlF parameters that can be achieved by using FOD spectra, particularly at low-order FOD levels. On the one hand, this study observed that in the low-order FOD spectral range, the number of wavelengths significantly correlated with the aforementioned ChlF parameters increased with an increasing derivative order, reflecting the amplification effect of the FOD transformation (Figure 4). On the other hand, this phenomenon may be explained by the ability of the low-order FOD spectra to enhance variations in the slope of the spectral reflectance. These spectral regions are closely associated with ChlF parameters or with leaf biochemical traits that strongly correlate with them. As demonstrated by [PERSON] et al. [26] in experiments on citrus leaves, spectral indices based on 0.9-1.1-order derivatives achieved the highest accuracy in estimating chlorophyll content, with accuracy declining as the derivative order increased. Similarly, [PERSON] et al. [48] reported that a 0.6-order FOD spectrum combined with a PLSR model achieved the best accuracy for estimating grassland chlorophyll content.