## 1 Introduction  
Chlorophyll fluorescence (ChIF), an indirect outcome of photosynthesis, serves as a crucial indicator of plant physiological status and the functionality of the photosynthetic apparatus. During the process of photosynthesis, light energy is absorbed by light-harvesting antennae and subsequently transferred through internal light-harvesting complexes to the reaction centers. The excess energy can be dissipated and released as heat through non-photochemical quenching or emitted in the form of ChIP [1]. Consequently, ChIP has been identified as a reliable indicator of energy dynamics within the photosynthetic apparatus [2]. ChIF can be measured using Pulse-Amplitude-Modulation (PAM) technology, which uses weak modulated light and strong saturation pulses at specific frequencies to measure key fluorescence signals. These signals include minimum fluorescence (F\({}_{0}\)), maximum fluorescence (F\({}_{\mathrm{m}}\)), and steady-state fluorescence (F\({}_{\mathrm{s}}\)). These measurements enable the calculation of various ChIF parameters, including the maximum quantum efficiency of PSII (PSII\({}_{\mathrm{max}}\)), the fraction of open PSII reaction centers (qL), the effective quantum yield of PSII (\(\Phi\)P), the yield of energy-dependent heat dissipation (\(\Phi\)N), and the quantum yield of fluorescence (\(\Phi\)F), which offer non-destructive approaches to assess photosynthetic potential, efficiency, and plant responses to stress [3]. Unfortunately, PAM technology relies on pulsed modulated light sources, which limits its applicability at larger spatial scales [4]. On the other hand, while retrieval techniques for sun-induced chlorophyll fluorescence (SIF) have been established based on the Fraunhofer line, such a signal is influenced by radiative transfer processes spanning from the photosystem level to the leaf and ultimately to the top of the canopy [5], making it difficult to detect, and leads to a complex relationship between the signal and gross primary productivity (GPP) [6]. In addition, SIF does not directly quantify the heat dissipation processes of the photosynthetic system. As a result, establishing an accurate and dependable approach for quantifying ChIF parameters at larger scales remains a pivotal challenge, particularly in the context of enhancing ecosystem process models. Fortunately, advances in hyperspectral technology have created new opportunities for monitoring ChIF parameters. In particular, hyperspectral indices associated with ChIF parameters have undergone rapid development and wide application due to their simplicity and effectiveness. [PERSON] et al. [7] measured ChIF parameters and the hyperspectral reflectance of _Suada salsa_ L. under salt stress and concluded that the spectral index constructed from 680 nm and 935 nm effectively estimates PSII\({}_{\mathrm{max}}\) and \(\Phi\)P. Furthermore, research has shown that drought stress strengthens the correlation between NPQ and the photochemical reflectance index (PRI) [8]. This occurs because NPQ is strongly linked to lutein cycle pigments, which influence the narrow spectral bands centered at 531 nm and 570 nm [9]. However, published spectral indices demonstrate a limited capacity to capture ChIF parameters, particularly in composite datasets. For example, one study evaluated the performance of 28 published spectral indices on ChIF parameters in mango leaves and found that empirical spectral indices performed poorly and were unstable across different leaf types (sunlit and shaded) [10]. A similar finding was reported in another study, which indicated that empirical spectral indices lacked universal applicability to ChIF parameters in deciduous forests, especially when applied to diverse datasets containing various leaf types and species [11]. These findings underscore the limitations of index-based methods that rely on restricted spectral information for estimating ChIF parameters.