Spectral Transformation Research Articles

Recovering orthogonality from quasi‐type kernel polynomials using specific spectral transformations

In this work, the concept of quasi‐type Kernel polynomials with respect to a moment functional is introduced. Difference equation satisfied by these polynomials along with the criterion for orthogonality conditions are discussed. The process of recovering orthogonality for the linear combination of a quasi‐type kernel polynomial with another orthogonal polynomial, which is identified by involving linear spectral transformation, is provided. This process involves an expression of ratio of iterated kernel polynomials. This leads to considering the limiting case of ratio of kernel polynomials involving continued fractions. Special cases of such ratios in terms of certain continued fractions are exhibited. Moreover, examples are discussed for obtaining the orthogonality of quasi‐type kernel polynomials of order 1.

Supercontinuum saturation of a femtosecond laser filament in pressurized gases.

Filamentation of high-power femtosecond optical pulses in high-pressure gases has gained increasing academic and practical interest from the viewpoint of studying large-scale spectral and temporal transformations occurring with pulsed laser radiation and obtaining super-broadened spectra and extremely short (attosecond) wave packets. Experimentally and theoretically, for the first time to the best of our knowledge, we show that as a result of a 45 fs Ti:sapphire laser pulse filamentation in an optical cell filled with pressurized up to 50 bar nitrogen or argon, the pulse spectrum can reach maximally about eightfold broadening. This limiting pulse spectral width is reached at a gas pressure of about 20 bar and with further pressure increase exhibits saturation and even a slight decrease relative to the limiting value. As a possible reason for this finding, we suppose the increase of pulse energy depletion in the self-created plasma at high gas pressure.

Use of a rugged mid-infrared spectrometer for in situ process analysis of liquids

The widespread application of mid-infrared (MIR) spectroscopy for process monitoring is currently limited by the poor transmission of MIR light through fibre optics. In this work, the performance of a novel and robust MIR spectrometer has been evaluated for practical deployment in a pilot plant or production environment. The spectrometer utilises a Sagnac interferometer design containing no moving parts and is directly attached to an attenuated total reflectance probe, eliminating the need for fibre optics. The quantitative performance of the spectrometer for the in situ analysis of ternary solvent mixtures was assessed. The predictions obtained by partial least squares were accurate (root mean square error of prediction of < 1% w/w) and comparable to those of a benchmark Michelson-based spectrometer with a fibre-coupled probe, which is more amenable to process development in a laboratory or pilot plant. Calibration transfer between the two spectrometers was performed using the spectral space transformation method to mimic the scenario of the scale-up of a process from the laboratory to pilot scale or from a pilot plant to production scale, where the two different MIR instruments might be deployed. The ability to perform in situ reaction monitoring with the robust Sagnac-based spectrometer was then demonstrated. Spectra acquired during an esterification reaction were resolved using multivariate curve resolution, to produce concentration profiles of each component. These results demonstrate the suitability of this rugged spectrometer for quantitative in situ monitoring of liquid processes, opening up new opportunities for process monitoring in the MIR region.

Estimation of Malondialdehyde Content in Medicago truncatula under Salt Stress Based on Multi-Order Spectral Transformation Characteristics

Salt stress is a significant abiotic factor affecting the growth and development of alfalfa. Malondialdehyde (MDA) serves as a critical biomarker for assessing alfalfa’s salt tolerance. Traditional methods for measuring MDA are often time-consuming and labor-intensive. Recent advances in remote sensing technology have made non-destructive estimation of metabolites feasible, positioning the accurate estimation of MDA content in alfalfa as a key focus in intelligent breeding. To address the challenge of detecting subtle changes in MDA content, this study developed a partial least squares regression (PLSR) model specifically for Medicago truncatula. This study utilized leaf reflectance hyperspectral data across the visible near-infrared–shortwave infrared (VIR-NIR-SWIR) spectrum, applying multi-order spectral transformation methods, including continuous wavelet transform (CWT), fractional differential (FD), and multi-granularity spectral segmentation (MGSS). Feature selection techniques, such as sequential forward selection (SFS), Least-Squares Boosting (LSBoost), and feature selection using neighborhood component analysis for regression (FSRNCA), were employed to enhance the efficiency of the MDA estimation. The findings revealed that the optimal PLSR model for MDA estimation was achieved by integrating CWT features across orders 1–30 with the SFS method. This model demonstrated robust estimation capabilities under varying salt stress conditions, significantly outperforming the original spectral data (R2 = 0.654, RMSE = 22.567 vs. R2 = 0.242, RMSE = 33.411). A comparative analysis of feature selection methods confirmed that SFS was the most effective for estimating MDA content in alfalfa. These results provide valuable insights and methodologies for MDA estimation and evaluating salt tolerance in alfalfa.

Synchronization of Gold sequences based on fast transform in a truncated basis of Walsh–Hadamard functions

Based on the analysis of the structures of isomorphic multiplicative groups of extended Galois fields, it is established that any cyclic shift of a pseudo-random Gold sequence can be transformed into a function belonging to the complete set of analogues of Rademacher functions of the corresponding dimension. This made it possible to develop a new algorithm for fast synchronization of Gold sequences based on the calculation of their discrete convolution using fast spectral transformation in a truncated basis of Walsh–Hadamard functions. The gain of the developed algorithm in terms of the number of arithmetic operations compared to the traditional method of calculating discrete convolution increases with increasing sequence length N and for N=511.1023 is approximately 3.4 times.

Soil Nutrients Inversion in Open‐Pit Coal Mine Reclamation Area of Loess Plateau, China: A Study Based on ZhuHai‐1 Hyperspectral Remote Sensing

ABSTRACTSoil nutrients are crucial to assess land reclamation quality, and the use of various types of remote sensing data for soil nutrient inversion has been a key focus for soil monitoring. However, fewer studies have been conducted using satellite‐based hyperspectral remote sensing. To explore the potential of satellite‐based hyperspectral remote sensing in soil nutrient monitoring, this study selected soil organic matter, total nitrogen, available phosphorus, and available potassium content data from 83 sample sites using ZhuHai‐1 hyperspectral data. After spectral transformation and feature extraction, various inversion models were constructed, including partial least squares regression, support vector machine, recurrent neural network, and random forest. After verification by accuracy, the best spectral‐model combination was used for inversion. The results showed that the R‐squared range of the inversion models was 0.67748–0.78115. High content areas of soil organic matter and available potassium exhibited concentrated and contiguous features, while high content areas of total nitrogen and available phosphorus were more fragmented and fine‐grained. Alfalfa grassland plays a vital role in improving reconstructed soil in the early reclamation stage, and agricultural activities have differential impacts on soil nutrient accumulation. This study provides a theoretical basis for verifying the application capability of ZhuHai‐1 hyperspectral satellite data in soil monitoring.

Hyperspectral Prediction Models of Chlorophyll Content in Paulownia Leaves under Drought Stress.

This study explored the quantitative inversion of the chlorophyll content in Paulownia seedling leaves under drought stress and analyzed the factors influencing the chlorophyll content from multiple perspectives to obtain the optimal model. Paulownia seedlings were selected as the experimental materials for the potted water control experiments. Four drought stress treatments were set up to obtain four types of Paulownia seedlings: one pair of top leaves (T1), two pairs of leaves (T2), three pairs of leaves (T3), and four pairs of leaves (T4). In total, 23 spectral transformations were selected, and the following four methods were adopted to construct the prediction model, select the best spectral preprocessing method, and explore the influence of water bands: partial least squares modeling with all spectral bands (all-band partial least squares, AB-PLS), principal component analysis partial least squares (PCA-PLS), correlation analysis partial least squares (CA-PLS), correlation analysis (water band) partial least squares, ([CA(W)-PLS]), and vegetation index modeling. Based on the prediction accuracy and the uniformity of different leaf positions, the optimal model was systematically explored. The results of the analysis of spectral reflectance showed significant differences at different leaf positions. The sensitive bands of chlorophyll were located near 550 nm, whereas the sensitive bands of water were located near 1440 and 1920 nm. The results of the vegetation index models indicate that the multiple-index models outperformed the single-index models. Accuracy decreased as the number of indicators decreased. We found that different model construction methods matched different optimal spectral preprocessing methods. First derivative spectra (R') was the best preprocessing method for the AB-PLS, PCA-PLS, and CA-PLS models, whereas the inverse log spectra (log(1/R)) was the best preprocessing method for the CA(W)-PLS model. Among the 14 indices, the green normalized difference vegetation index (GNDVI) was most correlated with the chlorophyll content sensitivity indices, and the water index (WI) was most correlated with the water sensitive indices. At the same time, the water band affected the cross validation accuracy. When characteristic bands were used for modeling, the cross validation accuracy was significantly increased. In contrast, when vegetation indices were used for modeling, the accuracy of the cross validation increased slightly but its predictive ability was reduced; thus, these changes could be ignored. We found that leaf position also affected the prediction accuracy, with the first pair of top leaves exhibiting the worst predictive ability. This was a bottleneck that limited predictive capability. Finally, we found that the CA(W)-PLS model was optimal. The model was based on 23 spectral transformations, four PLS construction methods, water bands, and different leaf positions to ensure systematicity, stability, and applicability.

Evaluation of the influence of temperature and time exposure on Peruvian Maca (Lepidium meyenii) flour samples through NIR spectroscopy and chemometrics

In this study, the three most known varieties of the Peruvian Maca (Lepidium meyenii), yellow, red, and black, were fractionated in multiple sub-samples and submitted to heating (from 25 to 200 °C) during one hour at 10-minute intervals to be measured using the near-infrared (NIR) spectroscopy after 24 h resting in a desiccator. The study focused on changes related to heating for considering new applications on Maca-enriched food products such as bread, cakes, and cookies, which are prepared over high temperatures during different time exposures. To perform the experiment, 9 portions of 1 g of each sample remained in an oven at each temperature (25, 50, 100, 150, 160, 170, 180, 190, and 200 °C) for different times (10, 20, 30, 40, 50, and 60 min). The principal component analysis (PCA) and the multivariate curve resolution with alternating least squares (MCR-ALS) were applied to interpret the results. In general, it was observed that spectral variations start to occur from heating samples from above 100 °C. The recovered signals indicated that most spectral variations occurred in regions assigned to NH vibrations, suggesting that heating influences the protein and/or macamides structures, which are responsible for most of the properties attributed to the Peruvian Maca. The yellow Maca phenotype was the most susceptible to spectral variations after heating, while the black Maca displayed the slowest spectral transformations. This study may encourage deeper investigations into the use of Maca-derived foods regarding their health-beneficial effects after heating to indicate the limitations of Maca applications.

Sentinel-2 Multispectral Satellite Remote Sensing Retrieval of Soil Cu Content Changes at Different pH Levels

With the development of multispectral imaging technology, retrieving soil heavy metal content using multispectral remote sensing images has become possible. However, factors such as soil pH and spectral resolution affect the accuracy of model inversion, leading to low precision. In this study, 242 soil samples were collected from a typical area of the Pearl River Delta, and the Cu content in the soil was detected in the laboratory. Simultaneously, Sentinel-2 remote sensing image data were collected, and two-dimensional and three-dimensional spectral indices were established. Constructing independent decision trees based on pH values, using the Successive Projections Algorithm (SPA) combined with the Boruta algorithm to select the characteristic bands for soil Cu content, and this was combined with Optuna automatic hyperparameter optimization for ensemble learning models to establish a model for estimating Cu content in soil. The research results indicated that in the SPA combined with the Boruta feature selection algorithm, the characteristic spectral indices were mainly concentrated in the spectral transformation forms of TBI2 and TBI4. Full-sample modeling lacked predictive ability, but after classifying the samples based on soil pH value, the R2 of the RF and XGBoost models constructed with the samples with pH values between 5.85 and 7.75 was 0.54 and 0.76, respectively, with corresponding RMSE values of 22.48 and 16.12 and RPD values of 1.51 and 2.11. This study shows that the inversion of soil Cu content under different pH conditions exhibits significant differences, and determining the optimal pH range can effectively improve inversion accuracy. This research provides a reference for further achieving the efficient and accurate remote sensing of heavy metal pollution in agricultural soil.

Estimating Cadmium Concentration in Agricultural Soils with ZY1-02D Hyperspectral Data: A Comparative Analysis of Spectral Transformations and Machine Learning Models

The accumulation of cadmium (Cd) in agricultural soils presents a significant threat to crop safety, emphasizing the critical necessity for effective monitoring and management of soil Cd levels. Despite technological advancements, accurately monitoring soil Cd concentrations using satellite hyperspectral technology remains challenging, particularly in efficiently extracting spectral information. In this study, a total of 304 soil samples were collected from agricultural soils surrounding a tungsten mine located in the Xiancha River basin, Jiangxi Province, Southern China. Leveraging hyperspectral data from the ZY1-02D satellite, this research developed a comprehensive framework that evaluates the predictive accuracy of nine spectral transformations across four modeling approaches to estimate soil Cd concentrations. The spectral transformation methods included four logarithmic and reciprocal transformations, two derivative transformations, and three baseline correction and normalization transformations. The four models utilized for predicting soil Cd were partial least squares regression (PLSR), support vector machine (SVM), bidirectional recurrent neural networks (BRNN), and random forest (RF). The results indicated that these spectral transformations markedly enhanced the absorption and reflection features of the spectral curves, accentuating key peaks and troughs. Compared to the original spectral curves, the correlation analysis between the transformed spectra and soil Cd content showed a notable improvement, particularly with derivative transformations. The combination of the first derivative (FD) transformation with the RF model yielded the highest accuracy (R2 = 0.61, RMSE = 0.37 mg/kg, MAE = 0.21 mg/kg). Furthermore, the RF model in multiple spectral transformations exhibited higher suitability for modeling soil Cd content compared to other models. Overall, this research highlights the substantial applicative potential of the ZY1-02D satellite hyperspectral data for detecting soil heavy metals and provides a framework that integrates optimal spectral transformations and modeling techniques to estimate soil Cd contents.

Scheme using a finite‐volume method for local horizontal derivatives in the spectral model: The non‐hydrostatic case

AbstractIn a typical non‐hydrostatic spectral dynamic numerical weather prediction (NWP) kernel, all forecast variables are transformed between grid point and spectral spaces to compute their gradients and solve the implicit problem. This kernel requires numerous spectral transformations, which depend heavily on extensive global communication and significantly hinder parallel computing efficiency. This paper introduces an innovative non‐hydrostatic spectral kernel that incorporates a finite‐volume method within the spectral framework. We have developed a horizontal divergence (D)‐based structure equation, allowing direct computation of most prognostic variables and their horizontal gradients at grid point space. By doing so, the need for spectral transformations is substantially decreased. Our experiments demonstrate that this new approach reduces the cost of spectral transformations by up to 40%, enhancing the overall model efficiency by 15%–22%. Additionally, a series of tests confirmed the accuracy and stability of this new solver.

Enhancing cross‐scale Raman in‐line monitoring capability of cell culture process in large‐scale manufacturing

AbstractThis study introduces an approach to enhance the Raman calibration model cross‐scale prediction capabilities in cell cultures. Our investigation centers on the improvement of Raman calibration models along with the scaling‐up of mammalian cell culture processes. Initially, we observed that integrating data from a 50 L run into the original dataset at lab‐scale was an effective strategy for improving prediction accuracy on 1000 L run. Further investigation indicated that this improvement could be attributed to the similarity between the spectra of 50 L with 1000 L. Subsequent investigation unveiled that the spectral variations primarily stemmed from differences between the instruments. This was verified through a direct comparison of two Raman analyzers. Then two pairwise spectral transformation methods, direct standardization (DS) and piecewise DS, both exhibited superior performance in predicting. This research underscores the importance of Raman spectral transfer method to alleviate instrument‐to‐instrument variations in cross‐scale monitoring of cell culture processes.

Classifying Plant Communities in the North American Coastal Plain With PRISMA Spaceborne Hyperspectral Imagery and the Spectral Mixture Residual

AbstractThe effort to map terrestrial biodiversity, in recent years limited mostly to the use of broadband multispectral remote sensing at decameter scales, can be greatly enhanced by harnessing hyperspectral imagery. Interpretation of hyperspectral imagery may be aided by the Mixture Residual (MR) spectral preprocessing transformation. MR integrates the benefits of spectral mixture analysis with the absorption peak‐enhancing characteristics of continuum removal. MR characterizes each pixel as a linear combination of generic end‐members estimating the spectral continuum, from which the residual of each wavelength is computed and treated as a source of additional information. Using Hyperspectral Precursor of the Application Mission (PRISMA) imagery, we tested the ability of MR‐transformed reflectance as compared to untransformed surface reflectance (SR) to map plant associations and land cover using ground truthing and random forest classifications across four landscapes within the North American Coastal Plain. We used a forward stepwise selection algorithm to choose bands for each classification and subsequently compared these between SR and MR. Our MR classifications distinguished land cover with 5% greater balanced accuracy on average than the SR‐based classifications across all four landscapes. The MR‐based classification that integrated data from all landscapes into a unified model encompassing all 21 land cover types achieved a 76% average balanced accuracy over three iterations. Generally, MR utilized the near‐infrared region to a greater degree than SR while deemphasizing the green peak. Based on our results, MR improves the accuracy of mapping terrestrial biodiversity, likely extending to other current and planned satellite hyperspectral missions.

[formula omitted]-dressing approach and [formula omitted]-soliton solutions of the general reverse-space nonlocal nonlinear Schrödinger equation

Using the ∂̄-dressing method, we study the general reverse-space nonlocal nonlinear Schrödinger (nNLS) equation. Beginning with a 3 × 3 matrix ∂̄-problem, the associated spatial and time spectral problems are obtained through two linear constraint equations. Furthermore, the gauge equivalence between the Heisenberg chain equation and the general reverse-space nNLS equation is established. By employing a recursive operator, a hierarchy for the general reverse-space nNLS equation is proposed. Moreover, by selecting a suitable spectral transformation matrix, the N-soliton solutions of the general reverse-space nNLS equation are calculated, yielding the explicit one-soliton and two-soliton solutions.

Modeling of Soil Cation Exchange Capacity Based on Chemometrics, Various Spectral Transformations, and Multivariate Approaches in Some Soils of Arid Zones

Cation exchange capacity is a crucial metric for managing soil fertility and promoting agricultural sustainability. An alternative technique for the non-destructive assessment of important soil parameters is reflectance spectroscopy. The main focus of this paper is on how to analyze and predict the content of various soil cation exchange capacities (CEC) in arid conditions (Sohag governorate, Egypt) at a low cost using laboratory analysis of CEC, visible near-infrared and shortwave infrared (Vis-NIR) spectroscopy, partial least-squares regression (PLSR), and Ordinary Kriging (OK). Utilizing reflectance spectroscopy with a spectral resolution of 10 nm and laboratory studies with a spectral range of 350 to 2500 nm, 104 surface soil samples were collected to a depth of 30 cm in the Sohag governorate, Egypt (which is part of the dry region of North Africa), in order to accomplish this goal. The association between the spectroradiometer and CEC averaged values was modeled using PLSR in order to map the predicted value using Ordinary Kriging (OK). Thirty-one soil samples were selected for validation. The predictive validity of the cross-validated models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), residual prediction deviation (RPD), and ratio of performance to interquartile distance (RPIQ). The results indicate that ten transformation methods yielded calibration models that met the study’s requirements, with R2 &gt; 0.6, RPQ &gt; 2.5, and RIQP &gt; 4.05. For evaluating CEC in Vis-NIR spectra, the most efficient transformation and calibration model was the reciprocal of Log R transformation (R2 = 0.98, RMSE = 0.40, RPD = 6.99, and RIQP = 9.22). This implies that combining the reciprocal of Log R with PLSR yields the optimal model for predicting CEC values. The CEC values were best fitted by four models: spherical, exponential, Gaussian, and circular. The methodology used here does offer a “quick”, inexpensive tool that can be broadly and quickly used, and it can be readily implemented again in comparable conditions in arid regions.

Risk Screening Environmental Indicators Model Change Based on Spectral Transformation Around New Yogyakarta International Airport

Risk Screening Environmental Indicators Model Change Based on Spectral Transformation Around New Yogyakarta International Airport

Improving the Estimation Accuracy of Soil Organic Matter Content Based on the Spectral Reflectance from Soils with Different Grain Sizes

Accurate and rapid estimation of soil organic matter (SOM) content is of great significance for advancing precision agriculture. Compared with traditional chemical methods, the hyperspectral estimation is superior in rapidly estimating SOM content. Soil grain size affects soil spectral reflectance, thereby affecting the accuracy of hyperspectral estimation. However, the appropriate soil grain size for the hyperspectral analysis is nearly unknown. This study propose a best hyperspectral estimation method for determining SOM content of farmland soil in the Ibinur Lake Irrigation Area (ILIA) of the northwest arid zones of China. The original spectral reflectance of the 20-mesh (0.85 mm) and 60-mesh (0.25 mm) sieved soil were obtained, and the feature wavebands were selected using five types of spectral transformations. Then, hyperspectral estimation models were constructed based on the partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) models. Results show that the SOM content had relatively higher correlation coefficient with spectral reflectance of the 0.85 mm sieved soil than that of the 0.25 mm sieved soil. The transformation of original spectral reflectance of soil effectively enhanced the spectral characteristics related to SOM content. Soil grain size obviously affected spectral reflectance and the accuracy of hyperspectral estimation models. The overall stability and estimation accuracy of RF model was significantly higher compared with the PLSR, SVM, and XGBoost. Finally, the RF model combined with the root mean first-order differentiation (RMSFD) of spectral reflectance of the 0.85 mm sieved soil (R2 = 0.82, RMSE = 2.37, RPD = 2.27) was identified as the best method for estimating SOM content of farmland soil in the ILIA.

Spatial-frequency gradient fusion based model augmentation for high transferability adversarial attack

In recent years, deep learning has gained widespread application across diverse fields, including image classification and machine translation. Nevertheless, the emergence of adversarial examples has revealed a vulnerability of deep learning techniques to potential attacks. Despite the introduction of diverse adversarial attack methods, they are still constrained by certain limitations. Specifically, global perturbations are easily discernible by humans, resulting in poor imperceptibility. Additionally, current methods encounter limited transferability due to their reliance on attacking specific models. To address these challenges, this paper proposed a spatial-frequency gradient fusion based model augmentation for adversarial attack. First, we utilize a Gaussian convolution kernel to pinpoint regions in images that exhibit significant pixel variation, aiming to generate locally imperceptible perturbations undetectable by humans. These areas, which we consider as complex texture regions, are ideal for adding perturbations. Then, we design a perceptual similarity constraint to regulate the generation of perturbations in smooth texture regions. Subsequently, to further enhance the transferability of our method, we propose a spatial-frequency gradient fusion based model augmentation, applying random spectral transformation to shift into the frequency domain for narrowing the differences between models. Additionally, we design complex region scaling transformations in the spatial domain, aimed at capturing common features shared across models. Finally, we integrate the gradients from both the spatial and frequency domains, leveraging the strengths of both to empower attack models in effectively simulating the target model. Extensive experiments conducted on ImageNet and CIFAR-10 datasets have shown that our method attains a remarkable black-box attack success rate of up to 93.1%, with a perceptual loss reduction of approximately 8.39%, while also exhibiting stronger robustness.

Spectral Decomposition and Transformation for Cross-domain Few-shot Learning

Cross-domain few-shot Learning (CDFSL) is proposed to first pre-train deep models on a source domain dataset where sufficient data is available, and then generalize models to target domains to learn from only limited data. However, the gap between the source and target domains greatly hampers the generalization and target-domain few-shot finetuning. To address this problem, we analyze the domain gap from the aspect of frequency-domain analysis. We find the domain gap could be reflected by the compositions of source-domain spectra, and the lack of compositions in the source datasets limits the generalization. Therefore, we aim to expand the coverage of spectra composition in the source datasets to help the source domain cover a larger range of possible target-domain information, to mitigate the domain gap. To achieve this goal, we propose the Spectral Decomposition and Transformation (SDT) method, which first randomly decomposes the spectrogram of the source datasets into orthogonal bases, and then randomly samples different coordinates in the space formed by these bases. We integrate the above process into a data augmentation module, and further design a two-stream network to handle augmented images and original images respectively. Experimental results show that our method achieves state-of-the-art performance in the CDFSL benchmark dataset.

Hyperspectral Indices Developed from Fractional-Order Derivative Spectra Improved Estimation of Leaf Chlorophyll Fluorescence Parameters.

Chlorophyll fluorescence (ChlF) parameters offer valuable insights into quantifying energy transfer and allocation at the photosystem level. However, tracking their variation based on reflectance spectral information remains challenging for large-scale remote sensing applications and ecological modeling. Spectral preprocessing methods, such as fractional-order derivatives (FODs), have been demonstrated to have advantages in highlighting spectral features. In this study, we developed and assessed the ability of novel spectral indices derived from FOD spectra and other spectral transformations to retrieve the ChlF parameters of various species and leaf groups. The results obtained showed that the empirical spectral indices were of low reliability in estimating the ChlF parameters. In contrast, the indices developed from low-order FOD spectra demonstrated a significant improvement in estimation. Furthermore, the incorporation of species specificity enhanced the tracking of the non-photochemical quenching (NPQ) of sunlit leaves (R2 = 0.61, r = 0.79, RMSE = 0.15, MAE = 0.13), the fraction of PSII open centers (qL) of shaded leaves (R2 = 0.50, r = 0.71, RMSE = 0.09, MAE = 0.08), and the fluorescence quantum yield (ΦF) of shaded leaves (R2 = 0.71, r = 0.85, RMSE = 0.002, MAE = 0.001). Our study demonstrates the potential of FOD spectra in capturing variations in ChlF parameters. Nevertheless, given the complexity and sensitivity of ChlF parameters, it is prudent to exercise caution when utilizing spectral indices for tracking them.