Influence of Surface Structure, Pigmentation and Particulate Matter on Plant Reflectance and Fluorescence

Development of Analytical Model to Describe Reflectance Spectra in Leaves with Palisade and Spongy Mesophyll

Remote sensing plays an important role in plant cultivation and ecological monitoring. This sensing is often based on measuring spectra of leaf reflectance, which are dependent on morphological, biochemical, and physiological characteristics of plants. However, interpretation of the reflectance spectra requires the development of new tools to analyze relations between plant characteristics and leaf reflectance. The current study was devoted to the development, parameterization, and verification of the analytical model to describe reflectance spectra of the dicot plant leaf with palisade and spongy mesophyll layers (on the example of pea leaves). Four variables (intensities of forward and backward collimated light and intensities of forward and backward scattered light) were considered. Light reflectance and transmittance on borders of lamina (Snell's and Fresnel's laws), light transmittance in the palisade mesophyll (Beer-Bouguer-Lambert law), and light transmittance and scattering in the spongy mesophyll (Kubelka-Munk theory) were described. The developed model was parameterized based on experimental results (reflectance spectra, contents of chlorophylls and carotenoid, and thicknesses of palisade and spongy mesophyll in pea leaves) and the literature data (final R2 was 0.989 for experimental and model-based reflectance spectra). Further model-based and experimental investigations showed that decreasing palisade and spongy mesophyll thicknesses in pea leaves (from 35.5 to 25.2 µm and from 58.6 to 47.8 µm, respectively) increased reflectance of green light and decreased reflectance of near-infrared light. Similarity between model-based and experimental results verified the developed model. Thus, the model can be used to analyze leaf reflectance spectra and, thereby, to increase efficiency of the plant remote and proximal sensing.

Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

Impact of atmospheric particulate matter retention on physiological characters of five plant species under different pollution levels in Zhengzhou

Atmospheric particulate matter (PM) pollution has become a major environmental risk, and green plants can mitigate air pollution by regulating their enzymatic activity, osmoregulatory substances, photosynthetic pigments, and other biochemical characteristics. The present investigation aims to evaluate the mitigation potential of five common evergreen tree species (Photinia serrulata, Ligustrum lucidum, Eriobotrya japonica, Euonymus japonicus, Pittosporum tobira) against air pollution and to assess the effect of dust retention on plant physiological functions exposed to three different pollution levels (road, campus, and park). The results found that the amount of dust retained per unit leaf area of the plants was proportional to the mass concentration of atmospheric particulate matter in the environment, and that dust accumulation was higher on the road and campus than in the park. There were significant differences in dust retention among the five tree species, with the highest leaf dust accumulation observed for E. japonica (5.45 g·m-2), and the lowest for P. tobira (1.53 g·m-2). In addition, the increase in PM adsorption by different plants was uneven with increasing pollution levels, with significant decreases in chlorophyll content, photosynthetic and transpiration rate. From a physiological perspective, P. tobira exhibited greater potential to respond to PM pollution. Biochemical indicators suggested that PM pollution caused changes in plant protective enzyme activities, with a decrease in superoxide dismutase (SOD) and peroxidase (POD) activities, as well as promoting membrane lipid peroxidation, and appropriate stress also enables plants to counteract oxidative damage. In particular, PM exposure also induced stomatal constriction. Overall, PM retention was significantly associated with physiological and photosynthetic traits. In conclusion, our study contributes to the understanding of the effects of PM on plant physiology. Furthermore, it also provides insights into the selection of plants that are tolerant to PM pollution.

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

Spectral Photoluminescent Parameters of Barley Seeds (Hordéum vulgáre) Infected with Fusarium ssp

We needed effective and sustainable technologies for better microbiological control of crops, including Fusarium. However, photoluminescent UV-Vis methods are potential for diagnosing plant diseases with Fusarium. It has not been sufficiently studied despite the application of these methods for other biological researches. The excitation spectrum of the seeds during infection shifts to the shorter wavelength and a new maximum appears in the region λ ≈ 232 nm. The photoluminescence of infected seeds increases with excitation by radiation of wavelengths λ_e,1  = 232 nm, λ_e,2  = 362 nm and λ_e,3  = 424 nm by 1.33-3.14 times, and λ_e,3  = 424 nm-decreases by 1.1 times. Statistical moments μ₃ and μ₄ , asymmetry and kurtosis change only with short-wave excitation. When analyzing the decomposition of the frequency spectrum into Gaussian curves, the most informative ratio is the ratio of right-handed and left-handed Gaussians under excitation λ_e,2  = 362 nm and λ_e,3  = 424 nm. The ratios of their maxima change during infection by 1.36-3.2 times, and for excitation by radiation λ_e,2 , the frequency boundaries of Gaussians change. The results of measurements and calculations provide a basis for the development of a method and device for photoluminescence diagnostics of fusarium seeds in UV-Vis ranges.

Imaging particulate matter exposed pine trees by vehicle exhaust experiment and hyperspectral analysis

This study analyzed spectral variations of the particulate matter (PM hereafter)-exposed pine trees using a spectrometer and a hyperspectral imager to derive the most effective spectral indices to detect the pine needle exposure to PM emission. We found that the spectral variation in the near-infrared (NIR hereafter) bands systemically coincided with the variations in PM concentration, showing larger variations for the diesel group whereas larger dust particles showed spectral variations in both visible and NIR bands. It is because the PM adsorption on needles is the main source of NIR band variation, and the combination of visible and NIR spectra can detect PM absorption. Fourteen bands were selected to classify PM-exposed pine trees with an accuracy of 82% and a kappa coefficient of 0.61. Given that this index employed both visible and NIR bands, it would be able to detect PM adsorption. The findings can be transferred to real-world applications for monitoring air pollution in an urban area.

The Optical Response of a Mediterranean Shrubland to Climate Change: Hyperspectral Reflectance Measurements during Spring

Remote sensing techniques in terms of monitoring plants’ responses to environmental constraints have gained much attention during recent decades. Among these constraints, climate change appears to be one of the major challenges in the Mediterranean region. In this study, the main goal was to determine how field spectrometry could improve remote sensing study of a Mediterranean shrubland submitted to climate aridification. We provided the spectral signature of three common plants of the Mediterranean garrigue: Cistus albidus, Quercus coccifera, and Rosmarinus officinalis. The pattern of these spectra changed depending on the presence of a neighboring plant species and water availability. Indeed, the normalized water absorption reflectance (R975/R900) tended to decrease for each species in trispecific associations (11–26%). This clearly indicates that multispecific plant communities will better resist climate aridification compared to monospecific stands. While Q. coccifera seemed to be more sensible to competition for water resources, C. albidus exhibited a facilitation effect on R. officinalis in trispecific assemblage. Among the 17 vegetation indices tested, we found that the pigment pheophytinization index (NPQI) was a relevant parameter to characterize plant–plant coexistence. This work also showed that some vegetation indices known as indicators of water and pigment contents could also discriminate plant associations, namely RGR (Red Green Ratio), WI (Water Index), Red Edge Model, NDWI1240 (Normalized Difference Water Index), and PRI (Photochemical Reflectance Index). The latter was shown to be linearly and negatively correlated to the ratio of R975/R900, an indicator of water status.