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Global climatic changes increase areas that are influenced by drought. Remote sensing based on the spectral characteristics of reflected light is widely used to detect the action of stressors (including drought) in plants. The development of methods of improving remote sensing is an important applied task for plant cultivation. Particularly, this improvement can be based on the calculation of reflectance indices and revealing the optimal spectral bandwidths for this calculation. In the current work, we analyzed the sensitivity of broadband-normalized difference reflectance indices and RGB indices to the action of soil drought on pea and wheat plants. Analysis of the heat maps of significant changes in reflectance indices showed that increasing the spectral bandwidths did not decrease this significance in some cases. Particularly, the index RI(659, 553) based on the red and green bandwidths was strongly sensitive to drought action in plants. The normalized red–green index (NRGI), which was the RGB-analog of RI(659, 553) measured by a color camera, was also sensitive to drought. RI(659, 553) and NRGI were strongly related. The results showed that broadband and RGB indices can be used to detect drought action in plants. [ABSTRACT FROM AUTHOR]

Remote sensing of stressor action on plants is an important step of their protection. Measurement of photochemical reflectance index (PRI) can be used to detect action of stressors including salinization; potentially, a small-scale spatial heterogeneity of PRI (within leaf or its part) can be an indicator of this action. The current work was devoted to analysis of sensitivity of the small-scale heterogeneity in PRI and in the reflected light intensity at 530 nm (approximately corresponding to the measuring wavelength for PRI) in leaves of pea (Pisum sativum) plants to action of salinization. Plants were cultivated under controlled conditions of a vegetation room and under open-air conditions. It was shown that both the standard deviation of PRI and coefficient of variation of the reflected light intensity at 530 nm were sensitive to action of salinization on plants. Moreover, this variation coefficient was negatively corelated to the potential quantum yield of PSII; i.e. increasing the coefficient could be used to estimate decreasing this yield caused by photodamage of PSII under salinization. Our results show that the small-scale spatial heterogeneity in PRI and the reflected light intensity at 530 nm can be used as additional tools of the remote sensing of plant responses under action of salinization. Remote sensing of action of stressors on plants is an important step of their protection. We showed that action of salinization stimulated spatial heterogeneity in the photochemical reflectance index, which was calculated on basis of the leaf reflectance at 530 and 575 nm, and in reflectance at 530 nm in pea (Pisum sativum) leaves. It means that measurements of the spatial heterogeneity of these parameters can be used as indicator of the action of salinization in the plant remote sensing. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Approaches for remote sensing can be used to estimate the influence of changes in environmental conditions on terrestrial plants, providing timely protection of their growth, development, and productivity. Different optical methods, including the informative multispectral and hyperspectral imaging of reflected light, can be used for plant remote sensing; however, multispectral and hyperspectral cameras are technically complex and have a high cost. RGB imaging based on the analysis of color images of plants is definitely simpler and more accessible, but using this tool for remote sensing plant characteristics under changeable environmental conditions requires the development of methods to increase its informativity. Our review focused on using RGB imaging for remote sensing the characteristics of terrestrial plants. In this review, we considered different color models, methods of exclusion of background in color images of plant canopies, and various color indices and their relations to characteristics of plants, using regression models, texture analysis, and machine learning for the estimation of these characteristics based on color images, and some approaches to provide transformation of simple color images to hyperspectral and multispectral images. As a whole, our review shows that RGB imaging can be an effective tool for estimating plant characteristics; however, further development of methods to analyze color images of plants is necessary. [ABSTRACT FROM AUTHOR]

Local damage (e.g., burning) induces a variation potential (VP), which is an important electrical signal in higher plants. A VP propagates into undamaged parts of the plant and influences numerous physiological processes, including photosynthesis. Rapidly increasing plant tolerance to stressors is likely to be a result of the physiological changes. Thus, developing methods of revealing VP-induced physiological changes can be used for the remote sensing of plant systemic responses to local damage. Previously, we showed that burning-induced VP influenced a photochemical reflectance index in pea leaves, but the influence of the electrical signals on other reflectance indices was not investigated. In this study, we performed a complex analysis of the influence of VP induction by local burning on difference reflectance indices based on 400–700 nm wavelengths in leaves of pea seedlings. Heat maps of the significance of local burning-induced changes in the reflectance indices and their correlations with photosynthetic parameters were constructed. Large spectral regions with significant changes in these indices after VP induction were revealed. Most changes were strongly correlated to photosynthetic parameters. Some indices, which can be potentially effective for revealing local burning-induced photosynthetic changes, are separately shown. Our results show that difference reflectance indices based on 400–700 nm wavelengths can potentially be used for the remote sensing of plant systemic responses induced by local damages and subsequent propagation of VPs. [ABSTRACT FROM AUTHOR]

A drought, which can be often accompanied by increased temperature, is a key adverse factor for agricultural plants. Remote sensing of early plant changes under water shortage is a prospective way to improve plant cultivation; in particular, the sensing can be based on measurement of difference reflectance indices (RIs). We complexly analyzed the efficiency of RIs based on 400–700 nm wavelengths for revealing the influences of water shortage and short-term heating on plant seedlings. We measured spectra of reflected light in leaves of pea, wheat, and pumpkin under control and stress conditions. All possible RIs in the 400–700 nm range were calculated, significances of differences between experimental and control indices were estimated, and heatmaps of the significances were constructed. It was shown that the water shortage (pea seedlings) changed absolute values of large quantity of calculated RIs. Absolute values of some RIs were significantly changed for 1–5 or 2–5 days of the water shortage; they were strongly correlated to the potential quantum yield of photosystem II and relative water content in leaves. In contrast, the short-term heating (pea, wheat, and pumpkin seedlings) mainly influenced light-induced changes in RIs. Our results show new RIs, which are potentially sensitive to the action of stressors. [ABSTRACT FROM AUTHOR]