Photochemical Reflectance Index Research Articles

Hyperspectral Imaging Reveals Differential Carotenoid and Chlorophyll Temporal Dynamics and Spatial Patterns in Scots Pine Under Water Stress.

Drought-related die-off events have been observed throughout Europe in Scots pine (Pinus sylvestris L.). Such events are exacerbated by carbon starvation that is, an imbalance of photosynthetic productivity and resource usage. Recent evidence suggests that optically measurable photosynthetic pigments such as chlorophylls and carotenoids respond to water stress (WS). However, there is a lack of measurements using imaging spectroscopy, and the mechanisms linking xanthophyll-related changes in reflectance captured by the photochemical reflectance index (PRI) and chlorophyll changes in red edge position (REP) to WS are not understood. To probe this, we conducted a greenhouse experiment where 3-year-old Pinus sylvestris saplings were subjected to water limitation and followed using hyperspectral imaging (HSI) spectroscopy, water status and photosynthetic measurements. Carotenoids (e.g., xanthophyll cycle) and chlorophylls responded to WS, which was observed using the HSI-derived indices PRI and REP respectively. The spatial-temporal response in these two pigment-reflectance groupings differed. The spatial distribution of PRI represented the light intensity around the time of the measurement, whereas REP reflected the daily averaged light intensity over the experimental course. A further difference was noted upon rewatering, where the carotenoid-related PRI partially recovered but the chlorophyll-related REP did not.

Ecophysiological variables retrieval and early stress detection: insights from a synthetic spatial scaling exercise

ABSTRACT The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (C ab), maximum carboxylation rate (V cmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and C ab were reliably retrieved in most cases, whereas V cmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field.

Identification of drought-salinity combined stress in tomato plants by vegetation indices

A major issue in several farming areas of the Mediterranean basin consists of drought and salinity stress. This stress is mainly due to a steady exposition of warm daily temperature and heatwaves, moreover with inevitable irrigation with saline water. Therefore, detecting the stress is essential to minimise significant yield loss and preserve agricultural sustainability. In this context, remote and proximal sensing can play a crucial role in allowing fast, not destructive, extensive, and reliable assessment of crop status. In this work, the effectiveness of several multispectral indices in detecting salinity and water stress in tomato plants, grown under controlled green-house conditions, was investigated. Three different classifiers (fine tree model, linear discriminant model, and linear support vector machines model) were used to verify whether, and the extent to which, the adopted multispectral indices can be adopted to identify a stress condition of the tomato plants. In the experimental campaign, the stress occurrence on tomato plants was assessed on the base of a set of ecophysiological measurements, such as transpiration, stomatal conductance, and photosynthesis rate. Obtained results showed that a classification model based on linear support vector machines, exploiting the combination of Photochemical Reflectance Index and the Chlorophyl Index, can detect drought and salinity stress in tomato plants with an accuracy higher than 94%.

Stomatal and Non-Stomatal Leaf Responses during Two Sequential Water Stress Cycles in Young Coffea canephora Plants

Understanding the dynamics of physiological changes involved in the acclimation responses of plants after their exposure to repeated cycles of water stress is crucial to selecting resilient genotypes for regions with recurrent drought episodes. Under such background, we tried to respond to questions as: (1) Are there differences in the stomatal-related and non-stomatal responses during water stress cycles in different clones of Coffea canephora Pierre ex A. Froehner? (2) Do these C. canephora clones show a different response in each of the two sequential water stress events? (3) Is one previous drought stress event sufficient to induce a kind of “memory” in C. canephora? Seven-month-old plants of two clones (’3V’ and ‘A1’, previously characterized as deeper and lesser deep root growth, respectively) were maintained well-watered (WW) or fully withholding the irrigation, inducing soil water stress (WS) until the soil matric water potential (Ψmsoil) reached ≅ −0.5 MPa (−500 kPa) at a soil depth of 500 mm. Two sequential drought events (drought-1 and drought-2) attained this Ψmsoil after 19 days and were followed by soil rewatering until a complete recovery of leaf net CO2 assimilation rate (Anet) during the recovery-1 and recovery-2 events. The leaf gas exchange, chlorophyll a fluorescence, and leaf reflectance parameters were measured in six-day frequency, while the leaf anatomy was examined only at the end of the second drought cycle. In both drought events, the WS plants showed reduction in stomatal conductance and leaf transpiration. The reduction in internal CO2 diffusion was observed in the second drought cycle, expressed by increased thickness of spongy parenchyma in both clones. Those stomatal and anatomical traits impacted decreasing the Anet in both drought events. The ‘3V’ was less influenced by water stress than the ‘A1’ genotype in Anet, effective quantum yield in PSII photochemistry, photochemical quenching, linear electron transport rate, and photochemical reflectance index during the drought-1, but during the drought-2 event such an advantage disappeared. Such physiological genotype differences were supported by the medium xylem vessel area diminished only in ‘3V’ under WS. In both drought cycles, the recovery of all observed stomatal and non-stomatal responses was usually complete after 12 days of rewatering. The absence of photochemical impacts, namely in the maximum quantum yield of primary photochemical reactions, photosynthetic performance index, and density of reaction centers capable of QA reduction during the drought-2 event, might result from an acclimation response of the clones to WS. In the second drought cycle, the plants showed some improved responses to stress, suggesting “memory” effects as drought acclimation at a recurrent drought.

Light Quality Influence on Growth Performance and Physiological Activity of Coleus Cultivars

This study investigates the influence of different light qualities, including red, green, blue, purple, and white lights, on the growth, physiological activity, and ornamental characteristics of two Coleus cultivars. Emphasizing the importance of leveraging phenotypic plasticity in plants within controlled environments, using light quality is a practice prevalent in the ornamental industry. The research explores the varied responses of two Coleus cultivars to distinct light spectra. The key findings reveal the efficacy of red light in enhancing shoot and leaf parameters in C. ‘Highway Ruby’, while red and green light exhibit comparable effects on shoot width and leaf dimensions in C. ‘Wizard Jade’. White light-emitting diodes (LEDs), particularly with color temperatures of 4100 K and 6500 K, promote root length growth in the respective cultivars. Moreover, chlorophyll content and remote sensing vegetation indices, including chlorophyll content (SPAD units), the normalized difference vegetation index (NDVI), the modified chlorophyll absorption ratio index (MCARI), and the photochemical reflectance index (PRI), along with the chlorophyll fluorescence, were significantly affected by light qualities, with distinct responses observed between the cultivars. In summary, this study highlights the transformative potential of LED technology in optimizing the growth and ornamental quality of foliage plants like Coleus, setting a benchmark for light quality conditions. By leveraging LED technology, producers and nursery growers access enhanced energy efficiency and unparalleled versatility, paving the way for significant advancements in plant growth, color intensity, and two-tone variations. This presents a distinct advantage over conventional production methods, offering a more sustainable and economically viable approach for increased plant reproduction and growth development. Likewise, the specific benefits derived from this study provide invaluable insights, enabling growers to strategically develop ornamental varieties that thrive under optimized light conditions and exhibit heightened visual appeal and market desirability.

Improving Soybean Gross Primary Productivity Modeling Using Solar-Induced Chlorophyll Fluorescence and the Photochemical Reflectance Index by Accounting for the Clearness Index

Solar-induced chlorophyll fluorescence (SIF) has been widely utilized to track the dynamics of gross primary productivity (GPP). It has been shown that the photochemical reflectance index (PRI), which may be utilized as an indicator of non-photochemical quenching (NPQ), improves SIF-based GPP estimation. However, the influence of weather conditions on GPP estimation using SIF and PRI has not been well explored. In this study, using an open-access dataset, we examined the impact of the clearness index (CI), which is associated with the proportional intensity of solar incident radiation and can represent weather conditions, on soybean GPP estimation using SIF and PRI. The midday PRI (xanthophyll de-epoxidation state) minus the early morning PRI (xanthophyll epoxidation state) yielded the corrected PRI (ΔPRI), which described the amplitude of xanthophyll pigment interconversion during the day. The observed canopy SIF at 760 nm (SIFTOC_760) was downscaled to the broadband photosystem-level SIF for photosystem II (SIFTOT_FULL_PSII). Our results show that GPP can be accurately estimated using a multi-linear model with SIFTOT_FULL_PSII and ΔPRI. The ratio of GPP measured using the eddy covariance (EC) method (GPPEC) to GPP estimated using SIFTOT_FULL_PSII and ΔPRI exhibited a non-linear correlation with the CI along both the half-hourly (R2 = 0.21) and daily scales (R2 = 0.25). The GPP estimates using SIFTOT_FULL_PSII and ΔPRI were significantly improved by the addition of the CI (for the half-hourly data, R2 improved from 0.64 to 0.71 and the RMSE decreased from 8.28 to 7.42 μmol•m−2•s−1; for the daily data, R2 improved from 0.71 to 0.81 and the RMSE decreased from 6.69 to 5.34 μmol•m−2•s−1). This was confirmed by the validation results. In addition, the GPP estimated using the Random Forest method was also largely improved by considering the influences of the CI. Therefore, our findings demonstrate that GPP can be well estimated using SIFTOT_FULL_PSII and ΔPRI, and it can be significantly enhanced by accounting for the CI. These results will be beneficial to vegetation GPP estimation using different remote sensing platforms, especially under various weather conditions.

Evaluation of the monitoring capability of various vegetation indices and mainstream satellite band settings for grassland drought

In the context of global climate change and increasing human activities, grassland drought has become increasingly severe and complex. The monitoring of grassland drought is crucial for reducing drought-related losses and ensuring national ecological security. This study used the coupled PROSPECT and SAIL radiative transfer models (PROSAIL) to simulate canopy reflectance, considering factors such as grassland growth stages and varying drought conditions. Our objective was to reveal the spectral response characteristics of grasslands to varying drought conditions and identify sensitive spectral bands suitable for drought monitoring during different grassland growth stages. We aligned commonly available satellite bands from moderate resolution imaging spectroradiometer (MODIS), Sentinel 2, Landsat 8, WorldView 2, and Gaofen 2 (GF 2) with these sensitive bands to assess the capabilities of existing satellite data for drought monitoring. Furthermore, this research evaluated the suitability of 16 commonly used remote sensing vegetation indices for grassland drought monitoring, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Difference Vegetation Index (DVI), Modified Soil Adjusted Vegetation Index (MSAVI), Atmospherically Resistant Vegetation Index (ARVI), Modified Normalized Difference Water Index (MNDWI), Global Vegetation Moisture Index (GVMI), Land Surface Water Index (LSWI), Visible and Shortwave Infrared Drought Index (VSDI), Water index(WI), Moisture Stress Index(MSI), Normalized Difference Water Index(NDWI), Normalized Difference Infrared Index (NDII), Photochemical Reflectance Index (PRI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). The simulation and analysis results revealed: 1) Grassland in different growth stages exhibit similar sensitivities to certain spectral bands, namely those within the ranges of 540 nm–720 nm, 1250 nm–1690 nm, 1805 nm–2190 nm, and 2264 nm–2500 nm, which are more sensitive to various drought conditions. 2) Suitable vegetation indices for both growing and stable stages include NDII, MSI, PRI, LSWI, and GVMI, with silhouette coefficients exceeding 0.6 for the growing stage and 0.7 for the stable stage. The least suitable vegetation index is DVI, with an average silhouette coefficient of 0.15 over the entire growth stage. 3) From the spectral band perspective, among the five assessed satellites, MODIS Band 7 exhibits the highest sensitivity to water content across all satellite bands. MODIS's band configuration is most suitable for monitoring grassland drought during different growth stages, while WorldView 2's band configuration is the least suitable.

Multitemporal airborne imaging spectrometry and fluorometry reveal contrasting photoprotective responses of trees

The Photochemical Reflectance Index (PRI) and solar induced fluorescence (SIF) provide information on plant photosynthetic activity. PRI and SIF are both strongly influenced by irradiance, but uncertainties related to the interpretation of these light responses at large spatial scales remain, partly due to a shortage of suitable data from aircraft or satellite platforms. The goal of this study was to explore interpretations of the PRI- and SIF-light responses of trees owing to species, functional types (evergreen and deciduous) and season. Using airborne hyperspectral and ultraspectral imagery in a North American urban forest, we derived PRI, SIF, and albedo (an indicator of illumination) at the 1-m pixel level. We then quantified crown-level PRI and SIF light responses of ten different tree species at three time points from late-summer to autumnal senescence using hierarchical models. Our results confirmed that both PRI and SIF were strongly influenced by illumination with PRI decreasing and SIF increasing with illumination. Both slope and intercept of the PRI-albedo relationship changed with season, but the pattern varied among species and functional types. SIF values decreased during autumnal senescence for all species, but evergreen species exhibited less seasonal decline in the slope of SIF-albedo relationship compared to deciduous species. The PRI and SIF light responses derived from the airborne imagery offer complementary information on dynamic photosynthesis responses presumably due to varying canopy structure, pigmentation and photoprotection among species and functional types. From airborne platforms, PRI- and SIF-light responses can be used to explore the contrasting physiological responses of individual tree crowns, providing a spatially and temporally explicit view of dynamic plant traits related to photoregulation and a novel view of functional diversity for entire landscapes.

PFOA accumulation in the leaves of basil (Ocimum basilicum L.) and its effects on plant growth, oxidative status, and photosynthetic performance

BackgroundPerfluoroalkyl substances (PFASs) are emerging contaminants of increasing concern due to their presence in the environment, with potential impacts on ecosystems and human health. These substances are considered “forever chemicals” due to their recalcitrance to degradation, and their accumulation in living organisms can lead to varying levels of toxicity based on the compound and species analysed. Furthermore, concerns have been raised about the possible transfer of PFASs to humans through the consumption of edible parts of food plants. In this regard, to evaluate the potential toxic effects and the accumulation of perfluorooctanoic acid (PFOA) in edible plants, a pot experiment in greenhouse using three-week-old basil (Ocimum basilicum L.) plants was performed adding PFOA to growth substrate to reach 0.1, 1, and 10 mg Kg− 1 dw.ResultsAfter three weeks of cultivation, plants grown in PFOA-added substrate accumulated PFOA at different levels, but did not display significant differences from the control group in terms of biomass production, lipid peroxidation levels (TBARS), content of α-tocopherol and activity of ascorbate peroxidase (APX), catalase (CAT) and guaiacol peroxidase (POX) in the leaves. A reduction of total phenolic content (TPC) was instead observed in relation to the increase of PFOA content in the substrate. Furthermore, chlorophyll content and photochemical reflectance index (PRI) did not change in plants exposed to PFAS in comparison to control ones. Chlorophyll fluorescence analysis revealed an initial, rapid photoprotective mechanism triggered by PFOA exposure, with no impact on other parameters (Fv/Fm, ΦPSII and qP). Higher activity of glutathione S-transferase (GST) in plants treated with 1 and 10 mg Kg− 1 PFOA dw (30 and 50% to control, respectively) paralleled the accumulation of PFOA in the leaves of plants exposed to different PFOA concentration in the substrate (51.8 and 413.9 ng g− 1 dw, respectively).ConclusionDespite of the absorption and accumulation of discrete amount of PFOA in the basil plants, the analysed parameters at biometric, physiological and biochemical level in the leaves did not reveal any damage effect, possibly due to the activation of a detoxification pathway likely involving GST.

Downscaling canopy photochemical reflectance index to leaf level by correcting for the soil effects

The photochemical reflectance index (PRI) is a promising remote sensing signal for monitoring vegetation physiology. Variations in leaf PRI are usually attributed to either the energy-dependent xanthophyll cycle or the carotenoid-chlorophyll ratio, both indicative of leaf physiology. However, canopy PRI is subject to soil, canopy structure, and incident and viewing angles, and thus has a weaker and more complicated relationship with vegetation photosynthetic activity or leaf pigment composition. Therefore, downscaling canopy PRI to the leaf level is essential for accurate remote sensing of vegetation physiology using PRI. An earlier investigation (P.Yang, RSE, 279, 113,133, 2022) illustrates that structural and angular variations in canopy PRI primarily result from varying degrees of soil interference. In this study, a soil correction method is proposed to mitigate the soil effects on top-of-canopy (TOC) reflectance at the PRI bands. The soil effects on TOC reflectance at 531 nm and 570 nm are respectively estimated as R531soil×R675/R675soil and R570soil×R675/R675soil, where R675 is TOC red reflectance, Rλsoil soil reflectance at wavelengthλ. R675/R675soil approximates the fraction of the observed sunlit soil, as leaves are nearly black at 675 nm due to strong absorption of chlorophyll, and R675 is mainly contributed from soil. To assess the effectiveness of the soil correction method, a wheat field dataset, a corn field dataset, and a simulated dataset, were utilized. The soil-adjusted and the original canopy PRI were compared with the leaf PRI for the real and synthetic scenarios that had various soil brightness, leaf chlorophyll content and canopy structure. Both the field and numerical experiments demonstrate that, for the canopies with low vegetation coverage and substantial soil contamination, the original canopy PRI was largely different from the leaf PRI, displaying substantial structural and angular dependence. In comparison, the soil-adjusted canopy PRI was more closely aligned with the PRI observed in the sunlit leaves in all three datasets. This study shows that accounting for the soil effects with TOC red reflectance allows downscaling canopy PRI to the leaf level. The soil-adjusted canopy PRI contributes to remote sensing of the xanthophyll cycle or the carotenoid-chlorophyll ratio from canopy PRI.

Detection of Emerging Stress in Trees Using Hyperspectral Indices as Classification Features

This research presents a classification methodology for the detection of new or emerging stress in trees using indices derived from hyperspectral data and tests whether existing hyperspectral indices are effective when used as the classification features for this problem. We tested six existing indices—Water Band Index (WBI), Gitelson–Merzlyak B Index (GMb), Normalized Phaeophytization Index (NPQI), Combined Carotenoid/Chlorophyll Ratio Index (CCRI), Photochemical Reflectance Index (PRI), and Red-Edge Chlorophyll Index (CIre)—along with a seventh Test Index—generated as a composite of PRI and Cire—as classification features. Analysis was conducted using data collected from trees with and without emerald ash borer (EAB) infestation to develop a methodology that could be adapted to measure emerging stress from other pathogens or invasive pests in other tree species. Previous work has focused specifically on the identification of damage or stress symptoms caused by a specific pathogen. In this study, we adapted that work to develop a system of classification that can be applied to the identification of stress symptoms from a range of sources, measurable in trees based on spectral response and, in some cases, detectable prior to the onset of visible symptoms that can be measured through human observation. Our data indicate that existing indices derived from hyperspectral data are effective as classification features when measuring spectral responses indicative of emerging stress in trees.

Classification and Identification of Pinecone Mulching in Blueberry Cultivation Based on Crop Leaf Characteristics and Hyperspectral Data

While crushed pinecone mulch holds promise as a beneficial material for blueberry cultivation, research on its effectiveness remains limited. Crop leaf characteristics can serve as parameters for assessing mulching effects, although there are several limitations, including the need to analyze various distinct characteristics separately. The combination of hyperspectral data and machine learning techniques is expected to enable the selection of only the most important features among these characteristics. In this study, we investigated the impact of various mulching treatments utilizing pine tree byproducts, including crushed pinecones. Mulching variations included non-mulching (NM), crushed pinecones (PCs), a mixture of crushed pinecones and sulfur (PCS), pine needles (PNs), and sulfur treatment (S). Conventional methods were employed to measure leaf growth (length and width) and physiological characteristics (chlorophyll content, chlorophyll fluorescence, and stomatal conductance). Hyperspectral reflectance was also measured, and classification models using Partial Least Squares Discriminant Analysis (PLS-DA) and eXtreme Gradient Boosting (XGBoost) were developed for crop characteristics, vegetation indices (VIs), visible and near-infrared (VNIR), and short-wave infrared (SWIR). The results showed that using crushed pinecones as the sole mulching material for blueberries, without sulfur treatment, had a positive impact on blueberry growth. The PC treatment exhibited a dual effect on plant growth by lowering the soil pH to 5.89 and maintaining soil moisture within the range of 26.33–35.20%. We observed distinct differences in soil inorganic nutrient content, with higher concentrations of organic matter, total nitrogen, and available P2O5 and K+, which positively influenced blueberry growth. Mulching treatments demonstrated superior physiological characteristics, with two classification models identifying stomatal conductance (gs) as a key parameter influencing treatment classification (VIP scores > 1 rank: 3, variable score rank: 1). The photochemical reflectance index (PRI) emerged as a major parameter among VIs, showing potential for measuring water stress (VIP scores > 1 rank: 2, variable score rank: 1). In the SWIR PLS-DA model, wavelength peaks were mainly observed in the O-H overtone (1410 nm, 1450 nm, 1930 nm, 1940 nm, and 2100 nm). Overall, crushed pinecones were found to positively impact the initial growth of blueberries by enhancing water status (plant respiration).

Assessing the Potential for Photochemical Reflectance Index to Improve the Relationship between Solar-Induced Chlorophyll Fluorescence and Gross Primary Productivity in Crop and Soybean

Photosynthesis is influenced by dynamic energy allocation under various environmental conditions. Solar-induced chlorophyll fluorescence (SIF), an important pathway for dissipating absorbed energy, has been extensively used to evaluate gross primary productivity (GPP). However, the potential for photochemical reflectance index (PRI), as an indicator of non-photochemical quenching (NPQ), to improve the SIF-based GPP estimation, has not been thoroughly investigated. In this study, using continually tower-based observations, we examined how PRI affected the link between SIF and GPP for corn and soybean at half-hourly and daily timescales. The relationship of GPP to SIF and PRI is impacted by stress indicated by vapor pressure deficit (VPD) and crop water stress index (CWSI). Moreover, the ratio of GPP to SIF of corn was more sensitive to PRI compared to soybean. Whether in Pearson or Partial correlation analysis, the relationships of PRI to the ratio of GPP to SIF were almost all significant, regardless of controlling structural-physiological (stomatal conductance, vegetation indices) and environmental variables (light intensity, etc.). Therefore, PRI significantly affects the SIF–GPP relationship for corn (r > 0.31, p < 0.01) and soybean (r > 0.22, p < 0.05). After combining SIF and PRI using the multi-variable linear model, the GPP estimation has been largely improved (the coefficient of determination, abbreviated as R2, increased from 0.48 to 0.49 to 0.78 to 0.84 and the Root Mean Square Error, abbreviated as RMSE, decreased from 6.38 to 10.22 to 3.56 to 6.60 μmol CO2·m−2·s−1 for corn, R2 increased from 0.54 to 0.62 to 0.78 to 0.82 and RMSE decreased from 6.25 to 9.59 to 4.34 to 6.60 μmol CO2·m−2·s−1 for soybean). It suggests that better GPP estimations for corn and soybean can be obtained when SIF is combined with PRI.

Non-destructive optical indices to estimate isoprenoids with nutritional value in packed rocket and spinach

The quantification of phytochemicals with nutritional benefits relies on accuracy yet time-consuming, expensive and destructive methodologies. In contrast, optical indices widely employed in disciplines such as ecology can serve as fast, low-cost, and non-destructive tools for tracking pigment changes. However, their potential application in postharvest phases and commercialization (i.e., supermarkets) remains underexplored. In this context, this work aimed to investigate the feasibility of using the normalized difference vegetation index (NDVI), the chlorophyll content index (CCI) and the photochemical reflectance index (PRI) to estimate phytochemicals of nutraceutical interest (chlorophylls, carotenoids and tocopherols). A fully factorial and randomized experimental design (combining different illumination, packaging, and collecting time) was conducted in two green leafy packaged vegetables (rocket and spinach). With this design that induced differences in physiological parameters a database with variable content of isoprenoids (carotenoids, chlorophylls, and tocopherols) based on treatments was created. By establishing correlations and models between isoprenoids and optical indices, the NDVI was revealed as a valuable tool to estimate chlorophyll content in both species, while the PRI tracked both tocopherol and carotenoid (except for β-carotene) content. Taking this approach, estimation of the sum of total carotenoids and tocopherols was achieved using the PRI. This study confirms the possibility of extending the use of the NDVI and PRI to real-time nutritional monitoring in different stages of production and commercialization. A future application envisions integrating this technology into refrigerators for enhanced consumer access to nutritional information.

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Conifers are a common type of plant used in ornamental horticulture. The prompt diagnosis of the phenological state of coniferous plants using remote sensing is crucial for forecasting the consequences of extreme weather events. This is the first study to identify the “Vegetation” and “Dormancy” states in coniferous plants by analyzing their annual time series of spectral characteristics. The study analyzed Platycladus orientalis, Thuja occidentalis and T. plicata using time series values of 81 vegetation indices and 125 spectral bands. Linear discriminant analysis (LDA) was used to identify “Vegetation” and “Dormancy” states. The model contained three to four independent variables and achieved a high level of correctness (92.3 to 96.1%) and test accuracy (92.1 to 96.0%). The LDA model assigns the highest weight to vegetation indices that are sensitive to photosynthetic pigments, such as the photochemical reflectance index (PRI), normalized PRI (PRI_norm), the ratio of PRI to coloration index 2 (PRI/CI2), and derivative index 2 (D2). The random forest method also diagnoses the “Vegetation” and “Dormancy” states with high accuracy (97.3%). The vegetation indices chlorophyll/carotenoid index (CCI), PRI, PRI_norm and PRI/CI2 contribute the most to the mean decrease accuracy and mean decrease Gini. Diagnosing the phenological state of conifers throughout the annual cycle will allow for the effective planning of management measures in conifer plantations.

Modeling transpiration using solar-induced chlorophyll fluorescence and photochemical reflectance index synergistically in a closed-canopy winter wheat ecosystem

The joint use of solar-induced chlorophyll fluorescence (SIF) and the photochemical reflectance index (PRI) has been shown to improve gross primary productivity (GPP) estimation across various plant functional types. However, the utility of PRI in combination with SIF for transpiration (T) estimation has not yet been explored. Additionally, current SIF-driven transpiration models including linear models, semi-mechanical models (combination of canopy conductance, gC, derived from a SIF and vapor pressure deficit, VPD, driven linear model with the Penman-Monteith model), and hybrid models (combination of gC derived from a SIF and VPD driven machine learning model with the Penman-Monteith model) have rarely been mutually assessed. Based on concurrent remotely sensed SIF and PRI, and eddy covariance flux measurements during one growing season for a winter wheat ecosystem in northern China, we investigated the mediating effect of PRI on SIF-driven T estimation under different VPD conditions and compared the performance of linear, semi-mechanical, and hybrid models in estimating T. Our results showed that the mediating effect of PRI on T described in the SIF-driven linear, semi-mechanistic, and hybrid models was significant under high VPD conditions rather than under low VPD conditions. Specifically, based on T partitioned using an underlying water use efficiency method as a benchmark, the root mean square error (RMSE) value of the PRI-mediated linear, semi-mechanistic, and hybrid models was 28.01 W/m2, 22.25 W/m2, and 28.71 W/m2 lower, respectively, than those of the corresponding models without PRI when VPD was >1.5 kPa. Based on T partitioned using a transpiration estimation algorithm as a benchmark, these three models also exhibited a significant reduction in RMSE under high VPD conditions after considering PRI. The main rationale behind the PRI improvement is that PRI can track photosynthetic dynamics under high VPD conditions. Based on the simulation results of the Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model, PRI can serve as an indicator for non-photochemical quenching (NPQ) within this ecosystem. Consequently, PRI can enhance the capability of SIF to characterize the energy dissipation of photosynthetically active radiation and help SIF to yield more accurate information on GPP and gc under high VPD conditions. Finally, the order of model performance in estimating T was generally hybrid model > semi-mechanistic model > linear model. Our findings show the effectiveness of PRI for improving SIF-driven transpiration estimation under high VPD conditions and provide a new hybrid model for estimating T from SIF.

Spectral phenology features of Platycladus orientalis (L.) Franco

Spectral plant phenology, based on time series of vegetation indices, is a useful tool for monitoring vegetation condition and climatic changes. The chlorophyll/carotenoid index (CCI) and photochemical reflectance index (PRI) show promise for spectral phenology of coniferous plants. This study evaluates the potential of using CCI, PRI, and the normalised difference vegetation index (NDVI) as phenological metrics for Platycladus orientalis. The study found a strong correlation between the annual dynamics of the chlorophyll/carotenoid ratio (Chl/Car) and the time series of CCI (R2 = 0.89, p < 0.001) and PRI (R2 = 0.85, p < 0.001). The CCI time series was used to determine the beginning and end of the phenological vegetation period (PVP) of P. orientalis through a double logistic regression model. The PRI model had an error of approximately two weeks in predicting the beginning and end of the PVP, with a tendency to overestimate the duration of the PVP. The use of NDVI as a model is unsuitable for describing the P. orientalis 2023 PVP.

Effect of elevated atmospheric CO2 on spectral reflectance of coffee leaves of plants cultivated at face facility

Climate change impacts are stressing many economic sectors worldwide, including agriculture, increasingly hindering efforts to meet human needs. Dioxide carbon is one of the main greenhouse gases and it affects directly the crop production. The objective of this study was to evaluate if established remote sensing indices could detect the effects of elevated atmospheric CO2 on the leaves of coffee (Coffea arabica L.) plantation growing under field conditions. Plots of coffee plants were exposed to ambient air (~390 µmol CO2 mol-1) and elevated CO2 (~550 µmol CO2 mol-1) at the free air CO2 enrichment (FACE) experiment. The statistical design was the completely randomized blocks with six replicates per treatment (ambient CO2 and elevated CO2), with 10m-diameter plots. Coffee leaves were spectrally characterized by reflectance spectra on their adaxial surfaces and seven vegetation indices were calculated from reflectance data: chlorophyll normalized difference index (Chl NDI), normalized difference nitrogen index (NDNI), normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), pigment specific simple ration indices for chlorophyll a (PSSRa) and chlorophyll b (PSSRb), and structural independent pigment index (SIPI). NDNI was a sensitive indicator of the atmospheric CO2 effects on coffee leaves. NDVI, PSSRa and PSSRb were sensitive to estimate the effect of elevated CO2 only under drought conditions. These indices identify the effect of CO2 when a long period with high precipitation deficit stressing the leaves occurred. Chl NDI, PRI and SIPI were not sensitive to atmospheric CO2.

Plant Biostimulants as an Effective Tool for Increasing Physiological Activity and Productivity of Different Sugar Beet Varieties

Drought and high temperatures are among most dangerous attributes of climate change, which negatively affects the quantity and quality of sugar beet production. One of the most effective tools for eliminating unwanted effects is the application of biostimulants during the growing season. In this study, a 4 × 3 factorial scheme was adopted: Two biostimulant treatments, namely (i) pure extract from brown seaweed Ascophylum nodosum (B1) and (ii) concentrate from the seaweed Ascophylum nodosum and humus substances (B2), were compared to a control treatment (B0) in an experiment with four sugar beet varieties (Fischer, Fabius, Nicolaus, Lucius). The two-year research proved the significant influence of biostimulants on all monitored physiological and production parameters of sugar beet, with the exception of potassium content. Biostimulants positively influenced the results of root yield, polarized and white sugar yield, and the values of LAI (leaf area index), NDVI (normalized difference vegetation index), and PRI (photochemical reflectance index), while the positive effect on sugar content was only in the case of B1 treatment. The production potential fluctuated significantly depending on the observed interaction, but it can be concluded that the most limiting factor of production is the course of weather conditions. However, after treatment with biostimulants, an increased root yield (B2) and sugar content (B1) were found. Moreover, in this experiment, a strong positive relationship between root yield and physiological parameters (NDVI and PRI) and LAI was proven, while the relationship of sugar content to these parameters was weak. Monitoring of the physiological response to biostimulant application shows a high potential from the sustainability perspective in the context of sugar beet production. In addition, the impact on the height and quality of production was evident.

Secondary metabolites of Urochloa ruziziensis on growth, nodulation, and spectral responses of soybean

Ruzigrass (Urochloa ruziziensis) is a widely cultivated plant alongside soybeans, particularly in integrated crop and livestock systems, due to its fast growth and adaptability to different conditions. This study aimed to assess the impact of secondary metabolites found in ethanolic extract from ruzigrass on soybean growth, nodulation, spectral responses, and soil microbiological attributes. The ethanolic extract of ruzigrass presents total concentration of flavonoids (852.5 µg g−1), including naringenin and kaempferol. Increasing doses (100, 200, 300, and 400 kg ha−1) of the extract were then applied to the soil in a greenhouse to cultivate soybean plants. The spectral analysis indicated that higher doses of ruzigrass extract enhanced photochemical reflectance, chlorophyll absorption, and total chlorophyll indices. However, the normalized differential vegetative index decreased in response to the treatments. Generally, increasing doses of ruzigrass extract led to linear improvements in plant height, leaf area, shoot dry mass, pod count, nodule count, nitrogen content, and soil microbial activity. On the other hand, root dry massand relative chlorophyll content decreased with higher doses of the extract. In conclusion, the application of Urochloa ruziziensis extract, containing secondary metabolites caused a reduction in root development and an increase in spectral indices related to abiotic stresses in soybeans. There was an increase in the biological attributes of the soil with a significant, increasing linear adjustment in the doses of ruzigrass extract applied, as well as in the soybean production components.