Photochemical Reflectance Index Research Articles

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.

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.

Coupled effects of solar illumination and phenology on vegetation index determination: an analysis over the Amazonian forests using the SuperDove satellite constellation

ABSTRACTDespite the importance of the Amazonian rainforests in the global carbon cycle, their phenological responses measured by large field-of-view satellite sensors are still not completely understood. In this scenario, close-to-nadir observations at high spatial and temporal resolutions made by satellite constellations may contribute to improve this knowledge. Here, we investigated the sensitivity of five vegetation indices (VIs) to canopy shadows over 15 protected forests of the Amazon, and the possible existence of coupled effects of solar illumination and vegetation phenology on the VI determination. The VIs are the Enhanced Vegetation Index (EVI), Green-Red Normalized Difference (GRND), Modified Photochemical Reflectance Index (MPRI), Normalized Difference Vegetation Index (NDVI), and RedEdge Normalized Difference (REND). They were calculated from 432 images obtained in 2022 by the Planet’s eight-band SuperDove instrument. Few daily images acquired on the same day with distinct Solar Zenith Angle (SZA) were used to disentangle the effects of solar illumination from those of vegetation phenology. The results showed the presence of coupled effects of solar illumination and vegetation phenology on the EVI determination regardless of the site location, especially over dense forests. Such effects were not observed significantly in the GRND, MPRI, NDVI, and REND data. When the vegetation phenology was kept fixed in the analysis, solar illumination generated pseudo-greening effects from the EVI, even for small differences in SZA between daily observations. As the most sensitive VI to illumination conditions, the EVI increased from the beginning to the end of the dry season tracking solar angles and shade fractions. This dry-season trend was not observed for GRND, MPRI, NDVI, and REND, which presented low correlations with SZA and shade fractions. These four VIs were correlated with each other over most sites, which explained the agreement observed between their seasonal profiles. From the analysis of 15 sites distributed throughout the Amazon, our findings did not confirm patterns of large-scale greening at the end of the dry season. Local changes in greening (vegetation productivity) and browning were captured by the VIs over a few sites but in different periods of the dry season (June to September). At the high spatial scale of SuperDove observations, our results highlight the necessity of correcting solar and, in some cases, terrain illumination effects on the EVI before retrieving phenological metrics over the Amazon.

Vegetation Indices for Early Grey Mould Detection in Lettuce Grown under Different Lighting Conditions.

Early detection of pathogenic fungi in controlled environment areas can prevent major food production losses. Grey mould caused by Botrytis cinerea is often detected as an infection on lettuce. This paper explores the use of vegetation indices for early detection and monitoring of grey mould on lettuce under different lighting conditions in controlled environment chambers. The aim was focused on the potential of using vegetation indices for the early detection of grey mould and on evaluating their changes during disease development in lettuce grown under different lighting conditions. The experiment took place in controlled environment chambers, where day/night temperatures were 21 ± 2/17 ± 2 °C, a 16 h photoperiod was established, and relative humidity was 70 ± 10% under different lighting conditions: high-pressure sodium (HPS) and light-emitting diode (LED) lamps. Lettuces were inoculated by 7-day-old fungus Botrytis cinerea isolate at the BBCH 21. As a control, non-inoculated lettuces were grown under HPS and LEDs (non-inoculated). Then, the following were evaluated: Anthocyanin Reflectance Index 2 (ARI2); Carotenoid Reflectance Index 2 (CRI2); Structure Intensive Pigment Index (SIPI); Flavanol Reflectance Index (FRI); Greenness (G); Greenness 2 (G2); Redness (R); Blue (B); Blue Green Index 2 (BGI2); Browning Index 2 (BRI2); Lichtenthaler Index 1 (LIC1); Pigment Specific Simple Ratio (PSSRa and PSSRb); Gitelson and Merzlyak (GM1 and GM2); Zarco Tejada-Miller Index (ZMI); Normalized Difference Vegetation Index (NDVI); Simple Ratio (SR); Red-Eye Vegetation Stress Index (RVSI); Photochemical Reflectance Index (PRI); Photochemical Reflectance Index 515 (PRI515); Water Band Index (WBI); specific disease index for individual study (fD); Healthy Index (HI); Plant Senescence Reflectance (PSRI); Vogelmann Red Edge Index (VREI1); Red Edge Normalized Difference Vegetation Index (RENDVI); and Modified Red Edge Simple Ratio (MRESRI). Our results showed that the PSRI and fD vegetation indices significantly detected grey mould on lettuce grown under both lighting systems (HPS and LEDs) the day after inoculation. The results conclusively affirmed that NDVI, PSRI, HI, fD, WBI, RVSI, PRI, PRI515, CRI2, SIPI, chlorophyll index PSSRb, and coloration index B were identified as the best indicators for Botrytis cinerea infection on green-leaf lettuce (Lactuca sativa L. cv Little Gem) at the early stage of inoculated lettuce's antioxidative response against grey mould with a significant increase in chlorophyll indices.

Traits contributing to salinity tolerance in rice genotypes from the Mekong Delta

AbstractIncreasing sea level rise and subsequent salinization in mega deltas, such as the Vietnamese Mekong Delta (VMD), pose a risk to rice (Oryza sativa L.) production during the dry season. This study investigated the salinity resistance of a selection of common rice genotypes from the VMD along with an international check, IR64. The 20 rice varieties were grown hydroponically for 5 weeks in a greenhouse and then exposed to three levels of NaCl concentration (0 mM, 50 mM and 100 mM) over a period of 2 weeks to determine their susceptibility to salinity. Rice plants were scored and SPAD (leaf greenness) and PRI (photochemical reflectance index) were measured on the youngest fully developed leaf on the main tiller. After harvesting the 7‐week‐old plants, biomass and ion (K+, Cl−, Na+) content were determined by organ across all tillers. Averaged over all varieties, both at 50 mM and 100 mM NaCl, there was a significant reduction in plant biomass, 39% and 52% respectively. However, the effect of the NaCl treatments and the uptake of Cl− and Na+ were significantly different between varieties (p < .0001). Using biomass and ion content as part of a multivariate analysis, varieties were classified according to their susceptibility to salinity and their predominant strategy towards managing ion accumulation. The grouped varieties were further characterized by patterns in Cl− and Na+ partitioning and nondestructive parameters such as SPAD and PRI.

Condition of Young Japanese Knotweed (Reynoutria japonica Houtt.) Offshoots in Response to Microwave Radiation of Their Rhizomes

Invasive Japanese knotweed is one of Poland’s most aggressive and difficult-to-control species. New methods of control are of utmost importance. This study aimed to analyze young knotweed offshoots’ regeneration and physiological condition after treatment of their rhizomes with microwaves of high frequency. The fresh rhizomes were microwaved in the laboratory once for different times (from 5 to 60 s, interval 5 s). Next, in the greenhouse, the growth of offshoots and their biochemical state (spectrophotometry of leaves) were recorded in three measurement series. It was shown that the microwave treatment for 35 s directly destroys knotweed rhizomes of a diameter of 1–3 cm, so it does not produce new offsprings. The treatment times from 5–25 s delay offspring regeneration and growth and development of leaves. The leaves initiate the mechanisms of protection against microwave stress in offshoots grown from the rhizomes exposed to microwaves for 10 s (increase in the anthocyanin index) and the 5 or 20 s treatments (higher values of the photochemical reflectance index). Based on the effective dose (ED50) analysis, the threshold of harmful effects of microwaves on the growth and development of Reynoutria japonica rhizomes was assumed to be 17–19 s.

Tracking photosynthetic phenology using spectral indices at the leaf and canopy scales in temperate evergreen and deciduous trees

Vegetation photosynthetic phenology is an important indicator to characterize the biological responses of terrestrial carbon cycle to climate change. Remote sensing-derived spectral indices have been used to estimate photosynthetic phenology of terrestrial ecosystems, yet there are large uncertainties due to differences in leaf structure and canopy function between evergreen and deciduous trees. In this study, we used in-situ measured meteorology, the maximum rate of leaf-level CO2 assimilation (Amax), chlorophyll fluorescence and canopy color changes in evergreen and deciduous species to evaluate and compare the abilities of spectral indices for photosynthetic phenology modeling. Seasonal variation in Amax is used as a proxy for photosynthetic phenology. Several indices were used, including the normalized difference vegetation index (NDVI), near-infrared reflectance vegetation index (NIRv), photochemical reflectance index (PRI), chlorophyll/carotenoid index (CCI), green-red vegetation index (GRVI), and green chromaticity coordinates (Gcc). We found that NIRv and PRI estimated phenological periods for Cedrus deodara and three deciduous trees all differed from Amax by less than 5 days, and all were linearly related to Amax (R2 > 0.6). CCI had less potential for Amax modeling, while NDVI had the largest error in predicting Amax. Gcc was a more accurate proxy for photosynthetic phenology than GRVI at the canopy scale. In addition, we found that the variability of different spectral indices in tracking photosynthetic phenology of evergreen and deciduous species was related to non-photochemical quenching processes. Especially for the fraction of absorbed light quenched by dynamic NPQ (ɸNPQ) and the fraction of absorbed light quenched by sustained NPQ (ɸf,D), they contribute more than 40 % to the variations of the NIRv and PRI spectral signals. Therefore, NIRv and PRI provide powerful tools for monitoring photosynthetic phenology, and future exploration of NIRv and PRI at larger spatial scales will favor the global plant phenology and carbon uptake modeling.

Early detection of pine shoot beetle attack using vertical profile of plant traits through UAV-based hyperspectral, thermal, and lidar data fusion

Pine shoot beetle (PSB) is one of the most damaging forest insects of Yunnan pine plantations in southwest China. However, the subtle symptoms of heterogeneous tree crowns make it difficult to accurately detect at early stage of PSB attack. Here, we evaluated the potential of a combination of plant traits (PTs) and vegetation indices (VIs) to distinguish different levels of tree damage by integrating hyperspectral, thermal imagery, and light detection and ranging (lidar) data based on unmanned airborne vehicle (UAV) systems. A voxelization method was used to fuse hyperspectral reflectance, temperature, and lidar point cloud data. Subsequently, PTs such as pigments (i.e., chlorophyll, carotenoid, and anthocyanin contents) were retrieved from a radiative transfer model inversion, and structural, fluorescence, and thermal traits, as well as VIs, were derived from the fusion data. We developed a novel analytical approach using random forest (RF) algorithm with predictors from different spatial distributions (i.e., horizontal directions, vertical layers, and vertical clusters) to compare the performance of tree severity classification. The results showed that the difference of both PTs and VIs in vertical layers between different severity levels are more than that in horizontal directions. The performance of RF model with predictors of the vertical layers (OA = 74 %, kappa = 0.65) was better than that using the predictive variables in horizontal directions (OA = 69 % and kappa = 0.60) of tree crowns. Using the vertical clustering features RF model increased the accuracy (OA = 78 % and kappa = 0.70), especially for slightly and moderately damaged trees, with improvements of 10 % and 12 %, respectively. Among all variables analyzed, chlorophylls were the most important predictor, followed by photochemical reflectance index and structural traits. Our work demonstrates the effectiveness of using fused UAV-based multi-sensor data for early detection of PSB attack, and can be applied other potential forest diseases and insect monitoring.

Age and Adaptive Changes in the Photosynthetic Apparatus of Leaves in Winter Green Herbaceous Plant Ajuga reptans L. in the Natural Conditions of the Taiga Zone

The formation and functioning of the photosynthetic apparatus (PSA) is under the general control of the plant organism and constantly changing environmental conditions. The authors studied age-related and adaptive changes in the pigment complex, spectral properties, and the state of photosystem II (PSII) leaves of partial shoots of a summer-winter-green herbaceous perennial Ajuga reptans L. (bugle) in connection with overwintering. Rosette leaves of a new generation appearing in May–June quickly accumulated photosynthetic pigments and formed PSA with a maximal quantum efficiency of PSII (Fv/Fm) and actual quantum yield of PSII (ФPSII). In autumn (September–October), the content of chlorophylls was 10 mg/g dry weight, the value of Fv/Fm was 0.8 rel. units, and the level of ФPSII at PAR 130 µmol quanta/(m2 s) was approximately 0.7 rel. units. Overwintered leaves contained half as much photosynthetic pigments, accumulated a significant amount of anthocyanins, and were characterized by low photochemical activity and a high level of de-epoxidation of pigments of the violaxanthin cycle. With the resumption of active vegetation of plants (May), a partial restoration of the pigment pool and the repair of PSA of overwintered leaves were noted, as evidenced by an increase in the quantum yield PSII. The completion of the leaf life cycle was accompanied by a decrease in ФPSII up to 0.5 rel. units and a sharp increase in the thermal dissipation of absorbed excitation energy (NPQ) to 0.9 rel. units. Seasonal changes of spectral properties of leaves and photochemical reflectance indices generally corresponded to the dynamics content pigments and the efficiency of the use of light in photosynthesis. The results obtained indicate a significant transformation of the structural and functional organization of PSA in the ontogeny of overwintering leaves. The genetically fixed property of winter green plants to preserve leaves is based on the ability of their PSA to restore functional activity after the shock impact of overwintering, which is facilitated by a complex of adaptive and protective mechanisms.

Suitability of the stress severity index combined with remote‐sensing data as a tool to evaluate drought resistance traits in potato

AbstractPotato is a drought susceptible crop and even short drought spells reduce tuber yields notably. In an earlier study we developed a stress severity index (SSI) based on the development stage of a genotype at the onset of drought and the soil water deficit based on soil water tension. Here, we test the suitability of the SSI combined with remotely sensed data as a screening tool to select drought‐tolerant potato genotypes. Normalized difference vegetation index (NDVI) and the photochemical reflectance index (PRI) were obtained from reflectance measurements and thermography. Temperature data from the thermography allow using the difference between leaf and air temperature (∆T) to estimate the transpirational cooling of the leaves. Via cluster analysis including SSI, tuber yield reduction under drought, NDVI, PRI and thermography, three groups were distinguished: 1. SSI < 1000 with fast decreasing NDVI, PRI and ∆T, 2. SSI 1000–2000 with almost constant NDVI and ∆T and 3. SSI > 2000 described by small changes of NDVI, PRI and temperature deficit. For SSI < 1000, ∆T, PRI and NDVI showed to be good indicators of genotypic performance under drought. Potential strategies for drought resistance in potato detectable through remote sensing are discussed.

Tracking canopy chlorophyll fluorescence with a low-cost light emitting diode platform.

Chlorophyll fluorescence measured at the leaf scale through pulse amplitude modulation (PAM) has provided valuable insight into photosynthesis. At the canopy- and satellite-scale, solar-induced fluorescence (SIF) provides a method to estimate the photosynthetic activity of plants across spatiotemporal scales. However, retrieving SIF signal remotely requires instruments with high spectral resolution, making it difficult and often expensive to measure canopy-level steady-state chlorophyll fluorescence under natural sunlight. Considering this, we built a novel low-cost photodiode system that retrieves far-red chlorophyll fluorescence emission induced by a blue light emitting diode (LED) light source, for 2 h at night, above the canopy. Our objective was to determine if an active remote sensing-based night-time photodiode method could track changes in canopy-scale LED-induced chlorophyll fluorescence (LEDIF) during an imposed drought on a broadleaf evergreen shrub, Polygala myrtifolia. Far-red LEDIF (720-740 nm) was retrieved using low-cost photodiodes (LEDIFphotodiode) and validated against measurements from a hyperspectral spectroradiometer (LEDIFhyperspectral). To link the LEDIF signal with physiological drought response, we tracked stomatal conductance (gsw) using a porometer, two leaf-level vegetation indices-photochemical reflectance index and normalized difference vegetation index-to represent xanthophyll and chlorophyll pigment dynamics, respectively, and a PAM fluorimeter to measure photochemical and non-photochemical dynamics. Our results demonstrate a similar performance between the photodiode and hyperspectral retrievals of LEDIF (R2 = 0.77). Furthermore, LEDIFphotodiode closely tracked drought responses associated with a decrease in photochemical quenching (R2 = 0.69), Fv/Fm (R2 = 0.59) and leaf-level photochemical reflectance index (R2 = 0.59). Therefore, the low-cost LEDIFphotodiode approach has the potential to be a meaningful indicator of photosynthetic activity at spatial scales greater than an individual leaf and over time.

Improving the ability of PRI in light use efficiency estimation by distinguishing sunlit and shaded leaves in rice canopy

ABSTRACT Light use efficiency (LUE) plays an important role in gross primary production (GPP), so accurate estimation of LUE is crucial. The estimation of LUE using the photochemical reflectance index (PRI) is considerably promising, but on the other hand there is always uncertainty in obtaining the PRI due to the changing viewing angle and sun position in remote sensing observations. The objective of this study was to distinguish between sunlit and shaded leaves to improve the performance of PRI estimating LUE, and we used rice canopy reflectance data collected by the automatic multi-angle remote sensing platform and flux data for each half hour from 9:00 to 15:00 in Shouxian, Anhui Province, China. We modified the two-leaf model based on the characteristics of the rice canopy, was thought of by distinguishing between sunlit and shaded leaves PRI by two-leaf model on the rice canopy. The results showed that the -LUE correlation improved relative to PRI-LUE on clear days and was essentially negligible on cloudy days. We found that the photosynthetically active radiation (PAR), temperature, and humidity had some influence on , the -LUE correlation was strongest at higher temperature and vapour pressure deficit (VPD) and was linear with the change of temperature. We conclude that the modified two-leaf model improves the integrity of the PRI remote sensing observation and increases the accuracy of its LUE estimation by adequately separating sunlit and shaded leaves in the rice canopy.

Dose dependent effect of nitrogen on the phyto extractability of Cd in metal contaminated soil using Wedelia trilobata

Cadmium (Cd) is one of the toxic heavy metal that negatively affect plant growth and compromise food safety for human consumption. Nitrogen (N) is an essential macronutrient for plant growth and development. It may enhance Cd tolerance of invasive plant species by maintaining biochemical and physiological characteristics during phytoextraction of Cd. A comparative study was conducted to evaluate the phenotypical and physiological responses of invasive W. trilobata and native W. chinensis under low Cd (10 µM) and high Cd (80 µM) stress, along with different N levels (i.e., normal 91.05 mg kg-1 and low 0.9105 mg kg-1). Under low-N and Cd stress, the growth of leaves, stem and roots in W. trilobata was significantly increased by 35–23%, 25–28%, and 35–35%, respectively, compared to W. chinensis. Wedelia trilobata exhibited heightened antioxidant activities of catalase and peroxidase were significantly increased under Cd stress to alleviate oxidative stress. Similarly, flavonoid content was significantly increased by 40–50% in W. trilobata to promote Cd tolerance via activation of the secondary metabolites. An adverse effect of Cd in the leaves of W. chinensis was further verified by a novel hyperspectral imaging technology in the form of normalized differential vegetation index (NDVI) and photochemical reflectance index (PRI) compared to W. trilobata. Additionally, W. trilobata increased the Cd tolerance by regulating Cd accumulation in the shoots and roots, bolstering its potential for phytoextraction potential. This study demonstrated that W. trilobata positively responds to Cd with enhanced growth and antioxidant capabilities, providing a new platform for phytoremediation in agricultural lands to protect the environment from heavy metals pollution.