Photochemical Reflectance Index Research Articles

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.

Combining Chlorophyll Fluorescence and Vegetation Reflectance Indices to Estimate Non-Photochemical Quenching (NPQ) of Rice at the Leaf Scale

Non-photochemical quenching (NPQ) is an indicator of crop stress. Until now, only a limited number of studies have focused on how to estimate NPQ using remote sensing technology. The main challenge is the complicated regulatory mechanism of NPQ. NPQ can be divided into energy-dependent (qE) and non-energy-dependent (non-qE) quenching. The contribution of these two components varies with environmental factors, such as light intensity and stress level due to the different response mechanisms. This study aims to explore the feasibility of estimating NPQ using photosynthesis-related vegetation parameters available from remote sensing by considering the two components of NPQ. We concurrently measured passive vegetation reflectance spectra by spectrometer, as well as active fluorescence parameters by pulse-amplitude modulated (PAM) of rice (Oryza sativa) leaves. Subsequently, we explored the ability of the selected vegetation parameters (including the photochemical reflectance index (PRI), inverted red-edge chlorophyll index (IRECI), near-infrared reflectance of vegetation (NIRv), and fluorescence quantum yield (ΦF)) to estimate NPQ. Based on different combinations of these remote sensing parameters, empirical models were established to estimate NPQ using the linear regression method. Experimental analysis shows that the contribution of qE and non-qE components varied under different illumination conditions. Under high illumination, the NPQ was attributed primarily to the qE component, while under low illumination, it was equally attributed to the qE and non-qE components. Among all tested parameters, ΦF was sensitive to the qE component variation, while IRECI and NIRv were sensitive to the non-qE component variation. Under high illumination, integrating ΦF in the regression model captured NPQ variations well (R2 > 0.74). Under low illumination, ΦF, IRECI, and NIRv explained 24%, 62%, and 65% of the variation in NPQ, respectively, while coupling IRECI or NIRv with ΦF considerably improved the accuracy of NPQ estimation (R2 > 0.9). For all the samples under both low and high illumination, the combination of ΦF with at least one of the other parameters (including IRECI, NIRv and PAR) offers a more versatile and reliable approach to estimating NPQ than using any single parameter alone. The findings of this study contribute to the further development of remote sensing methods for NPQ estimation at the canopy scale in the future.

Estimation of photosynthetic dynamics in forests from daily measured fluorescence and PRI data with adjustment for canopy shadow fraction

We conducted year-long measurements of the photochemical reflectance index (PRI) and solar-induced fluorescence in the O2A oxygen band (SIFA) at a Norway spruce forest and a European beech forest to study relationships of these remote sensing variables to photosynthesis by trees in grown forest stands. Measured PRI and SIFA values were linked to changes in forest gross primary productivity (GPP) and light-use efficiency (LUE). Changes in the shadow fraction (αS) within tree crowns influenced PRI and fluorescence signals. In the spruce forest, the quantum yield of SIFA (FYSIFA) decreased around midday together with photosynthesis and GPP. Such decreases in FYSIFA were accompanied by an increase in the αS. In the beech forest, we detected an increase in FYSIFA together with a decrease in αS in the afternoon hours. The overall sensitivity of PRI to LUE was variable according to the season, presumably influenced by complex changes in photosynthetic pigments. PRI and FYSIFA showed weak correlations with canopy LUE; however, when considered together, the correlation was strengthened (R2 was 0.63 and 0.34 in spruce and beech forest, respectively). Our model predicting LUE dynamics includes a diurnal minimum of PRI and canopy αS to make allowances for seasonal changes in photosynthetic pigments and for diurnal variability of the shadow fraction in forests. The incorporation of these correcting factors allowed us to estimate LUE at R2 = 0.68 (spruce) and 0.53 (beech). The modeling equations appeared sensitive to the absorbed photosynthetically active radiation (APAR), but less sensitive to the GPP of these forests. Substituting pigments correction with introducing differential PRI (ΔPRI) into the model did not significantly improve the LUE estimation across the season. Our results show that the joint use of PRI and fluorescence improves LUE and GPP estimation accuracy in both daily and seasonal observations.

Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study.

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.

Effect Analysis of Hydrogen Peroxide Using Hyperspectral Reflectance in Sorghum [Sorghum bicolor (L.) Moench] under Drought Stress.

Due to global climate change, adverse environments like drought in agricultural production are occurring frequently, increasing the need for research to ensure stable crop production. This study was conducted to determine the effect of artificial hydrogen peroxide treatment on sorghum growth to induce stress resistance in drought conditions. Hyperspectral analysis was performed to rapidly find out the effects of drought and hydrogen peroxide treatment to estimate the physiological parameters of plants related to drought and calculate the vegetation indices through PLS analysis based on hyperspectral data. The partial least squares (PLS) analysis collected chlorophyll fluorescence variables, photosynthetic parameters, leaf water potential, and hyperspectral reflectance during the stem elongation and booting stage. To find out the effect of hydrogen peroxide treatment in sorghum plants grown under 90% and 60% of field capacity in greenhouses, growth and hyperspectral reflectance were measured on the 10th and 20th days after foliar application of H2O2 at 30 mM from 1st to 5th leaf stage. The PLS analysis shows that the maximum variable fluorescence of the dark-adapted leaves was the most predictable model with R2 = 0.76, and the estimation model suitability gradually increased with O (R2 = 0.51), J (R2 = 0.73), and P (R2 = 0.75) among OJIP parameters of chlorophyll fluorescence analysis. However, the estimation suitability of predictions for moisture-related traits, vapor pressure deficit (VPD, R2 = 0.18), and leaf water potential (R2 = 0.15) using hyperspectral data was low. The hyperspectral reflectance was 10% higher at 20 days after treatment (DAT) and 3% at 20 DAT than the non-treatment in the far red and infra-red light regions under drought conditions. Vogelmann red edge index (VOG REI) 1, chlorophyll index red edge (CIR), and red-edge normalized difference vegetation index (RE-NDVI) efficiently reflected moisture stress among the vegetation indices. Photochemical reflectance index (PRI) can be used as an indicator for early diagnosis of drought stress because hydrogen peroxide treatment showed higher values than untreated in the early stages of drought damage.

Soil Respiration and Related Abiotic and Remotely Sensed Variables in Different Overstories and Understories in a High-Elevation Southern Appalachian Forest

Accurately predicting soil respiration (Rs) has received considerable attention recently due to its importance as a significant carbon flux back to the atmosphere. Even small changes in Rs can have a significant impact on the net ecosystem productivity of forests. Variations in Rs have been related to both spatial and temporal variation due to changes in both abiotic and biotic factors. This study focused on soil temperature and moisture and changes in the species composition of the overstory and understory and how these variables impact Rs. Sample plots consisted of four vegetation types: eastern hemlock (Tsuga canadensis L. Carriere)-dominated overstory, mountain laurel (Kalmia latifolia L.)-dominated understory, hardwood-dominated overstory, and cinnamon fern (Osmundastrum cinnamomeum (L.) C. Presl)-dominated understory, with four replications of each. Remotely sensed data collected for each plot, light detection and ranging, and hyperspectral data, were compiled from the National Ecological Observatory Network (NEON) to determine if they could improve predictions of Rs. Soil temperature and soil moisture explained 82% of the variation in Rs. There were no statistically significant differences between the average annual Rs rates among the vegetation types. However, when looking at monthly Rs, cinnamon fern plots had statistically higher rates in the summer when it was abundant and hemlock had significantly higher rates in the dormant months. At the same soil temperature, the vegetation types’ Rs rates were not statistically different. However, the cinnamon fern plots showed the most sensitivity to soil moisture changes and were the wettest sites. Normalized Difference Lignin Index (NDLI) was the only vegetation index (VI) to vary between the vegetation types. It also correlated with Rs for the months of August and September. Photochemical reflectance index (PRI), normalized difference vegetation index (NDVI), and normalized difference nitrogen index (NDNI) also correlated with September’s Rs. In the future, further research into the accuracy and the spatial scale of VIs could provide us with more information on the capability of VIs to estimate Rs at these fine scales. The differences we found in monthly Rs rates among the vegetation types might have been driven by varying litter quality and quantity, litter decomposition rates, and root respiration rates. Future efforts to understand carbon dynamics on a broader scale should consider the temporal and finer-scale differences we observed.

The Influence of Soil Salt Stress on Modified Photochemical Reflectance Indices in Pea Plants

Salinization is a significant adverse factor that suppresses productivity of agricultural plants. Multispectral imaging and calculation of reflectance indices, including the typical photochemical reflectance index (PRI), can be used for early revealing of changes in plants under salinization. However, the direction of change in typical PRI is dependent on the type of stressor and the experimental conditions. Earlier, we proposed modified PRIs with shifted measuring wavelengths and analyzed their changes under excess light, water deficit, and heating. In the current work, we investigated the sensitivity of these indices to changes in pea plants under salinization (100, 200, and 400 mM NaCl treatment). It was shown that short-wavelength PRIs were increased under this treatment; in contrast, long-wavelength PRIs were decreased. Most of the modified PRIs were strongly related to the maximal yield of photosystem II and the normalized difference vegetation index. Long-wavelength PRIs were more sensitive to plant changes than short-wavelength PRIs, typical PRI, and the normalized difference vegetation index because their stable decrease under moderate salinization (the 200 mM NaCl treatment) was initiated earlier. Our results show that long-wavelength PRIs, which also decreased under the action of the excess light, water deficit, and heating, can be potentially used as a universal tool for early revealing of stress changes in plants.

Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy.

Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV-VIS-NIR-SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA3) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA3 concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R2CV values ranging from 0.81 to 0.87 and RPDP values exceeding 2.09 for all parameters. Based on Pearson's coefficient XYZ interpolations and HVI algorithms, the NIR-SWIR band combination proved the most effective for predicting height and leaf area, while VIS-NIR was optimal for optimal energy yield, and VIS-VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s-PLS were most significant for SWIR1 and SWIR2, while i-PLS showed a more uniform distribution in VIS-NIR-SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

Analyzing the recovery mechanisms of patchy degradation and its response to mowing and plateau pika disturbances in alpine meadow

The alpine meadow is susceptible to degradation triggered by overgrazing and rodent outbreaks, leading to uneven patches of degradation. In this regard, we conducted a study that involved the implementation of nine combined treatments utilizing a two-factor randomized block design. The treatments were established based on three levels of plateau pika densities and three levels of simulated grazing intensity (mowing), and were replicated three times, resulting in 27 test plots. Hyperspectral images were used to map healthy meadow (HM), long-term vegetated patch (LVP), and short-term vegetated patch (SVP) and characterize their spectral properties. Four vegetation indices, including modified red edge simple ratio (MRESR), photochemical reflectance index (PRI), anthocyanin reflectance index (ARI), and plant senescence reflectance index (PSRI), were used to measure the recovery rate of various patches. In order to measure the resilience of various patches, the ratio of the LVP to degraded patch coverage (RLDC) and ratio of the SVP to degraded patch coverage (RSDC) were calculated from nadir-viewing drone images of the test plots. The research findings indicate that moderate mowing negatively affects the vegetative recovery rate of grass in healthy meadow. A moderate density of plateau pika facilitates plant growth in various types of patches. The joint action of plateau pika and mowing amplifies the negative effects of both factors while neutralizing their positive effects. SVP plants improve community resilience by shortening their life cycle and sprouting in cracks of soil crust. SVPs exhibit high resilience and can withstand local severe interference by plateau pika and grazing activities. LVPs require a stable environment for healthy meadow and cannot adapt to the disturbance of plateau pika at any density. Under the joint interferences of plateau pika and mowing, the SVPs could not be replaced by the LVPs. Therefore, the restoration and management of patchily degraded meadow must consider enhancing the resilience of plant patches and maintaining the long-term and stable survival of the restored grassland.

Spectral indices for characterizing lentil accessions in the dryland of Pacific Northwest

Lentil is a cool season legume crop suffering terminal stress in different parts of the world, especially in North America, Australia, Southwest Asia and North Africa. Drought stress, which is often linked with high temperatures, is very damaging to grain legumes in practically all agricultural environments. In this experiment, 49 accessions were sown with a check variety in an augmented design in Whitlow farm 2015, central ferry farm 2016 and Spillman farm 2016, Washington state university. The three different locations were in the dryland of southeastern Washington. The collected 49 accessions were screened, where 32 accession were taken from the United States Department of Agriculture's single plant-derived core collection and 16 from lentil reference population of ICARDA and one check variety was Avondale. Secondary traits such as green Normalized difference vegetation index (GNDVI), red Normalized difference vegetation index (RNDVI), photochemical reflectance index (PRI), red Normalized difference vegetation index (RNDVI), water band index (WBI), and normalized water index (NWI) were collected using a multi spectral reflectance device and agronomic metrics such as days to flowering (DF), biomass yield per plant (BYP) and seed yield per plant (SYP) data were also collected as well. Multi trait genotype ideotype distance (MGIDI) addresses the restrictions of AMMI and GGE analysis by using BLUP and ideotype distance, which can properly select genotypes considering higher yields as well as consider other desirable parameters GNDVI, RNDVI, PRI, WBI, NWI. The accessions Avondale, PI 368647, ILL7090, PI33690, PI 5187, ILL 86, ILL595, PI 533690, PI 518734, ILL 1649, ILL 4781 were selected as the ten best genotypes and Avondale, ILL7090, PI533690 were less affected by G x E interaction and selected for further evaluation based on AMMI, GGE and MGIDI index.