Spectral Reflectance Research Articles

Using coupled bulk-rock geochemistry and short-wave infrared (SWIR) spectral reflectance data as rapid exploration tools in metamorphosed VHMS deposits: insights from the King Zn deposit, Yilgarn Craton, Western Australia

AbstractBulk rock geochemistry and SWIR reflectance spectroscopy are widely used by companies for rapid and cost-effective exploration of volcanic-hosted massive sulfide (VHMS) deposits. However, few studies have integrated bulk-rock geochemistry with hyperspectral reflectance spectroscopy in greenstone belts that have undergone high-grade metamorphism. Here we present an extensive dataset combining bulk-rock geochemistry with chlorite and white mica SWIR spectral reflectance from the amphibolite-grade King VHMS deposit of the Yilgarn Craton, Western Australia. At King, the footwall stratigraphy is dominated by tholeiitic mafic rocks overlain by a sequence of calc-alkaline intermediate-felsic metavolcanic rocks. The hanging-wall stratigraphy is characterized by a thin metaexhalite layer, overlain by thick succession of interbedded metasedimentary and metavolcanic rocks. Chlorite spectral signatures are more Fe-rich in mafic lithologies and Mg-rich in felsic rocks, particularly where intense Mg-metasomatism occurred before metamorphism. In all units, Fe/Mg ratios of chlorite are strongly tied to bulk rock Fe/Mg ratios. White mica in the footwall is primarily muscovitic, with minor amounts of phengite in deep Fe-rich mafic rocks. By contrast, the hanging-wall sequence is dominated by phengitic signatures in both the Fe-rich metaexhalite, and weakly Ca-Mg altered volcanic rocks. This study concludes that chlorite SWIR reflectance is largely influenced by the bulk Fe/Mg composition of the host rock, whereas white mica reflectance correlates with the type and intensity of hydrothermal alteration prior to metamorphism. These findings underscore the potential of using chlorite and white mica spectral signatures to understand hydrothermal alteration patterns and detect new orebodies in metamorphosed VHMS systems.

Assessing intertidal sediment photopigment content from spectral reflectance with an UAV-mounted 10-band multispectral sensor

ABSTRACT Multispectral sensors mounted to unoccupied aerial vehicles (UAVs) can be leveraged to quantify microphytobenthos (MPB) biomass in intertidal mudflats, providing data products with cm-scale pixels. However, no standard protocol currently exists for calibrating UAV-acquired spectral information to sediment MPB content. Here, we present a new protocol for calibrating data from a UAV-mounted multispectral sensor to sediment MPB biomass as measured by photopigment content. To do so, we developed a methodology for acquiring and analyzing UAV imagery and sediment photopigment field data. We then implemented the protocol in the Fraser River Estuary, Canada to build a statistically valid calibration equation, testing the effectiveness of several spectral indices and photopigment measurements. Calibrated spectral index values can provide a very accurate measurement of MPB biomass, able to achieve 90 % correlation between the normalized difference vegetation index (NDVI) and sediment chlorophyl-a (chl-a) concentration. This high performance was achieved by closely pairing georeferenced sediment samples to corresponding multispectral imagery and minimizing the lag between sediment sample collection and UAV imagery acquisition. This protocol can facilitate the use of calibrated UAV-acquired multispectral imagery for investigating ecologically-important fine-scale spatial heterogeneity of MPB biomass.

Differential Study on Estimation Models for Indica Rice Leaf SPAD Value and Nitrogen Concentration Based on Hyperspectral Monitoring

Soil and plant analyzer development (SPAD) value and leaf nitrogen concentration (LNC) based on dry weight are important indicators affecting rice yield and quality. However, there are few reports on the use of machine learning algorithms based on hyperspectral monitoring to synchronously predict SPAD value and LNC of indica rice. Meixiangzhan No. 2, a high-quality indica rice, was grown at different nitrogen rates. A hyperspectral device with an integrated handheld leaf clip-on leaf spectrometer and an internal quartz-halogen light source was conducted to monitor the spectral reflectance of leaves at different growth stages. Linear regression (LR), random forest (RF), support vector regression (SVR), and gradient boosting regression tree (GBRT) were employed to construct models. Results indicated that the sensitive bands for SPAD value and LNC were displayed to be at 350–730 nm and 486–727 nm, respectively. Normalized difference spectral indices NDSI (R497, R654) and NDSI (R729, R730) had the strongest correlation with leaf SPAD value (R = 0.97) and LNC (R = −0.90). Models constructed via RF and GBRT were markedly superior to those built via LR and SVR. For prediction of leaf SPAD value and LNC, the model constructed with the RF algorithm based on whole growth periods of spectral reflectance performed the best, with R2 values of 0.99 and 0.98 and NRMSE values of 2.99% and 4.61%. The R2 values of 0.98 and 0.83 and the NRMSE values of 4.88% and 12.16% for the validation of leaf SPAD value and LNC were obtained, respectively. Results indicate that there are significant spectral differences associated with SPAD value and LNC. The model built with RF had the highest accuracy and stability. Findings can provide a scientific basis for non-destructive real-time monitoring of leaf color and precise fertilization management of indica rice.

Amyloid beta biomarker for dementia detection by hyperspectral ophthalmoscope images.

The escalating prevalence and economic burden of dementia underscore the urgency for innovative detection methods. This study investigates the potential of hyperspectral imaging (HSI) to detect dementia by analyzing retinal changes associated with amyloid beta (Aβ) formations. Leveraging a dataset of 3,256 ophthalmoscopic images from 137 participants aged 60 to 85 years, categorized into dementia and non-dementia groups via the Mini-Mental State Examination (MMSE), we extracted features from five key regions of interest (ROIs) identified for their pronounced changes in Aβ biomarkers. The analysis revealed that gender does not significantly influence dementia levels, and no substantial spectral differences were observed within the 380 nm to 600 nm wavelength range. However, significant variations in spectral reflection intensity were noted between 600 nm and 780 nm across both genders, suggesting a potential avenue for distinguishing stages of dementia. Despite the impact of diabetes on the vascular system, its stages did not significantly influence dementia development. This research highlights the utility of HSI in identifying dementia-related retinal changes and calls for further exploration into its effectiveness as a diagnostic tool, potentially offering a non-invasive method for early detection of dementia.

Reflectance spectral studies of spark plasma sintered tungsten carbide pellet

Abstract We report the first spectral reflectance of tungsten carbide (WC) as potential solar selective absorber. We developed an optical measurement system for visible to mid-infrared spectroscopy, covering the range of 0.1 to 2.5 eV, to evaluate the solar selectivity. A polycrystalline WC was prepared using spark plasma sintering method. The measured spectral reflectance of WC exhibits a low-energy plasma excitation around 0.6 eV corresponding to the cutoff energy of sunlight, consistent with ab initio calculations, thus making it preferable for the solar selective absorber. We also discuss effects of the sample quality on the spectral reflectance.

A text-based, generative deep learning model for soil reflectance spectrum simulation in the solar range (400–2499 nm)

Soil spectral reflectance is a necessary input for land surface and radiative transfer models, and can be used to infer soil properties. Numerous soil reflectance inversion models have been developed based on mechanistic approaches, each with their own limitations. Mechanistic models based on radiative transfer theory are usually based on only a few input soil properties, whereas data-driven approaches are limited by high non-uniformity of available published datasets that severely limits the amount of data usable for model calibration. To address these limitations, a fully data-driven soil optics generative model (SOGM) for simulation of soil reflectance spectra from soil property inputs was developed based on the denoising diffusion probabilistic model (DDPM). The model was trained on an extensive dataset comprising nearly 180,000 soil spectra-property set pairs from 17 published datasets. The model generates soil reflectance spectra from text-based inputs describing soil properties and their values rather than only numerical values and labels in binary vector format, which means the model can handle variable formats for property reporting. Because the model is generative, it can simulate reasonable output spectra based on an incomplete set of available input properties, which becomes more reliable as the input property set becomes more complete. Two additional sub-models were also built to complement the SOGM: a spectral padding model that can fill in the gaps for spectra shorter than the target solar range (400 to 2499 nm), and a wet soil spectra model that can estimate the effects of water content on soil reflectance spectra given the dry spectrum predicted by the SOGM. It can also be easily integrated with other soil–plant radiation models used for remote sensing research such as PROSAIL and Helios 3D plant modeling software. The testing results of the SOGM on new datasets not included in model training demonstrated that the model can generate reasonable soil reflectance spectra based on available property inputs. Results also show soil clay/sand/silt fraction, organic carbon content, nitrogen content, and iron content tended to be important properties for spectra simulation. Inclusion of some trace minerals like nickel as model inputs decreased model performance because of their low concentrations and large propensity for ground-truth measurement error.

Archetypal crop trait dynamics for enhanced retrieval of biophysical parameters from Sentinel-2 MSI

We present a new method for estimating biophysical parameters from Earth Observation (EO) data using a crop-specific empirical model based on the PROSAIL Radiative Transfer (RT) model, called an ‘archetype’ model. The first-order model presented uses maximum biophysical parameter magnitude, phenological and soil parameters to describe the spectral reflectance (400–2500 nm) of vegetation over time. The approach assumes smooth variation and archetypical coordination of crop biophysical parameters over time for a given crop. The form of coordination is learned from a large sample of observations. Using Sentinel-2 observations of maize from Northeast China in 2019, we map reflectance to biophysical parameters using an inverse model operator, synchronise the parameters to a consistent time frame using a double logistic model of LAI, then derive the model archetypes as the median value of the synchronised samples. We apply the model to estimate time series of biophysical parameters for different cereal crops using an ensemble framework with a weighted K-nearest neighbour solution, and validate the results with ground measurements of different crops collected near Munich, Germany in 2017 and 2018. The results show R values greater than 0.8 for leaf area index (LAI) and leaf brown pigment content (Cbrown), with an RMSE of 0.94 m2/m2 for LAI and 0.15 for Cbrown. The chlorophyll content (Cab) and canopy water content (CCw) were retrieved at a higher level of accuracy, with R values around 0.9 and an RMSE of 6.59μg/cm2 for Cab and 0.03 g/cm2 for CCw. Comparison of forward-modelled hyperspectral reflectance with independent ground measures shows that the retrieved parameters account for 90% of the variation in canopy reflectance, with an overall RMSE of around 0.05 in reflectance units. The retrievals for all terms are mostly within 1σ when measurement and prediction uncertainty are taken into account, except for some early and late season issues in leaf and canopy water due to the complexity of canopy structure and understory during these periods. The approach provides a new form of constraint for the simultaneous estimation of biophysical parameters from EO and greatly reduces the rank of the problem. It is suitable for monitoring crop conditions where biophysical parameters vary smoothly over time consistently with each archetype form. The approach can be refined for other canopy types and canopy representations and could provide strong constraints on expected smoothly-varying canopy features to aid in the interpretation of EO signals across different regions of the electromagnetic spectrum.

Composição química de revestimentos frios e a influência na refletância solar e emitância térmica

Abstract Cool materials have higher values of solar reflectance and lower values of thermal emittance, although still high when compared to conventional coatings, thus, their surfaces are less heated. In this regard, the goal was to identify the chemical elements present on cool surfaces that influence solar reflectance and thermal emittance. To this end, the spectral reflectance and the thermal emittance were measured with methods and equipment standardized by the Cool Roof Rating Council (CRRC) of 23 elastomeric cool coatings commercialized in the Brazilian market, and the chemical composition of the surfaces with EDS (Energy-dispersive X-ray detector) coupled to the field emission scanning electron microscope (FE-SEM) was analyzed. The presence of the metallic chemical element (titanium) in greater quantities on the surfaces was not decisive for lower thermal emittance values. The surfaces of materials with lower carbon and higher oxygen content have higher near infrared and solar reflectance. Therefore, surfaces with a matte finish have higher solar reflectance values because of the higher ratio of pigments in relation to resin than those with a glossy finish, which are mistakenly associated with higher reflectance due to its high surface gloss.

Effects of Melatonin on Ginseng Leaf Phenotypic and Photosynthetic Properties Following Chilling and Freezing Stress

Spring frost damage is a significant natural disaster, which can lead to large-scale ginseng (Panax ginseng C. A. Mey) yield reduction. To determine chilling and freezing stress damage to ginseng, we designed an experiment using three-year-old potted ginseng plants. We simulated chilling (0±0.3 ℃) and freezing (-2.5±0.3 ℃, -5±0.3 ℃) in an artificial room, with 16±2 ℃ of the room temperature at night as the control, to evaluate their physiological effects on ginseng leaf spectral reflectance, photosynthetic characteristics, and chlorophyll fluorescence parameters. We also investigated the mitigating effects of exogenous melatonin on ginseng leaves exposed to chilling and freezing stress. All ginseng leaves died under -5 ℃ freezing stress and the mortality rate reached 25∼57% under -2.5 ℃ freezing stress, while no plants died under 0 ℃ chilling stress. After -2.5 ℃ and 0 ℃ stress, leaf spectral reflectance in the 750∼1000 nm band was significantly lower than the control; transpiration rate, net photosynthetic rate, intercellular CO2 concentration, and stomatal conductance decreased significantly compared with the control, and there was no significant change in recovery time. The Fv/Fm ratio and qp decreased after low-temperature stress, while NPQ increased. After one week of melatonin application, leaf spectral reflectance in the 600∼650 and 750∼1000 nm bands reflected significant differences between the effects of melatonin application and no application on ginseng leaf chlorophyll content and structure under freezing stress. Ginseng leaf net photosynthetic rate increased by 23.79% and 4.48%, while intercellular CO2 concentration increased by 33.49 and 11.83% under stress at -2.5°C and 0°C. The ginseng leaf chlorophyll fluorescence parameter (Fv/Fm) recovered to 0.8 and above in one week after melatonin application, and qp decreased under -2.5°C and 0°C stress after melatonin application. Additionally, the NPQ values were 11.44% and 4.6% lower in -2.5+MT and 0+MT, respectively, compared to the treatment without MT applied. A sudden drop in temperature to 0 ℃ or even below caused ginseng plant growth inhibition, but melatonin application at a concentration of 100 nmol L−1 had certain mitigating effects on ginseng growth, which provides a theoretical basis and technical support for ginseng production.

Investigation of the three-wave lidar capabilities for monitoring the lower tier forest vegetation

Abstract The possibilities of the three-wave pulsed lidar for monitoring the lower tier of forest vegetation have been studied. The simulation analysis results of the lower tier forest vegetation groups identification on a neural net using measured vegetation spectral reflectance for different wavelengths of the three-wave lidar are presented. It is shown that the best options are lidars with wavelengths 532, 1064, 1570 nm and 532, 1064, 2030 nm. The three-wavelength lidar with these wavelengths allows the identification of five lower tier forest vegetation groups (undergrowth of deciduous trees; undergrowth of coniferous trees; areas of swamps, mosses and shrubby plants; soils with different percentages of vegetation coverage; areas of dry forest, soils without vegetation, lichens, burnt areas) with a probability of correct identification over 0.8 and a probability of incorrect identification under 0.08 (for a relative RMS noise values of 1%).

An advanced dorsiventral leaf radiative transfer model for simulating multi-angular and spectral reflection: Considering asymmetry of leaf internal and surface structure

Understanding the optical properties of dorsiventral leaves and quantifying leaf biochemical traits through physical models are important for interpreting canopy radiative transfer and monitoring plant growth. Previous models, such as the dorsiventral leaf model (DLM), have effectively accounted for the inner asymmetry of the leaf but neglected the asymmetry of surface structures between the upper and lower epidermis. In this study, we found marked differences in bidirectional reflectance factors (BRF) between the adaxial and abaxial surfaces of leaves under multi-angular measurements due to surface structural distinctions. To address this asymmetry in both internal and surface leaf structures, we subsequently proposed an advanced DLM model (MADLM) for simulating both multi-angular and spectral BRF of two leaf sides, linking the angular reflection of leaf adaxial and abaxial sides to surface structural parameters (roughness and refractive index) based on microfacet theory. Results show that MADLM accurately simulates multi-angular and spectral BRF for both sides of dorsiventral leaves, and yields satisfactory retrieval accuracy of leaf traits from all observation geometries. For close-range hyperspectral imaging applications, we further introduced a simplified version, sMADLM, which characterizes the surface reflection of two leaf sides in terms of the product of a leaf-side dependent parameter and the wavelength-dependent Fresnel factor. The sMADLM improves the mapping accuracy of leaf biochemical traits by effectively reducing the surface reflection effects in dorsiventral leaves. The MADLM and sMADLM deepen our understanding of the optical properties of dorsiventral leaves and provide practical methods for retrieving leaf biochemical traits via optical remote sensing.

Estimation of Soil Nitrogen Content Influenced by Different Nitrogen-Based Management Practices within Rice-Based Cropping Using Diffuse Reflectance Spectroscopy and Machine Learning

ABSTRACT Reflectance spectroscopy has been widely utilized by researchers to characterize and predict soil nitrogen (N). This study explores the potential of reflectance spectroscopy combined with machine learning to estimate soil N levels under different N-based management practices in rice-based cropping systems. A five-year field experiment (2015–2020) was conducted at Kalyani D Block Farm, Bidhan Chandra Krishi Viswavidyalaya, Nadia, West Bengal, India, testing rice-rice, rice-wheat, and rice-potato systems with N treatments: control (N₀), 100% Neem Coated Urea (NCU), 75% NCU with 25% organics, 100% Polymer Sulfur Coated Urea (PSCU), and 75% PSCU with 25% organics. Soil samples from 0 to 15 cm depth were collected post-harvest of rabi crops (wheat and potato) and summer rice for analysis, revealing the highest available N (AN) (189.6 kg ha− 1) in PSCU-treated summer rice plots, with maximum AN in wheat (290.6 kg ha− 1) and potato (431.4 kg ha− 1) found in 75% NCU + 25% organic plots. Rice yield reached a high of 5.58 t ha− 1 with PSCU treatment, while wheat and potato produced 3.87 and 16.07 t ha− 1 with NCU + vermicompost (VC), respectively. Spectral reflectance measurements were conducted in a controlled lab environment using the ASD Field Spec 4 spectroradiometer, showing significant variability in soil spectral behavior with soil organic carbon (SOC) and N levels. Pre-processing techniques, including first-order derivation (FOD), log (1/R) transformation, and continuum removal (CR), were applied to enhance spectral data quality. Machine learning models, Support Vector Machine Regression (SVMR) and Partial Least Squares Regression (PLSR) were used to estimate SOC, AN and total N (TN) from spectral data (350–2500 nm range). Results showed that SVMR consistently outperformed PLSR for all soil properties, with continuum-removed spectra achieving R2 values of 0.98, 0.97, and 0.92, and RMSEs of 0.02%, 37.1, and 948.8 kg ha− 1 for SOC, AN and TN, respectively. This study highlights the value of integrating spectral data with machine learning for rapid, accurate soil N estimation, enabling optimized fertilizer applications to enhance crop yields and reduce environmental impact.

A novel framework for dynamic and quantitative mapping of damage severity due to compound Drought–Heatwave impacts on tea Plantations, integrating Sentinel-2 and UAV images

In 2022, China experienced a historically rare compound drought–heatwave (CDH) event, which had more severe impacts on vegetation compared with individual extreme events. However, quantitatively mapping the damage severity of CDH on tea tree using satellite data remains a significant challenge. Here we proposed a novel framework for dynamic and quantitative mapping of tea trees damage severity caused by CDH in 2022 using Sentinel-2 and Unmanned Aerial Vehicle (UAV) data. The Extreme Gradient Boosting (XGBoost) was selected as the optimal machine learning algorithm to extract tea plantations using Sentinel-2 data from XGBoost, Random Forest (RF), Logistic regression (LR), and Naive Bayes. The User’s Accuracy and Producer’s Accuracy for the extraction of tea plantations are 92.20 % and 93.51 %, respectively. UAV images with 2.5 cm spatial resolution were utilized to detect the tea trees damaged caused by the CDH in 2022. A new index, named the CDH damage severity index (CDH_DSI), was proposed to quantitatively evaluate the damage severity of CDH on tea trees at pixel level, with a spatial resolution of 10 m x 10 m. Based on the results of tea plantations and damaged tea trees detection, UAV-derived CDH_DSI was calculated and used as ground truth data. Then, The XGBoost was selected as the optimal CDH_DSI prediction model from XGBoost, RF, and LR with the Sentnel-2 derived vegetation indices and spectral reflectance as predictors. The coefficient of determination was 0.81 and root mean squared error was 7.61 %. Finally, dynamic and quantitative CDH_DSI maps were generated with the optimal CDH_DSI prediction model. The results show that 50 percent of tea plantations in Wuyi were damaged by the prolonged CDH event in 2022. These results can be attributed to precipitation deficits and heatwaves. Given that more severe CDH events are projected for the future, quantifying their impacts can provide decision-making support for disaster mitigation and prevention.

Transformer-Based hyperspectral image analysis for phenotyping drought tolerance in blueberries

Drought-induced stress significantly impacted blueberry production due to the plants’ inefficient water regulation mechanisms to maintain yield and fruit quality under drought stress. Traditional methods of manual phenotyping for drought stress are not only time-consuming but also labor-intensive. To address the need for accurate and large-scale assessment of drought tolerance, we developed a high-throughput phenotyping (HTP) system to capture hyperspectral images of blueberry plants under drought conditions. A novel transformer-based model, LWC-former was introduced to predict leaf water content (LWC) utilizing spectral reflectance from hyperspectral images obtained from the developed HTP system. The LWC-former transformed the spectral reflectance into patch representations and embedded these patches into a lower dimensional to address multicollinearity issues. These patches were then passed to the transformer encoder to learn distributed features, followed by a regression head to predict LWC. To train the model, spectral reflectance data were extracted from hyperspectral images and pre-processed using log(1/R), mean scatter correction (MSC), and mean centering (MC). The results showed that our model achieved a coefficient of determination (R2) of 0.81 on the test dataset. The performance of the proposed model was also compared with TabTransformer, DeepRWC, multilayer perceptron (MLP), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), achieving R2 values of 0.65, 0.73, 0.71, 0.47, and 0.58, respectively. The results demonstrated that LWC-former outperformed other deep learning and statistical-based models. The high-throughput phenotyping system effectively facilitated large-scale data collection, while the LWC-former model addressed multicollinearity issues, significantly improving the prediction of LWC. These results demonstrate the potential of our approach for large-scale drought tolerance assessment in blueberries.

Effects of Foliar Application of a Lambda-Cyhalothrin Insecticide on Photosynthetic Characteristics of a Fodder Plant Malva moschata

Recently, a large number of pesticides with different chemical structures and modes of action (MOAs) have become regularly used in agriculture. They are used to control the insect populations in various crops. Foliar application of pesticides may negatively affect crop physiology, especially photosynthesis. However, the sensitivity of particular crops, especially their primary and secondary photosynthetic processes, to insecticide application is generally unknown. Our study aimed to evaluate the negative effects of lambda-cyhalothrin (λ-CY) on photosystem II (PSII) in Malva moschata (Musk mallow). We used fast chlorophyll fluorescence transients (i.e., OJIPs) and OJIP-derived parameters, the effective quantum yield of PSII (ΦPSII), induction curves of non-photochemical quenching (NPQ) and spectral reflectance curves and indices. The recommended concentration (0.05 μM) and a 10 times higher concentration (0.5 μM) of λ-CY did not cause any negative effect on photosynthetic parameters. An overdosed foliar application (100 times higher than recommended, i.e., 50 μM) led to changes in OJIP shape; a decrease in performance index (PIABS), maximum photosynthetic yield (FV/FM) and photosynthetic electron transport (ET0/RC); and an increase in protective mechanisms (unregulated quenching, DI0/RC). These changes lasted only tens of minutes after application, after which the parameters returned to pre-application values. An overdosed λ-CY application caused more rapid activation of NPQ, indicating the early response to stress in PSII. The application of 50 μM λ-CY caused an increase in spectral reflectance above 720 nm and changes in the indices that indicated λ-CY-induced stress.

Evaluation of vermicompost and vermicompost tea application on corn (Zea mays) growth and physiology using optical plant sensors

Corn is a vital global crop, yet its cultivation demands extensive agrochemical inputs, prompting the need for sustainable alternatives. This study investigates the impact of vermicompost (VC) and vermicompost tea (VCT) applications on corn growth, physiology, and resistance to Fall Armyworm (FAW) infestation using advanced optical plant sensors. Six treatments were employed: V0 (control), VC1, VCT100, VC1 + VCT50, VC3, and VC3 + VCT50. During the growing season, plant growth parameters, such as height, chlorophyll content, and spectral reflectance were measured using a chlorophyll meter, fluorometer, porometer, and spectroradiometer. Results indicated that VC-treated plants exhibited superior growth and higher chlorophyll content than control or untreated plants. The VC1 + VCT50-treated plants showed robust resistance to FAW, with no infestation throughout the season, while VC1-treated plants showed delayed attack by FAW. Soil chemical analysis showed that VC and VCT treatments had similar nutrient concentrations as the control. Plant nutrient content was higher in VCT100 compared to all treatments. These findings suggest that the combined application of VC and VCT, particularly at specific application rates, can enhance corn plant health, mitigate pest damage, and optimize yield potential.

The Utility of Visual and Olfactory Maize Leaf Cues in Host Finding by Adult Spodoptera frugiperda (Lepidoptera: Noctuidae).

The fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae) (FAW), is an invasive and destructive polyphagous pest that poses a significant threat to global agricultural production. The FAW mainly damages maize, with a particular preference for V3-V5 (third to fifth leaf collar) plant stages in northern China. How the FAW moth precisely locates maize plants in the V3-V5 stage at night remains unclear. The aims of this study were to evaluate the visual and olfactory cues used by the FAW to identify its host plant, maize, in order to select attractants with better trapping efficacy. Hyperspectral analysis of maize plants at different growth stages using the ASD Fieldspec 4 spectrometer was performed using mimics (moths or maize leaves sealed with transparent plastic sheets) and black cloth-covered plants for single visual and single olfactory attraction experiments. Gas chromatography-mass spectrometry (GC-MS) was used to analyze volatiles emitted from V3-V5 stage maize leaves. Volatile organic chemicals (VOCs) were screened using electroantennography (EAG) and Y-tube. Attractor efficacy was validated using mimics + VOCs. Results showed very little variance in the spectral reflectance curve of the maize at different growth stages. Fifteen VOCs were identified in the V3-V5 stage leaves of three different maize varieties, of which cis-3-hexenyl acetate and myrcene were found in relatively high concentrations in these maize varieties. The frequency of visits attracted by single visual stimuli was significantly lower than that attracted by single olfactory or olfactory + visual cues. The attractiveness of foliar cis-3-hexenyl acetate increased as its concentration decreased. The combination of mimics + cis-3-hexenyl acetate (1 ng/μL) increased host detection efficiency and stimulated mating behavior. These results indicate that the nocturnal insect FAW primarily uses olfactory cues for host identification, with visual cues serving as a complementary modality. The synergistic effect of olfactory and visual cues increases the efficiency of host recognition. We found that cis-3-hexenol acetate at a concentration from maize leaves is a reliable olfactory signal for the FAW. When using host plant VOCs as attractants to control adult FAWs, the role of visual cues must be considered.

Feasibility of spectro-polarimetric measurement in separating reflection components for improving water contaminant determination

Water bodies are critical to the environment, providing numerous ecological benefits; however, human activities increasingly threaten their quality. Natural water systems exhibit regional variability, dominated by organic and inorganic species, rendering in-situ measurements insufficient. Current remote sensing methods often overlook the impact of surface light components, which vary with solar radiation and wave intensity. This study demonstrates an approach for water quality monitoring utilizing spectral reflectance and polarization in the visible and near-infrared regions. A line spectrometer with a polarization filter was employed for hyperspectral measurements under simulated wave conditions. Chlorophyll (Chl) and suspended sediment (SS) pollution were simulated using locally sourced products at various concentrations. The contaminant reflectance was computed, and the polarization components were analyzed using the Pickering method and Stokes vectors. Normalization and continuum removal techniques ensured reliable comparisons across the spectra. The spectral angle mapper (SAM) algorithm quantified the similarity between unpolarized wave conditions and total reflectance spectra under calm conditions, while spectral entropy quantified wave effects compared to calm water. The results indicated that the polarized components of Chl and SS reflectance were minimal in calm water but increased under wave conditions, particularly at lower wavelengths. Higher contaminant concentrations exhibited greater spectral similarity, with lower SAM values indicating reduced specular reflections. The raw and normalized unpolarized reflectance spectra displayed characteristic features; however, the reflectance at high concentrations was lower than anticipated, likely due to spectrometer sensitivity and strong water absorption. The degree of Linear Polarization (DoLP) analysis revealed distinct scattering behaviors: Chl exhibited a lower DoLP than SS. Wave conditions enhanced the DoLP due to increased specular reflections. Overall, the method effectively separates reflection components but is sensitive to measurement conditions, emphasizing the necessity to account for angles and water conditions when estimating contaminants.

Control of Surface Plasmon Propagation and Terahertz Near-Field Waveforms in a Scanning Tunneling Microscope.

Coupling subcycle THz pulses to a scanning tunneling microscope (STM) enables ultrafast spectroscopy at the atomic scale. This technique critically depends on the shape of the THz near-field waveform in the tunnel junction. We characterize the THz electric field waveform in the STM junction by electro-optic sampling of tip-scattered THz light (s-EOS) and pulse correlation using the THz-induced current. Combined with full-wave simulations, we identify THz spectral distortions and reflections arising from THz surface plasmon propagation along the tip wire and cavity modes at the tip apex. By optimizing the tip shape, tip holder geometry and materials, we achieve a drastically flattened THz near-field waveform. This optimization ensures point-like coupling to the far-field and, thus, allows precise Gouy phase control at the STM tip. The improved THz waveforms facilitate atomically-resolved THz time-domain spectroscopy in the STM with high dynamic range for investigating local electron and phonon dynamics on surfaces.

Polarimetric BSSRDF Acquisition of Dynamic Faces

Acquisition and modeling of polarized light reflection and scattering help reveal the shape, structure, and physical characteristics of an object, which is increasingly important in computer graphics. However, current polarimetric acquisition systems are limited to static and opaque objects. Human faces, on the other hand, present a particularly difficult challenge, given their complex structure and reflectance properties, the strong presence of spatially-varying subsurface scattering, and their dynamic nature. We present a new polarimetric acquisition method for dynamic human faces, which focuses on capturing spatially varying appearance and precise geometry, across a wide spectrum of skin tones and facial expressions. It includes both single and heterogeneous subsurface scattering, index of refraction, and specular roughness and intensity, among other parameters, while revealing biophysically-based components such as inner- and outer-layer hemoglobin, eumelanin and pheomelanin. Our method leverages such components' unique multispectral absorption profiles to quantify their concentrations, which in turn inform our model about the complex interactions occurring within the skin layers. To our knowledge, our work is the first to simultaneously acquire polarimetric and spectral reflectance information alongside biophysically-based skin parameters and geometry of dynamic human faces. Moreover, our polarimetric skin model integrates seamlessly into various rendering pipelines.