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A significant challenge in global change research is understanding how vegetation interacts with the environment to influence ecosystem gross primary productivity (GPP) through carbon assimilation. One emerging objective is to consistently predict GPP fluctuations worldwide by establishing a robust scaling relationship between GPP measured at flux towers and satellite spectral reflectance data. However, a major hurdle in achieving this goal is the discrepancy in spatial resolution between early satellite measurements and eddy flux measurements. By using a large set of growing season data covering 100 site-years in North and Central America, we explored the potential of transforming incident and reflected shortwave (Rg) and photosynthetically active radiation (PAR) measurements into a broadband normalized difference vegetation index (NDVI) and near-infrared (NIR) reflectance of vegetation (NIRv) which simultaneously explains the GPP variability. We found that the broadband NDVI and NIRv derived from Rg and PAR measurements at the daily time scale were highly correlated with Planet Fusion, Landsat-8/9, and Sentinel-2 narrowband NDVI and NIRv across a wide range of climate and ecological gradients. The differences between satellite and broadband NDVI and NIRv were found to be significantly associated with soil background variations, phenological stages, water stress and signal saturation of broadband NIR reflectance at high biomass. The seasonal variability of broadband NDVI and NIRv remarkably captured the seasonality of vegetation phenology, evaporative fraction, GPP and rainfall in different ecosystems. Although saturation of GPP at high NDVI was evident, a linear relationship between broadband NIRv times incident PAR versus GPP indicated the effectiveness of NIRv-based approach to capture the hidden light use efficiency impacts on GPP. Our study concludes that inexpensive measurement of Rg and PAR components can provide reliable information on NDVI, NIRv, and GPP uninterruptedly. This enhances the sensing capability of flux tower sites without requiring additional spectrometer measurements. The proposed in-situ vegetation indices make a compelling case on using radiation signals for handshaking between ecosystem-scale measurements and remote sensing observables relevant to carbon uptake.

Accurately estimating leaf chlorophyll concentrations in a three-dimensional (3D) manner is crucial for precisely monitoring crop growth and development. To reduce destructive detection and achieve real-time estimation, researchers often utilize crop leaves' SPAD (Soil and Plant Analyzer Development) to represent relative chlorophyll content. However, SPAD measurement is time-consuming and laborious work. Many inversion methods have been proposed to estimate SPAD based on one-dimensional reflectance or two-dimensional spectral images, which rely on external light sources and also cannot obtain 3D distribution. Hyperspectral LiDAR (HSL), as an active remote sensing technology, can acquire target spatial and spectral data simultaneously, making 3D SPAD inversion and reconstruction possible. This research aims to invert SPAD values with HSL spectral information aided by the PROSPECT model and reconstruct 3D SPAD distribution with HSL spatial information. Firstly, we constructed new ratio chlorophyll indices (RCI) based on the multi-feature analysis method with the PROSPECT model and HSL spectral data. Next, we employed RCIs to modify the Look-Up Table (LUT) cost function (SPAD-LUT) of the PROSPECT model, which was validated with the ANGERS dataset. Then, we conducted SPAD inversion with HSL spectral information from vegetable crop samples and integrated the inverted SPAD values into their corresponding 3D coordinates, achieving 3D SPAD distribution reconstruction. The predicted values of the model inversion with the HSL dataset are consistent with the measured SPAD (R2 = 0.421, RMSE = 10.615). The results indicate that our method can invert sample SPAD values and reconstruct their distribution with HSL point cloud despite their complex spatial structure. [ABSTRACT FROM AUTHOR]

Summary Early stress detection of crops requires a thorough understanding of the signals showing the very first symptoms of the alterations in the photosynthetic light reactions. Detection of the activation of the regulated heat dissipation mechanism is crucial to complement passively induced fluorescence to resolve ambuiguities in energy partitioning. Using leaf spectroscopy, we evaluated the capability of pigment spectral unmixing to calculate the fluorescence quantum efficiency (FQE) and simultaneously retrieve fast absorption changes in a drought and nitrogen deficiency experiment with tomato. In addition, active fluorescence measurements and pigment analyses of xanthophylls, carotenes and chlorophylls were conducted. We observed notable responses in noninvasive proximal sensing‐retrieved FQE values under stress, but as expected, these alone were not enough to identify the constraints in photosynthetic efficiency. Reflectance‐based detection of the 535‐nm peak absorption change was able to complement FQE and indicate the activation of regulated heat dissipation for both stress treatments under growing light conditions. However, further complexity in the light harvesting energy regulation needs to be accounted for when considering additional light stress. Our results underscore the potential of complementary in vivo quantitative spectroscopy‐based products in the early and nondestructive stress diagnosis of plants, marking the path for further applications. [ABSTRACT FROM AUTHOR]

Floral mimics deceive their pollinators by developing visual and olfactory resemblance to various models. We report a flower that exhibits phenotypes like aerial litter and deceives an aerial litter specialist beetle to achieve pollination. We assessed the floral phenology and the effective pollinators of an Australian understorey treelet, Meiogyne heteropetala (Annonaceae). The similarities of morphology, colour and odour between the flowers and co‐occurring aerial litter were investigated. The terpene synthase involved in floral scent emission was identified by expression patterns and product profile. The behavioural responses of the pollinator to various odours were assessed using bioassays. The erotylid beetle Loberus sharpi is the most likely effective pollinator because it was the only pollen‐laden visitor during the pistillate phase. Loberus sharpi was exclusively found in aerial litter and M. heteropetala flowers. The flowers offer an honest shelter reward. The beetle also oviposits there, but most larvae eventually perished as the petals dropped onto the forest floor. The morphology and spectral reflectance of the flowers overlap with aerial litter. The floral scent was dominated by monoterpenes, especially 1,8‐cineole. The cineole synthase MhCINS was the only highly expressed floral terpene synthase and possessed a highly similar product profile to the floral scent composition. The volatile composition of M. heteropetala flowers is distinct from other congeners and highly similar to aerial litter, indicating advergence to aerial litter. Visual and odour resemblance, coupled with low larval survivorship, provides evidence that the beetles were deceived into pollinating the flowers. Behavioural experiments showed that the pollinator was attracted to both aerial litter and M. heteropetala flowers. The beetles were also attracted to 1,8‐cineole and synthetic mixes of floral odour and MhCINS products. The beetles were unable to distinguish floral scent from MhCINS products nor from 1,8‐cineole, suggesting MhCINS alone sufficed to attract the pollinator olfactorily. The beetles, however, preferred aerial litter over flowers. The beetles likely categorised the flower as a general, but not the most preferred, brood substrate. Synthesis. This study reports the first case of floral mimicry of aerial litter and characterises the biochemical process responsible for olfactory mimicry. [ABSTRACT FROM AUTHOR]

Predicting soil water status remotely is appealing due to its low cost and large‐scale application. During drought, plants can disconnect from the soil, causing disequilibrium between soil and plant water potentials at pre‐dawn. The impact of this disequilibrium on plant drought response and recovery is not well understood, potentially complicating soil water status predictions from plant spectral reflectance. This study aimed to quantify drought‐induced disequilibrium, evaluate plant responses and recovery, and determine the potential for predicting soil water status from plant spectral reflectance. Two species were tested: sweet corn (Zea mays), which disconnected from the soil during intense drought, and peanut (Arachis hypogaea), which did not. Sweet corn's hydraulic disconnection led to an extended 'hydrated' phase, but its recovery was slower than peanut's, which remained connected to the soil even at lower water potentials (−5 MPa). Leaf hyperspectral reflectance successfully predicted the soil water status of peanut consistently, but only until disequilibrium occurred in sweet corn. Our results reveal different hydraulic strategies for plants coping with extreme drought and provide the first example of using spectral reflectance to quantify rhizosphere water status, emphasizing the need for species‐specific considerations in soil water status predictions from canopy reflectance. Summary Statement: We developed models to accurately predict rhizosphere water status from hyperspectral imaging of roots and soil. We found canopies cannot always accurately predict rhizosphere water status—as our experiment revealed soil–plant disequilibrium in one species (sweet corn) but not another (peanut). [ABSTRACT FROM AUTHOR]

Pollination underlies plant yield, health and reproductive success in agricultural and natural systems worldwide. It is therefore concerning that declining animal pollinator populations compounded by growing demands for food are leading to rising pollination deficits, with globally significant economic and environmental implications.Despite this urgent issue, accurate and scalable tools to quantify and track pollination across useful spatiotemporal scales are lacking. Here, we propose to shed new light on pollination deficits, looking to remote sensing platforms as a transformative mapping and monitoring tool and a solution for pollinator conservation and crop management.Providing a synthesis of our current understanding of pollination‐triggered floral senescence and underlying ultrastructural and metabolic changes, we propose how spectral reflectance technologies could be applied to accurately detect pollination events in real‐time and at the landscape scale.Synthesis and applications: We highlight where research efforts can be targeted to produce scalable methods for identifying field‐relevant bioindicators of pollination. We provide guidance on how spectral imaging accompanied by machine learning and coupled with autonomous operation technologies will enable applications to detect pollination delivery across complex landscapes. Ultimately, such an ecological application will transform our quantitative understanding of pollination services and, by directly linking plant yields and health, will reveal pollination deficits at high resolution to help mitigate risks to food security and ecosystem functioning. [ABSTRACT FROM AUTHOR]

Nested association mapping (NAM) populations emerged as a multi-parental strategy that combines the high statistical power of biparental linkage mapping with greater allelic richness of association mapping. Several statistical models have been developed for marker-trait associations (MTAs) in genome-wide association studies (GWAS), which ranges from simple to increasingly complex models. These statistical models vary in their performance for detecting real association with the avoidance of false positives and false negatives. Furthermore, significant threshold methods play an equally important role for controlling spurious associations. In this study, we compared the performance of seven different statistical models ranging from single to multi-locus models on eight different simulated traits with varied genetic architecture for a NAM population of spring wheat (Triticum aestivum L.). The best identified model was further used to identify MTAs for 11 different agronomic and spectral reflectance traits, which were collected on the NAM population between 2014 and 2016. The "Bayesian information and linkage disequilibrium iteratively nested keyway (BLINK)" model performed better than all other models observed based on QQ plots and detection of real association in a simulated data set. The results from model comparison suggest that BLINK controls both false positives and false negatives under the different genetic architecture of simulated traits. Comparison of multiple significant threshold methods suggests that Bonferroni correction performed superior for controlling false positives and false negatives and complements the performance of GWAS models. BLINK identified 45 MTAs using Bonferroni correction of 0.05 for 11 different phenotypic traits in the NAM population. This study helps identify the best statistical model and significant threshold method for performing association analysis in subsequent NAM population studies. [ABSTRACT FROM AUTHOR]

Premise Methods Results Conclusions Volatile emissions from flowers and fruits play a key role in signalling to animals responsible for pollination and seed dispersal. Here, we investigated the pollination biology and chemical ecology of reproduction in Apodolirion buchananii, an African amaryllid that flowers in a leafless state soon after grassland vegetation is burnt in the dry late‐winter season.Pollinators were identified through field collection and pollen loads were quantified. Floral traits including spectral reflectance and scent chemistry were documented. Bioassays using cup traps were used to test the function of floral volatiles. Fruiting biology was investigated using controlled hand‐pollination experiments and chemical analysis of fruit scent. Seed germination was scored in greenhouse trials. Seed dispersal was monitored using observations and camera trapping.The sweetly scented white flowers of A. buchananii are pollen‐rewarding and pollinated mainly by a diverse assemblage of bees. Cup‐trap experiments demonstrated that pollinators are attracted to phenylacetaldehyde, the dominant volatile in the floral scent. Plants are shown to be self‐incompatible, and the fleshy fruits were found to emerge from the soil six months after pollination during the peak of the summer rains. Fruits emit a diverse blend of aliphatic and aromatic esters and contain large fleshy recalcitrant seeds which germinate within days of fruits splitting open. Seed dispersal by ants was recorded.This first account of the reproductive biology of a species in the genus Apodolirion highlights an outcrossing mating system involving bees attracted to color and scent as well as the unusual fruiting biology and ant‐mediated system of seed dispersal. [ABSTRACT FROM AUTHOR]

Efficient and affordable plant phenotyping methods are an essential response to global climatic pressures. This study demonstrates the continued potential of consumer-grade photography to capture plant phenotypic traits in turfgrass and derive new calculations. Yet the effects of image corrections on individual calculations are often unreported. Turfgrass lysimeters were photographed over 8 weeks using a custom lightbox and consumer-grade camera. Subsequent imagery was analyzed for area of cover, color metrics, and sensitivity to image corrections. Findings were compared to active spectral reflectance data and previously reported measurements of visual quality, productivity, and water use. Results confirm that Red–Green–Blue imagery effectively measures plant treatment effects. Notable correlations were observed for corrected imagery, including between yellow fractional area with human visual quality ratings (r = −0.89), dark green color index with clipping productivity (r = 0.61), and an index combination term with water use (r = −0.60). The calculation of green fractional area correlated with Normalized Difference Vegetation Index (r = 0.91), and its RED reflectance spectra (r = −0.87). A new chromatic ratio correlated with Normalized Difference Red-Edge index (r = 0.90) and its Red-Edge reflectance spectra (r = −0.74), while a new calculation correlated strongest to Near-Infrared (r = 0.90). Additionally, the combined index term significantly differentiated between the treatment effects of date, mowing height, deficit irrigation, and their interactions (p < 0.001). Sensitivity and statistical analyses of typical image file formats and corrections that included JPEG, TIFF, geometric lens distortion correction, and color correction were conducted. Findings highlight the need for more standardization in image corrections and to determine the biological relevance of the new image data calculations. [ABSTRACT FROM AUTHOR]

Grassland degradation poses a significant challenge to achieving the Sustainable Development Goals (SDGs) on the Qinghai–Tibetan Plateau (QTP). Effective monitoring of grassland degradation is essential for ecological restoration. Hyperspectral technology offers efficient and accurate identification of degradation. However, the influence of observation time, data analysis methods and classification techniques on the accuracy of identifying alpine grasslands remains unclear. In this study, the spectral reflectance of degraded alpine meadow, alpine meadow, alpine shrub and Tibetan barley was measured from May to September 2023 using a ground spectrometer in the northeastern QTP. First-order derivatives (FDR) and continuum removal were applied to the spectra, and characteristic parameters and vegetation indices were calculated. Support vector machine (SVM), random forest (RF), artificial neural network (ANN) and decision tree (DT) were then used to compare the classification accuracy between different months, transformation methods and characteristic parameters. The results showed that the spectral reflectance peaked in July, with significant differences in the near infrared (NIR) bands between alpine meadow and degraded alpine meadow. Alpine shrub and Tibetan barley showed greater differences in reflectance compared to other vegetation types, especially in the NIR bands. Data transformations improved reflectance and absorption characteristics in the NIR and visible bands. Indices such as DVI, RVI and NDGI effectively differentiated vegetation types. Optimal accuracy for the identification of degraded alpine meadow in July was achieved using FDR transformations and ANN or SVM for classification. This study provides methodological insights for monitoring grassland degradation on the QTP. [ABSTRACT FROM AUTHOR]

The Chinese mitten crab (Eriocheir sinensis) is highly valued by consumers for its delicious taste and high nutritional content, including proteins and trace elements, giving it significant economic value. However, variations in taste and nutritional value among crabs from different regions lead to considerable price differences, fueling the prevalence of counterfeit crabs in the market. Currently, there are no rapid detection methods to verify the origin of Chinese mitten crabs, making it crucial to develop fast and accurate detection techniques to protect consumer rights. This study focused on Chinese mitten crabs from different regions, specifically Hongze Lake, Tuo Lake, and Weishan Lake, by collecting near-infrared (NIR) diffuse reflectance spectral data from both the abdomen and carapace regions of the crabs. To eliminate noise from the spectral data, pretreatment was performed using Savitzky–Golay (SG) smoothing, Standard Normal Variate (SNV) transformation, and Multiplicative Scatter Correction (MSC). Key wavelengths reflecting the origin of Chinese mitten crabs were selected using Competitive Adaptive Reweighted Sampling (CARS), Bootstrap Soft Shrinkage (BOSS), and Uninformative Variable Elimination (UVE) algorithms. Finally, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Back Propagation Neural Network (BP) models were developed for rapid detection of crab origin. The results demonstrated that MSC provided the best preprocessing performance for NIR spectral data from both the abdomen and back of the crabs. For abdomen data, the SVM model developed using feature wavelengths selected by the CARS algorithm after MSC preprocessing achieved the highest accuracy (Acc) of 90.00%, with precision (P), recall (R), and F1-score for crabs from Weishan Lake at 89.29%, 86.21%, and 87.72%, respectively; for crabs from Tuo Lake at 86.96%, 95.24%, and 90.91%; and for crabs from Hongze Lake at 90.00%, 93.10%, and 91.53%. For carapace data, the SVM model based on wavelengths selected by the BOSS algorithm after MSC pretreatment achieved the best performance, with an Acc of 87.50%, and P, R, and F1 for crabs from Weishan Lake at 77.14%, 93.10%, and 84.38%; for Tuo Lake crabs at 100%, 90.47%, and 95.00%; and for Hongze Lake crabs at 92.31%, 80.00%, and 85.71%. In conclusion, NIR spectroscopy can effectively detect the origin of Chinese mitten crabs, providing technical support for developing rapid detection instruments and thereby safeguarding consumer rights. [ABSTRACT FROM AUTHOR]

The polarized bidirectional reflectance distribution function (pBRDF) can describe the changes between the Stokes vectors of incident and reflected light. The existing model can only describe the spatial distribution of the target's polarization characteristics at a single wavelength, so further research is needed for the description of the target's polarized spectral characteristics. In this paper, a modified three-component polarized spectral bidirectional reflectance distribution function (pSBRDF) is proposed, which combines Fresnel equation in the specular reflection component with Cauchy's empirical dispersion equation, by introducing the wavelength variable and dispersion constants that do not change with wavelength. The degree of the linear polarization (DoLP) of two types of coated fabric samples was measured at three incident angles and 400 to 760 nm wavelength. The error of the model in describing the spectral and the spatial distribution of DoLP are controlled within 0.00743 and 0.0757 respectively, which proves the accuracy of the model. This research provides the model basis for analysis of the target's polarization characteristics and the theoretical basis for the application of polarization detection. [ABSTRACT FROM AUTHOR]

Laboratory measurements of reflectance spectra of rocks and minerals at multiple viewing geometries are important for interpreting spacecraft data of planetary surfaces. However, efficiently acquiring such measurements is challenging, as it requires a custom goniometer that can accommodate multiple, bulky samples beneath a movable light source and detector. Most spectrogoniometric laboratory work to date has focused on mineral mixtures and particulates, yet it is also critical to characterize natural rock surfaces to understand the influence of texture and alteration. We designed the Three‐Axis N‐sample Automated Goniometer for Evaluating Reflectance (TANAGER) specifically to rapidly acquire spectra of natural rock surfaces across the full scattering hemisphere. TANAGER has its light source and the spectrometer's fiber optic mounted on rotating and tilting arcs, with a rotating azimuth stage and six‐position sample tray, all of which are fully motorized and integrated with a Malvern PanAnalytical ASD FieldSpec4 Hi‐Res reflectance spectrometer. Using well‐characterized color calibration targets, we have validated the accuracy and repeatability of TANAGER spectra. We also confirm that the system introduces no discernible noise or artifacts. All design schematics and control software for TANAGER are open‐source and available for use and modification by the larger scientific community. Plain Language Summary: To interpret spacecraft data sets of planetary surfaces, it is important to have similar data sets of rocks and minerals from Earth for comparison. Reflectance spectroscopy is a common technique in planetary science, and spacecraft make spectral reflectance measurements at multiple viewing geometries (with different positions of the sun relative to the planet's surface); therefore, it is important for laboratory spectral reflectance measurements to use multiple viewing geometries as well. However, such measurements are difficult, as they require custom instrumentation (a "goniometer") to control the angles of the light source and spectrometer detector. Most goniometers are designed for measuring small quantities of grains and particulates, but we have designed a new goniometer specifically for measuring the surfaces of bulky, natural rocks: the Three‐Axis N‐sample Automated Goniometer for Evaluating Reflectance (TANAGER). Using motorized, rotating and tilting arcs, TANAGER allows us to rapidly acquire reflectance spectra for multiple samples across a full range of viewing geometries. We have validated the accuracy and repeatability of TANAGER data, and we confirm that the system introduces no discernible noise or artifacts. All design schematics and control software for TANAGER are open‐source and available for use and modification by the larger scientific community. Key Points: Three‐Axis N‐sample Automated Goniometer for Evaluating Reflectance (TANAGER) is an automated goniometer designed to rapidly acquire reflectance spectra of rock surfaces across the full scattering hemisphereWe validated the accuracy and repeatability of TANAGER spectra and confirm that the system introduces no discernible noise or artifactsTANAGER's design schematics and control software are open‐source and available for use and modification by the scientific community [ABSTRACT FROM AUTHOR]

Soil salinization typically exerts a highly negative influence on soil productivity, crop yields, and ecosystem balance. As a typical region afflicted by soil salinization, the soda saline–alkali soils in the Songnen Plain of China demonstrate a clear cracking phenomena. Nevertheless, the overall spectral response to the cracked soil surface has scarcely been studied. This study intends to study the impact of salt parameters on the soil cracking process and enhance the spectral measurement method used for cracked salt-affected soil. To accomplish this goal, a controlled desiccation cracking experiment was carried out on saline soil samples. A gray-level co-occurrence matrix (GLCM) was calculated for the contrast (CON) texture feature to measure the extent of cracking in the dried soil samples. Additionally, spectroscopy measurements were conducted under different surface conditions. Principal component analysis (PCA) was subsequently performed to downscale the spectral data for band integration. Subsequently, the prediction accuracy of back-propagation artificial neural network (BP-ANN) models developed from the principal components of spectral reflectance was compared for different salt parameters. The results reveal that salt content is the dominant factor determining the cracking process in salt-affected soils, and that cracked soil samples had the highest model prediction accuracy for different salt parameters rather than uncracked blocks and 2 mm comparison soil samples. Furthermore, BP-ANN prediction models combining spectral response and CON were further developed, which can significantly enhance the prediction accuracy of different salt parameters with R2 values of 0.93, 0.91, and 0.74 and a ratio of prediction deviation (RPD) of 3.68, 3.26, and 1.72 for soil salinity, electrical conductivity (EC), and pH, respectively. These findings provide valuable insights into the cracking mechanism in salt-affected soils, thereby advancing the field of hyperspectral remote sensing for monitoring soil salinization. Furthermore, this study also aids in enhancing the design of spectral measurements for saline–alkali soils and is also helpful for local soil remediation with supporting data. [ABSTRACT FROM AUTHOR]

The study of chlorophyll fluorescence parameters is very important for understanding plant photosynthesis. Monitoring cotton chlorophyll fluorescence parameters via spectral technology can aid in understanding the photosynthesis, growth, and stress of cotton fields in real time and provide support for cotton growth regulation and planting management. In this study, cotton plot experiments with different water treatments were set up to obtain the spectral reflectance of the cotton canopy, the maximum photochemical quantum yield (Fv/Fm), and the photochemical quenching coefficient (qP) of leaves at different growth stages. Support vector machine regression (SVR), random forest regression (RFR), and artificial neural network regression (ANNR) were used to establish a fluorescence parameter inversion model of the cotton canopy leaves. The results show that the original spectrum was transformed by multivariate scattering correction (MSC), the standard normal variable (SNV), and continuous wavelet transform (CWT), and the model constructed with Fv/Fm passed accuracy verification. The SNV-SVR model at the budding stage, the MSC-SVR model at the early flowering stage, the SNV-SVR model at the full flowering stage, the MSC-SVR model at the flowering stage, and the CWT-SVR model at the full boll stage had the highest estimation accuracy. The accuracies of the three spectral preprocessing and qP models were verified, and the MSC-SVR model at the budding stage, SNV-SVR model at the early flowering stage, MSC-SVR model at the full flowering stage, SNV-SVR model at the flowering stage, and CWT-SVR model at the full boll stage presented the highest estimation accuracies. [ABSTRACT FROM AUTHOR]

This study assesses soil productivity in a 15-hectare cotton field using an integrated approach combining field data, laboratory analysis, and remote sensing techniques. Soil samples were collected and analyzed for key parameters including nitrogen (N), humus, phosphorus (P2O5), potassium (K2O), carbonates, pH, and electrical conductivity (EC). In addition to low salinity, these analyses showed low results for humus and nutrient parameters. A Pearson correlation analysis showed that low organic matter and high salinity had a strong negative correlation with crop productivity, explaining 37% of the variation in NDVI values. Remote sensing indices (NDVI, SAVI, NDMI, and NDSI) confirmed these findings by highlighting the relationship between soil properties and spectral reflectance. This research demonstrates the effectiveness of remote sensing in soil assessment, emphasizing its critical role in sustainable agricultural planning. By integrating traditional methods with advanced remote sensing technologies, this study provides actionable insights for policymakers and practitioners to improve soil productivity and ensure food security. [ABSTRACT FROM AUTHOR]

This study explored the quantitative inversion of the chlorophyll content in Paulownia seedling leaves under drought stress and analyzed the factors influencing the chlorophyll content from multiple perspectives to obtain the optimal model. Paulownia seedlings were selected as the experimental materials for the potted water control experiments. Four drought stress treatments were set up to obtain four types of Paulownia seedlings: one pair of top leaves (T1), two pairs of leaves (T2), three pairs of leaves (T3), and four pairs of leaves (T4). In total, 23 spectral transformations were selected, and the following four methods were adopted to construct the prediction model, select the best spectral preprocessing method, and explore the influence of water bands: partial least squares modeling with all spectral bands (all-band partial least squares, AB-PLS), principal component analysis partial least squares (PCA-PLS), correlation analysis partial least squares (CA-PLS), correlation analysis (water band) partial least squares, ([CA(W)-PLS]), and vegetation index modeling. Based on the prediction accuracy and the uniformity of different leaf positions, the optimal model was systematically explored. The results of the analysis of spectral reflectance showed significant differences at different leaf positions. The sensitive bands of chlorophyll were located near 550 nm, whereas the sensitive bands of water were located near 1440 and 1920 nm. The results of the vegetation index models indicate that the multiple-index models outperformed the single-index models. Accuracy decreased as the number of indicators decreased. We found that different model construction methods matched different optimal spectral preprocessing methods. First derivative spectra ( R ′ ) was the best preprocessing method for the AB-PLS, PCA-PLS, and CA-PLS models, whereas the inverse log spectra ( l o g (1 / R) ) was the best preprocessing method for the CA(W)-PLS model. Among the 14 indices, the green normalized difference vegetation index (GNDVI) was most correlated with the chlorophyll content sensitivity indices, and the water index (WI) was most correlated with the water sensitive indices. At the same time, the water band affected the cross validation accuracy. When characteristic bands were used for modeling, the cross validation accuracy was significantly increased. In contrast, when vegetation indices were used for modeling, the accuracy of the cross validation increased slightly but its predictive ability was reduced; thus, these changes could be ignored. We found that leaf position also affected the prediction accuracy, with the first pair of top leaves exhibiting the worst predictive ability. This was a bottleneck that limited predictive capability. Finally, we found that the CA(W)-PLS model was optimal. The model was based on 23 spectral transformations, four PLS construction methods, water bands, and different leaf positions to ensure systematicity, stability, and applicability. [ABSTRACT FROM AUTHOR]

This study focuses on synthesizing and characterizing supramolecular cocrystals derived from 4-cyano-1-methylpyridinium iodide and 18-crown-6. The synthesis employed a solvent evaporation technique, followed by a thorough structural analysis utilizing XRD methods. The compound's purity is confirmed by powder XRD analysis, while single-crystal XRD confirms its triclinic system packing with a centrosymmetric space group ( P 1 ‾ ). The findings unveiled the emergence of unique cocrystals characterized by distinct molecular configurations supported by hydrogen bonding and ion-dipole interactions. Through diffuse reflectance spectral measurements, the direct band gap energy was assessed at 2.36 eV. Thermogravimetric analysis was utilized to evaluate the thermal properties of the compound across varying temperatures. The compound's elemental composition and surface morphology were analyzed through investigations using EDS and SEM. [ABSTRACT FROM AUTHOR]

The Visible Infrared Imaging Radiometer Suite (VIIRS) is one of the key instruments on the recently launched NOAA-21 (previously known as JPSS-2) satellite. The VIIRS, like its predecessors on the SNPP and NOAA-20 satellites, provides daily global coverage in 22 spectral bands from 412 nm to 12 μm. The geometrically and radiometrically calibrated observations are the basis for many operational applications and scientific research studies. A total of 14 of the 22 bands are reflective solar bands (RSBs), covering photon wavelengths from 412 nm to 2.25 μm. The RSBs were radiometrically calibrated prelaunch and have been regularly calibrated on orbit through the onboard solar diffuser (SD) and scheduled lunar observations. The on-orbit SD's reflectance change is determined by the onboard solar diffuser stability monitor (SDSM). We review the calibration algorithms and present the early mission performance of the NASA N21 VIIRS RSBs. Using the calibration data collected at both the yaw maneuver and regular times, we derive the screen transmittance functions. The visible and near-infrared bands' radiometric gains have been stable, nearly independent of time, and so were the radiometric gains of the shortwave-infrared bands after the second mid-mission outgassing. Further, we assess the Earth-view striping observed in the immediate prior collection (Collection 2.0) and apply a previously developed algorithm to mitigate the striping. The N21 VIIRS RSB detector signal-to-noise ratios are all above the design values with large margins. Finally, the uncertainties of the retrieved Earth-view top-of-the-atmosphere spectral reflectance factors at the respective typical spectral radiance levels are estimated to be less than 1.5% for all the RSBs, except band M11 whose reflectance factor uncertainty is 2.2%. [ABSTRACT FROM AUTHOR]

To assess the prediction accuracy of recent optical and numerical models for the spectral reflectance and color of monolithic samples of dental materials with different thicknesses. Samples of dental resin composites of Aura Easy Flow (Ae1, Ae3 and Ae4 shades) and Estelite Universal Flow Super Low (A1, A2, A3, A3.5, A4 and A5 shades) with thicknesses between 0.3 and 1.8 mm, as well as Estelite Universal Flow Medium (A2, A3, OA2 and OA3 shades) with thicknesses between 0.4 and 2.0 mm, were used. Spectral reflectance and transmittance factors of all samples were measured using a X-Rite Color i7 spectrophotometer. Four analytical optical models (2 two-flux models and 2 four-flux models) and two numerical models (PCA-based and L*a*b*-based) were implemented to predict spectral reflectance of all samples and then convert them into CIE-L*a*b* color coordinates (D65 illuminant, 2°Observer). The CIEDE2000 total color difference formula (Δ E 00 ) between predicted and measured colors, and the corresponding 50:50% acceptability and perceptibility thresholds (A T 00 and P T 00 ) were used for performance assessment. The best performing optical model was the four-flux model RTE-4F-RT, with an average Δ E 00 = 0.72 over all samples, 94.87% of the differences below A T 00 and 65.38% below P T 00 . The best performing numerical model was L*a*b*-PCHIP (interpolation mode), with an average Δ E 00 = 0.48, and 100% and 79.69% of the differences below A T 00 and P T 00 , respectively. Both optical and numerical models offer comparable color prediction accuracy, offering flexibility in model choice. These results help guide decision-making on prediction methods by clarifying their strengths and limitations. • Reflectance data of dental materials can be estimated by numerical algorithms. • Reflectance data of dental materials can be estimated by optical models. • Predicted — measured color differences are generally lower than AT 00. [ABSTRACT FROM AUTHOR]

• During non-glint season, the main source of light reflected by GEO satellites is MLI. • GEO satellite reflectance spectra remain stable at varying phase angles. • Normalized reflectance spectra remain stable despite solar declination changes. Geostationary orbit (GEO) satellites have extensive application in fields like communications and navigation due to their stationary properties. Using additional methods to identify and characterise GEO satellites is crucial for enhancing cataloguing capabilities and verifying their operation status. Reflectance spectroscopy is a promising technique for characterising satellites, but a deeper understanding of the primary factors influencing satellite reflectance spectra is required. So we carried out slitless spectroscopic observations on six GEO satellites using the 80 cm high-precision telescope of the Purple Mountain Observatory located in Yaoan, Yunnan. Reflectance spectra and by-product light curves were then obtained after necessary spectral extraction and correction procedures. Our experiment and observation lasted over ten months, and this paper utilizes a subset of data with high Signal-to-Noise Ratio(SNR). Based on the reflectance spectral data obtained, we discovered that: The dominant factor impacting the reflectance spectra is the Multi-layer insulation(MLI) on the surface of three-axis stabilized GEO satellites. Reflectance spectral trends of GEO satellites do not change significantly over the course of a night with changes in the sun-satellite-observatory phase angle. The normalised reflectance spectral trends remain unchanged despite variations in the solar declination. The aforementioned findings establish a strong basis for utilising reflectance spectra to recognise and classify non-cooperative GEO satellites. [ABSTRACT FROM AUTHOR]

Estimating foliar dust (FD) is essential in understanding the complex interaction between FD, vegetation, and the environment. The elevated FD has a significant impacts on vegetation physiological processes. The present study aims to explore the potential of multi‐sensor optical satellite data sets (e.g., Landsat‐8, 9; Sentinel‐2B, and PlanetScope) in conjunction with in situ data sets for FD estimation over the Jharsuguda coal mining region in Eastern India. The efficacy of different spectral bands and various radiometric indices (RIs) was tested using linear regression models for FD estimation. Furthermore, the study attempts to quantify the impacts of FD on vegetation's physiological processes (e.g., carbon uptake, transpiration, water use efficiency, leaf temperature) through proxy data sets. The key findings of the study uncovered sensor‐specific and common trends in vegetation spectral profiles under varying FD concentrations. A saturation threshold was observed around 50 g/m2 of FD concentration, beyond which additional FD concentration exhibited limited impact on spectral reflectance. On the other hand, the assessment of FD estimation models revealed distinct performances and shared trends across various satellite sensors. Notably, near‐infrared and shortwave infrared‐1 bands, along with certain RIs, such as the Global Environmental Monitoring Index and the Non‐Linear Index, emerged as pivotal for accurate FD estimation. Besides, the study results revealed that vegetation‐associated carbon uptake experienced a ∼2 to 3 gC reduction for every additional gram of FD per square meter. Moreover, the vegetation transpiration reduction per unit of FD ranged from approximately 0.0005 to 0.0006 mm/m2/day, highlighting a moderate impact on transpiration levels. These findings aid a significant evidence base to our understanding of FD's impact on vegetation physiological processes. Plain Language Summary: Estimating foliar dust (FD) cover is essential to understand its impact on vegetation and the environment. Elevated FD levels can reduce photosynthesis, hinder carbon sequestration, and affect transpiration, thereby altering plant health and ecosystem functions. Accurate FD estimation helps in assessing these ecological impacts and developing strategies to mitigate adverse effects. Furthermore, FD can affect remote sensing‐based analysis by altering the spectral reflectance properties of vegetation, potentially leading to inaccuracies in vegetation‐related studies (e.g., health assessment, productivity estimation, species identification, etc.). By understanding these effects, the study shall aid in refining remote sensing models for more precise vegetation analysis. So, the present study utilizes multi‐sensor optical satellite data sets (Landsat‐8, Landsat‐9, Sentinel‐2B, and PlanetScope) along with in situ data to estimate FD in the Jharsuguda coal mining region in Eastern India. Vegetation's spectral profiles under dusty and non‐dusty conditions are also studied. This study also quantifies the impacts of FD on vegetation physiological processes, providing critical insights into the ecological consequences of dust accumulation on plant surfaces. Key Points: Demonstrated an approach to predict foliar dust concentration from satellite dataEvaluated the efficacy of spectral bands of 4 satellite sensors and 26 radiometric indicesQuantified the effect of foliar dust on vegetation physiological processes [ABSTRACT FROM AUTHOR]

This study quantitatively analyzes the influence of the spectral characteristics, reflectance or transmittance, of different materials on the lighting of an interior space with natural and artificial light. For this purpose, a three-dimensional simulated classroom is used, where each of the components is assigned specific materials with an associated reflectance or transmittance. Additionally, two types of lighting are available: 6500 K daylight and light from six continuous spectrum LED luminaires. The lighting is evaluated on two planes: the work plane and the corneal plane (80 cm and 120 cm from the floor, respectively). Three versions of the same classroom were analyzed by varying the walls (white, blue, and red), each with a different neutral-colored floor. Furthermore, calculations were performed in each situation considering two different types of glazing in the windows, with 20% and 88% transmittance. The photopic and melanopic lighting analysis was carried out with the ALFA calculation program to verify the necessary requirements for adequate lighting. The results show that the white classroom is the best lit, followed by the blue and finally the red, due to the reflectance characteristics of the walls and floor although slight differences among them are found. It was found that in some cases, additional auxiliary luminaires would be required for proper lighting depending on the transmittance of the glazing. This study highlights the critical role of material selection in optimizing both photopic and melanopic lighting, with practical implications for energy efficiency and occupant well-being in educational spaces. [ABSTRACT FROM AUTHOR]

Visible and near-infrared (Vis-NIR) reflectance spectroscopy has recently emerged as an efficient and cost-effective tool for monitoring soil parameters and provides an extensive array of measurements swiftly. This study sought to predict fundamental biological attributes of calcareous soils using spectral reflectance data in the Vis-NIR range through the application of partial least square regression (PLSR) and stepwise multiple linear regression (SMLR) techniques. The objective was to derive spectrotransfer functions (STFs) to predict selected soil biological attributes. A total of 97 composite samples were collected from three distinct agricultural land uses, i.e., sugarcane, wheat, and date palm, in the Khuzestan Province, Iran. The samples were analyzed using both standard laboratory analysis and proximal sensing approach within the Vis-NIR range (400–2500 nm). Biological status was evaluated by determining soil enzyme activities linked to nutrient cycling including acid phosphatase (ACP), alkaline phosphatase (ALP), dehydrogenase (DEH), soil microbial respiration (SMR), microbial biomass phosphorus (Pmic), and microbial biomass carbon (Cmic). The results indicated that the developed PLSR models exhibited superior predictive performance in most biological parameters compared to the STFs, although the differences were not significant. Specifically, the STFs acceptably accurately predicted ACP, ALP, DEH, SMR, Pmic, and Cmic with R2val (val = validation dataset) values of 0.68, 0.67, 0.65, 0.65, 0.76, and 0.72, respectively. These findings confirm the potential of Vis-NIR spectroscopy and the effectiveness of the associated STFs as a rapid and reliable technique for assessing biological soil quality. Overall, in the context of predicting soil properties using spectroscopy-based approaches, emphasis must be placed on developing straightforward, easily deployable, and pragmatic STFs. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivum L.) production in the Northern Great Plains of North America has been challenged by wheat stem sawfly (WSS), Cephus cinctus Norton, for a century. Damaging WSS populations have increased, highlighting the need for reliable surveys. Remote sensing (RS) can be used to correlate reflectance measurements with nuanced phenomena like cryptic insect infestations within plants, yet little has been done with WSS. To evaluate interactions between WSS-infested wheat and spectral reflectance, we grew wheat plants in a controlled environment, experimentally infested them with WSS and recorded weekly hyperspectral measurements (350–2500 nm) of the canopies from prior to the introduction of WSS to full senescence. To assess the relationships between WSS infestation and wheat reflectance, we employed sparse multiway partial least squares regression (N-PLS), which models multidimensional covariance structures inherent in multitemporal hyperspectral datasets. Multitemporal hyperspectral measurements of wheat canopies modeled with sparse N-PLS accurately estimated the proportion of WSS-infested stems (R2 = 0.683, RMSE = 13.5%). The shortwave-infrared (1289–1380 nm) and near-infrared (942–979 nm) spectral regions were the most important in estimating infestation, likely due to internal feeding that decreases plant-water content. Measurements from all time points were important, suggesting aerial RS of WSS in the field should incorporate the visible through shortwave spectra collected from the beginning of WSS emergence at least weekly until the crop reaches senescence. [ABSTRACT FROM AUTHOR]

Crop monitoring is a fundamental practice in seaweed aquaculture. Seaweeds are vulnerable to several threats such as ice-ice disease (IID) causing a whitening of the thallus due to depigmentation. Crop condition assessment is important for minimizing yield losses and improving the biosecurity of seaweed farms. The recent influence of modern technology has resulted in the development of precision aquaculture. The present study focuses on the exploitation of spectral reflectance in the visible and near-infrared regions for characterizing the crop condition of two of the most cultivated Eucheumatoids species: Kappaphycus alvareezi and Eucheuma denticulatum. In particular, the influence of spectral resolution is examined towards discriminating: (a) species and morphotypes, (b) different levels of seaweed health (i.e., from healthy to completely depigmented) and (c) depigmented from silted specimens (thallus covered by a thin layer of sediment). Two spectral libraries were built at different spectral resolutions (5 and 45 spectral bands) using in situ data. In addition, proximal multispectral imagery using a drone-based sensor was utilised. At each experimental scenario, the spectral data were classified using a Random Forest algorithm for crop condition identification. The results showed good discrimination (83–99% overall accuracy) for crop conditions and morphotypes regardless of spectral resolution. According to the importance scores of the hyperspectral data, useful wavelengths were identified for discriminating healthy seaweeds from seaweeds with varying symptoms of IID (i.e., thalli whitening). These wavelengths assisted in selecting a set of vegetation indices for testing their ability to improve crop condition characterisation. Specifically, five vegetation indices (the RBNDVI, GLI, Hue, Green–Red ratio and NGRDI) were found to improve classification accuracy, making them recommended for seaweed health monitoring. Image-based classification demonstrated that multispectral library data can be extended to photomosaics to assess seaweed conditions on a broad scale. The results of this study suggest that proximal sensing is a first step towards effective seaweed crop monitoring, enhancing yield and contributing to aquaculture biosecurity. [ABSTRACT FROM AUTHOR]

Monitoring soil salinity with remote sensing is difficult, but knowing the link between saline soil surface spectra, soil water, and salt transport processes might help in modeling for soil salinity monitoring. In this study, we used an indoor soil column experiment, an unmanned aerial vehicle multispectral sensor camera, and a soil moisture sensor to study the water and salt transport process in the soil column under different water addition conditions and investigate the relationship between the soil water and salt transport process and the spectral reflectance of the image on the soil surface. The observation results of the soil column show that the soil water and salt transportation process conforms to the basic transportation law of "salt moves together with water, and when water evaporates, salt is retained in the soil weight". The salt accumulation phenomenon increases the image spectral reflectance of the surface layer of the soil column, while soil temperature has no effect on the reflectance. As the water percolates down, water and salt accumulate at the bottom of the soil column. The salinity index decreases instantly after the addition of brine and then tends to increase slowly. The experimental results indicate that this work can capture the relationship between the water and salt transport process and remote sensing spectra, which can provide theoretical basis and reference for soil water salinity monitoring. [ABSTRACT FROM AUTHOR]

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), invasion endangered the maize production worldwide, including India. The objective of this study was to quantify the FAW damage severity and its impact on leaf area index (LAI), biomass and grain yield of maize and to detect the field damage using high-resolution multispectral spaceborne remote sensing data. Maize growing fields in the Kurnool District of Andhra Pradesh and the Gadwal District of Telangana, India, were randomly surveyed to collect detailed ground-truth information. Foliar damage due to FAW was recorded, and the fields were categorized into various severity grades (healthy, low, medium and severe). FAW infestation caused significant change in LAI between the severity grades, which formed the basis for its damage detection using multispectral spaceborne remote sensing. Severe FAW infestation caused significant reduction in LAI, biomass and grain yield ranging between 36.9 and 39.9% compared to healthy grade. The infestation at the leaf collar (LC) stage caused significant yield loss of up to 26.5% compared to the tassel initiation (TI) and tasselling and silking (TS) stages. Canopy spectral reflectance from healthy and FAW-infested plants showed significant differences in the visible and near infrared (NIR) regions. A reflectance peak was observed in the NIR region of healthy plants compared to infested plants. Among various spaceborne vegetation indices, the Soil Adjusted Vegetation Index (SAVI) performed better in identifying the FAW infestation (R2 = 0.61**), biomass (R2 = 0.70**) and yield loss (R2 = 0.82**). These findings indicate the feasibility of utilizing multispectral remote sensing data for monitoring FAW infestation on a spatial scale, thus enabling the site-specific management. [ABSTRACT FROM AUTHOR]

Wheat breeding programs are currently focusing on using non-destructive and cost-effective hyperspectral sensing tools to expeditiously and accurately phenotype large collections of genotypes. This approach is expected to accelerate the development of the abiotic stress tolerance of genotypes in breeding programs. This study aimed to assess salt tolerance in wheat genotypes using non-destructive canopy spectral reflectance measurements as an alternative to direct laborious and time-consuming phenological selection criteria. Eight wheat genotypes and sixteen F8 RILs were tested under 150 mM NaCl in real field conditions for two years. Fourteen spectral reflectance indices (SRIs) were calculated from the spectral data, including vegetation SRIs and water SRIs. The effectiveness of these indices in assessing salt tolerance was compared with four morpho-physiological traits using genetic parameters, SSR markers, the Mantel test, hierarchical clustering heatmaps, stepwise multiple linear regression, and principal component analysis (PCA). The results showed significant differences (p ≤ 0.001) among RILs/cultivars for both traits and SRIs. The heritability, genetic gain, and genotypic and phenotypic coefficients of variability for most SRIs were comparable to those of measured traits. The SRIs effectively differentiated between salt-tolerant and sensitive genotypes and exhibited strong correlations with SSR markers (R2 = 0.56–0.89), similar to the measured traits and allelic data of 34 SSRs. A strong correlation (r = 0.27, p < 0.0001) was found between the similarity coefficients of SRIs and SSR data, which was higher than that between measured traits and SSR data (r = 0.20, p < 0.0003) based on the Mantel test. The PCA indicated that all vegetation SRIs and most water SRIs were grouped with measured traits in a positive direction and effectively identified the salt-tolerant RILs/cultivars. The PLSR models, which were based on all SRIs, accurately and robustly estimated the various morpho-physiological traits compared to using individual SRIs. The study suggests that various SRIs can be integrated with PLSR in wheat breeding programs as a cost-effective and non-destructive tool for phenotyping and screening large wheat populations for salt tolerance in a short time frame. This approach can replace the need for traditional morpho-physiological traits and accelerate the development of salt-tolerant wheat genotypes. [ABSTRACT FROM AUTHOR]

Introduction: The effects of salinity memory and its interaction with genetic diversity for drought tolerance and pollination system in terms of morphological, physiological, root characteristics and spectral reflectance indices (SRIs) in tall fescue is still unknown. Methods: Four tall fescue genotypes (two drought-sensitive and two drought-tolerant) were manually controlled to produce four selfed (S1) and four open-pollinated (OP) progeny genotypes (finally eight progeny genotypes). Then all genotypes were assessed for two years in greenhouse under five salinity treatments including control treatment (C), twice salinity stress treatment (primary mild salinity stress in two different stages and secondary at the end stage) (S1t1S2 and S1t2S2), once severe salinity stress treatment (secondary only, S2), and foliar spray of salicylic acid (SA) simultaneously with secondary salinity stress (H2S2). Results: Results indicated that obligate selfing (S1) caused to inbreeding depression in RWC, plant growth, catalase activity, root length and the ratio of root/shoot (R/S). Once salinity stress treatment (S2) led to depression in most measured traits, while pre-exposure to salinity (salinity memory) (S1t1S2 and S1t2S2) improved photosynthetic pigments, proline, antioxidant enzymes and R/S. Conclusion: Salinity memory was more pronounced in drought-sensitive genotypes, while it was more evident in OP than S1 population. Foliar spray of salicylic acid (SA) was almost equally effective in reducing the effects of salinity stress in both populations. The efficacy of application was more pronounced in tolerant genotypes compared to sensitive ones. The possibility of modeling correlated spectral reflectance indices (SRIs) for prediction of different morphological, physiological and root characteristics will be discussed. [ABSTRACT FROM AUTHOR]

Heavy mineral deposits occur in several coastal areas of the world, formed over a long period due to variations in mean sea level, wave action, and winds. These are the main sources of ilmenite (FeTiO3), which in turn is the source of more than 80% of the TiO2 produced and applied in various industries, most recently in nanotechnology. The present study mapped heavy mineral deposits on the coast of Rio Grande do Sul in southern Brazil using integrated proximal and orbital thermal infrared (TIR) remote sensing techniques. Mineral groups, such as oxides and silicates, have spectral features in the TIR wavelengths. Using laboratory spectroscopy at TIR using Nicolet 6700 Thermo Scientific Spectrometer, we measured the spectral signature of the local sample of heavy minerals (between 8 and 14 μm) and identified a diagnostic spectral feature at 10.75 μm. The signature was resampled to be compatible with the Advanced Spaceborne Thermal Emission Radiometer (ASTER) sensor bandwidth values and used as a reference endmember for the Spectral Angle Mapper (SAM) and Linear Spectral Unmixing (LSU) digital image classification algorithms. Thus, we identified the presence of the reference endmember (heavy minerals) in the pixels of the ASTER scene. In pixels classified by SAM as the presence of heavy minerals, LSU was applied to estimate the surface concentration within the pixel. The results showed a concentration of up to 20% of heavy minerals, with the highest concentration on the beach and dune fields. Opaque minerals such as ilmenite do not have spectral reflectance features in visible, near-infrared, and short-wave infrared, which makes their identification by remote sensing difficult. The present study showed that the integration of proximal and orbital as well as hyperspectral and multispectral thermal data can be considered as an alternative for detecting and mapping heavy minerals in coastal areas. [ABSTRACT FROM AUTHOR]

Surface water assumes a pivotal role in sustaining a wide range of wildlife species in semi‐arid protected areas. However, differences in surface water body typology, underlying soil type, wildlife activity, the presence of phytoplankton amongst other factors, result in high variability of surface water spectral reflectance and detection accuracy. In this study, the performance of radar and optical methods was evaluated in detecting surface water of variable spectral reflectance in Hwange National Park, Zimbabwe using Sentinel‐1 radar and Sentinel‐2 optical images for the period 2016–2023. Results demonstrated that radar methods had low surface water detection accuracy which was highly variable as shown by overall accuracy and kappa statistic measures which continuously changed over time compared with optical methods. The overall best‐performing method was the optical AWEInsh (sharpened) which showed high surface water detection accuracy and consistency (OA: 94%–100%) and (κ: 0.88–1.00) from 2016 to 2023. Therefore, optical methods present a stable and robust way for surface water monitoring in heterogeneous semi‐arid protected areas. However, radar‐based methods should be continually explored where optical‐based technologies are impeded as a result of vegetation cover and cloud conditions. [ABSTRACT FROM AUTHOR]

Context and results: The structure, electronic and optical properties of single-layer transition metallic chalcogenides ZrX3 (X = S, Se, Te) have been studied by density functional theory. The electron energy dispersion curve shows that ZrX3 has semiconductor properties, in which the conduction band is mainly contributed by the correlated states of the Zr-d orbital, and the valence band is mainly contributed by the correlated states of the X-p orbital. It is found that b-axis and biaxial strain have great influence on the bandgap and the shift of density of states is also large. At the same time, the peak value of density of states increases greatly when biaxial strain is applied. It is of guiding significance for selecting suitable substrates to prepare two-dimensional ZrX3 materials to study their electronic properties. The calculation of optical constants confirms that ZrX3 has strong optical anisotropy. In the visible range, the light absorption efficiency of ZrX3 in the direction of electric field polarization [100] is higher than that in the direction of [010]. The reflectance spectral results show that ZrS3 and ZrSe3 in the [100] directions have the highest reflectance, and ZrTe3 in the [010] direction has the highest reflectance, even in the long electromagnetic radiation range (up to 10 eV), which is of great significance for the construction of visible optical devices. Computational method: All computations have been carried out based on density functional theory (DFT) as implemented in the CASTEP code. The pseudo-potential is adopted by the norm conserving, and the exchange correlation functional is adopted by the Perdew-Burke-Ernzerhof in local generalized gradient approximation (GGA). [ABSTRACT FROM AUTHOR]

Human–computer interaction (HCI) with screens through gestures is a pivotal method amidst the digitalization trend. In this work, a gesture recognition method is proposed that combines multi-band spectral features with spatial characteristics of screen-reflected light. Based on the method, a red-green-blue (RGB) three-channel spectral gesture recognition system has been developed, composed of a display screen integrated with narrowband spectral receivers as the hardware setup. During system operation, emitted light from the screen is reflected by gestures and received by the narrowband spectral receivers. These receivers at various locations are tasked with capturing multiple narrowband spectra and converting them into light-intensity series. The availability of multi-narrowband spectral data integrates multidimensional features from frequency and spatial domains, enhancing classification capabilities. Based on the RGB three-channel spectral features, this work formulates an RGB multi-channel convolutional neural network long short-term memory (CNN-LSTM) gesture recognition model. It achieves accuracies of 99.93% in darkness and 99.89% in illuminated conditions. This indicates the system's capability for stable operation across different lighting conditions and accurate interaction. The intelligent gesture recognition method can be widely applied for interactive purposes on various screens such as computers and mobile phones, facilitating more convenient and precise HCI. [ABSTRACT FROM AUTHOR]

The leaf chlorophyll content (LCC) of winter wheat, an important food crop widely grown worldwide, is a key indicator for assessing its growth and health status in response to CO2 stress. However, the remote sensing quantitative estimation of winter wheat LCC under CO2 stress conditions also faces challenges such as an unclear spectral sensitivity range, baseline drift, overlapping spectral peaks, and complex spectral response due to CO2 stress changes. To address these challenges, this study introduced the fractional order derivative (FOD) and continuous wavelet transform (CWT) techniques into the estimation of winter wheat LCC. Combined with the raw hyperspectral data, we deeply analyzed the spectral response characteristics of winter wheat LCC under CO2 stress. We proposed a stacking model including multiple linear regression (MLR), decision tree regression (DTR), random forest (RF), and adaptive boosting (AdaBoost) to filter the optimal combination from a large number of feature variables. We use a dual-band combination and vegetation index strategy to achieve the accurate estimation of LCC in winter wheat under CO2 stress. The results showed that (1) the FOD and CWT methods significantly improved the correlation between the raw spectral reflectance and LCC of winter wheat under CO2 stress. (2) The 1.2-order derivative dual-band index (RVI (R720, R522)) constructed by combining the sensitive spectral bands of the CO2 response of winter wheat leaves achieved a high-precision estimation of the LCC under CO2 stress conditions (R2 = 0.901). Meanwhile, the red-edged vegetation stress index (RVSI) constructed based on the CWT technique at specific scales also demonstrated good performance in LCC estimation (R2 = 0.880), verifying the effectiveness of the multi-scale analysis in revealing the mechanism of the CO2 impact on winter wheat. (3) By stacking the sensitive spectral features extracted by combining the FOD and CWT methods, we further improved the LCC estimation accuracy (R2 = 0.906). This study not only provides a scientific basis and technical support for the accurate estimation of LCC in winter wheat under CO2 stress but also provides new ideas and methods for coping with climate change, optimizing crop-growing conditions, and improving crop yield and quality in agricultural management. The proposed method is also of great reference value for estimating physiological parameters of other crops under similar environmental stresses. [ABSTRACT FROM AUTHOR]

Heavy metal contamination in soils and vegetation poses a significant problem due to its toxicity and persistence. Toxic effects on vegetation include not only impaired growth, reduced yields, and even plant death but also biodiversity loss and ecosystem degradation. Addressing this issue requires comprehensive monitoring and remediation efforts to mitigate the environmental, human health, and ecological impacts. This review examines the state-of-the-art methodologies and advancements in remote sensing applications for detecting and monitoring heavy metal contamination in soil and its subsequent effects on vegetation. By synthesizing the current research findings and technological developments, this review offers insights into the efficacy and potential of remote sensing for monitoring heavy metal contamination in terrestrial ecosystems. However, current studies focus on regression and AI methods to link spectral reflectances and indices to heavy metal concentrations, which poses limited transferability to other areas, times, spectral discretizations, and heavy metal elements. We conclude that one important way forward is the more thorough understanding and simulation of the related physico-chemical processes in soils and plants and their effects on the spectral signatures. This would offer a profound basis for remote sensing applications for individual circumstances and would allow disentangling heavy metal effects from other stressors such as droughts or soil salinity. [ABSTRACT FROM AUTHOR]

The increase in atmospheric and soil temperatures in recent decades has led to unfavorable conditions for plants in many Mediterranean coastal environments. A typical example can be found along the coast of the Campania region in Italy, within the "Volturno Licola Falciano Natural Reserve", where a pine forest suffered a dramatic loss of trees in 2021. New pines were planted in 2023 to replace the dead ones, with a larger tree layout and interspersed with Mediterranean bushes to replace the dead pine forest. A direct (in situ) monitoring program was planned to analyze the determinants of the pine salinity stress, coupled with Sentinel-2 L2A data; in particular, multispectral indices NDVI and NDMI were provided by the EU Copernicus service for plant status and water stress level information. Both the vadose zone and shallow groundwater were monitored with continuous logging probes. Vadose zone monitoring indicated that salinity peaked at a 30 cm soil depth, with values up to 1.9 g/L. These harsh conditions, combined with air temperatures reaching peaks of more than 40 °C, created severe difficulties for pine growth. The results of the shallow groundwater monitoring showed that the groundwater salinity was low (0.35–0.4 g/L) near the shoreline since the dune environment allowed rapid rainwater infiltration, preventing seawater intrusion. Meanwhile, salinity increased inland, reaching a peak at the end of the summer, with values up to 2.8 g/L. In November 2023, salts from storm-borne aerosols ("sea spray") deposited on the soil caused the sea-facing portion of the newly planted pines to dry out. Differently, the pioneer vegetation of the Mediterranean dunes, directly facing the sea, was not affected by the massive deposition of sea spray. The NDMI and NDVI data were useful in distinguishing the old pine trees suffering from increasing stress and final death but were not accurate in detecting the stress conditions of newly planted, still rather short pine trees because their spectral reflectance largely interfered with the adjacent shrub growth. The proposed coupling of direct and remote sensing monitoring was successful and could be applied to detect the main drivers of plant stress in many other Mediterranean coastal environments. [ABSTRACT FROM AUTHOR]

• The spectral characteristics of different types of deterioration are analyzed. • The normalized spectral index is constructed to preliminarily identify the deterioration. • The relationship between spectral reflectance and Schmidt rebound value is established. • The deterioration identification models are established based on hyperspectral image texture features. Deterioration investigation is an essential foundation for understanding the preservation status of stone cultural heritage, as well as for carrying out emergency and preventive conservation. Traditional photogrammetry method for deterioration investigation in stone cultural heritage heavily relies on personnel experience and has low automation. To accurately evaluate the degree of deterioration and quantify its scale, different algorithms are used to establish the rebound value prediction model and deterioration identification model based on the hyperspectral image. The effects of different wavelength selection methods and different classification models are compared. The results show that the rebound value inversion model constructed by CARS and PLS delivers the most accurate forecasts, with R2 being no less than 0.85. The maximum error of the model when applied in the field does not exceed 20%. Different types of deterioration can be initially identified by the normalized spectral index constructed from the 530 nm and 675 nm wavelengths. In addition, all four classification models based on hyperspectral imaging texture features can identify different types of deterioration. The LGBM model has the highest identification accuracy of 0.98. It also has good performance in field identification. This study provides a new method for deterioration investigation in stone cultural heritage. [ABSTRACT FROM AUTHOR]

Rice is one of the major grain crops in the world. It is of great significance to accurately inverse the chlorophyll content during growth and development in the field. However, there are some deficiencies using conventional interpretability that are driven by data inversion of the physical and chemical parameters. The leaf radiative transfer model can be expected to effectively simulate the spectral information of rice leaves, and then describe the effects of leaf parameters on spectral reflectance. A strong mechanism model can be used for the inversion of chlorophyll content of rice leaves, according to the physically driven method. The PIOSL (PROSPECT considering the internal optical structure of the leaves) model can be utilized to assume that the internal structure of the leaf blade is composed of the superposition of two layers of optical properties, which is much more in line with the actual growth of the plant. This study aims to verify the feasibility of retrieving rice leaf chlorophyll content by the PIOSL model. Firstly, the sensitivity analysis was carried out on each input parameter into the PIOSL model. The parameters Cab and Cab12 were determined as the high sensitivity in the 400-750 nm band. The inversion of chlorophyll content (Cab) was then implemented. The projection (SPA) was used to extract the features of spectral data. Five characteristic bands were sensitive to chlorophyll: 490, 575, 645, 675, and 725 nm. A multiple regression model was constructed to realize the inversion of rice leaf chlorophyll. The inversion performance of the mechanism model was then evaluated using the PIOSL model. The chlorophyll content of rice leaves was also inverted to construct an initial lookup table. LSE (least squares estimate) was used to screen the simulated sample data in the initial lookup table closer to the measured spectra. A classification prediction model was constructed using a support vector machine (SVM), in order to determine whether the combination of the screened sample parameters was in line with the actual growth of the leaves. The improved lookup table was randomly split into the training and test sets in the ratio of 7:3. The hierarchical inversion of chlorophyll content of rice leaves was performed to construct the WOA-ELM (whale optimization, WOA; extreme learning machine, ELM) inversion model. A comparison was made on the commonly-used PROSPECT model with the same data. An improved lookup table was then obtained using the PROSPECT model. The WOA-ELM model was used for the chlorophyll inversion. The results showed that the R² and RMSE of the inversion model using PIOSL-WOA-ELM were 0.977 and 2.356 μg/cm2, respectively, and the inversion accuracies of both models were above 0.9, compared with the PROSPECT-WOA-ELM model. According to the numerically driven inversion model and the physically-driven radiative transfer model, the inversion accuracy of the PIOSLWOA-ELM model was higher than that of the multivariate regression with continuous projection. Therefore, it is feasible for a more effective inversion with the radiative transfer mechanism. The finding can provide new ideas for the accurate inversion of the distribution of rice chlorophyll in the leaf, further effectively retrieving the crop physicochemical parameters. [ABSTRACT FROM AUTHOR]

Radiative cooling pigments offer new opportunities for sustainable carbon neutrality as zero‐energy and zero‐pollution functional materials. However, it is a challenge to prepare inorganic yellow pigments simultaneously with environmentally friendly, high color value, and weather resistance. In this study, a series of alkali metal‐doped γ‐Sm2S3 yellow pigments with near‐infrared reflectance were synthesized. The doping of alkali metal ions reduced synthesis temperature of Sm2S3 from 1300°C to 1000°C. The change in energy band structure induced by alkali metal doping allows the products to exhibit modifiable colors. Typical γ‐[Na]‐Sm2S3 pigment powders have a high near‐infrared reflectance and impressive colors (b* = 78.6, C* = 77.6). Furthermore, colored films made by prepared yellow powders with polyvinylidene fluoride in certain ratios had high near‐infrared reflectance and excellent environmental weathering, resulting in an effective cooling of 4–8°C in outdoor experiments. This work expands the range of options for energy‐efficient building and yellow colorant. [ABSTRACT FROM AUTHOR]

In this study, a novel method is proposed to estimate surface-spectral reflectance from camera responses that combine model-based and training-based approaches. An imaging system is modeled using the spectral sensitivity functions of an RGB camera, spectral power distributions of multiple light sources, unknown surface-spectral reflectance, additive noise, and a gain parameter. The estimation procedure comprises two main stages: (1) selecting the local optimal reflectance dataset from a reflectance database and (2) determining the best estimate by applying a neural network to the local optimal dataset only. In stage (1), the camera responses are predicted for the respective reflectances in the database, and the optimal candidates are selected in the order of lowest prediction error. In stage (2), most reflectance training data are obtained by a convex linear combination of local optimal data using weighting coefficients based on random numbers. A feed-forward neural network with one hidden layer is used to map the observation space onto the spectral reflectance space. In addition, the reflectance estimation is repeated by generating multiple sets of random numbers, and the median of a set of estimated reflectances is determined as the final estimate of the reflectance. Experimental results show that the estimation accuracies exceed those of other methods. [ABSTRACT FROM AUTHOR]

Spatio-ecological heterogeneity has a significant impact on various ecosystem properties, such as biodiversity patterns, variability in ecosystem resources, and species distributions. Given this perspective, remote sensing has gained widespread recognition as a powerful tool for assessing the spatial heterogeneity of ecosystems by analyzing the variability among different pixel values in both space and, potentially, time. Several measures of spatial heterogeneity have been proposed, broadly categorized into abundance-related measures (e.g., Shannon's H) and dispersion-related measures (e.g., Variance). A measure that integrates both abundance and distance information is the Rao's quadratic entropy (Rao's Q index), mainly used in ecology to measure plant diversity based on in-situ based functional traits. The question arises as to why one should use a complex measure that considers multiple dimensions and couples abundance and distance measurements instead of relying solely on simple dispersion-based measures of heterogeneity. This paper sheds light on the spatial version of the Rao's Q index, based on moving windows for its calculation, with a particular emphasis on its mathematical and statistical properties. The main objective is to theoretically demonstrate the strength of Rao's Q index in measuring heterogeneity, taking into account all its potential facets and applications, including (i) integrating multivariate data, (ii) applying differential weighting to pixels, and (iii) considering differential weighting of distances among pixel reflectance values in spectral space. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of mapping land cover types in Senegal and recognition of vegetation systems in the Saloum River Delta on the satellite images. Multi-seasonal landscape dynamics were analyzed using Landsat 8-9 OLI/TIRS images from 2015 to 2023. Two image classification methods were compared, and their performance was evaluated in the GRASS GIS software (version 8.4.0, creator: GRASS Development Team, original location: Champaign, Illinois, USA, currently multinational project) by means of unsupervised classification using the k-means clustering algorithm and supervised classification using the Support Vector Machine (SVM) algorithm. The land cover types were identified using machine learning (ML)-based analysis of the spectral reflectance of the multispectral images. The results based on the processed multispectral images indicated a decrease in savannas, an increase in croplands and agricultural lands, a decline in forests, and changes to coastal wetlands, including mangroves with high biodiversity. The practical aim is to describe a novel method of creating land cover maps using RS data for each class and to improve accuracy. We accomplish this by calculating the areas occupied by 10 land cover classes within the target area for six consecutive years. Our results indicate that, in comparing the performance of the algorithms, the SVM classification approach increased the accuracy, with 98% of pixels being stable, which shows qualitative improvements in image classification. This paper contributes to the natural resource management and environmental monitoring of Senegal, West Africa, through advanced cartographic methods applied to remote sensing of Earth observation data. [ABSTRACT FROM AUTHOR]

Old-growth forests are essential to preserve biodiversity and play an important role in sequestering carbon and mitigating climate change. However, their existence across Europe is vulnerable due to the scarcity of their distribution, logging, and environmental threats. Therefore, providing the current status of old-growth forests across Europe is essential to aiding informed conservation efforts and sustainable forest management. Remote sensing techniques have proven effective for mapping and monitoring forests over large areas. However, relying solely on remote sensing spectral or structural information cannot capture comprehensive horizontal and vertical structure complexity profiles associated with old-growth forest characteristics. To overcome this issue, we combined spectral information from Sentinel-2A multispectral imagery with 3D structural information from high-density point clouds of airborne laser scanning (ALS) imagery to map old-growth forests over an extended area. Four features from the ALS data and fifteen from Sentinel-2A comprising raw band (spectral reflectance), vegetation indices (VIs), and texture were selected to create three datasets used in the classification process using the random forest algorithm. The results demonstrated that combining ALS and Sentinel-2A features improved the classification performance and yielded the highest accuracy for old-growth class, with an F1-score of 92% and producer's and user's accuracies of 93% and 90%, respectively. The findings suggest that features from ALS and Sentinel-2A data sensitive to forest structure are essential for identifying old-growth forests. Integrating open-access satellite imageries, such as Sentinel-2A and ALS data, can benefit forest managers, stakeholders, and conservationists in monitoring old-growth forest preservation across a broader spatial extent. [ABSTRACT FROM AUTHOR]

Salt-sensitive trees in coastal wetlands are dying as forests transition to marsh and open water at a rapid pace. Forested wetlands are experiencing repeated saltwater exposure due to the frequency and severity of climatic events, sea-level rise, and human infrastructure expansion. Understanding the diverse responses of trees to saltwater exposure can help identify taxa that may provide early warning signals of salinity stress in forests at broader scales. To isolate the impacts of saltwater exposure on trees, we performed an experiment to investigate the leaf-level physiology of six tree species when exposed to oligohaline and mesohaline treatments. We found that species exposed to 3–6 parts per thousand (ppt) salinity had idiosyncratic responses of plant performance that were species-specific. Saltwater exposure impacted leaf photochemistry and caused early senescence in Acer rubrum, the most salt-sensitive species tested, but did not cause any impacts on plant water use in treatments with <6 ppt. Interestingly, leaf spectral reflectance was correlated with the operating efficiency of photosystem II (PSII) photochemistry in A. rubrum leaves before leaf physiological processes were impacted by salinity treatments. Our results suggest that the timing and frequency of saltwater intrusion events are likely to be more detrimental to wetland tree performance than salinity concentrations. [ABSTRACT FROM AUTHOR]

Abstract The European cranberry bush, known for its health benefits, can only be consumed through fermentation. This study aimed to develop a fruit leather made from European cranberry bush using quince seed gel and the foam drying method. For this purpose, quince seed gel was added to European cranberry juice to increase consistency. Then, European cranberry fruit leather was obtained by drying at 70, 80, and 90 °C air temperatures using foam mat drying technology. Spectral reflectance, color, drying kinetics, anthocyanin, ascorbic acid, and total phenolic content, antiradical activity, and macro-micronutrient concentrations of the resulting fruit pulp were investigated. The foam mat drying process at 90 °C had the greatest values of ascorbic acid (0.996 mg g− 1), anthocyanin (275.9 mg kg− 1), DPPH (47.77%), and ABTS.+ (68.76 µg TE g− 1). In addition, the highest value of total phenolic content (37.75 mg g− 1) was obtained in the foam mat drying process at 80 °C. The highest concentration of P, Na, Mg, K, Ca, and Mn in fruit leather was obtained at 70 °C, and the highest concentration of S, Cu, and Zn was obtained at 90 °C. The lowest spectral reflectance values were measured at 90 °C. In conclusion, the present study explored the fact that adding quince seed gel, extremely rich in biochemical content, significantly enhanced the bioactivity properties of European cranberry bush fruit leather.

Plant physiological status is the interaction between the plant genome and the prevailing growth conditions. Accurate characterization of plant physiology is, therefore, fundamental to effective plant phenotyping studies; particularly those focused on identifying traits associated with improved yield, lower input requirements, and climate resilience. Here, we outline the approaches used to assess plant physiology and how these techniques of direct empirical observations of processes such as photosynthetic CO2 assimilation, stomatal conductance, photosystem II electron transport, or the effectiveness of protective energy dissipation mechanisms are unsuited to high-throughput phenotyping applications. Novel optical sensors, remote/proximal sensing (multi- and hyperspectral reflectance, infrared thermography, sun-induced fluorescence), LiDAR, and automated analyses of below-ground development offer the possibility to infer plant physiological status and growth. However, there are limitations to such indirect approaches to gauging plant physiology. These methodologies that are appropriate for the rapid high temporal screening of a number of crop varieties over a wide spatial scale do still require calibration or validation with direct empirical measurement of plant physiological status. The use of deep-learning and artificial intelligence approaches may enable the effective synthesis of large multivariate datasets to more accurately quantify physiological characters rapidly in high numbers of replicate plants. Advances in automated data collection and subsequent data processing represent an opportunity for plant phenotyping efforts to fully integrate fundamental physiological data into vital efforts to ensure food and agro-economic sustainability.

Characterizing potato cultivars is essential for diagnosing the specific production and management requirements. Employing techniques that facilitate rapid, objective, and cost-effective identification of various aspects of plant physiology is crucial for generating this valuable information. In this study, our goal was to identify different potato cultivars across three distinct phenological phases utilizing reflectance spectroscopy within the visible and near-infrared ranges. Reflectance spectra were measured using a portable spectrometer on leaves obtained from five different potato cultivars. We constructed classification models to determine the accurate classification percentages for each cultivar within specific phenological stages. General trends in the reflectance spectra were observed, which were consistent across all five cultivars within the measured range. During the vegetative phase, there was a uniform reflectance of around 5% in the green region. This value experienced a slight decrease during the flowering phase and subsequently rose to approximately 12% during the senescence phase. In the near-infrared (NIR) range, a reduction in reflectance from 50 to 30% was noted during the flowering phase, followed by a cultivar-dependent increase towards senescence. The specific wavelengths identified in the spectra enabled the accurate classification of plants from each cultivar across the vegetative, flowering, and senescence phases. The classification rates were notably high, with a success rate of 87% for Var. Paola, 96% for Var. Paysandú, 93% for Var. Violeta, and 100% for cv. 448 and 440. The Var. Paola exhibited superior correct classification rates, ranging from 85% for Paysandú and Violeta, 90% for cv. 440, to 100% for cv. 448. The characterization of potato cultivars using spectroscopy techniques yields valuable information that serves as fundamental input for precision agriculture systems. [ABSTRACT FROM AUTHOR]

This study explores the detection and classification of floating marine debris in the Nagapattinam area using Sentinel-2 satellite imagery. Data were enhanced through histogram stretching, facilitating the identification of marine plastic debris. Various indices were derived to facilitate the creation of training sets for six models. Each model underwent five tests using a support vector machine and random forest classifiers. Key spectral bands such as B6 (red-edge 2), B8 (near infrared), and B11 (SWIR 1) were pivotal in the classification process. This study illustrates the effectiveness of SVM and RF algorithms in detecting plastic debris, showcasing true and false detection rates. Spectral profiles of various plastic items, including bags, fishnets, and bottles, were analyzed, revealing strong near-infrared reflectance and low red-edge reflectance. Plastic pixels were categorized based on the percentage of plastic coverage, influencing reflectance intensity. Accuracy assessments for the six tests in both the models are presented, highlighting three significant plastic debris groupings near the coast and one near Vedaranyam, with the remaining detections not classified as plastic debris. An automatic floating plastic recognition system was developed and tested in the Nagapattinam area using the best-performing model, identifying floating plastic with an 80–95% accuracy. The Floating Debris Index was the most crucial for identifying marine floating plastic. The study concludes that the distinct reflectance characteristics of plastic facilitate its identification among marine debris, with model accuracies. [ABSTRACT FROM AUTHOR]