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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]

Abstract Background Precise evaluation of fungal conidia production may facilitate studies on resistance mechanisms and plant breeding for disease resistance. In this study, hyperspectral imaging (HSI) was used to quantify the sporulation of Magnaporthe oryzae on the leaves of rice cultivars grown under controlled conditions. Three rice genotypes (CO 39, Nipponbare, IR64) differing in susceptibility to blast were inoculated with M. oryzae isolates Guy 11 and Li1497. Spectral information (450–850 nm, 140 wavebands) of typical leaf blast symptoms was recorded before and after induction of sporulation of the pathogen. Results M. oryzae produced more conidia on the highly susceptible genotype than on the moderately susceptible genotype, whereas the resistant genotype resulted in no sporulation. Changes in reflectance spectra recorded before and after induction of sporulation were significantly higher in genotype CO 39 than in Nipponbare. The spectral angle mapper algorithm for supervised classification allowed for the classification of blast symptom subareas and the quantification of lesion areas with M. oryzae sporulation. The correlation between the area under the difference spectrum (viz. spectral difference without and with sporulation) and the number of conidia per lesion and the number of conidia per lesion area was positive and count-based differences in rice - M. oryzae interaction could be reproduced in the spectral data. Conclusions HSI provided a precise and objective method of assessing M. oryzae conidia production on infected rice plants, revealing differences that could not be detected visually.

Estimation of fruit quality parameters are usually based on destructive techniques which are tedious, costly and unreliable when dealing with huge amounts of fruits. Alternatively, non–destructive techniques such as image processing and spectral reflectance would be useful in rapid detection of fruit quality parameters. This research study aimed to assess the potential of image processing, spectral reflectance indices (SRIs), and machine learning models such as decision tree (DT) and random forest (RF) to qualitatively estimate characteristics of mandarin and tomato fruits at different ripening stages. Quality parameters such as chlorophyll a (Chl a), chlorophyll b (Chl b), total soluble solids (TSS), titratable acidity (TA), TSS/TA, carotenoids (car), lycopene and firmness were measured. The results showed that Red-Blue-Green (RGB) indices and newly developed SRIs demonstrated high efficiency for quantifying different fruit properties. For example, the R2 of the relationships between all RGB indices (RGBI) and measured parameters varied between 0.62 and 0.96 for mandarin and varied between 0.29 and 0.90 for tomato. The RGBI such as visible atmospheric resistant index (VARI) and normalized red (Rn) presented the highest R2 = 0.96 with car of mandarin fruits. While excess red vegetation index (ExR) presented the highest R2 = 0.84 with car of tomato fruits. The SRIs such as RSI 710,600, and R730,650 showed the greatest R2 values with respect to Chl a (R2 = 0.80) for mandarin fruits while the GI had the greatest R2 with Chl a (R2 = 0.68) for tomato fruits. Combining RGB and SRIs with DT and RF models would be a robust strategy for estimating eight observed variables associated with reasonable accuracy. Regarding mandarin fruits, in the task of predicting Chl a, the DT-2HV model delivered exceptional results, registering an R2 of 0.993 with an RMSE of 0.149 for the training set, and an R2 of 0.991 with an RMSE of 0.114 for the validation set. As well as for tomato fruits, the DT-5HV model demonstrated exemplary performance in the Chl a prediction, achieving an R2 of 0.905 and an RMSE of 0.077 for the training dataset, and an R2 of 0.785 with an RMSE of 0.077 for the validation dataset. The overall outcomes showed that the RGB, newly SRIs as well as DT and RF based RGBI, and SRIs could be used to evaluate the measured parameters of mandarin and tomato fruits. [ABSTRACT FROM AUTHOR]

Precise prediction of soil salinity using visible, and near-infrared (vis-NIR) spectroscopy is crucial for ensuring food security and effective environmental management. This paper focuses on the precise prediction of soil salinity utilizing visible and near-infrared (vis-NIR) spectroscopy, a critical factor for food security and effective environmental management. The objective is to utilize vis-NIR spectra alongside a multiple regression model (MLR) and a random forest (RF) modeling approach to predict soil salinity across various land use types, such as farmlands, bare lands, and rangelands accurately. To this end, we selected 150 sampling points representatives of these diverse land uses. At each point, we collected soil samples to measure the soil salinity (ECe) and employed a portable spectrometer to capture the spectral reflectance across the full wavelength range of 400 to 2400 nm. The methodology involved using both individual spectral reflectance values and combinations of reflectance values from different wavelengths as input variables for developing the MLR and RF models. The results indicated that the RF model (RMSE = 4.85 dS m-1, R2 = 0.87, and RPD = 3.15), utilizing combined factors as input variables, outperformed others. Furthermore, our analysis across different land uses revealed that models incorporating combined input variables yielded significantly better results, particularly for farmlands and rangelands. This study underscores the potential of combining vis-NIR spectroscopy with advanced modeling techniques to enhance the accuracy of soil salinity predictions, thereby supporting more informed agricultural and environmental management decisions. [ABSTRACT FROM AUTHOR]

Accurate and timely monitoring of drought conditions in grape-producing regions is crucial for achieving healthy growth of grapes. Current soil moisture (SM) products are primarily available at coarse resolutions (e.g. several to tens of kilometres), constraining its applications at fine scales. Here, we trained a weighted stacking ensemble model including three tree-based models (categorical boosting, random forest, and gradient boosting decision tree), using seven forcing parameters related to spectral reflectance (SR), land surface temperature (LST), and evapotranspiration (ET), in conjunction with the digital elevation model (DEM) feature. The weighted stacking ensemble model exhibited an average R2 of 0.86 and an average RMSE of 0.021 m3/m3 in simulating SM in the vegetive stage and the mid-ripening stage of grape. Then we generated high spatiotemporal downscaled SM (HSM) data at a grape growing area at high spatiotemporal resolutions (30 m, 8-day) from 2009 to 2018. Our HSM dataset demonstrated strong spatial, seasonal and interannual dynamics that align with 500 m SM dataset derived from single MODIS data, and the HSM dataset shows more details in SM distribution. Additionally, the SM time series in the HSM is consistently correlated with drought events, offering intricate spatiotemporal information for drought monitoring. The application of downscaled SM results identified a concentration of drought events in the eastern foothills of the Helan Mountains, particularly severe drought conditions were observed in the Hongsipu production area. Drought occurrences in the Hongsipu production area ranged from 90% to 91% during May and June, decreasing to 73% and 41% in July and August, respectively. These findings significantly contribute to enhancing high spatiotemporal SM monitoring capabilities, offering valuable guidance for timely water management in grape-growing regions. [ABSTRACT FROM AUTHOR]

Spectral imaging has many applications, from methane detection using satellites to disease detection on crops. However, spectral cameras remain a costly solution ranging from 10 thousand to 100 thousand euros for the hardware alone. Here, we present a low-cost multispectral camera (LC-MSC) with 64 LEDs in eight different colors and a monochrome camera with a hardware cost of 340 euros. Our prototype reproduces spectra accurately when compared to a reference spectrometer to within the spectral width of the LEDs used and the ±1 σ variation over the surface of ceramic reference tiles. The mean absolute difference in reflectance is an overestimate of 0.03 for the LC-MSC as compared to a spectrometer, due to the spectral shape of the tiles. In environmental light levels of 0.5 W m−2 (bright artificial indoor lighting) our approach shows an increase in noise, but still faithfully reproduces discrete reflectance spectra over 400 nm–1000 nm. Our approach is limited in its application by LED bandwidth and availability of specific LED wavelengths. However, unlike with conventional spectral cameras, the pixel pitch of the camera itself is not limited, providing higher image resolution than typical high-end multi- and hyperspectral cameras. For sample conditions where LED illumination bands provide suitable spectral information, our LC-MSC is an interesting low-cost alternative approach to spectral imaging. [ABSTRACT FROM AUTHOR]

Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) explore the potential of the soil spectral signature obtained in different spectral bands (VIS-NIR, SWIR, and VIS-NIR-SWIR) and, by using hyperspectral imaging and non-imaging sensors, in the predictive modeling of soil attributes; and (b) analyze the accuracy of different ML models in predicting particle size and soil organic carbon (SOC) applied to the spectral signature of different spectral bands. Six soil monoliths, located in the central north region of Parana, Brazil, were collected and scanned via hyperspectral cameras (VIS-NIR camera and SWIR camera) and spectroradiometer (VIS-NIR-SWIR) in the laboratory. The spectral signature of the soils was analyzed and subsequently applied to ML models to predict particle size and SOC. Each set of data obtained by the different sensors was evaluated separately. The algorithms used were k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), linear regression (LR), artificial neural network (NN), and partial least square regression (PLSR). The most promising predictive performance was observed for the complete VIS-NIR-SWIR spectrum, followed by SWIR and VIS-NIR. Meanwhile, KNN, RF, and NN models were the most promising algorithms in estimating soil attributes for the dataset obtained from both sensors. The general mean R2 (determination coefficient) values obtained using these models, considering the different spectral bands evaluated, were around 0.99, 0.98, and 0.97 for sand prediction, and around 0.99, 0.98, and 0.96 for clay prediction. The lower performances, obtained for the datasets from both sensors, were observed for silt and SOC, with R2 results between 0.40 and 0.59 for these models. KNN demonstrated the best predictive performance. Integrating effective ML models with robust sample databases, obtained by advanced hyperspectral imaging and spectroradiometers, can enhance the accuracy and efficiency of soil attribute prediction. [ABSTRACT FROM AUTHOR]

Chlorophyll (Chl) concentration is one of the factors that affects crop productivity. This study investigated the prediction of chlorophyll concentrations in Mombaça grass' leaves using hyperspectral data and machine learning techniques. Chlorophyll variations were induced by different levels of nitrogen fertilization (104, 208, 312, and 416 kg ha−1). Spectral signatures (400–2500 nm) and chlorophyll contents of the leaves were obtained in October, November, and December 2017, and January 2018. Models were generated using Partial Least Square Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Two validation techniques were employed: holdout, dividing the data into training (75%) and testing (25%) sets; and leave‐one‐date‐out cross‐validation (LOOCV), in which one date was omitted during model training and used to predict the omitted date's value. Chlorophyll concentrations varied according to N doses, with the highest concentrations observed in October and December. In these months, there were greater variations in spectral reflectance in the green and red bands (530–680 nm). December was identified as the ideal period for chlorophyll quantification, for both holdout and LOOCV validation techniques. The SVR technique performed best (R2 = 0.71, RMSE = 0.23 mg g−1, dr = 0.72) compared to RF (R2 = 0.63, RMSE = 0.27 mg g−1, dr = 0.66) and PLSR (R2 = 0.60, RMSE = 0.27 mg g−1, dr = 0.67). Therefore, the prediction of chlorophyll in Mombaça grass using spectroradiometry is promising and applicable across different cultivation periods. [ABSTRACT FROM AUTHOR]

This study aims to improve the accuracy of the detection of adulterated minced beef with chicken, duck, and pork. A series of tests were carried out to quantitatively detect the adulterants in beef using hyperspectral reflectance (R) and transmittance (T) spectral data fusion. Firstly, three types of adulterants (chicken, duck, and pork) were added to the beef with a mass percentage of 2% to 50% concentration interval of 2%, respectively. Then the reflectance spectral data was collected to combine with SVM (support vector machine, SVM), random forest (RF), and long short-term memory (LSTM). A classification model was established to classify the samples into chicken, duck, and pork adulterated and pure beef samples (4 groups). Then, a partial least squares model (PLSR) was built from the single reflectance and transmittance spectral data to quantify the concentrations of adulterants. The performance of the model was optimized by competitive adaptive reweighted sampling (CARS), irrelevant variable elimination (UVE), and primary and intermediate data fusion. The predictions were also conducted on adulterants such as chicken, duck, and pork, according to the intermediate-level data fusion. The results showed the greatest effectiveness values of improvement were 3.9%, 9.3%, and 4.5% over those in the optimal model with single spectral data, respectively. The models with the best prediction results for chicken and duck samples withadulterants were the de trending (DT) pre-processed UVE-PLSR, with corresponding coefficients of determination (r-squaredprediction, ) and root mean square error (RMSEP) of 0.984 5 and 1.8651、 0.986 0 and 1.7711, respectively, and the bestprediction model for adulterated pork samples was RAW-CARS-PLSR, with corresponding and RMSEP of 0.975 1 and 2.366 5. Hyperspectral imaging combined with data fusion can effectively detect the adulterants in beef with high accuracy and speed. The intermediate-level data fusion can maximize the performance of the improved model. [ABSTRACT FROM AUTHOR]

Spectral encoding is an important way of describing spectral features and patterns. Traditional methods focused on encoding the spectral amplitude information (SAI). Abundant spectral shape information (SSI) was wasted. In addition, traditional statistical encoding methods might only gain local adaptability since different objects should have their own best encoding scales. In order to obtain differential signals from hyperspectral images (HSI) for detecting ground objects correctly, a multi-scale encoding (MSE) method with SSI and two optimization strategies were proposed in this research. The proposed method concentrated on describing the SAI and SSI of the spectral reflectance signals. Four widely used open data sets were adopted to validate the performance of the proposed method. Experimental results indicated that the MSE method with SSI could describe the details of spectral signals accurately. It could obtain excellent performance for detecting similar objects with a small number of samples. In addition, the optimization strategies contributed to obtaining the best result from dynamic encoding scales. [ABSTRACT FROM AUTHOR]

Research suggested that color vision evolved for the purpose of seeing skin color modulations signaling emotion or mood. Hence people want to find the connection of the cone sensitivities with the skin spectral reflectance. In this article the W and V shapes based on skin spectral reflectance measured at different positions and from different ethnic groups (Kurdish, Caucasian, Thai, Chinese) were investigated. First, it was found that the measured skin spectral reflectance from all body positions and ethnic groups have a V shape between 400 and 450 nm and a W shape between 520 and 610 nm. The W and V shapes from the measured skin spectral reflectance are clearly different from those from the spectral reflectance generated from the physical model. The V shape and W shape do not exist with spectral reflectances measured from artificial skin color. Second, it was found that the wavelengths where the left trough assumes for the W shape are different from the wavelength where the M cone peaks, and the wavelengths where the central peak of the W shape assumes are different from the wavelength where the L cone peaks, which are different from the results from literature. Third, it was found that the skin spectral reflectance varies with body positions and ethnic groups. The W shapes turn to be a “U” shape for the spectral reflectance measured at back of the hand and inner forearm positions. The spectral reflectance measured from Kurdish starts decreasing for each body positions for wavelength greater than 700 nm, which is clearly different from the spectral reflectance measured from the Chinese and Caucasian ethnic groups. The findings of this paper are valuable for improving the physical model for generating skin spectral reflectance, reconstructing skin spectral reflectance based on image, identifying human skin color from spectral imaging and rendering of human skin color in computer graphics. [ABSTRACT FROM AUTHOR]

Cuscuta chinensis have a significant regulatory effect on plant growth, but the response mechanism of functional traits to the parasitism of C. chinensis and the trade-off relationship between traits and hyperspectral characteristics are not clear. We investigated the functional trait response and hyperspectral characteristics of Euonymus japonicus, the most common urban hedge plant in China, to the parasitism of C. chinensis. The results showed that the parasitism of C. chinensis led to the difference of leaf functional traits: the leaf thickness, stomatal density, and leaf dry matter content were significantly increased, whereas the leaf area, leaf weight, specific leaf area, chlorophyll content index, and leaf tissue density were significantly decreased. Notably, the parasitism of C. chinensis changed the spatial distribution pattern of stomata and promoted the stomata to be evenly distributed. Furthermore, the spectral reflectance of leaves treated with the parasitism of C. chinensis tended to increase. The parasitism of C. chinensis led to the "blue shift" of hyperspectral reflectance of leaves. There was a significant correlation between spectral parameters and leaf functional traits, and leaf biomass accounted for 83% of the variation in reflectance of the water stress band. In general, the parasitism of C. chinensis determines the strategic way of plant utilization of resources and affects the change of plant strategy by affecting the difference of traits. Urban plants were more inclined to invest resources in nutrient storage capacity at the expense of resources investment in photosynthetic capacity and defense mechanism. The plant ecological strategy changed from resource acquisition to resource conservation. This finding comes up with a new strategy that urban tree species can modify the plasticity of functional traits for survival and growth under the interference of parasitic plants. [ABSTRACT FROM AUTHOR]

In order to realize the rapid and quantitative monitoring of soil organic carbon content in farmland, nine conventional pretreatment analysis methods were used to optimize the original spectral information and analyze the relationship between each pretreatment spectrum and wheat soil organic carbon content, and the continuous projection algorithm (SPA) was used to extract the spectral characteristics of soil organic carbon, and two types of soil organic carbon spectral monitoring models based on full spectrum and spectral characteristic bands were established. The results showed that the correlation between the pretreatment spectrum and soil organic carbon in wheat could be significantly improved compared with the original spectrum. In the meantime, the SPA method was used to extract and confirm the important information of soil organic carbon content in the spectral regions of 400-450 nm, 510-620 nm, 1010-1 060 nm and 2 000-2 400 nm. Comparing the performance of the two types of models, it can be seen that the continuous projection algorithm-multiple linear regression (SPA-MLR) model is better than the partial least squares regression (PLSR) model under the same pretreatment spectra, and the overall performance of the soil organic carbon content estimation model based on multivariate scattering correction (MSC) pretreatment spectra is the best (Rv2=0.726, RMSEv=0.109, RPDv=1.956), and it has practical application potential. This study confirms that spectral pretreatment can improve the correlation between spectral reflectance and soil organic carbon content in wheat to a certain extent, and affect the performance of the monitoring model, and the model construction method may have a more positive effect on the accuracy of model estimation. The results of this study can provide a theoretical basis and practical exploration for the detection of soil organic carbon content in farmland. [ABSTRACT FROM AUTHOR]

Quantifying the effect of maize tassel on canopy reflectance is essential for creating a tasseling progress monitoring index, aiding precision agriculture monitoring, and understanding vegetation canopy radiative transfer. Traditional field measurements often struggle to detect the subtle reflectance differences caused by tassels due to complex environmental factors and challenges in controlling variables. The three-dimensional (3D) radiative transfer model offers a reliable method to study this relationship by accurately simulating interactions between solar radiation and canopy structure. This study used the LESS (large-scale remote sensing data and image simulation framework) model to analyze the impact of maize tassels on visible and near-infrared reflectance in heterogeneous 3D scenes by modifying the structural and optical properties of canopy components. We also examined the anisotropic characteristics of tassel effects on canopy reflectance and explored the mechanisms behind these effects based on the quantified contributions of the optical properties of canopy components. The results showed that (1) the effect of tassels under different planting densities mainly manifests in the near-infrared band of the canopy spectrum, with a variation magnitude of ±0.04. In contrast, the impact of tassels on different leaf area index (LAI) shows a smaller response difference, with a magnitude of ±0.01. As tassels change from green to gray during growth, their effect on reducing canopy reflectance increases. (2) The effect of maize tassel on canopy reflectance varied with spectral bands and showed an obvious directional effect. In the red band at the same sun position, the difference in tassel effect caused by the observed zenith angle on canopy reflectance reaches 200%, while in the near-infrared band, the difference is as high as 400%. The hotspot effect of the canopy has a significant weakening effect on the shadow effect of the tassel. (3) The non-transmittance optical properties of maize tassels reduce canopy reflectance, while their high reflectance increases it. Thus, the dual effects of tassels create a game in canopy reflectance, with the final outcome mainly depending on the sensitivity of the canopy spectrum to transmittance. This study demonstrates the potential of using 3D radiative transfer models to quantify the effects of crop fine structure on canopy reflectance and provides some insights for optimizing crop structure and implementing precision agriculture management (such as selective breeding of crop optimal plant type). [ABSTRACT FROM AUTHOR]

Large-scale yield estimation in the field or plot during wheat grain filling can contribute to high-throughput plant phenotyping and precision agriculture. To overcome the challenges of poor yield estimation at a large scale and for multiple species, this study employed a combination of multispectral and RGB drones to capture images and generation of time-series data on vegetation indices and canopy structure information during the wheat grubbing period. Five machine learning methods, partial least squares, random forest, support vector regression machine, BP neural networks, and long and short-term memory networks were used. The yield estimation of wheat grain filling period data was executed using a long and short-term memory network based on the preferred machine learning model, with a particular focus on distinguishing different heat-tolerant genotypes of wheat. The results unveiled a declining trend in the spectral reflectance characteristics of vegetation indices as the filling period progressed. Among the time-series data of the wheat filling period, the long and short-term memory network exhibited the highest estimation effectiveness, surpassing the BP neural network, which displayed the weakest estimation performance, by an impressive improvement in R2 of 0.21. The three genotypes of wheat were categorized into heat-tolerant genotype, moderate heat-tolerant genotype, and heat-sensitive genotype. Subsequently, the long and short-term memory network, which exhibited the most accurate yield estimation effect, was selected for regression prediction. The results indicate that the yield estimation effect was notably better than that achieved without distinguishing genotypes. Among the wheat genotypes, the heat-sensitive genotype demonstrated the most accurate prediction with an R2 of 0.91 and RMSE% of 3.25%. Moreover, by fusing the vegetation index with canopy structure information, the yield prediction accuracy (R2) witnessed an overall enhancement of about 0.07 compared to using the vegetation index alone. This approach also displayed enhanced adaptability to spatial variation. In conclusion, this study successfully utilized a cost-effective UAV for data fusion, enabling the extraction of canopy parameters and the application of a long and short-term memory network for yield estimation in wheat with different heat-tolerant genotypes. These findings have significant implications for informed crop management decisions, including harvesting and contingency forecasting, particularly for vast wheat areas. [ABSTRACT FROM AUTHOR]

Advances in reconstructing the East Asian monsoon have provided important insights into the natural climate variability in Asia during the pre-instrumental period. However, there are still unresolved paleoclimate issues that necessitate the use of geological proxy data to further our understanding of past climate changes. This study focused on core B13, located in the muddy area of the North Yellow Sea (NYS), to investigate the evolutionary history over the past 1500 years and reconstruct the records of the East Asian summer monsoon (EASM) and Yellow Sea warm current (YSWC). The mean grain size of sediment ranged from 4.2 Φ to 5.6 Φ, with the sorting coefficient ranging from 1.9 to 2.2, indicating poor sorting. The C–M pattern showed a limited range of values, with the M values being between 33 and 83 μm and the C values being between 165 and 287 μm, suggesting uniform-suspension transport. The L* index ranged from 40.41 to 44.12, while the a* and b* indexes ranged from 0.55 to 1.78 and 2.86 to 5.94, respectively. A stable and relatively strong sedimentary environment is indicated through a comprehensive analysis of the C–M plot, triangular plot, the relationship between the mean grain size and sorting, and the changes in grain-size and color parameters. The sedimentary evolution in the muddy area of the NYS over the past 1500 years can be categorized into three distinct stages. In this study, proxies for the EASM and YSWC were extracted using the VPCA method from the sediment grain size and diffuse spectral reflectance (DSR) data, respectively. The reliability of these proxies has been confirmed through comparison with other validated proxies. The results indicated that the strength of the EASM and YSWC also exhibited three stages, corresponding to the Dark Ages Cold Period (DACP), Medieval Warm Period (MWP), and Little Ice Age (LIA), respectively. On a centennial scale, the correlation between the EASM and YSWC was predominantly negative. This research validates the reliability of the VPCA method for paleoclimate reconstruction, contributes important climate records in a special muddy area, and provides a new perspective on how to eliminate temporal errors in verifying the correlation between the two climate systems. [ABSTRACT FROM AUTHOR]

The study explores the potential of next-generation satellite hyperspectral imaging systems for screening and predicting surface-soil contamination and degradation by exploiting various spectral indices and signature-matching techniques at a heavily industrialized area in India. The soil moisture content, desertification and salinity status, clay or fine material content, heavy metal content, vegetation health status, and stress levels were assessed from continuum-removed spectral reflectance values. Results indicated the presence of water in two tailings ponds, high salinity, and desertification values in most of the tailings ponds and dump sites, clay boundary liner along four ponds, high heavy metal indices along three ponds and all dump sites, highly stressed vegetation near all tailings ponds and coal dump sites, and pollutants in nearby water channels. The results suggest a strategy for the initial identification of priority areas for ground-based investigations and an alternative rapid methodology to monitor large industrial hubs in India. [ABSTRACT FROM AUTHOR]

Accurate whiteness measurement is critical across various industries including textiles, paper, and detergents. With the advent of fluorescent whitening agents (FWAs), the assessment of whiteness has evolved from simply measuring reflectance to determining the spectral radiance factor (SRF) of materials. While multispectral imaging (MSI) can effectively measure object reflectance and colour, its accuracy is compromised by FWAs that significantly augment spectral reflectance. Thus, reliable whiteness measurement requires the determination of both reflectance and SRF. This article presents the design of a specialised light source equipped with an ultraviolet (UV) filter to provide adjustable illumination within an integrating sphere system. By tuning the position of the UV filter, multispectral images are captured of a fabric under varying UV exposures. These images are subsequently processed and combined to reconstruct full‐range visible spectrum information. This approach is shown to achieve high accuracy and spatial uniformity in quantifying the whiteness of textiles using MSI. [ABSTRACT FROM AUTHOR]

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Biological soil crusts (biocrusts) are widespread soil photosynthetic communities covering about 12% of Earth's land surface, and play crucial roles in terrestrial carbon (C) and nitrogen (N) cycles, yet scalable quantifications of biocrusts and their biogeochemical contributions are notably lacking. While remote sensing has enormous potential to assess, scale, and contextualize biocrusts and their functions, the applicability of hyperspectral data in predicting C‐ and N‐related biocrust traits remains largely unexplored. We address this issue by evaluating the potential of in situ hyperspectral data to predict C and N across a range of biocrust species and different environmental conditions. We found that in situ hyperspectral reflectance measurements can be used to predict biocrust tissue C/N ratios and N concentrations with relatively high accuracy but to a lesser extent for potential biocrust N2 fixation rates. Critical wavelength domains included the visible region of the spectrum from roughly 490–600 nm, which most effectively captured variations in biocrust tissue C, and the shortwave infrared region from 1,150 to 1,350 nm and 1,550–1,650 nm, which most effectively captured biocrust tissue N and N2 fixation potential. Finally, we provide evidence that multi‐ and hyperspectral missions with targeted band placement, such as the proposed 26‐band Landsat Next, could be effective in predicting biocrust traits. This work provides a critical step in understanding how to apply data from new and upcoming satellite missions to the monitoring of biocrusts. Plain Language Summary: Biological soil crusts (biocrusts) are communities of organisms such as cyanobacteria, fungi, and lichens existing on soil surfaces. Biocrusts play an important role in the carbon (C) and nitrogen (N) cycles of terrestrial ecosystems. The C and N status of biocrusts can be examined in various ways. However, the applicability of studying C and N status of biocrusts using spectral reflectance is poorly explored. We combined statistical models and spectral reflectance measured using a spectroradiometer within the wavelength range from 400 to 2,450 nm to predict variations in C and N status of samples from 14 representative biocrust species, and under both wet and dry conditions. We found that variations in biocrust tissue C/N ratios and N concentrations could be accurately predicted. We also found that the wavelength range of 490–600 nm, and 1,150–1,350 nm as well as around 1,600 nm are the most important wavelength domains to study C and N status of biocrusts. Finally, we provide evidence that upcoming satellite missions with proper band placement, such as the proposed 26‐band Landsat Next, could be effective in predicting biocrust traits. Results from this work shed light on applying data from upcoming satellite missions to the monitoring of biocrusts. Key Points: The applicability of hyperspectral data in predicting carbon‐ and nitrogen‐related biocrust functional traits is poorly understoodVisible and shortwave infrared region of the spectrum best predicted variations in carbon‐ and nitrogen‐related biocrust functional traitsMulti‐ and hyperspectral satellite missions with targeted band placement could be effective in predicting biocrust functional traits [ABSTRACT FROM AUTHOR]

Some paintings may have hidden depictions beneath the visible surface, which can provide valuable insights into the artist's creative process and the genesis of the artwork. Studies have shown that these covered paintings can be revealed through image-based techniques and integrated data processing. This study analyzes an oil painting by Beceri from the mid-16th century depicting the Holy Family, owned by the Uffizi Galleries. During the analysis of the materials, we discovered evidence of pictorial layers beneath the visible scene. To uncover the hidden figuration, we applied a multimodal approach that included microprofilometry, reflectance imaging spectroscopy, macro X-ray fluorescence, and optical coherence tomography. We analyzed the brushstrokes of the hidden painting, visualized the underdrawing, located the painted areas beneath the outermost painting, and quantified the thicknesses of the pictorial layers. The pigments used for the underpainting were identified through cross-analysis of X-ray fluorescence and spectral correlation maps. The underlying pictorial subject, Leda and the Swan, appears to be inspired by a long-lost and replicated work by Michelangelo. This information places Beceri and his production in a more defined context. [ABSTRACT FROM AUTHOR]

Light quality can be stated to be the identity of an artificial light source, and although the response of light quality may vary depending on the crop, the effect is clearly visible and can produce various results depending on the combination of an artificial light source. This study investigated the spectral reflectance, photosynthetic performance, and chlorophyll fluorescence of mini green romaine lettuce based on light quality. Most parameters related to spectral reflectance showed the best results under blue light, and photosynthetic performance was more effective with mixed light than with single-colored light, among which blue + red (BR)-LED was the most suitable. Red light was ineffective, showing mostly low results in parameters of spectral reflectance and photosynthetic performance. In the case of chlorophyll fluorescence, the light quality influenced photomorphogenesis, resulting in increased leaf length and width with R- and quantum dot (QD)-LED, which expanded the leaf area and allowed for more external light to be captured (ABS/RC and TRo/RC). However, the ratio of electronized energy (ETo/RC) was low, and the amount of energy dissipated as heat (DIo/RC) was high. Consequently, the degree of electron acceptor reduction and overall photosynthetic performance (PIABS and PItotal) were lower compared to other light qualities. Additionally, the contrasting results of QD-LED and BR-LED were attributed to the form of red light and the presence or absence of far-red light when comparing spectra. Principal component analysis also clearly distinguished light qualities for photosynthesis and growth. Growth was increased by red (R)- and QD-LED, while photosynthetic performance was increased by BR- and blue (B)-LED. [ABSTRACT FROM AUTHOR]

The fast and effective monitoring of agrochemical residues is essential for assuring food safety, since many agricultural products are sprayed with pesticides and commercialised without waiting for the pre-harvest interval. In this study, we investigated the use of spectral reflectance combined with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to evaluate the discrimination of fungicide-contaminated lettuces, considering three maximum residue limits (MRLs) [3.5, 5, and 7 mg carbon disulphide (CS2) kg−1]. The non-systemic Mancozeb fungicide (dithiocarbamate) was adopted in this research. Spectral reflectance (Vis/NIR) was measured by a hand-held spectrometer connected to a clip probe with an integrating sphere. The lettuce spectra were pre-treated (centring, standard normal variate, and first derivative) before data processing. Our findings suggest that PCA recognised inherent similarities in the fungicide-contaminated lettuce spectra, categorising them into two distinct groups. The PLS-DA models for all MRLs resulted in high accuracy levels, with correct discriminations ranging from 94.5 to 100% for the external validation dataset. Overall, our study demonstrates that spectroscopy combined with discriminating methods is a promising tool for non-destructive and fast discrimination of fungicide-contaminated lettuces. This methodology can be used in industrial food processing, enabling large-scale individual analysis and real-time decision making. [ABSTRACT FROM AUTHOR]

Accurate intraoperative assessment of organ perfusion is a pivotal determinant in preserving organ function e.g. during kidney surgery including partial nephrectomy or kidney transplantation. Hyperspectral imaging (HSI) has great potential to objectively describe and quantify this perfusion as opposed to conventional surrogate techniques such as ultrasound flowmeter, indocyanine green or the subjective eye of the surgeon. An established live porcine model under general anesthesia received median laparotomy and renal mobilization. Different scenarios that were measured using HSI were (1) complete, (2) gradual and (3) partial malperfusion. The differences in spectral reflectance as well as HSI oxygenation (StO2) between different perfusion states were compelling and as high as 56.9% with 70.3% (± 11.0%) for "physiological" vs. 13.4% (± 3.1%) for "venous congestion". A machine learning (ML) algorithm was able to distinguish between these perfusion states with a balanced prediction accuracy of 97.8%. Data from this porcine study including 1300 recordings across 57 individuals was compared to a human dataset of 104 recordings across 17 individuals suggesting clinical transferability. Therefore, HSI is a highly promising tool for intraoperative microvascular evaluation of perfusion states with great advantages over existing surrogate techniques. Clinical trials are required to prove patient benefit. [ABSTRACT FROM AUTHOR]

It is challenging to make nocturnal observations of the natural behaviour of flower‐visiting moths as they are highly sensitive to visible artificial light sources. One solution, implemented here, is to use specially‐adapted close‐focusing camera traps that rely on infrared wavelengths for detection and illumination of small insect subjects. We investigated the pollination system of Bonatea saundersiodes, a rare African terrestrial orchid that is phylogenetically nested in the large Habenaria clade. We used modified camera traps and direct observations to study the pollination system of B. saundersioides. We quantified floral traits, including morphology, nectar, spectral reflectance and scent chemistry. Flowers of this orchid species were found to be pollinated exclusively by settling noctuid moths. Moth visits to the orchid were recorded throughout the night, with peaks of activity after dusk and just before dawn. Pollinaria of the orchid are attached between the palps of visiting moths. Floral traits generally consistent with settling moth pollination include the relatively short floral spurs (ca. 20 mm) containing small amounts of nectar, white and green perianth segments, and the floral scent which is produced at night and is dominated by β‐ocimene and other terpenoid compounds. This study demonstrates that close‐focusing camera traps have tremendous potential to improve our understanding of plant–pollinator interactions which are not easy to study using traditional methods. [ABSTRACT FROM AUTHOR]

Laser therapy has been widely used to treat port-wine stains (PWS) and other cutaneous vascular lesions via selective photothermolysis. Animal models are a valuable tool for investigating thermal responses beneath the skin. However, in previous animal experiments, such as the dorsal skin chamber model, one side of the skin was removed, resulting in the loss of mechanical support for the target blood vessel. In this study, the optical clearing technique was applied to the dorsal skin, allowing direct observation of real thermal responses within the tissue without removing the covering skin. The target blood vessels were irradiated with a pulsed 1064 nm Nd: YAG laser. The corresponding thermal responses were recorded using a CCD camera. Additionally, variations in skin reflectance spectra were measured before and after laser irradiation. Due to the optical clearing and reflectance spectra measurement, vessel responses such as contraction, reperfusion, and full occlusion were correlated with specific variation patterns in reflectance spectral signals. [ABSTRACT FROM AUTHOR]

The structural integrity of myelin sheaths in the central nervous system (CNS) is crucial for the maintenance of its function. Electron microscopy (EM) is the gold standard for visualizing individual myelin sheaths. However, the tissue processing involved can induce artifacts such as shearing of myelin, which can be difficult to distinguish from true myelin abnormalities. Spectral confocal reflectance (SCoRe) microscopy is an imaging technique that leverages the differential refractive indices of compacted CNS myelin in comparison to surrounding parenchyma to detect individual compact myelin internodes with reflected light, positioning SCoRe as a possible complementary method to EM to assess myelin integrity. Whether SCoRe is sensitive enough to detect losses in myelin compaction when myelin quantity is otherwise unaffected has not yet been directly tested. Here, we assess the capacity of SCoRe to detect differences in myelin compaction in two mouse models that exhibit a loss of myelin compaction without demyelination: microglia-deficient mice (Csf1r-FIREΔ/Δ) and wild-type mice fed with the CSF1R inhibitor PLX5622. In addition, we compare the ability to detect compact myelin sheaths using SCoRe in fixedfrozen versus paraffin-embedded mouse tissue. Finally, we show that SCoRe can successfully detect individual sheaths in aged human paraffin-embedded samples of deep white matter regions. As such, we find SCoRe to be an attractive technique to investigate myelin integrity, with sufficient sensitivity to detect myelin ultrastructural abnormalities and the ability to perform equally well in tissue preserved using different methods. [ABSTRACT FROM AUTHOR]

The insensitivity and saturation of sensor signals significantly limit the application of Landsat data on estimating biomass in tropical forests with a high level of biomass. The previous studies with single-date Landsat data have lower accuracies in tropical forests compared to boreal and temperate forests. Landsat time-series data provide a promising opportunity to improve the accuracy by enhancing the relationship between Landsat spectral reflectance and forest aboveground biomass with disturbance and recovery dynamics. Compared to the single-date image, Landsat time-series data can capture abrupt spectral changes (e.g. harvesting and fire) and show the regrowth process in forested pixels. However, very limited studies take advantage of Landsat time-series data to estimate aboveground biomass in tropical forests. Recurrent neural networks (RNNs) are powerful deep learning techniques to capture time dependencies in sequence data. However, the application of RNNs in estimating forest biomass has not been explored yet. Therefore, we integrate the long short-term memory network (LSTM) and the fully connected neuron network (FNN) to establish an RNN-FNN model for estimating forest biomass with Landsat time-series imagery and airborne LiDAR data. We compared the proposed model with the commonly used Random Forest model and linear regression model which are implemented with single-date data. The results show that the RNN-FNN model can deal with Landsat time-series sequence data to enhance the relationship between Landsat spectral reflectance and forest aboveground biomass. The proposed model achieves the R2 of 0.63 and RMSE of 25.5 Mg/ha, which significantly outperformed the Random Forest model and linear regression model with Landsat single-date data. This study demonstrates the value of RNN and Landsat time-series imagery in estimating forest biomass for tropical forests. [ABSTRACT FROM AUTHOR]

Exploring the impact of different monitoring angles from unmanned aerial vehicle (UAV) on the monitoring accuracy of soil moisture content (SMC) is crucial for precision irrigation. To this end, experiment was conducted to monitor the SMC of winter wheat at different growth stages under different irrigation treatments in Yangling, Shaanxi, China. At a solar zenith angle of 45°, multispectral remote sensing data from a UAV were collected at thirteen different monitoring zenith angles. Simultaneously, the SMC in the wheat root zone was measured. From the UAV multispectral images, spectral reflectance was extracted for the construction of vegetation indices. Then the correlation between the vegetation indices and the measured SMC was analyzed. With the vegetation indices as input variables, SMC monitoring models were constructed using Extreme Learning Machine (ELM), Random Forest (RF), and Back Propagation Neural Network (BPNN). The study also examined the effect of specific angles (hotspot and dark spot angles) on the estimation accuracy of the SMC at the nadir angle. The results indicated that different monitoring angles significantly impact the SMC estimation accuracy. Band reflectance and vegetation indices exhibited significant peak values and angular effects at the monitoring zenith angle of 45°. The models achieved the optimal inversion accuracy at the hotspot angle (at a monitoring zenith angle of 45° in the solar principal plane), and the accuracy was ranked as follows: BPNN (R2 = 0.71; RMSE = 1.69) > ELM (R2 = 0.52; RMSE = 1.94) > RF (R2 = 0.48; RMSE = 2.10). By eliminating shadows at the nadir angle through the threshold of dark spot, inversion accuracy similar to the hotspot direction is achieved. This study provides a basis for appropriate selection of UAV flight angles for the monitoring of SMC in the root zone of winter wheat. [ABSTRACT FROM AUTHOR]

Total nitrogen (TN) is a critical nutrient for plant growth, and its monitoring in agricultural soil is vital for farm managers. Traditional methods of estimating soil TN levels involve laborious and costly chemical analyses, especially when applied to large areas with multiple sampling points. Remote sensing offers a promising alternative for identifying, tracking, and mapping soil TN levels at various scales, including the field, landscape, and regional levels. Spaceborne hyperspectral sensing has shown effectiveness in reflecting soil TN levels. This study evaluates the efficiency of spectral reflectance at visible near-infrared (VNIR) and shortwave near-infrared (SWIR) regions to identify the most informative hyperspectral bands responding to the TN content in agricultural soil. In this context, we used PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral imagery with ensemble learning modeling to identify N-specific absorption features. This ensemble consisted of three multivariate regression techniques, partial least square (PLSR), support vector regression (SVR), and Gaussian process regression (GPR) learners. The soil TN data (n = 803) were analyzed against a hyperspectral PRISMA imagery to perform spectral band selection. The 803 sampled data points were derived from open-access soil property and nutrient maps for Africa at a 30 m resolution over a bare agricultural field in southern Morocco. The ensemble learning strategy identified several bands in the SWIR in the regions of 900–1300 nm and 1900–2200 nm. The models achieved coefficient-of-determination values ranging from 0.63 to 0.73 and root-mean-square error values of 0.14 g/kg for PLSR, 0.11 g/kg for SVR, and 0.12 g/kg for GPR, which had been boosted to an R2 of 0.84, an RMSE of 0.08 g/kg, and an RPD of 2.53 by the ensemble, demonstrating the model's accuracy in predicting the soil TN content. These results underscore the potential for using spaceborne hyperspectral imagery for soil TN estimation, enabling the development of decision-support tools for variable-rate fertilization and advancing our understanding of soil spectral responses for improved soil management. [ABSTRACT FROM AUTHOR]

Chlorophyll fluorescence (ChlF) parameters offer valuable insights into quantifying energy transfer and allocation at the photosystem level. However, tracking their variation based on reflectance spectral information remains challenging for large-scale remote sensing applications and ecological modeling. Spectral preprocessing methods, such as fractional-order derivatives (FODs), have been demonstrated to have advantages in highlighting spectral features. In this study, we developed and assessed the ability of novel spectral indices derived from FOD spectra and other spectral transformations to retrieve the ChlF parameters of various species and leaf groups. The results obtained showed that the empirical spectral indices were of low reliability in estimating the ChlF parameters. In contrast, the indices developed from low-order FOD spectra demonstrated a significant improvement in estimation. Furthermore, the incorporation of species specificity enhanced the tracking of the non-photochemical quenching (NPQ) of sunlit leaves (R2 = 0.61, r = 0.79, RMSE = 0.15, MAE = 0.13), the fraction of PSII open centers (qL) of shaded leaves (R2 = 0.50, r = 0.71, RMSE = 0.09, MAE = 0.08), and the fluorescence quantum yield (ΦF) of shaded leaves (R2 = 0.71, r = 0.85, RMSE = 0.002, MAE = 0.001). Our study demonstrates the potential of FOD spectra in capturing variations in ChlF parameters. Nevertheless, given the complexity and sensitivity of ChlF parameters, it is prudent to exercise caution when utilizing spectral indices for tracking them. [ABSTRACT FROM AUTHOR]

Studying the influence of the host plant genotype on the spectral reflectance of crops infected by a pathogen is one of the key directions in the development of precision methods for monitoring the phytosanitary state of wheat agrocenoses. The purpose of this research was to study the influence of varietal factors and disease development on the spectral characteristics of winter wheat varieties of different susceptibility to diseases during the growing seasons of 2021, 2022 and 2023. The studied winter wheat crops were represented by three varieties differing in susceptibility to phytopathogens: Grom, Svarog and Bezostaya 100. Over three years of research, a clear and pronounced influence of the varietal factor on the spectral characteristics of winter wheat crops was observed, which in most cases manifested itself as an immunological reaction of specific varieties to the influence of pathogen development. The nature of the influence of the pathogenic background and the spectral characteristics of winter wheat crops were determined by the complex interaction of the development of individual diseases under the conditions of a particular year of research. A uniform and clear division of the spectral characteristics of winter wheat according to the intensity of the disease was recorded only at a level of pathogen development of more than 5%. Moreover, this gradation was most clearly manifested in the spectral channels of the near-infrared range and at a wavelength of 720 nm. [ABSTRACT FROM AUTHOR]

This paper quantifies the annual dynamics of the shortwave radiation reflected from bare arable land as a result of smoothing previously plowed land located in three different agricultural subregions of the European Union and associated countries. This estimate takes into account the annual variation of the bare arable land area, obtained from Sentinel 2 satellite imagery; the spatial variability of soil units within croplands, obtained from digital soil and land-cover maps; and the laboratory spectral reflectance characteristics of these units, obtained from soil samples stored in the LUCAS soil database. The properties of the soil units, which cover an area of at least 4% of each subregion, were characterized. The highest amounts of shortwave radiation reflected under clear-sky conditions from air-dried, bare arable land surfaces—approximately 850 PJ day−1 and 1.10 EJ day−1 for land shaped by a plow (Pd) and smoothing harrow (Hs), respectively—were found in the summer around 8 August in the western subregion. However, the lowest radiation occurred in the spring on 10 April at 340 PJ day−1 for Pd and 430 PJ day−1 for Hs in the central subregion. The largest and the smallest amounts of this radiation throughout the year—only as a result of smoothing, by Hs, land that was previously treated by Pd—was estimated at 42 EJ for the western and southern subregions and 19 EJ for the central subregion, respectively. [ABSTRACT FROM AUTHOR]

Optical satellite data products (e.g., Sentinel-2, PlanetScope, Landsat) require proper validation across diverse ecosystems. This has conventionally been achieved using airborne and more recently unmanned aerial vehicle (UAV) based hyperspectral sensors which constrain operations by both their cost and complexity of use. The MicaSense Altum is an accessible multispectral sensor that integrates a radiometric thermal camera with 5 bands (475 nm–840 nm). In this work we assess the spectral reflectance accuracy of a UAV-mounted MicaSense Altum at 25, 50, 75, and 100 m AGL flight altitudes using the manufacturer provided panel-based reflectance conversion technique for atmospheric correction at the Mer Bleue peatland supersite near Ottawa, Canada. Altum derived spectral reflectance was evaluated through comparison of measurements of six known nominal reflectance calibration panels to in situ spectroradiometer and hyperspectral UAV reflectance products. We found that the Altum sensor saturates in the 475 nm band viewing the 18% reflectance panel, and for all brighter panels for the 475, 560, and 668 nm bands. The Altum was assessed against pre-classified hummock-hollow-lawn microtopographic features using band level pair-wise comparisons and common vegetation indices to investigate the sensor's viability as a validation tool of PlanetScope Dove 8 band and Sentinel-2A satellite products. We conclude that the use of the Altum needs careful consideration, and its field deployment and reflectance output does not meet the necessary cal/val requirements in the peatland site. [ABSTRACT FROM AUTHOR]

Background: Breeding programs for nutrient-efficient tea plant varieties could be advanced by the combination of genotyping and phenotyping technologies. This study was aimed to search functional SNPs in key genes related to the nitrogen-assimilation in the collection of tea plant Camellia sinensis (L.) Kuntze. In addition, the objective of this study was to reveal efficient vegetation indices for phenotyping of nitrogen deficiency response in tea collection. Methods: The study was conducted on the tea plant collection of Camellia sinensis (L.) Kuntze of Western Caucasus grown without nitrogen fertilizers. Phenotypic data was collected by measuring the spectral reflectance of leaves in the 350–1100 nm range calculated as vegetation indices by the portable hyperspectral spectrometer Ci710s. Single nucleotide polymorphisms were identified in 30 key genes related to nitrogen assimilation and tea quality. For this, pooled amplicon sequencing, SNPs annotation and effect prediction with SnpEFF tool were used. Further, a linear regression model was applied to reveal associations between the functional SNPs and the efficient vegetation indices. Results: PCA and regression analysis revealed significant vegetation indices with high R2 values (more than 0.5) and the most reliable indices to select ND-tolerant genotypes were established: ZMI, CNDVI, RENDVI, VREI1, GM2, GM1, PRI, and Ctr2, VREI3, VREI2. The largest SNPs frequency was observed in several genes, namely F3'5'Hb, UFGTa, UFGTb, 4Cl, and AMT1.2. SNPs in NRT2.4, PIP, AlaDC, DFRa, and GS1.2 were inherent in ND-susceptible genotypes. Additionally, SNPs in AlaAT1, MYB4, and WRKY57, were led to alterations in protein structure and were observed in ND-susceptible tea genotypes. Associations were revealed between flavanol reflectance index (FRI) and SNPs in ASNb and PIP, that change the amino acids. In addition, two SNPs in 4Cl were associated with water band index (WBI). Conclusions: The results will be useful to identify tolerant and susceptible tea genotypes under nitrogen deficiency. Revealed missense SNPs and associations with vegetation indices improve our understanding of nitrogen effect on tea quality. The findings in our study would provide new insights into the genetic basis of tea quality variation under the N-deficiency and facilitate the identification of elite genes to enhance tea quality. [ABSTRACT FROM AUTHOR]

This paper presents the results of a mesocosm experiment for the evaluation of remote sensing chlorophyll-a (chl-a) concentration estimations in an anthropogenic water reservoir. The chl-a presence in the water causes changes in the water surface reflectance spectrum, especially in the green and red part, but many factors could affect the remote measurements of chl-a content. The in situ mesocosm method of the experiment was used for investigating the spectral reflectance of the inland water surface in a wide range of chl-a concentrations. Eight specially designed measurement boxes were placed into the water. In half of the boxes, the devices to support the development of the submerged water plant were installed. During the experiment, simultaneously, spectral data from the water surface were gathered and physical–chemical analyses of water were carried out. The obtained results confirm the usefulness of the mesocosm experiment for the remote sensing chl-a concentration algorithms being developed. The concentration of dissolved organic carbon was identified as a key factor that interfered with remote chl-a estimations in the analyzed reservoir. [ABSTRACT FROM AUTHOR]

Accurate and rapid estimation of soil organic matter (SOM) content is of great significance for advancing precision agriculture. Compared with traditional chemical methods, the hyperspectral estimation is superior in rapidly estimating SOM content. Soil grain size affects soil spectral reflectance, thereby affecting the accuracy of hyperspectral estimation. However, the appropriate soil grain size for the hyperspectral analysis is nearly unknown. This study propose a best hyperspectral estimation method for determining SOM content of farmland soil in the Ibinur Lake Irrigation Area (ILIA) of the northwest arid zones of China. The original spectral reflectance of the 20-mesh (0.85 mm) and 60-mesh (0.25 mm) sieved soil were obtained, and the feature wavebands were selected using five types of spectral transformations. Then, hyperspectral estimation models were constructed based on the partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) models. Results show that the SOM content had relatively higher correlation coefficient with spectral reflectance of the 0.85 mm sieved soil than that of the 0.25 mm sieved soil. The transformation of original spectral reflectance of soil effectively enhanced the spectral characteristics related to SOM content. Soil grain size obviously affected spectral reflectance and the accuracy of hyperspectral estimation models. The overall stability and estimation accuracy of RF model was significantly higher compared with the PLSR, SVM, and XGBoost. Finally, the RF model combined with the root mean first-order differentiation (RMSFD) of spectral reflectance of the 0.85 mm sieved soil (R2 = 0.82, RMSE = 2.37, RPD = 2.27) was identified as the best method for estimating SOM content of farmland soil in the ILIA. [ABSTRACT FROM AUTHOR]

This paper explores the intersection of colorimetry and biomimetics in textile design, focusing on mimicking natural plant colors in dyed textiles via instrumental colorant formulation. The experimental work was conducted with two polyester substrates dyed with disperse dyes using the exhaustion process. Textiles dyed with different dye colors and concentrations were measured in a spectrophotometer and a database was created in Datacolor Match Textile software version 2.4.1 (0) with the samples' colorimetric properties. Colorant recipe formulation encompassed the definition and measurement of the pattern colors (along four defined natural plants), the selection of the colorants, and the software calculation of the recipes. After textile dyeing with the lowest expected CIELAB color difference (ΔE*) value recipe for each pattern color, a comparative analysis was conducted by spectral reflectance and visual assessment. Scanning electron microscopy and white light interferometry were also used to characterize the surface of the natural elements. Samples dyed with the formulated recipe attained good chromatic similarity with the respective natural plants' colors, and the majority of the samples presented ΔE* between 1.5 and 4.0. Additionally, recipe optimization can also be conducted based on the colorimetric evaluation. This research contributes a design framework for biomimicking colors in textile design, establishing a systematic method based on colorimetry and color theory that enables the reproduction of nature's color palette through the effective use of colorants. [ABSTRACT FROM AUTHOR]

Purpose: Accurate assessment of soil moisture content (SMC) is crucial for applications in climate science, hydrology, ecology, and agriculture. However, conventional SMC characterization and measurement are expensive, time-consuming, and have negative effects on soil. Recently, the application of multispectral technology provides a new idea for SMC accurate detection. The objective of this study was to develop and compare regression and machine learning algorithms to estimate SMC from multispectral images. Materials and methods: A multispectral sensor was used to collect spectral images of 125 soil samples from five distinct soil textures in Shanxi province at varying degrees of soil moisture, ranging from arid to fully saturated. A set of seven spectral parameters was derived from images, and predictive relationships were developed against laboratory-measured SMC. A linear regression (LR) model and a backpropagation neural network model based on genetic algorithm optimization (GA-BP) were compared in this study to predict SMC. Results and discussion: The results showed that (1) the spectral reflectance and SMC exhibit a clear negative correlation, and the lower the SMC, the larger the spectral reflectance is. (2) The GA-BP neural network model exhibits higher prediction accuracy and performance (R2 = 0.978 ~ 0.990, RMSE = 0.366 ~ 0.799%, MAE = 0.360 ~ 0.890%). (3) The GA-BP model exhibits the excellent inversion precision for the fine sand soil (R2 = 0.990, RMSE = 0.518%, MAE = 0.360%). Conclusions: This study introduces an effective methodology for accurate estimation of SMC using multispectral remote sensing technology. It further underscores the significant effectiveness of employing backpropagation neural networks and genetic algorithms in SMC prediction, providing a rapid, precise, non-intrusive, and practical approach towards precision agriculture. [ABSTRACT FROM AUTHOR]

Soil acidity (pH <5.5) limits agricultural production due to aluminum (Al) toxicity. The primary target of Al toxicity is the plant root. However, symptoms can be observed on the shoots. This study aims to determine the potential use of chlorophyll fluorescence imaging, multispectral imaging, and 3D multispectral scanning technology to quantify the effects of Al toxicity on corn. Corn seedlings were grown for 13 days in nutrient solutions (pH 4.0) with four Al treatments: 50, 100, 200, and 400 μM and a control (0 μM AlCl3 L−1). During the experiment, four measurements were performed: four (MT1), six (MT2), 11 (MT3), and 13 (MT4) days after the application of Al treatments. The most sensitive traits affected by Al toxicity were the reduction of plant growth and increased reflectance in the visible wavelength (affected at MT1). The reflectance of red wavelengths increased more significantly compared to near‐infrared and green wavelengths, leading to a decrease in the normalized difference vegetation index and the Green Leaf Index. The most sensitive chlorophyll fluorescence traits, effective quantum yield of PSII, and photochemical quenching coefficient were affected after prolonged Al exposure (MT3). This study demonstrates the usability of selected phenotypic traits in remote sensing studies to map Al‐toxic soils and in high‐throughput phenotyping studies to screen Al‐tolerant genotypes. [ABSTRACT FROM AUTHOR]

The accurate identification of different restoration stages of degraded alpine meadow patches is essential to effectively curb the deterioration trend of 'Heitutan' (areas of severely degraded alpine meadows in western China). In this study, hyperspectral imaging (HSI) and machine learning techniques were used to develop a method for accurately distinguishing the different restoration stages of alpine meadow patches. First, hyperspectral images representing the four restoration stages of degraded alpine meadow patches were collected, and spectral reflectance, vegetation indexes (VIs), color features (CFs), and texture features (TFs) were extracted. Secondly, valid features were selected by competitive adaptive reweighted sampling (CARS), ReliefF, recursive feature elimination (RFE), and F-test algorithms. Finally, four machine learning models, including the support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and extreme gradient boosting (XGBoost), were constructed. The results demonstrated that the SVM model based on the optimal wavelengths (OWs) and prominent VIs achieved the best value of accuracy (0.9320), precision (0.9369), recall (0.9308), and F1 score (0.9299). In addition, the models that combine multiple sets of preferred features showed a significant performance improvement over the models that relied only on a single set of preferred features. Overall, the method combined with HSI and machine learning technology showed excellent reliability and effectiveness in identifying the restoration stages of meadow patches, and provided an effective reference for the formulation of grassland degradation management measures. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central European Agriculture is the property of Journal of Central European Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central European Agriculture is the property of Journal of Central European Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We report on the detection of three icy cavities on the nucleus of comet 67P/Churyumov–Gerasimenko. They were identified on high-resolution anaglyphs built from images acquired by the Optical, Spectroscopic, and Infrared Remote Imaging System instrument aboard the Rosetta spacecraft on 2016 April 9 and 10. Visually, they appear as bright patches of typically 15 to 30 m across whose large reflectances and spectral slopes in the visible substantiate the presence of sub-surface water ice. Using a new high-resolution photogrammetric shape model, we determined the three-dimensional shape of these cavities whose depth ranges from 20 to 47 m. Spectral slopes were interpreted with models combining water ice and refractory dark material and the water ice abundances in the cavities were found to amount to a few per cent. The determination of the lifetime of the icy cavities was strongly biased by the availability of appropriate and favourable observations, but we found evidence of values of up to 2 yr. The icy cavities were found to be connected to jets well documented in past studies. A thermal model allowed us to track their solar insolation over a large part of the orbit of the comet and a transitory bright jet on 2015 July 18 was unambiguously linked to the brief illumination of the icy bottom of one of the cavities. These cavities are likely to be the first potential subsurface access points detected on a cometary nucleus and their lifetimes suggest that they reveal pristine sub-surface icy layers or pockets rather than recently recondensed water vapour. [ABSTRACT FROM AUTHOR]

Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample distance (GSD)) to regional scales (>250 m GSD). Understanding the spatial and spectral dependencies of these models is imperative to result interpretation, scaling, and deploying models. We leveraged high spatial resolution hyperspectral data collected with an unmanned aerial system mounted sensor (272 spectral bands from 0.4–1 μm at 0.063 m GSD) to estimate silage yield. We subjected our imagery to three band selection algorithms to quantitatively assess spectral reflectance features applicability to yield estimation. We then derived 11 spectral configurations, which were spatially resampled to multiple GSDs, and applied to a support vector regression (SVR) yield estimation model. Results indicate that accuracy degrades above 4 m GSD across all configurations, and a seven-band multispectral sensor which samples the red edge and multiple near-infrared bands resulted in higher accuracy in 90% of regression trials. These results bode well for our quest toward a definitive sensor definition for global corn yield modeling, with only temporal dependencies requiring additional investigation. [ABSTRACT FROM AUTHOR]

Background: Precise evaluation of fungal conidia production may facilitate studies on resistance mechanisms and plant breeding for disease resistance. In this study, hyperspectral imaging (HSI) was used to quantify the sporulation of Magnaporthe oryzae on the leaves of rice cultivars grown under controlled conditions. Three rice genotypes (CO 39, Nipponbare, IR64) differing in susceptibility to blast were inoculated with M. oryzae isolates Guy 11 and Li1497. Spectral information (450–850 nm, 140 wavebands) of typical leaf blast symptoms was recorded before and after induction of sporulation of the pathogen. Results: M. oryzae produced more conidia on the highly susceptible genotype than on the moderately susceptible genotype, whereas the resistant genotype resulted in no sporulation. Changes in reflectance spectra recorded before and after induction of sporulation were significantly higher in genotype CO 39 than in Nipponbare. The spectral angle mapper algorithm for supervised classification allowed for the classification of blast symptom subareas and the quantification of lesion areas with M. oryzae sporulation. The correlation between the area under the difference spectrum (viz. spectral difference without and with sporulation) and the number of conidia per lesion and the number of conidia per lesion area was positive and count-based differences in rice - M. oryzae interaction could be reproduced in the spectral data. Conclusions: HSI provided a precise and objective method of assessing M. oryzae conidia production on infected rice plants, revealing differences that could not be detected visually. [ABSTRACT FROM AUTHOR]

The objective of this paper is to assess soil surface properties in northern Jordan using optical remote sensing and field sampling. Soil spectral reflectance has been measured in the range of 350 and 2500 nm, and the soil chemical properties such as iron oxides, and organic and inorganic carbon were analyzed by using an infrared cell in a high-frequency induction oven. In the same manner, the traditional soil profile description, routine samples analysis, and soil classification were used to classify the same soil according to the USDA classification system. The distribution pattern of the soil reflectance behavior in relation to its soil organic carbon forms different soil groups in the feature space which are, iron affecting organic carbon affecting, and inorganic carbon affecting soil. These groups reflect the effect of different soil mineralogy, conditions, and land use. This approach facilitates the quantification of soil quality in high performance and proves that both organic and inorganic carbons are important indicators of the characteristics of soil conditions using remote sensing. Investigation of the relationship between the reflectance properties of soil groups using the USDA soil classification system and soil profile description confirms the presence of a strong relationship between the two systems. The first trend is using the soil’s organic carbon content to correspond to the clay content and cation exchange capacity, and the second trend is using inorganic carbon content to correspond to the silt and carbonate content under different climatic conditions. The results show that soil spectroscopy (350-2500 nm) is a promising technique for preliminary soil description and can classify soils according to soil chemical properties especially soil organic carbon only instead of using traditional soil profile classification and analysis. [ABSTRACT FROM AUTHOR]

A new supramolecular cocrystal of bis(3-hydroxy-1-methylpyridin-1-ium iodide)-18-crown-6 (I) has been synthesized successfully from 3-hydroxypyridine, methyl iodide and 18-crown-6 (18C6). The cocrystal was characterized by FT-IR, UV-DRS and PL spectroscopy, single crystal and powder X-ray diffraction (XRD) analysis, SEM and EDS analysis, and thermogravimetric analysis. The bulk phase purity of the compound is confirmed by powder XRD analysis, whereas single-crystal XRD confirms that it packs in a monoclinic system with centrosymmetric space group (P21/c). Single crystal XRD shows that the asymmetric unit is C6H12O3, [C6H8NO]+[I]¯. The presence of C–H⋯O, C–H⋯I and O–H⋯I intermolecular contacts helps to achieve crystal cohesion. The direct band gap energy has been estimated to be 3.21 eV using diffuse reflectance spectral measurements. Thermogravimetric analysis was employed to determine the compound's thermal behaviour concerning temperature. EDS and SEM investigations were used to examine the elemental composition and surface morphology of the compound. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is made up of decomposing biotic material in various stages, as well as compounds generated by plant roots and soil organisms--it helps the soil's biophysical functions. Laboratory spectroscopy, for example, provides a novel technique to analyse SOM content because it is both cost and timeefficient. Because of its composition and biophysical features, SOM has a distinct spectral reflectance; this relationship has been effectively exploited to estimate and predict SOM. The purpose of this study is to investigate the link between SOM concentration and laboratory-based soil spectral reflectance in the Emakhosaneni subplace of uThukela District, KwaZulu-Natal, South Africa. We collected 13 random soil samples from each of the four major land use types (agricultural, residential, eroded land, and rangeland), totalling 52 samples. To assess the SOM content, we oven-dried the materials overnight (105 ℃), crushed them, sieved them (1 mm), and analysed them using the Walkley-Black method. The spectral reflectance of the soil samples was then acquired using the Analytical Spectral Device (ASD) FieldSpec3 and pre-processed for noise reduction before analysis. We found that the area's average SOM content was 2.19%, with agricultural land having the greatest average SOM content (2.98%), followed by rangeland (2.46%), residential (1.77%), and eroded land area (1.56%). However, the average reflectance of the spectra was higher on eroded areas and decreased to a minimum on agricultural areas. This is attributed to the relationship between soil colour and SOM. Correlation analysis demonstrated a moderately negative relationship between SOM concentration and spectral reflectance over the whole spectral range covered (400-2400 nm). Our partial least squares regression analysis revealed that pre-processed spectra data models (FDT; R2 = 0.46 and SG; R2 = 0.34) performed better than raw data models, in both calibration and validation data sets, respectively. Despite the influence of noise on raw data model performance, they modestly predicted SOM content, particularly on the validation data set. However, the developed model cannot be used due to the very low coefficient of determination. Our results highlight the importance of spectroscopy to assess SOM content, and further research can be carried out on a larger scale. [ABSTRACT FROM AUTHOR]