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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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 glass phase in volcanic rocks presents a challenge to obtaining compositional data from visible and short‐wave‐infrared (VSWIR) and mid‐infrared (MIR) spectral data of remote surfaces due to its amorphous structure and variable composition. Nonetheless, glass is a common phase in volcanic materials because it forms via the rapid quench of magma and can constitute up to the entirety of a volcanic deposit. Use of partial least squares regression (PLS) to predict glass contents creates models that are insensitive to viewing geometry and sample conditions such as grain size and spectrally inactive compositional variables, enhancing the ability to detect glasses with remote sensing. PLS models are used here to predict crystallinity and oxide composition of samples from VSWIR and MIR spectral data using training spectra from natural volcanic rocks and geologically relevant synthetic samples. Three spectral resolutions of VSWIR and MIR spectra (1, 10, and 100 nm/band, and 1.9, 19, and 190 cm−1/band, respectively) were tested to assess the effects of collection configuration on different spectrometers. PLS models trained on 1 nm and 1.9 cm−1 data sets have the lowest uncertainties of glass modal abundance for VSWIR and MIR, respectively. MIR models predicting sample wt. % SiO2 and FeO, and VSWIR models of wt. % FeO provide accurate estimates (e.g., RMSE‐P of 3.4 wt. % FeO) at all spectral resolutions. Results are based on training data sets skewed to mafic compositions, which affects model accuracies. Plain Language Summary: Glass is difficult to identify and characterize from remote sensing observations of volcanic rocks. Differences in the number of spectral bands between laboratory and currently orbiting instruments further complicate interpretation of remote observations because glass features are subtle. A machine learning regression technique (partial least squares, or PLS) is used to predict the amount of glass and chemical composition using reflectance and emission spectral data of natural volcanic and synthetic glass samples. Results from three spectral resolutions were compared and affirm that spectral data can quantify phase abundance and composition in natural glassy samples such as volcanic materials. Key Points: PLS models can predict the modal glass abundance of volcanic samples with accuracies of 12%–15% from VSWIR and MIR spectraPLS models can predict bulk sample oxide abundances with accuracies of 1.5–6.9 and 0.9–4.2 wt. % from VSWIR and MIR spectra, respectivelyPLS model accuracy is minimally impacted by the number of available spectral bands [ABSTRACT FROM AUTHOR]

The exposure to irradiation from high‐energy particles alters the reflectance properties of asteroid surfaces and is referred to as space weathering. This process leads to an increase in spectral slope in visible and near‐infrared wavelengths. However, changes in the regolith particle size, which can vary dramatically among the asteroid population, are known to influence the spectral properties of meteorites and asteroids. In this context, we investigate the changes in spectral slope and absorption band depths of fresh and irradiated ordinary chondrite meteorites to quantitatively compare the effects of space weathering and grain size variations. To do so, we develop and employ the Spectral Analysis for Asteroid Reflectance Investigation routine that calculates the band parameters of reflectance spectra. We then formulate a parameter called the Space Weathering Index (SWI) that is designed to encapsulate spectral changes due to space weathering. We find that the SWI is strongly dependent on the spectral slope which complicates the interpretation of asteroid spectra in the context of grain size variations and space weathering. We also show that a second parameter, the Band Depth Index, is indicative of petrologic type. Finally, we use a linear discriminant analysis to classify asteroid reflectance spectra into H, L, LL, and unequilibrated ordinary chondrites. [ABSTRACT FROM AUTHOR]

Antarctic moss beds are sensitive to climatic conditions, and both their survival and community composition are particularly influenced by the availability of liquid water over summer. As Antarctic regions increasingly face climate pressures (e.g., changing hydrology and heat waves), advancing capabilities to efficiently and non‐destructively monitor water content in moss communities becomes a key research priority. Because of the complexity induced by multiple micro‐climatic drivers and its fragility, tracking the evolution and responses of moss bed moisture requires monitoring methods that are non‐intrusive, efficient, and spatially significant, such as the use of unoccupied aerial systems (UAS). In this study, we combine a multi‐species drying laboratory experiment with short‐wave infrared (SWIR) spectroscopy analyses to first develop a Random Forest regression Model (RFM) capable of predicting Antarctic moss turf water content (~5% error). The RFM was then applied to UAS‐borne SWIR imaging data (900–1700 nm, <16 nm spectral resolution) of the moss beds at high spatial resolution (2 cm) across three sites in the vicinity of Casey Station, Antarctica. The sites differed in terrain, snow cover, and moisture availability to evaluate method capabilities under different conditions. Optimum RFM parameters and input variables (spectral indices and reflectance spectra) were determined. Maps of moss moisture were validated via acquiring moss spectra and water content (using sponges inserted into the moss turf) collected in situ, for which an exponential correlation (R2 = 0.72) was reported. RFM further allowed investigation of the influential spectral variables to model water content in moss and associated spectral water absorption features. We demonstrated that UAS‐borne SWIR imaging is a promising new tool to map and quantify water content in Antarctic moss beds. Hyperspectral mapping facilitates the exploration of the spatial variability of moss health and enables the creation of a baseline against which changes in these moss communities can be measured. [ABSTRACT FROM AUTHOR]

Summary: Turgor loss point (TLP) is an important proxy for plant drought tolerance, species habitat suitability, and drought‐induced plant mortality risk. Thus, TLP serves as a critical tool for evaluating climate change impacts on plants, making it imperative to develop high‐throughput and in situ methods to measure TLP.We developed hyperspectral pressure–volume curves (PV curves) to estimate TLP using leaf spectral reflectance. We used partial least square regression models to estimate water potential (Ψ) and relative water content (RWC) for two species, Frangula caroliniana and Magnolia grandiflora. RWC and Ψ's model for each species had R2 ≥ 0.7 and %RMSE = 7–10. We constructed PV curves with model estimates and compared the accuracy of directly measured and spectra‐predicted TLP.Our findings indicate that leaf spectral measurements are an alternative method for estimating TLP. F. caroliniana TLP's values were −1.62 ± 0.15 (means ± SD) and −1.62 ± 0.34 MPa for observed and reflectance predicted, respectively (P > 0.05), while M. grandiflora were −1.78 ± 0.34 and −1.66 ± 0.41 MPa (P > 0.05).The estimation of TLP through leaf reflectance‐based PV curves opens a broad range of possibilities for future research aimed at understanding and monitoring plant water relations on a large scale with spectral ecophysiology. [ABSTRACT FROM AUTHOR]

Multiple raptors show juvenile plumage that is substantially different from that of their parents. Here, we test the prediction that the colouration of the juvenile northern goshawk Accipiter gentilis resembling the colouration of the common buzzard Buteo buteo acts as a form of aggressive mimicry. The goshawk specialises in hunting larger birds and mammals up to the size of geese or hares, while the buzzard preys mostly on small rodents. Larger birds may thus consider juvenile goshawks as less dangerous raptors, and the juvenile goshawk may thus gain an advantage when hunting. We used the Eurasian magpie Pica pica, a common prey of the goshawk, to test this prediction. We compared the behavioural responses of magpie parents defending their freshly fledged young towards mounts of an adult goshawk, juvenile goshawk, and buzzard. To be able to assess whether this behaviour differs from responses to a nest predator and a harmless bird we also presented a common raven Corvus corax and common pheasant Phasianus colchicus as baseline stimuli. Both juvenile and adult goshawks elicited antipredatory behaviour, but magpies took more risks facing juvenile goshawks. Additionally, the intensity of antipredatory behaviour towards the juvenile goshawk was also higher than towards the buzzard. We thus conclude that magpies do distinguish between juvenile and adult goshawks, as well as they do distinguish juvenile goshawks from buzzards. They are able to assess the threat particular raptors represent and respond accordingly. Analysis of spectral reflectance of stuffed specimens of these three raptors suggests there are differences, which can be used for the appropriate recognition. In conclusion, we cannot confirm the hypothesis of aggressive mimicry in juvenile goshawks that interact with magpies. [ABSTRACT FROM AUTHOR]

Premise: Spectroscopy is a powerful remote sensing tool for monitoring plant biodiversity over broad geographic areas. Increasing evidence suggests that foliar spectral reflectance can be used to identify trees at the species level. However, most studies have focused on only a limited number of species at a time, and few studies have explored the underlying phylogenetic structure of leaf spectra. Accurate species identifications are important for reliable estimations of biodiversity from spectral data. Methods: Using over 3500 leaf‐level spectral measurements, we evaluated whether foliar reflectance spectra (400–2400 nm) can accurately differentiate most tree species from a regional species pool in eastern North America. We explored relationships between spectral, phylogenetic, and leaf functional trait variation as well as their influence on species classification using a hurdle regression model. Results: Spectral reflectance accurately differentiated tree species (κ = 0.736, ±0.005). Foliar spectra showed strong phylogenetic signal, and classification errors from foliar spectra, although present at higher taxonomic levels, were found predominantly between closely related species, often of the same genus. In addition, we find functional and phylogenetic distance broadly control the occurrence and frequency of spectral classification mistakes among species. Conclusions: Our results further support the link between leaf spectral diversity, taxonomic hierarchy, and phylogenetic and functional diversity, and highlight the potential of spectroscopy to remotely sense plant biodiversity and vegetation response to global change. [ABSTRACT FROM AUTHOR]

Diffuse reflectance spectroscopy is emerging as a reliable soil testing approach. However, few studies reported on the applicability of DRS for estimating the overall soil quality index (SQI) in smallholder farms. Specifically, the DRS approach has not been evaluated in cocoa production systems with multi‐year soil data. We evaluated the DRS approach in typical cocoa production systems from Papua New Guinea (PNG) by collecting soil samples before and after implementing soil test‐based nutrient recommendations. Specifically, 379 soil samples were collected in 2017 from cocoa farms in East New Britain, New Ireland, and Bouganville provinces of PNG. A second soil testing campaign was undertaken in 2019 to analyse 432 soil samples from the same farms to test the consistency of the DRS approach for SQI assessment. In all these samples, 15 soil parameters were measured using the conventional wet chemistry approach. Reflectance spectra in each sample were also measured over the visible to near‐infrared (wavelength: 350–2500 nm). A minimum dataset approach was followed to estimate SQIs from laboratory‐measured soil properties. With available N, clay content, available P, and available Zn adjudged as significant soil quality parameters for the cocoa plantations of PNG, estimated mean SQI values ranged from 0.30 in the cocoa farm from Bouganville province (BOKA‐PAN) in 2017 to 0.46 for the New Ireland province (LAU‐PAN) soil samples collected during 2019. Overall, SQI values increased from about 13%–21% following the nutrient applications. Based on the validation of two commonly‐used chemometric models (PLSRFS: partial‐least‐squares regression with feature selection, SVR: support vector regression), the SQI values were estimated with the coefficient of determination (R2) values as high as.94. Our results showed that a DRS model calibrated in soil samples collected in a specific year can be used for estimating SQI values in soil samples collected in different years. These results suggest that the principal component analysis (PCA) based SQI may be estimated from the DRS data with good accuracy and SQI values may potentially capture the management effects in smallholder farms. [ABSTRACT FROM AUTHOR]

In order to realize the evaluation of salted duck eggs quality, this study constructed salts‐sensitive colorimetric sensor array (CSA) based on silver nanoprisms (AgNPRs) etching principle and utilized hyperspectral imaging (HSI) system to capture the reflectance spectral of CSA and section of salted duck eggs. To minimize the noise in spectral signals, three preprocessing methods including Savitzky–Golay Smoothing (SG), standard normal variate (SNV) transformation, and multiplicative scatter correction were used. The competitive adaptive reweighted sampling and uninformative variables elimination were used to select the optimal wavelengths, and then partial least squares regression was used to predict moisture, lipid, and salt content (SC). Results demonstrated coefficients of determination of 0.8200, 0.8969, 0.8709, and 0.9239 for protein moisture, yolk moisture, yolk lipid, and protein SC. Visualization distribution maps successfully display the distribution of moisture and lipid content during salting periods. The results indicated that the CSA based on AgNPRs etching principle can be an effective tool in quantitative measurement of salt and HSI combining CSA can be applied to evaluate the quality of salted duck eggs comprehensively. The study offers a rapid detection method for the production and processing of salted duck eggs in the industry. Practical application: A novel salts‐sensitive CSA was constructed to evaluate quality of salted duck eggs based on AgNPRs etching. This study designed and developed a portable sample collection device using 3D printing technology to acquire the information of samples and CSA at the same time. This study combined with HSI system and colorimetric sensor technology to evaluate the quality of salted duck eggs during the salting process. [ABSTRACT FROM AUTHOR]

Machine learning (ML) methods have gained widespread attention in optimizing nanostructures for target transport properties and discovering unexpected physics. Here, an ML method is developed to discover and experimentally confirm binary CeO2‐MgO aperiodic multilayers (AMLs) for thermal barrier coatings with significantly enhanced reflectance. The effect of varying AML design parameters like total thickness, average period, and randomness in layer thicknesses, on the spectral and total reflectance is demonstrated. Introducing aperiodicity in layer thicknesses is shown to lead to a broadband increase in spectral reflectance due to photon localization. Since the number of possible AML structures increases exponentially with total thickness, a Genetic Algorithm optimizer is developed to efficiently discover AMLs with enhanced reflectance, for total thicknesses of 5–50 µm. Surprisingly, all the optimized structures show an odd number of layers with a CeO2 layer at both ends, deviating from the traditional way of designing binary superlattices with paired layers. The optimized AML and a reference periodic superlattice of 5 µm thickness are fabricated by Pulsed Laser Deposition and characterized by optical reflectance measurements. The fabricated AML, despite considerable fabrication uncertainty, still enhances the reflectance to 48% from 40% of the reference superlattice, validating the effectiveness of our ML‐based optimization process. [ABSTRACT FROM AUTHOR]

The spectral reflectance of a colour is its most essential characteristic, and it does not change with changes of equipment or light source. Therefore, the reconstruction of spectral reflectance from parameters such as colour tristimulus value and RGB has attracted the attention of researchers. To avoid the occurrence of metamerism in the colour reproduction process and to achieve the purpose of colour spectral reproduction, the spectral reflectance and the optimal parameters of the reconstruction using the weighted fitting method were discussed experimentally with the colour data measured from the Sino Colour Book as the research object. The results show that the best results are obtained when the weighted fitting parameters are weighted by the third power and eight colour samples are involved in the fitting. The reconstructed spectral reflectance obtained by the weighted fitting method has little variation and high overlap with the actual spectral reflectance obtained at each hue and lightness, thus meets the requirements of practical applications. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing has the potential to map numerous attributes of the Earth's surface, including spatial patterns of biological diversity. Grasslands are one of the largest biomes on Earth. Accurate mapping of grassland biodiversity relies on spectral discrimination of endmembers of species or plant functional types. We focused on spectral separation of grass lineages that dominate global grassy biomes: Andropogoneae (C4), Chloridoideae (C4), and Pooideae (C3). We examined leaf reflectance spectra (350–2,500 nm) from 43 grass species representing these grass lineages from four representative grassland sites in the Great Plains region of North America. We assessed the utility of leaf reflectance data for classification of grass species into three major lineages and by collection site. Classifications had very high accuracy (94%) that were robust to site differences in species and environment. We also show an information loss using multispectral sensors, that is, classification accuracy of grass lineages using spectral bands provided by current multispectral satellites is much lower (accuracy of 85.2% and 61.3% using Sentinel 2 and Landsat 8 bands, respectively). Our results suggest that hyperspectral data have an exciting potential for mapping grass functional types as informed by phylogeny. Leaf‐level hyperspectral separability of grass lineages is consistent with the potential increase in biodiversity and functional information content from the next generation of satellite‐based spectrometers. Plain Language Summary: Understanding and identifying changes in plant diversity along broad environmental gradients requires scalable and reliable data. Spectroscopy has been shown to provide data across scales with the ability to measure plant reflectance at various extents (e.g., leaf, plot, and landscapes) with high spectral resolution and broad coverage of the electromagnetic spectrum (350–2,500 nm). In grasses, evolutionary lineage captures major axes in plant biodiversity and functional variation. We show that identifying grass evolutionary lineages from spectroscopy is possible based on common characteristics in their leaf‐level spectra. We classified 43 grass species from four sites in North America into their respective evolutionary lineages with very high accuracy (>90%) based on similarities in their leaf spectra. Classifying grass species into lineages using coarser spectral resolution data, similar to existing multispectral satellites, Sentinel 2 and Landsat 8, resulted in lower accuracy due to a loss of information from decreasing the spectral resolution. Grass lineages likely have similar spectra because of common leaf traits that evolved under similar ecological contexts. The importance of these distinctions found in the spectral reflectance of dominant grass lineages, should help our efforts in mapping and understanding grassland ecosystem function and patterns of biodiversity. Key Points: Globally dominant grass lineages are identifiable using hyperspectral leaf signaturesKey wavelengths for separating grass lineages were visible, red‐edge and shortwave infrared regions, rarely measured by multispectral sensorsHyperspectral sensors have the potential to improve remote sensing identification of grass functional types over multispectral sensors [ABSTRACT FROM AUTHOR]

The lunar surface evolves over time due to space weathering, and the visible–near‐infrared spectra of more mature (i.e., heavily weathered) soils are lower in reflectance and steeper in spectral slope (i.e., darker and redder) than their immature counterparts. These spectral changes have traditionally been attributed to the space‐weathered rims of soil grains (and particularly nanophase iron therein). However, understudied thus far is the spectral role of agglutinates—the agglomerates of mineral and lithic fragments, nanophase iron, and glass that are formed by micrometeoroid impacts and are ubiquitous in mature lunar soils. We separated agglutinates and non‐agglutinates from six lunar soils of varying maturity and composition, primarily from the 125–250 μm size fraction, and measured their visible–near‐infrared reflectance spectra. For each soil, the agglutinate spectra are darker, redder, and have weaker absorption bands than the corresponding non‐agglutinate and unsorted soil spectra. Moreover, greater soil maturity corresponds to darker agglutinate spectra with weaker absorption bands. These findings suggest that agglutinates (rather than solely the space‐weathered rims) play an important role in both the darkening and reddening of mature soils—at least for the size fractions examined here. Comparisons with analog soils suggest that high nanophase iron abundance in agglutinates is likely responsible for their low reflectance and spectrally red slope. Additional studies of agglutinates are needed both to more comprehensively characterize their spectral properties (across size fractions and in mixing with non‐agglutinates) and to assess the relative roles of agglutinates and rims in weathering‐associated spectral changes. Plain Language Summary: In the scientific study of the Moon, one key focus is surface processes: how do the physical and chemical properties of the Moon's surface change over time due to weathering (e.g., bombardment by micrometeoroids and by particles from the Sun)? Such investigations provide valuable insights into the Moon's history (such as the ages of impact craters) that are often deduced from measurements of reflected light; as a soil is weathered, it reflects light differently, which manifests visually as a progressive darkening of the soil. This phenomenon had primarily been attributed to weathering‐associated development of rims on individual soil grains, but in this work we explored an alternative cause: soil particles known as agglutinates (misshapen, vesicular agglomerates of mineral fragments, iron, and glass that form due to weathering processes). We isolated agglutinates of six soil samples from the Moon and measured how they reflect light. We find that they reflect light in patterns reminiscent of how the Moon's surface behaves when weathered. These findings suggest that agglutinates play a more important role than previously thought in determining the light‐reflecting properties of the Moon's surface, thus warranting a greater and more nuanced consideration in future studies of how the Moon's surface changes over time. Key Points: We measured the reflectance spectra of agglutinates and non‐agglutinates separated from six lunar soils of varying composition and maturitySpectral comparisons suggest that spectral reddening of mature soils is expressed mainly via agglutinates (for the size fractions measured)These findings reframe our understanding of the relative spectral roles of agglutinates and soil grain rims in weathered lunar soils [ABSTRACT FROM AUTHOR]

Porous silicon 1D photonic crystals, namely rugate filters, are covered with a loosely packed hexagonally ordered array of stimuli‐responsive hydrogel microgels, and the optical properties of the resulting hybrid sensor are thoroughly investigated. For this purpose, both rugate filters with and without hydrogel microgel on top are immersed in ethanol/water mixtures possessing different compositions and NaCl solutions. Reflectance spectra of all samples are taken and analyzed concerning the spectral positions and reflectance intensity of the strong peak related to the photonic crystal rugate peak as well as the side bands resulting from Fabry–Pérot interference at the interfaces bordering the porous silicon. For the latter analysis, a Fourier transform is applied to the side bands for calculating the effective optical thickness (EOT). Thereby it can be shown that the spectral position of both the rugate peak and the EOT peak is best suited for monitoring refractive index changes in the immersion medium whereas the swelling and collapse of the stimuli‐responsive hydrogel microgel can be only detected by variations in the amplitude of the rugate peak and the EOT peak. These results are confirmed by simulations using a simple geometrical model and shall serve as guide for developing tailor‐made optical sensors. [ABSTRACT FROM AUTHOR]

Despite advances in microfabrication, compact spectrometers still face challenges in shrinking their size without sacrificing optical performance. Here, the solid immersion grating microspectrometer (SIG‐µSPEC) for high spectral resolution in a broad operational wavelength range is reported. The spectroscopic module incorporates a silicon microslit, index‐matched lens, plane mirrors, solid immersion grating (SIG), and a CMOS line sensor within a small form factor. The SIG facilitates high angular dispersion of light on a planar focal plane, resulting in an average spectral resolution of 5.8 nm, with over 76% maximum sensitivity from 400 to 800 nm. SIG‐µSPEC measures the spectral reflectance of fruits at different ripening stages, clearly revealing changes in the chlorophyll absorption band. The measured spectrum is further utilized for the precise prediction of the soluble solid content (SSC) levels, achieving a high correlation (R2 = 0.91) and a ratio of prediction‐to‐deviation of 2.36. This compact microspectrometer holds the potential for precise and non‐invasive spectral analysis across point‐of‐care fields. [ABSTRACT FROM AUTHOR]

Remote automated surveillance of insect abundance and diversity is poised to revolutionize insect decline studies. The study reveals spectral analysis of thin‐film wing interference signals (WISs) can discriminate free‐flying insects beyond what can be accomplished by machine vision. Detectable by photonic sensors, WISs are robust indicators enabling species and sex identification. The first quantitative survey of insect wing thickness and modulation through shortwave‐infrared hyperspectral imaging of 600 wings from 30 hover fly species is presented. Fringy spectral reflectance of WIS can be explained by four optical parameters, including membrane thickness. Using a Naïve Bayes Classifier with five parameters that can be retrieved remotely, 91% is achieved accuracy in identification of species and sexes. WIS‐based surveillance is therefore a potent tool for remote insect identification and surveillance. [ABSTRACT FROM AUTHOR]

Assessing plant community traits is important for understanding how terrestrial ecosystems respond and adapt to global climate change. Field hyperspectral remote sensing is effective for quantitatively estimating vegetation properties in most terrestrial ecosystems, although it remains to be tested in areas with dwarf and sparse vegetation, such as the Tibetan Plateau. We measured canopy reflectance in the Tibetan Plateau using a handheld imaging spectrometer and conducted plant community investigations along an alpine grassland transect. We estimated community structural and functional traits, as well as community function based on a field survey and laboratory analysis using 14 spectral vegetation indices (VIs) derived from hyperspectral images. We quantified the contributions of environmental drivers, VIs, and community traits to community function by structural equation modelling (SEM). Univariate linear regression analysis showed that plant community traits are best predicted by the normalized difference vegetation index, enhanced vegetation index, and simple ratio. Structural equation modelling showed that VIs and community traits positively affected community function, whereas environmental drivers and specific leaf area had the opposite effect. Additionally, VIs integrated with environmental drivers were indirectly linked to community function by characterizing the variations in community structural and functional traits. This study demonstrates that community‐level spectral reflectance will help scale plant trait information measured at the leaf level to larger‐scale ecological processes. Field imaging spectroscopy represents a promising tool to predict the responses of alpine grassland communities to climate change. [ABSTRACT FROM AUTHOR]

The intangible cultural heritage of Yangjiabu New Year woodblock paintings (YNYWPs) has numerous potential cultural, artistic, and economic values. The quantitative color characteristics of YNYWPs, which have always been disregarded, can offer evidence in support of their inheritance and conservation. Utilizing the principles and methods of color science and spectral imaging technology, the color characteristics, including spectral characteristics, colorimetric characteristics, and color combination regularity, were quantitatively investigated by measuring and capturing the representative works. The results show that the spectral reflectances of the seven colors (i.e., red, yellow, green, cyan, violet, black, and paper color) adopted in the selected paintings have no significant structured features and can be accurately described using the first six derived eigenvectors by principal component analysis. The Xuan paper and the yellow color have higher reflectance than the other five colors. Correspondingly, their Commission Internationale de l'Eclairage (CIE) 1964 XYZ stimulus values are also relatively high. The others cover a narrow range in the CIE XYZ system. When transformed into the CIELAB color space, the seven colors can be relatively uniformly distributed in the color space. With regards to the color combination regularity, red, yellow, and cyan tend to occupy a large area in the selected paintings. In contrast, the other colors, especially green, are not frequently used. The resulting data of the color combination regularity quantitatively verify the color combination knack that is transmitted by word of mouth from generation to generation. [ABSTRACT FROM AUTHOR]

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

A data fusion strategy based on hyperspectral imaging (HSI) and electronic nose (e‐nose) systems was developed in this study to inspect the postharvest ripening process of Hayward kiwifruit. The extracted features from the e‐nose and HSI techniques, in single or combined mode, were used to develop machine learning algorithms. Performance evaluations proved that the fusion of olfactory and reflectance data improves the performance of discriminative and predictive algorithms. Accordingly, with high classification accuracies of 100% and 94.44% in the calibration and test stages, the data fusion‐based support vector machine (SVM) outperformed the partial least square discriminant analysis (PLSDA) for discriminating the kiwifruit samples into eight classes based on storage time. Moreover, the data fusion‐based support vector regression (SVR) was a better predictor than partial least squares regression (PLSR) for kiwifruit firmness, soluble solids content (SSC), and titratable acidity (TA) measures. The prediction R2 and RMSE criteria of the SVR algorithm on the test data were 0.962 and 0.408 for firmness, 0.964 and 0.337 for SSC, and 0.955 and 0.039 for TA, respectively. It was concluded that the hybrid of e‐nose and HSI systems coupled with the SVM algorithm delivers an effective tool for accurate and nondestructive monitoring of kiwifruit quality during storage. [ABSTRACT FROM AUTHOR]

Purpose: To determine the degree of conversion (DC) and spectral diffuse reflectance of four different gingiva‐colored composites and to evaluate their color stability after various aging processes. Materials and Methods: The gingiva‐colored composites were assigned to four experimental groups (Anaxgum (AG), Crea.lign paste Gum (CB), Gradia Gum (GR), SR Nexco Gum (NC)). A total of 120 disc‐shaped specimens (10 × 2 mm) (n = 30/group) were polymerized in a Teflon mold. The nature of chemical bonding was studied by Fourier transform infrared spectroscopy (FTIR). Diffuse reflection spectra of the polymerized specimens were gathered using an ultraviolet‐visible‐near infrared (UV‐Vis‐NIR) spectrophotometer. Specimens subjected to aging methods were divided into three subgroups (n = 10): ultraviolet aging, hydrothermal aging, and autoclave aging. Color differences (ΔE*ab and ΔE00) were determined by colorimetry before and after aging. The statistical analysis was done using a two‐way ANOVA along with paired sample t‐test and Bonferroni's post hoc test. Results: Conversion degrees varied between 26.9% and 59.7% and all groups showed 3 or 4 maxima at different positions in the visible region of the spectrum. Both ΔE*ab and ΔE00 values were significantly different from the groups of different brands for all types of aging processes. Similarly, there were significantly different ΔE*ab and ΔE00 values according to the aging procedure for all groups of particular brands, except for ΔE00 of SR Nexco Gum (NC). Conclusions: The aging procedures resulted in significant color differences between similar shades of four commercial gingiva‐colored composites. The composite resins showed different degrees of conversion and diffuse reflectance spectra. The aging conditions tested affected the color stability. Patients with gingiva‐colored indirect restorations should be informed about time‐dependent discoloration. [ABSTRACT FROM AUTHOR]

Microscopic hyperspectral imaging technology is a potential non‐destructive and non‐contact method for colour measurement of micrometre‐sized textile fibres. However, specularity on the fibre surface can distort the accurate colour information and affect the accuracy of the colour measurement. This paper proposed a specular‐constrained sparse approximation (SCSA) for specular‐diffuse reflection separation from hyperspectral images of wool fibres. First, a specular prior map is generated based on the lightness dissimilarity. Then the SCSA model is used to decompose the processed hyperspectral image A into low‐rank data L, sparse specularity data S constrained by the specular prior map, sparse noise E, and Gaussian noise N. A non‐linear logistic sigmoid function and a sparse approximation of A – L – N to S are used to improve the performance of specularity removal during iterative optimization. The experimental results show that the proposed method significantly preserves diffuse reflectance and texture details in the specular highlight regions to obtain actual spectral reflectance and chromatic values from hyperspectral images of wool fibres. [ABSTRACT FROM AUTHOR]

The Landsat satellites provide decades of near‐global surface reflectance measurements that are increasingly used to assess interannual changes in terrestrial ecosystem function. These assessments often rely on spectral indices related to vegetation greenness and productivity (e.g. Normalized Difference Vegetation Index, NDVI). Nevertheless, multiple factors impede multi‐decadal assessments of spectral indices using Landsat satellite data, including ease of data access and cleaning, as well as lingering issues with cross‐sensor calibration and challenges with irregular timing of cloud‐free acquisitions. To help address these problems, we developed the 'LandsatTS' package for R. This software package facilitates sample‐based time series analysis of surface reflectance and spectral indices derived from Landsat sensors. The package includes functions that enable the extraction of the full Landsat 5, 7, and 8 records from Collection 2 for point sample locations or small study regions using Google Earth Engine accessed directly from R. Moreover, the package includes functions for 1) rigorous data cleaning, 2) cross‐sensor calibration, 3) phenological modeling, and 4) time series analysis. For an example application, we show how 'LandsatTS' can be used to assess changes in annual maximum vegetation greenness from 2000 to 2022 across the Noatak National Preserve in northern Alaska, USA. Overall, this software provides a suite of functions to enable broader use of Landsat satellite data for assessing and monitoring terrestrial ecosystem function during recent decades across local to global geographic extents. [ABSTRACT FROM AUTHOR]

Wavelength‐dependent mid‐infrared (400–1,800 cm−1; 5.56–25 μm) optical constants (real and imaginary indices of refraction; n and k) are determined using reflectance spectra at a spectral sampling of 4 cm−1 for several silicate glasses of varying SiO2 wt% which include (a) basaltic volcanic glass from Kīlauea, Hawaii, (b) synthetic andesite, (c) two synthetic dacites, (d) obsidian volcanic glass from Mount Lassen, California, (e) synthetic rhyodacite, and (f) rhyolitic volcanic glass from Mexico. Because glasses are optically isotropic, no specific orientation was required for spectral measurements, and polished glass samples were measured at random orientations using micro‐FTIR spectrometer. Lorentz‐Lorenz dispersion theory and Fresnel reflectance model for high symmetry materials were used to model the optical constants by optimizing oscillator parameters in modeled spectra to match laboratory spectra. A genetic algorithm (GA) approach automatically finds the natural oscillators and their parameters for each spectrum, and then uses these parameters in a non‐linear least squares optimization routine. The study compared spectral parameters such as Christiansen feature, reststrahlen bands (RBs), and the peaks centered around 860–1,100 (peak 1) and 400–480 cm−1 (peak 2) of n and k to their respective SiO2 wt% of the glasses. CF, RBs, peak 1 of n and k, and peak 2 of n shift to higher wavenumbers with increased SiO2 wt%, whereas peak 2 of k shifts to lower wavenumbers with increased SiO2 wt%. Derived optical constants of these glasses will improve quantitative abundance mapping of volcanic materials on the surfaces of silicate targets in the Solar System. Plain Language Summary: The effective mapping of minerals and their abundances on a planet's surface from orbit is enabled by understanding the nature of the interaction of light of varying wavelengths with the planetary surface. The reflected/emitted light from the surface directly depends on the optical properties of the materials and their interaction with each other. In this study, we numerically modeled the mid‐infrared optical constants of silicate glasses using a genetic algorithm (GA) approach. The GA allows the model to automatically locate the natural harmonic oscillators and their parameters that are responsible for creating the spectral signals that can be detected by spectrometers on orbital spacecraft. Derived optical constants of the glasses could be used in radiative transfer models for quantitative mineral mapping applications. Glasses on planetary surfaces are common in volcanic and/or impact materials and the mid‐infrared spectral range has been widely used to map the surface mineralogy of planets including the Moon and Mars. Mapping the composition and abundance of these glasses therefore will help us to understand the thermal evolution of the planets themselves. Key Points: MIR optical constants (5.56–25 μm; 400–1,800 cm−1) and their oscillator parameters were modeled for seven silicate glasses of varying SiO2 wt%MicroFTIR reflectance spectra of the glasses studied were measured for the spectral region 5.56–25 μm (400–1,800 cm−1)Genetic algorithm was adopted to automatically find the natural oscillators and their parameters for the glasses studied [ABSTRACT FROM AUTHOR]

This study is part of the requirements consolidation study for the European Copernicus Hyperspectral Imaging Mission for Environment (CHIME). It explores the value added by existing hyperspectral data of similar characteristics to CHIME, namely AVIRIS‐NG and PRecursore IperSpettrale della Missione Applicativa (PRISMA), for detecting topsoil texture properties. The spatial variability is retrieved using the linear spectral mixture analysis, an image‐based algorithm that breaks down the hyperspectral data set into fractional abundance of spectral classes within each pixel. The fractional abundance of image‐based endmembers is broken into categories to find intervals having a spatial relation with texture components in terms of fine (clay and silt) or coarse (sand) abundance. The fraction maps obtained show similar spatial patterns to the USDA soil texture classification, obtained with a geostatistical approach. Specifically, AVIRIS CHIME‐like FAM1 > 0.45 presented an agreement of 86% with clay and/or silt higher than 45% which, according to the United Stated Agriculture Department (USDA) intervals, correspond to loam‐clay loam classes. Similar results are obtained with PRISMA with FAM2 0.20–0.35, overlapping 63% of the kriging‐based USDA clay‐loam class. The fractional abundance categories showing the highest overlap percentages are correlated with the short‐wave infrared spectral range, showing average coefficients of 0.7 where wavelengths are over 1,500 nm. From 1700 nm, CHIME‐like shows values of 0.8. In conclusion, this exploratory research and results leverage the opportunity of extending the processing chain to a larger number of case studies to better understand the physical relation between the spectral reflectance captured by new spaceborne hyperspectral sensors and the spatial patterns of soil texture classes. Plain Language Summary: In the current context led by the constant availability, almost in near‐real time, of remote sensing data with high spatial, spectral and temporal resolutions, the monitoring of soil properties should no longer be solely limited to field data observations. There is a growing need of knowledge for preserving and exploiting soils and the upcoming generation of hyperspectral satellite remote sensing has popped up new opportunities for developing and adapting algorithms that break up the image pixels into several spectral behaviors that correspond to different soil properties. This paper contributes to the application of these image analysis techniques to soil texture classes retrieval considering the United Stated Agriculture Department model. The paper uses the principles of the spectral mixture concept to explore the opportunity of detecting soil textures in two farmland units and provides some highlights on the observational requirements needed to integrate the future satellite missions' architecture within the agriculture and food security application domain. Key Points: Hyperspectral imaging is used to retrieve United Stated Agriculture Department soil texture classification in two farmland units applying linear spectral mixture analysisCopernicus Hyperspectral Imaging Mission for Environment‐like and PRecursore IperSpettrale della Missione Applicativa‐derived fractional abundance intervals are found to spatially overlap the clay‐loam class obtained with field dataFractional abundances categories having the highest overlap percentage are strongly correlated with the Short‐Wave InfraRed > 1,500 nm [ABSTRACT FROM AUTHOR]

Summary: Kleptomyiophily, where flowers imitate wounded insects to attract 'kleptoparasitic' flies as pollinators, is one of the most specialized types of floral mimicry and often involves physical trapping devices. However, the diversity of pollinators and functional floral traits involved in this form of mimicry remain poorly understood. We report a novel example of kleptomyiophily in the nontrapping flowers of Ceropegia gerrardii and explore the floral traits responsible for attracting pollinators.The pollinators, reproductive biology and floral traits (including epidermal surfaces, spectral reflectance and the composition of nectariferous petal secretions and scent) were investigated. Attractive volatiles were identified using electrophysiological and behavioural experiments.Ceropegia gerrardii was predominantly pollinated by kleptoparasitic Desmometopa spp. (Milichiidae) flies. The flower corollas extrude a protein‐ and sugar‐containing secretion, similar to the haemolymph of wounded insects, on which the flies feed. Floral scent was chemically similar to that of injured honey bees. Four out of 24 electrophysiologically active compounds, all released by injured honey bees, were identified as key players in pollinator attraction.Our results suggest that C. gerrardii flowers chemically mimic wounded honey bees to attract kleptoparasitic flies and reward them with a secretion similar to the haemolymph on which they would normally feed. See also the Commentary on this article by Raguso, 239: 1164–1165. [ABSTRACT FROM AUTHOR]

The purpose of the study was to evaluate the possibility of parameterizing the state of soil surface roughness (SSR) based on proximal measurements of spectral reflectance in the VIS‐NIR range, which is important for the needs of monitoring the state of soil surfaces. SSR should be constantly monitored as it provides an insight into a range of hydrological and erosive soil processes and improves the interpretation of remote sensing data. SSR and the spectral reflectance of three texturally different soils were measured under simulated rainfall in laboratory condition. The relationship between the SSR parameters and soil spectra was determined using regression random forest models. Various spectral data processing methods were tested and the best wavelengths for SSR description after rainfall were found. Two roughness indices were used to describe the SSR: Height Standard Deviation (HSD) and T3D (Tortuosity index). Although both shared a significant correlation with SSR, the T3D index demonstrated a more pronounced rainfall effect and a closer correlation with spectral data than HSD. The best determination of T3D was obtained with the raw spectra (RAW) (R2 = 0.71), as well as with spectra transformed with the baseline alignment first derivative (BA1d) method (R2 = 0.71) or the Savitzky‐Golay (SG) method (R2 = 0.69). Different wavelengths were the best SSR predictors depending on the spectral transformation method (VIPs ‐ Variable Importance in Projection). For both roughness indices, the NIR wavelengths (725–820 nm) yielded the highest VIP Score in models based on RAW spectra, while those in the VIS region (450–772 nm) were most important in models based on transformed spectra. Plain Language Summary: The intensity of agricultural land use is increasing globally, contributing to soil degradation. Such degradation requires constant monitoring using various soil physical characteristics, including soil surface roughness (SSR). The SSR changes over time under the influence of natural and anthropogenic factors, such as rain and farming practices in agricultural fields. SSR can be quantified using remote sensing methods based on the interpretation of airborne and satellite images. However, to do so, it is necessary to understand the relationship between the SSR and the amount of light reflected from the soil surface, first in controlled laboratory condition using hyperspectral data and statistical models. Our study presents results of the reflectance measurements from soil surfaces, which were gradually smoothed by simulated rainfall in the laboratory condition. Based on these measurements, and machine learning methods, models were developed that can predict the SSR from the VNIR‐SWIR spectra with a reliability of up to 71%. Key Points: The impact of simulated rainfall on the soil surface roughness and their spectral characteristics was investigatedThe changes in the soil surface roughness were closely related to the changes in spectral reflectance from the soilsThe spectral data was more closely related to the T3D index than the height standard deviation index [ABSTRACT FROM AUTHOR]

We present a method for estimating the main properties of human skin, leveraging a hyperspectral dataset of skin tones synthetically generated through a biophysical layered skin model and Monte Carlo light transport simulations. Our approach learns the mapping between the skin parameters and diffuse skin reflectance in such space through an encoder‐decoder network. We assess the performance of RGB and spectral reflectance up to 1 μm, allowing the model to retrieve visible and near‐infrared. Instead of restricting the parameters to values in the ranges reported in medical literature, we allow the model to exceed such ranges to gain expressiveness to recover outliers like beard, eyebrows, rushes and other imperfections. The continuity of our albedo space allows to recover smooth textures of skin properties, enabling reflectance manipulations by meaningful edits of the skin properties. The space is robust under different illumination conditions, and presents high spectral similarity with the current largest datasets of spectral measurements of real human skin while expanding its gamut. [ABSTRACT FROM AUTHOR]

Spectral rendering is essential for the production of physically‐plausible synthetic images, but requires to introduce several changes in the content generation pipeline. In particular, the authoring of spectral material properties (e.g., albedo maps, indices of refraction, transmittance coefficients) raises new problems. While a large panel of computer graphics methods exists to upsample a RGB color to a spectrum, they all provide a one‐to‐one mapping. This limits the ability to control interesting color changes such as the Usambara effect or metameric spectra. In this work, we introduce a one‐to‐many mapping in which we show how we can explore the set of all spectra reproducing a given input color. We apply this method to different colour changing effects such as vathochromism – the change of color with depth, and metamerism. [ABSTRACT FROM AUTHOR]

The spectral reflectance is recognized as the fingerprint of an object surface and has been used to achieve accurate color measurement in textile and other fields. Spectral reflectance can be recovered from color images to preserve high spectral and spatial resolutions simultaneously. However, a color camera commonly supplies a non‐raw color image, which is non‐linear with respect to the scene radiance, and is inappropriate for quantitative analysis. In this study, for non‐raw color images, different nonlinearity correction models are designed and evaluated with respect to different spectral estimation algorithms. The colorimetric and spectral accuracy of spectral estimation after the nonlinearity correction is assessed through both simulation and practical experiments. In the simulation, a large number of spectral images from several datasets are employed to directly verify the effectiveness of the nonlinearity correction. In the practical experiments, the spectral estimation accuracy following the nonlinearity correction is verified directly and indirectly based on actual color images. The resulting linear color image data after the nonlinearity correction can provide better spectral estimation accuracy especially for the PI algorithm with one power‐function based model. Besides, the combination of the simple PI algorithm with the power‐function based model can exceed other combinations comprising complex algorithms and models in both accuracy and efficiency. For the linear color image data, the PI algorithm even surpasses the deep learning‐based methods in certain metric, thus indicating a shallow relationship exists between the linear color image data and the spectral reflectance. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile imaging devices, there is a strong desire for accurate image reconstruction. However, the conventional colorimetry system is often related to environmental illuminations. Spectral reflectance is the fingerprint of color and is invariant with environmental illuminations. Therefore, the goal of the present study is to verify different algorithms to reconstruct the spectral reflectance from the camera red, green, and blue responses. In this research, the weighted local sample selection method was first combined with pseudo inverse matrix method (PI) and Wiener estimation method (WE) to investigate the optimal sample number on the model accuracy. The optimum local sample numbers of the two combination methods were established. The performance of five methods was evaluated, including PI, WE, smoothing constraint method, weighted pseudo inverse matrix method (WPI) and weighted Wiener estimation method (WWE) under lightings varying a wide range of correlated color temperature (CCT) from 3000 to 10 000 K. The best algorithm (WPI) in different lighting environments was established. The metamerism of different materials was revealed, the impact of materials on training and testing samples was reported. Finally, the methods' performance under different CCTs was revealed in terms of root mean square error and CIEDE2000, and the results from the theoretical simulation and real camera capturing were compared. [ABSTRACT FROM AUTHOR]

Wheat (Triticum aestivum L.) leaf rust is the most common and widely distributed wheat disease. Non‐destructive and real‐time methods for monitoring wheat leaf rust can help prevent and control plant diseases in agricultural production. In this study, we obtained multispectral imagery of the wheat canopy acquired by an unmanned aerial vehicle, selected the vegetation index using the K‐means algorithm (KA) and genetic algorithm (GA), and established a wheat leaf rust monitoring model based on the backpropagation neural network (BPNN) method. The results showed that the R2 and RMSE of the KA‐BPNN model were 0.902% and 5.45% for the modeling set, respectively, and 0.784% and 4.76% for the validation set, respectively; and the R2 and RMSE of the GA‐BPNN model was 0.922% and 4.88% for the modeling set, respectively, and 0.780% and 4.28% for the validation set, respectively. The prediction model after optimizing the variables using KA and GA had higher accuracy than the BPNN model, implying that using variable dimensionality reduction methods and complex machine learning algorithms to construct estimation models can improve model accuracy significantly. These models accurately monitored leaf rust in winter wheat, providing a theoretical basis and technical support for assessing plant diseases and screening disease‐resistant wheat varieties. [ABSTRACT FROM AUTHOR]

This study integrated field‐level sensor data into the FAO‐56 Penman–Monteith algorithm to provide a site‐specific estimate of crop evapotranspiration. This was carried out at two contrasting sites for pea and bean (Manawatū) and barley (Hawke's Bay) crops managed within two irrigation management zones, at each site, under variable‐rate irrigation systems in New Zealand. Daily crop evapotranspiration estimates were calculated using data from a weather station situated at the field site combined with in‐field crop sensing data (spectral reflectance, canopy temperature, and canopy height). In addition, calibrated soil moisture data were used with a soil water balance model to compare estimations of daily crop evapotranspiration with those estimated using the crop sensing method. The results indicated that variable crop responses to different irrigation strategies and soil types provided a good opportunity to quantify different levels of spectral reflectance, canopy temperature, and consequently the estimation of crop water use. The statistical comparisons revealed that the modified FAO‐56 Penman–Monteith using crop sensor data compared well with the more conventional soil water balance approach using soil moisture data (R2 = 0.70, 0.83, 0.91 for barley, pea, and bean, respectively). Overall, the results from this study indicated that crop sensing approaches combined with the FAO‐56 Penman–Monteith model have potential to provide a more easily determined site‐specific field estimation of crop evapotranspiration than other methods, and it can take into consideration the spatiotemporal variability of crop growth in a field. Core Ideas: Field‐level sensor method was used for water use estimates.The method gave accurate estimates of crop evapotranspiration.The method detected spatial variations in the crop response to irrigation.The method successfully modeled crop coefficients and crop evapotranspiration.The method simulated the benefits of variable rate irrigation. [ABSTRACT FROM AUTHOR]

Background: The real‐time non‐destructive estimation of soil total nitrogen content via hyperspectral remote sensing is important for crop fertilization and precision agriculture development. Aims: The study aimed to use hyperspectral technology for the construction of an estimation model using different methods for assessing total nitrogen content in Shajiang black soil. Methods: In our study, soil samples were obtained from Shangshui County, Henan Province, China. Using hyperspectral data of soil samples, the original spectral reflectance was transformed into logarithmic, reciprocal, and first derivative spectra. Hyperspectral estimation models for assessing the total nitrogen content were built with spectral indices, competitive adaptive reweighted sampling (CARS) algorithms, and a combination of machine learning methods, including the partial least square regression, support vector machine (SVM), and random forest methods. Results: The results showed that after the first derivative transformation, the performance of normalized indices constructed with a combination of two bands at 1401 and 776 nm was better. The coefficient of determination (R2), root mean square error (RMSE), ratio of performance to deviation (RPD), and ratio of performance to the inter‐quartile range (RPIQ) values of the model were 0.84, 0.10 g kg–1, 2.25, and 4.04, respectively. Validation was performed using data independent of the modeling samples, and validation R2, RMSE, RPD, and RPIQ values were 0.92, 0.07 g kg–1, 2.56, and 4.19, respectively. The same sample was analyzed using a CARS algorithm for screening feature bands, and the 61 reciprocal reflectance bands selected as the input for the SVM exhibited the best performance. A modeling R2 value of 0.92, RMSE of 0.07 g kg–1, RPD of 3.09, RPIQ of 5.97, and validation R2 of 0.96, RMSE of 0.06 g kg–1, RPD of 3.24, and RPIQ of 8.47 were observed. Conclusions: The estimation models constructed using the two methods exhibited good ability to determine the total nitrogen content. The accuracy of the SVM model was slightly higher than that of the indices model, but both models could facilitate the rapid estimation of total nitrogen content in Shajiang black soil. The findings can provide a technical reference for the estimation of the levels of soil total nitrogen and other nutrients. [ABSTRACT FROM AUTHOR]

Accurate preharvest yield estimation is an important issue for agricultural planning purposes and precision farming. Machine learning (ML) based on readily obtained information on the cropping system, typically including spectral reflectance measurements, is an essential approach for achieving practical solutions. We tested in a 9‐year soil compaction experiment the accuracy of ML‐based yield predictions made up to 2 months before harvest from a Ratio Vegetation Index (RVI) and recordings of precipitation and reference evapotranspiration. The applied data set comprises 224 combinations of plots and years with measured grain yields in the range of 4.22–9.34 Mg/ha. The best ML model [i.e., with the smallest mean absolute error (MAE)] was selected automatically by the AutoML interface included in the R program package H2O. Its cross‐validated predictions made on June 30 more than 1 month before harvest showed an MAE of 0.38 Mg/ha when trained on all data from all years except the one under consideration. MAE increased to about 0.68 Mg/ha when determined 3 weeks earlier on June 10. MAE values in the range of 0.32–0.42 Mg/ha were obtained for predictions made on June 30 when based on data from at least six consecutive years; however, MAE showed no generally decreasing trend with the number of years. Yield estimations were robust towards a considerable soil variation observed within the experimental area due in part to the experimental treatments. The results show a potential of making yield predictions in barley 1–2 months before harvest, which, however, is not sufficiently early to support decisions on top‐dress N fertilization. [ABSTRACT FROM AUTHOR]

Soil moisture controls the exchange of energy between the land surface and the atmosphere and significantly affects plant growth and productivity. Hyperspectral (350–2500 nm) monitoring of soil moisture content (SMC) could provide a theoretical basis for the real‐time estimation of spatial and temporal variations in soil moisture. During this study, we have developed SMC prediction models in different soil moisture ranges by using the combination of spectral preprocessing methods and optimized spectral indices. Results indicated that first‐order derivative (FD) preprocessing method can highlight the effective information of the spectra and improve the correlation between spectral reflectance and SMC. The spectral reflectance exhibited a strong correlation with SMC near 1400, 1700, 1900, and 2200 nm. The FD‐NDSI‐W0‐model had the best SMC prediction and applicability (R2 = 0.977, root mean square error = 3.413%, relative percent deviation = 6.171) to predict SMCs. Overall, pretreatment methods, combined with the two‐band random combination to optimize the spectral index to build a prediction model of SMC, could be applied for accurate monitoring of SMC. Core Ideas: The spectral features were enhanced using preprocessing methods.The characteristic bands of soil moisture content are clarified.The spectral indices were optimized using a two‐band random combination. [ABSTRACT FROM AUTHOR]