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Abstract: 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.

The increasing threats to grassland ecosystems from land-use/land-cover change, disturbances, and invasive species underscore the importance of monitoring grassland plant diversity. While most remote sensing studies have mainly focused on quantifying α-diversity (diversity within communities), less attention has been given to remotely estimating β-diversity (diversity between communities) in naturally-assembled grasslands. In this study, we used remote sensing to map plant β-diversity in a naturally-assembled grassland managed using prescribed fire and grazing in a North American tallgrass prairie ecosystem within the US Southern Great Plains. We aimed to assess the impact of time since fire on the relationship between in situ plant β-diversity and remotely–sensed β-diversity, also known as spectral β-diversity. We collected in situ observations at 60 m × 60 m, 120 m × 120 m, 180 m × 180 m, and 240 m × 240 m plots, alongside airborne and spaceborne DESIS imaging spectroscopy data with spatial resolutions of 1 m and 30 m, respectively. To assess how management practices influenced β-diversity, we grouped our in situ observations into three categories based on time since fire, including recently-burned (burn age <1 year), transitional (burn age of 1–2 years), and unburned (burn age >2 years). Our findings showed that the association between in situ and spectral β-diversity was strongly influenced by time since fire. Excluding plots with high soil cover (≥50%) improved our remote estimation of β-diversity, despite soil exposure effects. Our study demonstrated that remote sensing of β-diversity in grassland ecosystems is influenced by spatial scale, time since fire, and soil exposure, all of which can be addressed with scale-appropriate imaging spectrometry. This study also highlighted the capability of spaceborne imaging spectrometers to estimate β-diversity of grassland ecosystems across large spatial domains. [ABSTRACT FROM AUTHOR]

The standardized spectral mixture model combines the specificity of a physically based representation of a spectrally mixed pixel with the generality and portability of a spectral index. Earlier studies have used spectrally and geographically diverse collections of broadband and spectroscopic imagery to show that the reflectance of the majority of ice-free landscapes on Earth can be represented as linear mixtures of rock and soil substrates (S), photosynthetic vegetation (V) and dark targets (D) composed of shadow and spectrally absorptive/transmissive materials. However, both broadband and spectroscopic studies of the topology of spectral mixing spaces raise questions about the completeness and generality of the Substrate, Vegetation, Dark (SVD) model for imaging spectrometer data. This study uses a spectrally diverse collection of 40 granules from the EMIT imaging spectrometer to verify the generality and stability of the spectroscopic SVD model and characterize the SVD topology and plane of substrates to assess linearity of spectral mixing. New endmembers for soil and non-photosynthetic vegetation (NPV; N) allow the planar SVD model to be extended to a tetrahedral SVDN model to better accommodate the 3D topology of the mixing space. The SVDN model achieves smaller misfit than the SVD, but does so at the expense of implausible fractions beyond [0, 1]. However, a refined spectroscopic SVD model still achieves small (<0.03) RMS misfit, negligible sensitivity to endmember variability and strongly linear scaling over more than an order of magnitude range of spatial resolution. [ABSTRACT FROM AUTHOR]

Remote sensing is now a valued solution for more accurately budgeting water supply by identifying spectral and spatial information. A study was put in place in a Vitis vinifera L. cv. Cabernet-Sauvignon vineyard in the San Joaquin Valley, CA, USA, where a variable rate automated irrigation system was installed to irrigate vines with twelve different water regimes in four randomized replicates, totaling 48 experimental zones. The purpose of this experimental design was to create variability in grapevine water status, in order to produce a robust dataset for modeling purposes. Throughout the growing season, spectral data within these zones was gathered using a Near InfraRed (NIR) - Short Wavelength Infrared (SWIR) hyperspectral camera (900 to 1700 nm) mounted on an Unmanned Aircraft Vehicle (UAV). Given the high water-absorption in this spectral domain, this sensor was deployed to assess grapevine stem water potential, Ψstem, a standard reference for water status assessment in plants, from pure grapevine pixels in hyperspectral images. The Ψstem was acquired simultaneously in the field from bunch closure to harvest and modeled via machine-learning methods using the remotely sensed NIR-SWIR data as predictors in regression and classification modes (classes consisted of physiologically different water stress levels). Hyperspectral images were converted to bottom of atmosphere reflectance using standard panels on the ground and through the Quick Atmospheric Correction Method (QUAC) and the results were compared. The best models used data obtained with standard panels on the ground and allowed predicting Ψstem values with an R2 of 0.54 and an RMSE of 0.11 MPa as estimated in cross-validation, and the best classification reached an accuracy of 74%. This project aims to develop new methods for precisely monitoring and managing irrigation in vineyards while providing useful information about plant physiology response to deficit irrigation. [ABSTRACT FROM AUTHOR]

Purpose: Cover crops and reduced tillage are two key climate smart agricultural practices that can provide agroecosystem services including improved soil health, increased soil carbon sequestration, and reduced fertilizer needs. Crop residue carbon traits (i.e., lignin, holocellulose, non-structural carbohydrates) and nitrogen concentrations largely mediate decomposition rates and amount of plant-available nitrogen accessible to cash crops and determine soil carbon residence time. Non-destructive approaches to quantify these important traits are possible using spectroscopy. Methods: The objective of this study was to evaluate the efficacy of spectroscopy instruments to quantify crop residue biochemical traits in cover crop agriculture systems using partial least squares regression models and a combination of (1) the band equivalent reflectance (BER) of the PRecursore IperSpettrale della Missione Applicativa (PRISMA) imaging spectroscopy sensor derived from laboratory collected Analytical Spectral Devices (ASD) spectra (n = 296) of 11 cover crop species and three cash crop species, and (2) spaceborne PRISMA imagery that coincided with destructive crop residue collections in the spring of 2022 (n = 65). Spectral range was constrained to 1200 to 2400 nm to reduce the likelihood of confounding relationships in wavelengths sensitive to plant pigments or those related to canopy structure for both analytical approaches. Results: Models using laboratory BER of PRISMA all demonstrated high accuracies and low errors for estimation of nitrogen and carbon traits (adj. R2 = 0.86 − 0.98; RMSE = 0.24 − 4.25%) and results indicate that a single model may be used for a given trait across all species. Models using spaceborne imaging spectroscopy demonstrated that crop residue carbon traits can be successfully estimated using PRISMA imagery (adj. R2 = 0.65 − 0.75; RMSE = 2.71 − 4.16%). We found moderate relationships between nitrogen concentration and PRISMA imagery (adj. R2 = 0.52; RMSE = 0.25%), which is partly related to the range of nitrogen in these senesced crop residues (0.38–1.85%). PRISMA imagery models were also influenced by atmospheric absorption, variability in surface moisture content, and some presence of green vegetation. Conclusion: As spaceborne imaging spectroscopy data become more widely available from upcoming missions, crop residue trait estimates could be regularly generated and integrated into decision support tools to calculate decomposition rates and associated nitrogen credits to inform precision field management, as well as to enable measurement, monitoring, reporting, and verification of net carbon benefits from climate smart agricultural practice adoption in an emerging carbon marketplace. [ABSTRACT FROM AUTHOR]

The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay minerals and quantifying the montmorillonite abundance are a challenge since they are usually intimately mixed with other minerals, soil organic carbon and soil moisture content. Imaging spectroscopy coupled with unmixing methods can address these issues, but the quality of the estimation degrades the coarser the spatial resolution is due to pixel heterogeneity. With the advent of UAV-borne and proximal hyperspectral acquisitions, it is now possible to acquire images at a centimeter scale. Thus, the objective of this paper is to evaluate the accuracy and limitations of unmixing methods to retrieve montmorillonite abundance from very-high-resolution hyperspectral images (1.5 cm) acquired from a camera installed on top of a bucket truck over three different agricultural fields, in Loiret department, France. Two automatic endmember detection methods based on the assumption that materials are linearly mixed, namely the Simplex Identification via Split Augmented Lagrangian (SISAL) and the Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF), were tested prior to unmixing. Then, two linear unmixing methods, the fully constrained least square method (FCLS) and the multiple endmember spectral mixture analysis (MESMA), and two nonlinear unmixing ones, the generalized bilinear method (GBM) and the multi-linear model (MLM), were performed on the images. In addition, several spectral preprocessings coupled with these unmixing methods were applied in order to improve the performances. Results showed that our selected automatic endmember detection methods were not suitable in this context. However, unmixing methods with endmembers taken from available spectral libraries performed successfully. The nonlinear method, MLM, without prior spectral preprocessing or with the application of the first Savitzky–Golay derivative, gave the best accuracies for montmorillonite abundance estimation using the USGS library (RMSE between 2.2–13.3% and 1.4–19.7%). Furthermore, a significant impact on the abundance estimations at this scale was in majority due to (i) the high variability of the soil composition, (ii) the soil roughness inducing large variations of the illumination conditions and multiple surface scatterings and (iii) multiple volume scatterings coming from the intimate mixture. Finally, these results offer a new opportunity for mapping expansive soils from imaging spectroscopy at very high spatial resolution. [ABSTRACT FROM AUTHOR]

This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products. [ABSTRACT FROM AUTHOR]

An existing technique for quickly obtaining the spatial distribution data of soil organic matter (SOM) involves combining an unmanned aerial vehicle (UAV) platform and imaging spectroscopy technology; this technique is suitable for precision agricultural management and real-time monitoring. In this study, the average spectral data were first extracted via circular regions of interest (ROIs) of different radii and modelled using the measured SOM to determine the optimal extraction range for the spectral data. Partial least-squares regression (PLSR) and support vector machine (SVM) models were constructed based on the raw spectra, standard normal transform spectra, multiple scattering-corrected spectra, and first-order differential spectra. The difference index, ratio index, and normalized index were calculated and analysed for their correlation with SOM content, and the spectral indices were screened to establish the SOM prediction model. Finally, the accuracy of the model was evaluated, and we selected the optimal model to map the spatial distribution of SOM. The results of our study show that 1) the radius of the ROI significantly affects the model's accuracy; 2) the accuracy of the nonlinear SVM model is much higher than that of the linear PLSR model; 3) the prediction model based on spectral indices is better than full-band model. This study can serve as a model for the application of UAV imaging spectroscopy technology to field-scale SOM estimations and rapid monitoring, as well as provide technical support for precision agriculture and climate change studies. [ABSTRACT FROM AUTHOR]

The effort to map terrestrial biodiversity, in recent years limited mostly to the use of broadband multispectral remote sensing at decameter scales, can be greatly enhanced by harnessing hyperspectral imagery. Interpretation of hyperspectral imagery may be aided by the Mixture Residual (MR) spectral preprocessing transformation. MR integrates the benefits of spectral mixture analysis with the absorption peak‐enhancing characteristics of continuum removal. MR characterizes each pixel as a linear combination of generic end‐members estimating the spectral continuum, from which the residual of each wavelength is computed and treated as a source of additional information. Using Hyperspectral Precursor of the Application Mission (PRISMA) imagery, we tested the ability of MR‐transformed reflectance as compared to untransformed surface reflectance (SR) to map plant associations and land cover using ground truthing and random forest classifications across four landscapes within the North American Coastal Plain. We used a forward stepwise selection algorithm to choose bands for each classification and subsequently compared these between SR and MR. Our MR classifications distinguished land cover with 5% greater balanced accuracy on average than the SR‐based classifications across all four landscapes. The MR‐based classification that integrated data from all landscapes into a unified model encompassing all 21 land cover types achieved a 76% average balanced accuracy over three iterations. Generally, MR utilized the near‐infrared region to a greater degree than SR while deemphasizing the green peak. Based on our results, MR improves the accuracy of mapping terrestrial biodiversity, likely extending to other current and planned satellite hyperspectral missions. Plain Language Summary: Multispectral imagery, which captures light reflectance from the earth in only a few wide frequency ranges, has traditionally dominated satellite remote sensing. In contrast, newer hyperspectral imagery captures reflectance in many, narrower frequency ranges, providing much more information. We explored a method exploiting that added information to identify plant communities and land uses. The method removes variability associated with the general reflectance patterns of dominant land cover categories, green foliage, soil, and shadow. We then used the remaining reflectance to distinguish finer differences among land cover based on the types of plants or soil present. We tested this method against untransformed data using known areas to train and then predict land cover types on four landscapes dominated by pine savannas in Georgia and Florida. The transformation changed the most useful part of the spectrum, using more wavelengths in the infrared range instead of visible light. When applied to these landscapes, the transformation produced models with 5% better accuracy on average. Using this transformed data to predict the presence of all 21 land cover types at once resulted in an accuracy of 76% on average. Ultimately, this process could improve biodiversity mapping for conservation in the future. Key Points: The spectral Mixture Residual (MR) transformation applied to PRISMA scenes consistently produced higher accuracies in classifying four landscapes in the North American Coastal Plain by 5% on average over untransformed surface reflectance (SR)The MR transformation achieved 76% balanced accuracy when all 21 land cover types across the four landscapes were combined into one random forest modelThe forward stepwise selection of bands within the MR models favored near‐infrared wavelengths while deemphasizing those in the green peak when compared with SR models [ABSTRACT FROM AUTHOR]

Developing accurate landscape‐scale aboveground biomass (AGB) maps is critical to understanding coastal deltaic wetland resilience, as AGB influences stability and elevation dynamics in herbaceous wetlands. Here we used AVIRIS‐NG imaging spectrometer (or "hyperspectral") data from NASA's 2021 Delta‐X mission in coastal Louisiana to map seasonal changes in herbaceous AGB across two deltaic basins with contrasting sediment delivery and hydrologic regimes: the Atchafalaya (active) and Terrebonne (inactive). We assessed the impact of atmospheric effects on our retrievals, as high water vapor content in August 2021 caused significant noise in the 880–1,000 and 1,080–1,200 nm near‐infrared (NIR) wavelengths. We hypothesized that correcting these wavelengths with our conditional Gaussian interpolation algorithm would improve AGB estimates due to their association with plant canopy water content. We empirically assessed the performance of the corrected spectra on AGB estimates using Partial Least Squares Regression (PLSR), finding that the corrected NIR bands attained high variable importance and reduced estimation errors. Our Random Forest regression approach based on the corrected spectra attained equivalent error metrics via leave‐one‐out‐cross‐validation as the PLSR models (R2 = 0.43, mean absolute error = 257.3 g/m2) while greatly improving the AGB maps' visual quality, having better captured variability while reducing noise and discontinuities in AGB estimates across flightlines. The maps show differing seasonal growth, with the Atchafalaya and Terrebonne Basins' AGB increasing from means of 4.3–9.4 and 4.6–8.9 Mg/ha, respectively. We demonstrated that imaging spectroscopy can be applied to assess herbaceous biomass stocks, growth patterns, and resilience in coastal ecosystems. Plain Language Summary: Developing accurate landscape‐scale aboveground biomass (AGB) maps is critical to understanding the sustainability of coastal deltaic systems, as plant biomass aids in a wetland maintaining its elevation in the face of relative sea level rise. Here we use imaging spectroscopy, or "hyperspectral" remote sensing, to map wetland biomass across seasons in coastal Louisiana. In doing so, we correct for the noise caused by high atmospheric water vapor content in important infrared bands, thereby improving our biomass models. We test empirical and machine learning models for estimating wetland biomass, determining that a machine learning regression model using our corrected data delivers the best model metrics and map quality. We thus map AGB in April and August 2021, across the Atchafalaya and Terrebonne Basins. Our maps show various seasonal growth patterns, with the Atchafalaya and Terrebonne Basins' herbaceous AGB increasing from means of 4.3–9.4 and 4.6–8.9 Mg/ha, respectively. Key Points: We developed an interpolation algorithm to correct noise in near‐infrared bands that improved wetland aboveground biomass (AGB) estimatesWe assessed model approaches and mapped wetland AGB in coastal Louisiana with a machine learning algorithm for March and August 2021The active Atchafalaya and inactive Terrebonne Basins' AGB increased from means of 4.3–9.4 and 4.6–8.9 Mg/ha, respectively [ABSTRACT FROM AUTHOR]

An existing technique for quickly obtaining the spatial distribution data of soil organic matter (SOM) involves combining an unmanned aerial vehicle (UAV) platform and imaging spectroscopy technology; this technique is suitable for precision agricultural management and real-time monitoring. In this study, the average spectral data were first extracted via circular regions of interest (ROIs) of different radii and modelled using the measured SOM to determine the optimal extraction range for the spectral data. Partial least-squares regression (PLSR) and support vector machine (SVM) models were constructed based on the raw spectra, standard normal transform spectra, multiple scattering-corrected spectra, and first-order differential spectra. The difference index, ratio index, and normalized index were calculated and analysed for their correlation with SOM content, and the spectral indices were screened to establish the SOM prediction model. Finally, the accuracy of the model was evaluated, and we selected the optimal model to map the spatial distribution of SOM. The results of our study show that 1) the radius of the ROI significantly affects the model’s accuracy; 2) the accuracy of the nonlinear SVM model is much higher than that of the linear PLSR model; 3) the prediction model based on spectral indices is better than full-band model. This study can serve as a model for the application of UAV imaging spectroscopy technology to field-scale SOM estimations and rapid monitoring, as well as provide technical support for precision agriculture and climate change studies.

Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA's EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy is emerging as a leading remote sensing method for quantifying plant biodiversity. The spectral variation hypothesis predicts that variation in plant hyperspectral reflectance is related to variation in taxonomic and functional identity. While most studies report some correlation between spectral and field‐based (i.e., taxonomic and functional) expressions of biodiversity, the observed strength of association is highly variable, and the utility in applying spectral community properties to examine environmental drivers of communities remains unknown. We linked hyperspectral data acquired by airborne imaging spectrometers with precisely geolocated field plots to examine the spectral variation hypothesis along a temperate‐to‐boreal forest gradient in southern Québec, Canada. First, we examine the degree of association between spectral and field‐based dimensions of canopy tree composition and diversity. Second, we ask whether the relationships between field‐based community properties and the environment are reproduced when using spectral community properties. We found support for the spectral variation hypothesis with the strength of association generally greater for the functional than taxonomic dimension, but the strength of relationships was highly variable and dependent on the choice of method or metric used to quantify spectral and field‐based community properties. Using a multivariate approach (comparisons of separate ordinations), spectral composition was moderately well correlated with field‐based composition; however, the degree of association increased when univariately relating the main axes of compositional variation. Spectral diversity was most tightly associated with functional diversity metrics that quantify functional richness and divergence. For predicting canopy tree composition and diversity using environmental variables, the same qualitative conclusions emerge when hyperspectral or field‐based data are used. Spatial patterns of canopy tree community properties were strongly related to the turnover from temperate‐to‐boreal communities, with most variation explained by elevation. Spectral composition and diversity provide a straightforward way to quantify plant biodiversity across large spatial extents without the need for a priori field observations. While commonly framed as a potential tool for biodiversity monitoring, we show that spectral community properties can be applied more widely to assess the environmental drivers of biodiversity, thereby helping to advance our understanding of the drivers of biogeographical patterns of plant communities. [ABSTRACT FROM AUTHOR]

Surface, aircraft, and satellite measurements indicate pervasive early cold season (Augut–September) CO2 emissions across Arctic regions, consistent with increased ecosystem metabolism in plants and soils. A key remaining question is whether cold season sources will become large enough to permanently shift the Arctic into a net carbon source. Polar orbiting GHG satellites provide robust estimation of regional carbon budgets but lack sufficient spatial coverage and repeat frequency to track sink‐to‐source transitions in the early cold season. Mission concepts such as the Arctic Observing Mission (AOM) advocate for flying imaging spectrometers in highly elliptical orbits (HEO) over the Arctic to address sampling limitations. We perform retrieval and flux inversion simulation experiments using the AURORA mission concept, leveraging a Panchromatic imaging Fourier Transform Spectrometer (PanFTS) in HEO. Our simulations demonstrate the potential benefits of increased CO2 sampling for detecting emissions during the early cold season. Plain Language Summary: The Arctic is warming rapidly, leading to an acceleration of carbon exchange across tundra and boreal ecosystems. These changing flows of carbon can be difficult to detect using traditional observing systems. We propose that measurement systems which observe Arctic ecosystems continuously from a quasi‐GEO orbit can more accurately detect these important changes, such that unexpected and dangerous feedbacks to climate warming can be monitored and accounted for. Key Points: The Arctic Fourier Transform Spectrometer Investigation (AURORA) mission concept addresses GHG sampling limitations in the ArcticAURORA uses a highly elliptical orbit (HEO) to increase repeat frequency, and a panchromatic iFTS for SWIR O2 and CO2 bands (0.7–2.5 μm)Simulation experiments show potential benefits of increased sampling frequency for grid scale detection of early cold season CO2 emissions [ABSTRACT FROM AUTHOR]

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

Crustose coralline algae (CCA) are a highly diverse group of habitat‐forming, calcifying red macroalgae (Rhodophyta) with unique adaptations to diverse irradiance regimes. A distinctive CCA phenotype adaptation, which allows them to maximize photosynthetic performance in low light, is their content of a specific group of light‐harvesting pigments called phycobilins. In this study, we assessed the potential of noninvasive hyperspectral imaging (HSI) in the visible spectrum (400–800 nm) to describe the phenotypic variability in phycobilin content of an Antarctic coralline, Tethysphytum antarcticum (Hapalidiales), from two distinct locations. We validated our measurements with pigment extractions and spectrophotometry analysis, in addition to DNA barcoding using the psbA marker. Targeted spectral indices were developed and correlated with phycobilin content using linear mixed models (R2 = 0.64–0.7). Once applied to the HSI, the models revealed the distinct phycoerythrin spatial distribution in the two site‐specific CCA phenotypes, with thin and thick crusts, respectively. This study advances the capabilities of hyperspectral imaging as a tool to quantitatively study CCA pigmentation in relation to their phenotypic plasticity, which can be applied in laboratory studies and potentially in situ surveys using underwater hyperspectral imaging systems. [ABSTRACT FROM AUTHOR]

Drought stress is a significant factor affecting soybean growth and yield. A lack of suitable high-throughput phenotyping techniques hinders the drought tolerance evaluation of multi-genotype samples. A method for evaluating drought tolerance in soybeans is proposed based on multimodal remote sensing data from an unmanned aerial vehicle (UAV) and machine learning. Hundreds of soybean genotypes were repeatedly planted under well water (WW) and drought stress (DS) in different years and locations (Jiyang and Yazhou, Sanya, China), and UAV multimodal data were obtained in multiple fertility stages. Notably, data from Yazhou were repeatedly obtained during five significant fertility stages, which were selected based on days after sowing. The geometric mean productivity (GMP) index was selected to evaluate the drought tolerance of soybeans. Compared with the results of manual measurement after harvesting, support vector regression (SVR) provided better results (N = 356, R2 = 0.75, RMSE = 29.84 g/m2). The model was also migrated to the Jiyang dataset (N = 427, R2 = 0.68, RMSE = 15.36 g/m2). Soybean varieties were categorized into five Drought Injury Scores (DISs) based on the manually measured GMP. Compared with the results of the manual DIS, the accuracy of the predicted DIS gradually increased with the soybean growth period, reaching a maximum of 77.12% at maturity. This study proposes a UAV-based method for the rapid high-throughput evaluation of drought tolerance in multi-genotype soybean at multiple fertility stages, which provides a new method for the early judgment of drought tolerance in individual varieties, improving the efficiency of soybean breeding, and has the potential to be extended to other crops. [ABSTRACT FROM AUTHOR]

Coral reefs are threatened globally by compounding stressors of accelerating climate change and deteriorating water quality. Water quality plays a central role in coral reef health. Yet, accurately quantifying water quality at large scales meaningful for monitoring impacts on coral health remains a challenge due to the complex optical conditions typical of shallow water coastal systems. Here, we report the performance of 32 remote sensing water quality models for suspended particulate matter and chlorophyll concentrations as well as colored dissolved organic matter absorption, over concentration ranges relevant for reef ecology using airborne imaging spectroscopy and field measurements across 62 stations in nearshore Hawaiian waters. Models were applied to reflectance spectra processed with a suite of approaches to compensate for glint and other above-water impacts on reflectance spectra. Results showed reliable estimation of particulate matter concentrations (RMSE = 2.74 mg L−1) and accurate but imprecise estimation of chlorophyll (RMSE = 0.46 μg L−1) and colored dissolved organic matter (RMSE = 0.03 m−1). Accurately correcting reflectance spectra to minimize sun and sky glint effects significantly improved model performance. Results here suggest a role for both hyperspectral and multispectral platforms and rapid application of simple algorithms can be useful for nearshore water quality monitoring over coral reefs. [ABSTRACT FROM AUTHOR]

Soils are the largest terrestrial carbon pool and a valuable good that provides important ecosystem services. Since soils are threatened by degradation and in order to fight climate change the knowledge of the status quo especially of its soil organic carbon (SOC) content is required. A promising tool to map and monitor our soils are spaceborne imaging spectrometers which are able to produce up-to-date, inexpensive and spatially explicit maps. Especially the recent launch of new imaging spectroscopy sensors with a high signal-to-noise ratio opens up new possibilities. One of those is the combination of multitemporal spaceborne imaging spectroscopy data into SOC composite maps with a higher spatial coverage. This study explores different multitemporal combination workflows in order to support finding a best practice. To our knowledge for the first time, a spatially more complete SOC composite map was generated using four PRISMA images recorded over the same study site in northern Germany. Two different workflows of computation were compared: workflow one, creates a synthetical bare soil composite using averaged spectra as a basis for model development. Workflow two uses compositing after individual SOC modeling for each image. Within these workflows, different approaches were tested to estimate the SOC content, amongst them are a range of SOC spectral features and a two-step local PLSR which replaces the wet-chemistry SOC analyses for model calibration and validation by laboratory spectra and a large scale soil spectral library. Results show that the best method to produce a multitemporal composite SOC map based on imaging spectroscopy data was workflow two: the SOC maps composite, using the SOC spectral feature approach (R2 = 0.83, RPD = 2.42). While workflow two and the traditional PLSR approach was more robust for all input dates (R2 = 0.79, RPD = 2.21). Best results of the single images reached R2 values of 0.76-0.91 and RPD values ranging between 2.06-3.42. Three of the tested SOC spectral features provided accuracies comparable to the modeling approaches. These results are promising regarding the improvement of the spatial coverage and the refinement of the mapping and monitoring of SOC and other soil parameters. Further investigations in this direction are needed as they are precursors of what will be feasible by upcoming operational imaging spectroscopy missions with increased availability.

In this study, hyperspectral data of two geographically distant study areas in Germany were analyzed to find the most effective spectral wavelengths for red-edge-based vegetation indices to differentiate bark beetle-infested Norway spruce into three infestation classes, including green-attack. Considering that the relationship between red-edge position (REP) and reflection shoulder position (RESP) is crucial for the relative quantification of chlorophyll concentration and LAI and their changes due to bark beetle infestation, we tested normalized difference red-edge indices (NDRE) formed from these parameters. Using combinations of 75 spectral bands in the range 685 nm to 850 nm, we analyzed 1187 different NDREs, and two additional vegetation indices, and show that the sorted, normalized Kruskal-Wallis H-value, followed by a frequency analysis of the RESP and REP bands of the 10 % of the highest ranked NDREs is an effective way to identify the global optima of these parameters. The optimal REP and RESP estimated using this approach were determined at 714 nm and 758 nm, respectively. The derived normalized difference red-edge index NDRE758_714 could be successfully applied to both test sites. Moreover, we found that aggregating hyperspectral bands into broader bands – 703 nm to 729 nm for a REP band and 741 nm to 767 nm for a RESP band – did not degrade model accuracy when used in a multispectral NDRE, suggesting that a hyperspectral camera is not required to perform the task. Multinomial logistic regression (MNLR) and random forest (RF) classification models were successfully transferred between the two geographically distant study sites. The predictive accuracies of the models for the test data indicated MNLR to be more robust than the RF. With the MLNR models applied, overall accuracies 77.4 % were achieved in the first study area imaged during the green-attack core period, and 88.2 % in the second study area imaged at the final green-attack stage. Our results contribute to forest health monitoring with imaging spectroscopy data and provide practical recommendations for sensor design with broader bands.

Invasive plants are threats to biodiversity, ecosystem functioning, and services. Previous studies have reported that the impacts of biological invasions on ecosystem characteristics can be scale- and ecosystem-dependent. Current methods to assess the impacts of biological invasions have mainly focused on traditional field observations, limiting the scale at which biological invasions can be studied. With its synoptic view, remote sensing can contribute to improving our understanding of the impacts of biological invasions across large spatial scales. However, the application of remote sensing to determine the impacts of invasive plants on ecosystem characteristics, including aboveground and belowground, has not yet been explored. Therefore, our goals were to (1) determine the impacts of invasive plants on aboveground functional traits and productivity, (2) assess the underlying mechanisms through which species invasion impacts belowground characteristics, and (3) determine the capability of remotely-sensed data to capture the impacts of species invasion on aboveground and belowground characteristics. To address our goals, we focused on Lespedeza cuneata (L. cuneata), an invasive legume at the Joseph H. Williams Tallgrass Prairie Preserve in Oklahoma, U.S. We measured percent cover of L. cuneata, quantified aboveground biomass, collected top-of-canopy foliage samples and soil samples in the field, and collected airborne imaging spectroscopy. We used remotely-sensed spectral data and in situ-measured traits to estimate plant functional traits and aboveground biomass. We then assessed the impacts of L. cuneata invasion on aboveground functional traits and biomass using generalized additive models. We also identified the mechanisms through which L. cuneata impacted belowground characteristics using structural equation models. We developed generalized joint attribute models using in situ aboveground and belowground characteristics and predicted belowground characteristics throughout our study site by applying the developed model to remotely-sensed aboveground characteristics. Our findings showed that L. cuneata invasion shifted aboveground functional traits toward those of L. cuneata by significantly increasing community-weighted mean (CWM) foliar nitrogen and phosphorus concentrations. Moreover, L. cuneata significantly increased aboveground biomass, a proxy for aboveground productivity. We also showed that imaging spectroscopy captured the impacts of species invasion on aboveground functional traits and productivity. More importantly, we provided substantial evidence suggesting that imaging spectroscopy can be used to predict belowground characteristics through the aboveground-belowground linkages. These findings can significantly advance our understanding of the impacts of biological invasions on belowground characteristics across large scales which is often challenging to quantify using field methods.

Imaging spectroscopy allows us to identify surface materials by analyzing the spectra resulting from the light–material interaction. In this preliminary study, we analyze a pair of hyperspectral cubes acquired by PRISMA (on 20 April 2021) and EO1- Hyperion (on 4 July 2015) over the Indonesian Lusi mud eruption. We show the potential suitability of using the two sensors for characterizing the mineralogical features in demanding "wet and muddy" environments such as Lusi. We use spectral library reflectance spectra like Illite Chlorite from the USGS spectral library, which are known to be associated with Lusi volcanic products, to identify minerals. In addition, we have measured the reflectance spectra and composition of Lusi sampled mud collected in November 2014. Finally, we compare them with reflectance spectra from EO1-Hyperion and PRISMA. The use of hyperspectral sensors at improved SNR, such as PRISMA, has shown the potential to determine the mineral composition of Lusi PRISMA data, which allowed the distinction of areas with different turbidities as well. Artifacts in the VNIR spectral region of the L2 PRISMA reflectance product were found, suggesting that future work needs to take into account an independent atmospheric correction rather than using the L2D PRISMA product. [ABSTRACT FROM AUTHOR]

With the increasing availability and volume of remote sensing data, imaging spectroscopy is an expanding tool for agricultural studies. One of the fundamental applications in agricultural research is crop mapping and classification. Previous studies have mostly focused at local to regional scales, and classifications were usually performed for a limited number of crop types. Leveraging fine spatial resolution (60 cm) imaging spectroscopy data collected by the Global Airborne Observatory (GAO), we investigated canopy-level spectral variations in 16 crop species from different agricultural regions in the U.S. Inter-specific differences were quantified through principal component analysis (PCA) of crop spectra and their Euclidean distances in the PC space. We also classified the crop species using support vector machines (SVM), demonstrating high classification accuracy with a test kappa of 0.97. A separate test with an independent dataset also returned high accuracy (kappa = 0.95). Classification using full reflectance spectral data (320 bands) and selected optimal wavebands from the literature resulted in similar classification accuracies. We demonstrated that classification involving diverse crop species is achievable, and we encourage further testing based on moderate spatial resolution imaging spectrometer data. [ABSTRACT FROM AUTHOR]

Accurate retrieval of canopy nutrient content has been made possible using visible-to-shortwave infrared (VSWIR) imaging spectroscopy. While this strategy has often been tested on closed green plant canopies, little is known about how nutrient content estimates perform when applied to pixels not dominated by photosynthetic vegetation (PV). In such cases, contributions of bare soil (BS) and non-photosynthetic vegetation (NPV), may significantly and nonlinearly reduce the spectral features relied upon for nutrient content retrieval. We attempted to define the loss of prediction accuracy under reduced PV fractional cover levels. To do so, we utilized VSWIR imaging spectroscopy data from the Global Airborne Observatory (GAO) and a large collection of lab-calibrated field samples of nitrogen (N) content collected across numerous crop species grown in several farming regions of the United States. Fractional cover values of PV, NPV, and BS were estimated from the GAO data using the Automated Monte Carlo Unmixing algorithm (AutoMCU). Errors in prediction from a partial least squares N model applied to the spectral data were examined in relation to the fractional cover of the unmixed components. We found that the most important factor in the accuracy of the partial least squares regression (PLSR) model is the fraction of photosynthetic vegetation (PV) cover, with pixels greater than 60% cover performing at the optimal level, where the coefficient of determination ( R 2 ) peaks to 0.66 for PV fractions of more than 60% and bare soil (BS) fractions of less than 20%. Our findings guide future spaceborne imaging spectroscopy missions as applied to agricultural cropland N monitoring. [ABSTRACT FROM AUTHOR]

The MAJIS (Moons And Jupiter Imaging Spectrometer) instrument on board the ESA JUICE (JUpiter ICy moon Explorer) mission is an imaging spectrometer operating in the visible and near-infrared spectral range from 0.50 to 5.55 μm in two spectral channels with a boundary at 2.3 μm and spectral samplings for the VISNIR and IR channels better than 4 nm/band and 7 nm/band, respectively. The IFOV is 150 μrad over a total of 400 pixels. As already amply demonstrated by the past and present operative planetary space missions, an imaging spectrometer of this type can span a wide range of scientific objectives, from the surface through the atmosphere and exosphere. MAJIS is then perfectly suitable for a comprehensive study of the icy satellites, with particular emphasis on Ganymede, the Jupiter atmosphere, including its aurorae and the spectral characterization of the whole Jupiter system, including the ring system, small inner moons, and targets of opportunity whenever feasible. The accurate measurement of radiance from the different targets, in some case particularly faint due to strong absorption features, requires a very sensitive cryogenic instrument operating in a severe radiation environment. In this respect MAJIS is the state-of-the-art imaging spectrometer devoted to these objectives in the outer Solar System and its passive cooling system without cryocoolers makes it potentially robust for a long-life mission as JUICE is. In this paper we report the scientific objectives, discuss the design of the instrument including its complex on-board pipeline, highlight the achieved performance, and address the observation plan with the relevant instrument modes. [ABSTRACT FROM AUTHOR]

This study addresses the complexity and importance of developing a method of calibrating digital observations of meteor spectra with all-sky cameras. It aims to present novel approaches to spectral sensitivity, atmospheric extinction and flat-field corrections. Images of a known line emission spectrum were captured at various positions within the field of view using a camera with a fish-eye lens and plastic holographic grating. The flat-field correction was separated into a wavelength-independent and wavelength-dependent component, both dependent on the position of the spectral line in the field of view (FoV). Total profile intensities of spectra obtained from the images were compared throughout the spectral range at different positions in the FoV. The flat-field was constructed by fitting those dependencies with high-degree polynomial functions. Using a simplified atmospheric model, a novel approach was constructed to determine the atmospheric extinction curve throughout the spectral range, allowing it to be separately considered from the spectral sensitivity which was previously not the case. A comparison of the newly developed and previously used methodology was tested on several meteor spectra of the same meteor captured from different stations of the European Fireball Network. It revealed a significantly improved correspondence of the analysed spectra in the part of the spectral range unaffected by the limitations imposed by the newly developed methodology. Failing to follow the correct calibration methodology precisely may introduce varying degrees of uncertainty in computations of elemental abundances and other physical properties, depending on the equipment’s specific effect magnitude. [ABSTRACT FROM AUTHOR]

Corals are habitat-forming organisms on tropical and sub-tropical reefs, often displaying diverse phenotypic behaviors that challenge field-based monitoring and assessment efforts. Symbiont chlorophyll (Chl) is a long-recognized indicator of intra- and inter-specific variation in coral's response to environmental variability and stress, but the quantitative Chl assessment of corals at the reef scale continues to prove challenging. We integrated field, airborne, and laboratory techniques to test and apply the use of reflectance spectroscopy for in situ and reef-scale estimation of Chl a and Chl c2 concentrations in a shallow reef environment of Kāne'ohe Bay, O'ahu. High-fidelity spectral signatures (420–660 nm) derived from field and airborne spectroscopy quantified Chl a and Chl c2 concentrations with demonstrable precision and accuracy. Airborne imaging spectroscopy revealed a 10-fold range of Chl concentrations across the reef ecosystem. We discovered a differential pattern of Chl a and Chl c2 use in symbiont algae in coexisting corals indicative of a physiological response to decreasing light levels with increasing water depth. The depth-dependent ratio of Chl c2:a indicated the presence of two distinct light-driven habitats spanning just 5 m of water depth range. Our findings provide a pathway for further study of coral pigment responses to environmental conditions using field and high-resolution airborne imaging spectroscopy. [ABSTRACT FROM AUTHOR]

Imaging spectroscopy offers great potential to characterize plant traits at fine resolution across broad regions and then assess controls on their variation across spatial resolutions. We applied permutational partial least‐squares regression to map seven key foliar chemical and morphological traits using NASA's Airborne Visible/Infrared Imaging Spectrometer‐Next Generation (AVIRIS‐NG) for six sites spanning a climatological gradient in the Western Ghats of India. We studied the variation of trait space at spatial resolutions from the plot level (4 m), community level (30 and 100 m) to the ecosystem level (1,000 m). We observed a consistent pattern of trait space across different resolutions, with one axis defined by foliar nitrogen and leaf mass per area (LMA) and another axis representing leaf structure and defense defined by fiber, lignin, and total phenolics. We also observed consistent directionality of environment‐trait correlations across resolutions with generally higher predictive capacity of our environment‐traits models at coarser resolutions. Among the seven traits, total phenolics, fiber, and lignin were strongly influenced by environmental factors (model R2 > 0.5 at 1,000 m). Calcium, sugars, and nitrogen were significantly affected by site conditions, incorporating site as a fixed effect largely improved model performance. LMA showed little dependence on environmental factors or site conditions, suggesting a stronger influence of species composition and site history on LMA variation. Our results show that reliable trait‐trait relationships can be identified in coarse resolution imagery, but that local scale trait‐trait relationships (resolutions finer than 30 m) are not sensitive to broad‐scale abiotic/biotic factors. Plain Language Summary: We combined field leaf measurements and airborne hyperspectral images to map seven key foliar functional traits across the Western Ghats in India. We analyzed the traits at different spatial resolutions, from plot‐level (4 m), community level (30 and 100 m) to ecosystem‐level (1,000 m). We found that the trait‐trait relationship remains the same at different resolutions, which shows that we can learn a lot about the trait relationships by using images that aren't very detailed. We also looked at how traits are linked to the environment. Traits like chemicals that help plants defend themselves, as well as physical defenses like fiber and lignin, were strongly influenced by broad climate conditions. We could predict these traits well at 1,000 and 100 m. Other traits like calcium, sugars, and nitrogen were more affected by where the plants were growing, and we could predict them well when we considered the specific location. However, leaf mass per area didn't seem to depend much on the environment or location, which means it's probably influenced more by species composition and site history. When we looked at traits on a finer scale (30 m), our predictions weren't as accurate, suggesting that other things might also be affecting traits at those levels. Key Points: We mapped seven key plant functional traits for six sites along the Western Ghats of India‐a diverse but under‐represented areaOur study showed that the trait‐trait relationship (or trait space) is well conserved during the spatial aggregation from 4 to 1,000 mMost traits could be predicted well at 1,000 m and moderately at 100 m by environmental parameters but not at local scales finer than 30 m [ABSTRACT FROM AUTHOR]

Effective defect detection of blueberries is important to ensuring supplies of high-quality products to the fresh market. In this study, hyperspectral reflectance imaging with machine learning was evaluated for discriminating between defective and normal blueberries. Fresh blueberries hand-harvested were scanned in the wavelength range of 400–1000 nm. An image analysis pipeline was developed to segment individual blueberries and extract mean spectra and spatial features. Defective blueberries were found to have lower near-infrared reflectance than sound samples, and spectral features produced a better separation between defective and sound samples than the spatial features in the scattering plots of their first two principal components. Nine types of machine learning models were built for classifying defective and sound samples using the spectral and spatial features separately as well as their concatenation. The regularized linear discriminant analysis (RLDA) model trained on the spectral features achieved the best overall accuracy of 95.7 %, as opposed to the best accuracy of 85.3 % based on spatial features, which was obtained by LDA. Simply concatenating spectral and spatial features did not improve over modeling using spectral or spatial features alone. A model ensemble strategy integrating the spectral features-based RLDA and the spatial features-based LDA resulted in a statistically significant improvement in the overall accuracy to 96.6 %. Model-level feature integration offers an effective means for improving the discrimination between defective and normal blueberries. Both the hyperspectral data11 https://doi.org/10.5281/zenodo.11200576. and the software programs22 https://github.com/vicdxxx/Blueberry-Defect-Detection-by-Hyperspectral-Imaging. of this study are made publicly available.

Abstract Mitigating impacts of global change on biodiversity is a pressing goal for land managers, but understanding impacts is often limited by the spatial and temporal constraints of traditional in situ data. Advances in remote sensing address this challenge, in part, by enabling standardized mapping of biodiversity at large spatial scales and through time. In particular, hyperspectral imagery can detect functional and compositional characteristics of vegetation by measuring subtle differences in reflected light. The spectral variance hypothesis (SVH) expects spectral diversity, or variability in reflectance across pixels, to predict vegetation diversity. However, the majority of research testing the SVH to date has been conducted in systems with controlled conditions or spatially homogenous assemblages, with little generalizability to heterogeneous real‐world systems. Here, we move the field forward by testing the SVH in a species‐rich system with high heterogeneity resulting from variable species composition and a recent fire. We use very high spatial resolution (~1 mm) hyperspectral imagery to compare spectrally derived estimates of vegetation diversity with in situ measures collected in Boulder, CO, USA. We find that spectral diversity and taxonomic diversity are positively correlated only for low to moderate diversity transects, or in transects that were recently burned where vegetation diversity is low and composed primarily of C3 grasses. Additionally, we find that the relationship between spectral and taxonomic diversity depends on spatial resolution, indicating that pixel size should remain a priority for biodiversity monitoring. Practical implication: The context dependency of this relationship, even with high spatial resolution data, confirms previous work that the SVH does not hold across landscapes and demonstrates the necessity for repeated, high‐resolution data in order to tease apart the biological conditions underpinning the SVH. With refinement, however, the remote sensing techniques described here will offer land managers a cost‐effective approach to monitor biodiversity across space and time.

The increasing importance of Earth observation information for society and environment, particularly in the expanding domain of hyperspectral remote sensing, along with the significantly increasing data availability, emphasizes the demand for online education to address the growing need for expertise in this domain. We present a comprehensive review of existing online education programs on Earth observation that underline the limited availability of online resources for hyperspectral remote sensing.To respond to this demand, we introduce HYPERedu, an online education program on hyperspectral remote sensing that has been developed as part of the EnMAP satellite mission since 2019. The program’s core components, content, and participation statistics, along with insights from participant feedback, are presented. HYPERedu offers a wide range of resources, including slide collections, tutorials, video screencasts, and online courses, making them accessible to a broad audience for university teaching, training schools and individual learning. The program’s effectiveness as well as challenges and insights encountered during program development are discussed. We emphasize the importance of collaborative learning, sustainability efforts for long-term material accessibility and quality assurance, and the need to bridge the digital divide with offline options, open-source tools, and multilingual content. Effective content presentation is vital, focusing on consistent branding, concise modules, interactive features, and narrative-style videos. Additionally, we explore the future of online Earth observation learning, considering enhanced participant interaction and the integration of new learning concepts. Finally, we underscore the significance of outreach and training for satellite mission utilization and advocate for open education to promote open science, data, software, and accessibility for inclusive education.

Abstract Surface, aircraft, and satellite measurements indicate pervasive early cold season (Augut–September) CO2 emissions across Arctic regions, consistent with increased ecosystem metabolism in plants and soils. A key remaining question is whether cold season sources will become large enough to permanently shift the Arctic into a net carbon source. Polar orbiting GHG satellites provide robust estimation of regional carbon budgets but lack sufficient spatial coverage and repeat frequency to track sink‐to‐source transitions in the early cold season. Mission concepts such as the Arctic Observing Mission (AOM) advocate for flying imaging spectrometers in highly elliptical orbits (HEO) over the Arctic to address sampling limitations. We perform retrieval and flux inversion simulation experiments using the AURORA mission concept, leveraging a Panchromatic imaging Fourier Transform Spectrometer (PanFTS) in HEO. Our simulations demonstrate the potential benefits of increased CO2 sampling for detecting emissions during the early cold season.

Snow albedo is a crucial component to the energy balance of our seasonal snowpack on Earth, reflecting incoming solar irradiance and altering the Earth system. Seasonal snow surface properties undergo constant change (e.g., melt-freeze cycle, faceting, sublimation, and windblown compaction) and have high spatial variability, especially in mountain regions, making it difficult to scale ground measurements. Snow albedo, fractional snow-covered area, and snow-specific surface area can be modeled using top of atmosphere radiance measurements from spaceborne imaging spectroscopy. We model these snow properties by testing inversions geared specifically for complex topography, as well as incorporating the impacts of the mixed snow pixel. Additionally, we avoid computing every pixel in an image by creating tens-of-thousands of k-means clusters based on the rounded values of the cosine of the local illumination angle to the nearest ten-thousandth digit. This computation is further sped up by leveraging message passing interface to scale with more nodes. We present this work as an open-source algorithm, which we refer to as Global Optical Snow properties via High-speed Algorithm With K-means clustering (GOSHAWK). We validate our algorithm with PRecursore IperSpettrale della Missione Applicativa L1 radiance imagery across eight sites in the Northern Hemisphere from 2021 to 2023 and compare outputs with field spectroscopy, specific surface area measurements, airborne LiDAR surveys, and four-component net radiometers. More work in algorithm development and calibration–validation work is needed in steep terrain and dense canopy to improve snow property retrieval prior to the Surface Biology and Geology and Copernicus Hyperspectral Imaging Mission for the Environment missions.

Investigating the application of remote sensing to water pollution in industrial ponds is of great interest for rapid and cost-effective pollution monitoring. This article presents a method to detect pollutants and map their spatial distribution in industrial ponds using the water inherent optical properties (IOPs), namely the absorption and backscattering coefficients, derived from imaging spectroscopy data. The IOPs of industrial water pollutants remain poorly known. Current remote sensing methods are site-specific and require in situ measurements to calibrate empirically based models. Here, a generic approach is proposed based on the semianalytical radiative transfer model adapted to take into account both the absorption and backscattering coefficients of pollutant particles. The model is then inverted using an alternating multipixel method, named industrial wastewater optical characterization (IWOC), to map the spatial distribution of the pollutants. The performances of IWOC are evaluated using noise-free and noisy simulated datasets for an absorption-dominated water case and a backscattering-dominated water case. The water reflectance spectra (Rrs) for noise-free synthetic datasets are satisfactorily retrieved by the IWOC method. The optical properties of the pollutants are also well retrieved, with maximum root mean square error (RMSE) values of 2.43 × 10−3 m−1 for the absorption-dominated case and fairly zero for the backscattering-dominated case. A sensitivity study shows that the impact of noise is the highest on the estimates of the spectral slope exponent of the backscattering coefficient. The performances of the IWOC method are also examined through hyperspectral airborne images acquired over relevant study areas. The reflectance Rrs is well retrieved with RMSE values ranging from 7.5 × 10−5 sr−1 to 5.82 × 10−4 sr−1. The a priori knowledge of the properties of the study areas is consistent with the spatial distribution of the effluents within the ponds as derived from the remote sensing observations. The approach conducted in this article is a first step toward a generic inversion method for the optical characterization of pollution sources in water, which could further lead to an operational method.

Surface reflectance is an important data product in imaging spectroscopy for obtaining surface information. The complex retrieval of surface reflectance, however, critically relies on accurate knowledge of atmospheric absorption and scattering, and the compensation of these effects. Furthermore, illumination and observation geometry in combination with surface reflectance anisotropy determine dynamics in retrieved surface reflectance not related to surface absorption properties. To the best of authors' knowledge, no comprehensive assessment of the impact of atmospheric, topographic, and anisotropy effects on derived surface information is available so far. This study systematically evaluates the impact of these effects on reflectance, albedo, and vegetation products. Using three well-established processing schemes (ATCOR F., ATCOR R., and BREFCOR), high-resolution APEX imaging spectroscopy data, covering a large gradient of illumination and observation angles, are brought to several processing states, varyingly affected by mentioned effects. Pixel-wise differences of surface reflectance, albedo, and spectral indices of neighboring flight lines are quantitatively analyzed in their respective overlapping area. We found that compensation of atmospheric effects reveals actual anisotropy-related dynamics in surface reflectance and derived albedo, related to an increase in pixel-wise relative reflectance and albedo differences of more than 40%. Subsequent anisotropy compensation allows us to successfully reduce apparent relative reflectance and albedo differences by up to 20%. In contrast, spectral indices are less affected by atmospheric and anisotropy effects, showing relative differences of 3% to 10% in overlapping regions of flight lines. We recommend to base decisions on the use of appropriate processing schemes on individual use cases considering envisioned data products.

This document is an editorial published in the journal Frontiers in Plant Science. It discusses the application of deep learning approaches to spectral images for plant phenotyping. Spectral imaging is a sensor technology used in agriculture and plant science to quantify the composition of agricultural products and detect plant stresses and diseases. The editorial highlights the challenges of using deep learning networks for spectral image data, such as the complexity of the datasets and the lack of pre-trained networks. It also mentions research on reconstructing spectral images from RGB images and presents several research papers on disease detection, weed density, apple disease recognition, gray mold disease diagnosis, Raman spectroscopy, and chlorophyll estimation. The editorial concludes that deep learning approaches show promise for analyzing spectral images in plant phenotyping and can lead to new insights into plant biology and responses to environmental stressors. [Extracted from the article]

The standardized spectral mixture model combines the specificity of a physically based representation of a spectrally mixed pixel with the generality and portability of a spectral index. Earlier studies have used spectrally and geographically diverse collections of broadband and spectroscopic imagery to show that the reflectance of the majority of ice-free landscapes on Earth can be represented as linear mixtures of rock and soil substrates (S), photosynthetic vegetation (V) and dark targets (D) composed of shadow and spectrally absorptive/transmissive materials. However, both broadband and spectroscopic studies of the topology of spectral mixing spaces raise questions about the completeness and generality of the Substrate, Vegetation, Dark (SVD) model for imaging spectrometer data. This study uses a spectrally diverse collection of 40 granules from the EMIT imaging spectrometer to verify the generality and stability of the spectroscopic SVD model and characterize the SVD topology and plane of substrates to assess linearity of spectral mixing. New endmembers for soil and non-photosynthetic vegetation (NPV; N) allow the planar SVD model to be extended to a tetrahedral SVDN model to better accommodate the 3D topology of the mixing space. The SVDN model achieves smaller misfit than the SVD, but does so at the expense of implausible fractions beyond [0, 1]. However, a refined spectroscopic SVD model still achieves small (

Imaging spectroscopy (hyperspectral sensing) is a proven tool for mapping and monitoring the spatial distribution of vegetation species composition. However, there exists a gap when it comes to the availability of high-resolution spatial and spectral imagery for accurate tree species mapping, particularly in complex forest environments, despite the continuous advancements in operational remote sensing and field sensor technologies. Here, we aim to bridge this gap by enhancing our fundamental understanding of imaging spectrometers via complex simulated environments. We used DIRSIG, a physics-based, first-principles simulation approach to model canopy-level reflectance for 3D plant models and species-level leaf reflectance in a synthetic forest scene. We simulated a realistic scene, based on the same species composition, found at Harvard Forest, MA (USA). Our simulation approach allowed us to better understand the interplay between instrument parameters and landscape characteristics, and facilitated comprehensive traceability of error budgets. To enhance our understanding of the impact of sensor design on classification performance, we simulated image samples at different spatial, spectral, and scale resolutions (by modifying the pixel pitch and the total number of pixels in the sensor array, i.e., the focal plane dimension) of the imaging sensor and assessed the performance of a deep learning-based convolutional neural network (CNN) and a traditional machine learning classifier, support vector machines (SVMs), to classify vegetation species. Overall, across all resolutions and species mixtures, the highest classification accuracy varied widely from 50 to 84%, and the number of genus-level species classes identified ranged from 2 to 17, among 24 classes. Harnessing this simulation approach has provided us valuable insights into sensor configurations and the optimization of data collection methodologies to improve the interpretation of spectral signatures for accurate tree species mapping in forest scenes. Note that we used species classification as a proxy for a host of imaging spectroscopy applications. However, this approach can be extended to other ecological scenarios, such as in evaluating the changing ecosystem composition, detecting invasive species, or observing the effects of climate change on ecosystem diversity. [ABSTRACT FROM AUTHOR]

Knowing how functional diversity varies across environmental gradients is crucial in investigating biodiversity formation and community assembly processes. The majority of studies concerning functional diversity are based on one fixed plot size, resulting in weakened sensitivity of the spatial patterns to sampling resolution. This weakness may obscure the true mechanisms behind community assemblage. In this study, we utilized hyperspectral imagery collected by Orbita hyperspectral satellites (OHS) to explore the spatial distribution patterns of functional traits and diversity in alpine meadows at four sampling radii (25 m, 50 m, 100 m, 200 m). We examined how functional traits and their diversity responded to variations in climatic and soil conditions at different sampling resolutions. Our findings revealed a significant influence of solar radiation and soil depth on the patterns of functional traits in alpine meadows. Scale dependency was evident in the functional richness (FRic), functional evenness (FEve), and functional dispersion (FDis) of different traits, indicated by a notable difference from the null model. In the presence of stronger soil nutrient and water limitation, chlorophyll a content (CAC), specific leaf area (SLA), leaf thickness (LT), and plant height (Height) exhibited increased clustering and convergence at larger sampling resolutions, illustrating environmental filtering in habitat patch assembly in the alpine meadow. For the first time, this study explores scale dependency in functional diversity patterns in alpine meadows using satellite-based hyperspectral imagery. It unveils the environmental filtering effect of soil nutrient limitation on shaping vegetation patch structures at a landscape level from a remote sensing perspective. The study underscores the significance of accounting for sampling resolution in future related research.

CNES is currently carrying out a Phase A study to assess the feasibility of a future hyperspectral imaging sensor (10 m spatial resolution) combined with a panchromatic camera (2.5 m spatial resolution). This mission focuses on both high spatial and spectral resolution requirements, as inherited from previous French studies such as HYPEX, HYPXIM, and BIODIVERSITY. To meet user requirements, cost, and instrument compactness constraints, CNES asked the French hyperspectral Mission Advisory Group (MAG), representing a broad French scientific community, to provide recommendations on spectral sampling, particularly in the Short Wave InfraRed (SWIR) for various applications.This paper presents the tests carried out with the aim of defining the optimal spectral sampling and spectral resolution in the SWIR domain for quantitative estimation of physical variables and classification purposes. The targeted applications are geosciences (mineralogy, soil moisture content), forestry (tree species classification, leaf functional traits), coastal and inland waters (bathymetry, water column, bottom classification in shallow water, coastal habitat classification), urban areas (land cover), industrial plumes (aerosols, methane and carbon dioxide), cryosphere (specific surface area, equivalent black carbon concentration), and atmosphere (water vapor, carbon dioxide and aerosols). All the products simulated in this exercise used the same CNES end-to-end processing chain, with realistic instrument parameters, enabling easy comparison between applications. 648 simulations were carried out with different spectral strategies, radiometric calibration performances and signal-to-noise Ratios (SNR): 24 instrument configurations × 25 datasets (22 images + 3 spectral libraries).The results show that spectral sampling up to 20 nm in the SWIR range is sufficient for most applications. However, 10 nm spectral sampling is recommended for applications based on specific absorption bands such as mineralogy, industrial plumes or atmospheric gases. In addition, a slight performance loss is generally observed when radiometric calibration accuracy decreases, with a few exceptions in bathymetry and in the cryosphere for which the observed performance is severely degraded. Finally, most applications can be achieved with a realistic SNR, with the exception of bathymetry, shallow water classification, as well as carbon dioxide and methane estimation, which require the optimistic SNR level tested. On the basis of these results, CNES is currently evaluating the best compromise for designing the future hyperspectral sensor to meet the objectives of priority applications.

The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay minerals and quantifying the montmorillonite abundance are a challenge since they are usually intimately mixed with other minerals, soil organic carbon and soil moisture content. Imaging spectroscopy coupled with unmixing methods can address these issues, but the quality of the estimation degrades the coarser the spatial resolution is due to pixel heterogeneity. With the advent of UAV-borne and proximal hyperspectral acquisitions, it is now possible to acquire images at a centimeter scale. Thus, the objective of this paper is to evaluate the accuracy and limitations of unmixing methods to retrieve montmorillonite abundance from very-high-resolution hyperspectral images (1.5 cm) acquired from a camera installed on top of a bucket truck over three different agricultural fields, in Loiret department, France. Two automatic endmember detection methods based on the assumption that materials are linearly mixed, namely the Simplex Identification via Split Augmented Lagrangian (SISAL) and the Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF), were tested prior to unmixing. Then, two linear unmixing methods, the fully constrained least square method (FCLS) and the multiple endmember spectral mixture analysis (MESMA), and two nonlinear unmixing ones, the generalized bilinear method (GBM) and the multi-linear model (MLM), were performed on the images. In addition, several spectral preprocessings coupled with these unmixing methods were applied in order to improve the performances. Results showed that our selected automatic endmember detection methods were not suitable in this context. However, unmixing methods with endmembers taken from available spectral libraries performed successfully. The nonlinear method, MLM, without prior spectral preprocessing or with the application of the first Savitzky–Golay derivative, gave the best accuracies for montmorillonite abundance estimation using the USGS library (RMSE between 2.2–13.3% and 1.4–19.7%). Furthermore, a significant impact on the abundance estimations at this scale was in majority due to (i) the high variability of the soil composition, (ii) the soil roughness inducing large variations of the illumination conditions and multiple surface scatterings and (iii) multiple volume scatterings coming from the intimate mixture. Finally, these results offer a new opportunity for mapping expansive soils from imaging spectroscopy at very high spatial resolution.

This paper presents an innovative, holistic, and comprehensive approach to drone-based imaging spectroscopy based on a small, cost-effective, and lightweight Unmanned Aerial Vehicle (UAV) payload intended for remote sensing applications. The payload comprises a push-broom imaging spectrometer built in-house with readily available Commercial Off-The-Shelf (COTS) components. This approach encompasses the entire process related to drone-based imaging spectroscopy, ranging from payload design, field operation, and data processing to the extraction of scientific data products from the collected data. This work focuses on generating directly georeferenced imaging spectroscopy datacubes using a Do-It-Yourself (DIY) imaging spectrometer, which is based on COTS components and freely available software and methods. The goal is to generate a remote sensing reflectance datacube that is suitable for retrieving chlorophyll-A (Chl-A) distributions as well as other properties of the ocean spectra. Direct georeferencing accuracy is determined by comparing landmarks in the directly georeferenced datacube to their true location. The quality of the remote sensing reflectance datacube is investigated by comparing the Chl-A distribution on various days with in situ measurements and satellite data products.

Spaceborne spectroscopic imaging offers the potential to improve our understanding of biodiversity and ecosystem services, particularly for challenging and rich environments like mangroves. Understanding the signals present in large volumes of high-dimensional spectroscopic observations of vegetation communities requires the characterization of seasonal phenology and response to environmental conditions. This analysis leverages both spectroscopic and phenological information to characterize vegetation communities in the Sundarban riverine mangrove forest of the Ganges–Brahmaputra delta. Parallel analyses of surface reflectance spectra from NASA’s EMIT imaging spectrometer and MODIS vegetation abundance time series (2000–2022) reveal the spectroscopic and phenological diversity of the Sundarban mangrove communities. A comparison of spectral and temporal feature spaces rendered with low-order principal components and 3D embeddings from Uniform Manifold Approximation and Projection (UMAP) reveals similar structures with multiple spectral and temporal endmembers and multiple internal amplitude continua for both EMIT reflectance and MODIS Enhanced Vegetation Index (EVI) phenology. The spectral and temporal feature spaces of the Sundarban represent independent observations sharing a common structure that is driven by the physical processes controlling tree canopy spectral properties and their temporal evolution. Spectral and phenological endmembers reside at the peripheries of the mangrove forest with multiple outward gradients in amplitude of reflectance and phenology within the forest. Longitudinal gradients of both phenology and reflectance amplitude coincide with LiDAR-derived gradients in tree canopy height and sub-canopy ground elevation, suggesting the influence of surface hydrology and sediment deposition. RGB composite maps of both linear (PC) and nonlinear (UMAP) 3D feature spaces reveal a strong contrast between the phenological and spectroscopic diversity of the eastern Sundarban and the less diverse western Sundarban.

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

Drought stress is a significant factor affecting soybean growth and yield. A lack of suitable high-throughput phenotyping techniques hinders the drought tolerance evaluation of multi-genotype samples. A method for evaluating drought tolerance in soybeans is proposed based on multimodal remote sensing data from an unmanned aerial vehicle (UAV) and machine learning. Hundreds of soybean genotypes were repeatedly planted under well water (WW) and drought stress (DS) in different years and locations (Jiyang and Yazhou, Sanya, China), and UAV multimodal data were obtained in multiple fertility stages. Notably, data from Yazhou were repeatedly obtained during five significant fertility stages, which were selected based on days after sowing. The geometric mean productivity (GMP) index was selected to evaluate the drought tolerance of soybeans. Compared with the results of manual measurement after harvesting, support vector regression (SVR) provided better results (N = 356, R2 = 0.75, RMSE = 29.84 g/m2). The model was also migrated to the Jiyang dataset (N = 427, R2 = 0.68, RMSE = 15.36 g/m2). Soybean varieties were categorized into five Drought Injury Scores (DISs) based on the manually measured GMP. Compared with the results of the manual DIS, the accuracy of the predicted DIS gradually increased with the soybean growth period, reaching a maximum of 77.12% at maturity. This study proposes a UAV-based method for the rapid high-throughput evaluation of drought tolerance in multi-genotype soybean at multiple fertility stages, which provides a new method for the early judgment of drought tolerance in individual varieties, improving the efficiency of soybean breeding, and has the potential to be extended to other crops.