Spectral Signatures Research Articles

Spectral analysis comparison of pushbroom and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification.

Hyperspectral imaging sensors have rapidly advanced, aiding in tumor diagnostics for in vivo brain tumors. Linescan cameras effectively distinguish between pathological and healthy tissue, whereas snapshot cameras offer a potential alternative to reduce acquisition time. Our research compares linescan and snapshot hyperspectral cameras for in vivo brain tissues and chromophore identification. We compared a linescan pushbroom camera and a snapshot camera using images from 10 patients with various pathologies. Objective comparisons were made using unnormalized and normalized data for healthy and pathological tissues. We utilized the interquartile range (IQR) for the spectral angle mapping (SAM), the goodness-of-fit coefficient (GFC), and the root mean square error (RMSE) within the 659.95 to 951.42nm range. In addition, we assessed the ability of both cameras to capture tissue chromophores by analyzing absorbance from reflectance information. The SAM metric indicates reduced dispersion and high similarity between cameras for pathological samples, with a 9.68% IQR for normalized data compared with 2.38% for unnormalized data. This pattern is consistent across GFC and RMSE metrics, regardless of tissue type. Moreover, both cameras could identify absorption peaks of certain chromophores. For instance, using the absorbance measurements of the linescan camera, we obtained SAM values below 0.235 for four peaks, regardless of the tissue and type of data under inspection. These peaks are one for cytochrome b in its oxidized form at , two for at and , and one for water at . The spectral signatures of the cameras show more similarity with unnormalized data, likely due to snapshot sensor noise, resulting in noisier signatures post-normalization. Comparisons in this study suggest that snapshot cameras might be viable alternatives to linescan cameras for real-time brain tissue identification.

A new algorithm to determine the spatial coverage of carob (Neltuma piurensis) by ecological floor: Chira-Piura River Basin case

The carob tree (Neltuma piurensis) is characteristic of the forests of northern Peru, withstand extreme climatic events such as “El Niño” and droughts, in addition to the influence of climate change, affecting its distribution of coverage at different altitudes. The objective of this article is to propose an algorithm to determine the Spatial Coverage of Carob by Ecological Floor (SCCEF) in the Chira-Piura River Basin, Peru. The method used consisted of measuring the spectral signature of the carob tree with the FieldSpec4 spectroradiometer at three sampling points corresponding to the localities of Cardal, Lancones and Macacará, located on different ecological floors. The comparison of the spectral signatures for Cardal and Lancones gives an R2 = 0.9459, for Cardal and Macacará an R2 = 0.9866 and for Lancones with Macacará an R2 = 0.9469, which allows an accurate identification of the carob tree in the satellite images. The Mann-Whitney-Wilcoxon U test validates the spectral signatures extracted from the satellite images with the spectral signatures measured with the spectroradiometer at Lancones (p-value = 0.9705 >α = 0.05), Cardal (p-value = 0.9819 > 0.05) and Macacará (p-value = 0.7959 > 0.05). The results show that the SCCEF in the Tropical (T) ecological floor represents 1.55 % of the T area, in the Tropical Pre-Montane (TPM) ecological floor it is 1.47 % of the TPM area, in the Low Tropical Montane (LTM) ecological floor it is 0.78 % of the LTM area, in the Montane (M) ecological floor it is 0.69 % of the M area and in the Paramo (P) ecological floor it is 0.35 % of the P area. Therefore, the SCCEF decreases in each ecological floor as its altitude increases.

Chestnut quality classification by THz Time-Domain Hyperspectral Imaging combined with unsupervised learning analysis

Chestnut crops are threatened by fungal pathogens such as Gnomoniopsis castaneae, which cause significant degradation of quality. Early detection of such infections is crucial to maintain the quality of chestnuts in the food industry. This study explores the application of Terahertz Time-Domain Hyperspectral Imaging (THz-TDHIS) combined with unsupervised learning techniques to identify fungal infections in chestnuts. Unlike conventional methods that rely on light attenuation, this approach leverages the unique spectral signatures of infected tissues. By employing Principal Component Analysis, K-Means Clustering, and Agglomerative Clustering, we effectively differentiate between healthy and infected portions of chestnuts. Our findings indicate that spectral features, rather than just intensity variations, provide more reliable markers for infection. In addition, we demonstrate that these methods enable the quantification of the degree of infection in chestnuts. The robustness of these unsupervised learning methods in handling large and heterogeneous data sets further underscores their potential in agricultural applications. This integrated THz-TDHIS and machine learning approach presents a promising solution to ensure chestnut quality and safety.

Sequence Segmentation of Nematodes in Atlantic Cod with Multispectral Imaging Data.

Nematodes pose significant challenges for the fish processing industry, particularly in white fish. Despite technological advances, the industry still depends on manual labor for the detection and extraction of nematodes. This study addresses the initial steps of automatic nematode detection and differentiation from other common defects in fish fillets, such as skin remnants and blood spots. VideometerLab 4, an advanced Multispectral Imaging (MSI) System, was used to acquire 270 images of 50 Atlantic cod fillets under controlled conditions. In total, 173 nematodes were labeled using the Segment Anything Model (SAM), which is trained to automatically segment objects of interest from only few representative pixels. With the acquired dataset, we study the potential of identifying nematodes through their spectral signature. We incorporated normalized Canonical Discriminant Analysis (nCDA) to develop segmentation models trained to distinguish between different components within the fish fillets. By incorporating multiple segmentation models, we aimed to achieve a satisfactory balance between false negatives and false positives. This resulted in 88% precision and 79% recall for our annotated test data. This approach could improve process control by accurately identifying fillets with nematodes. Using MSI minimizes unnecessary inspection of fillets in good condition and concurrently boosts product safety and quality.

Integrating Cu2O Colloidal Mie Resonators in Structurally Colored Butterfly Wings for Bio-Nanohybrid Photonic Applications.

Colloidal Cu2O nanoparticles can exhibit both photocatalytic activity under visible light illumination and resonant Mie scattering, but, for their practical application, they have to be immobilized on a substrate. Butterfly wings, with complex hierarchical photonic nanoarchitectures, constitute a promising substrate for the immobilization of nanoparticles and for the tuning of their optical properties. The native wax layer covering the wing scales of Polyommatus icarus butterflies was removed by simple ethanol pretreatment prior to the deposition of Cu2O nanoparticles, which allowed reproducible deposition on the dorsal blue wing scale nanoarchitectures via drop casting. The samples were investigated by optical and electron microscopy, attenuated total reflectance infrared spectroscopy, UV-visible spectrophotometry, microspectrophotometry, and hyperspectral spectrophotometry. It was found that the Cu2O nanoparticles integrated well into the photonic nanoarchitecture of the P. icarus wing scales, they exhibited Mie resonance on the glass slides, and the spectral signature of this resonance was absent on Si(100). A novel bio-nanohybrid photonic nanoarchitecture was produced in which the spectral properties of the butterfly wings were tuned by the Cu2O nanoparticles and their backscattering due to the Mie resonance was suppressed despite the low refractive index of the chitinous substrate.

Phenol identification and mapping of rangeland species at laboratory and landscape scales using PRISMA hyperspectral data

ABSTRACT Secondary metabolites have high ecological and economic values. Measuring the amounts of these metabolites in plants through laboratory analysis is time-consuming and expensive. On the other hand, the laboratory results cannot be generalized to a wide area of rangelands with diverse climates, topography, and soil. Therefore, the present study aims to evaluate the potential of PRISMA hyperspectral data to estimate the rangeland phenol concentration at landscape scale in western Isfahan province, Iran. Phenol concentration and spectral signatures were extracted from 42 plant individuals, including Astragalus adscendens, Astragalus verus, Daphne mucronata, Anabasis aphylla, and Phlomis olivieri using laboratory methods and spectroscopy in the range of 350 to 2500 nm. Phenol-sensitive bands were identified by establishing a relationship between phenol concentration and all spectral bands by the Partial Least Square Regression (PLSR) method. The map of phenol spatial changes was then produced with the Bayesian Regularized Artificial Neural Network Model (BRNN), Random Forest (RF), Generalized Linear Model (GAM), Generalized Additive Model (GLM), and Gaussian Process Regression (GPR) in rangelands. The results showed that the phenol concentration in different individuals was between 3.65 and 20.13 (mgGAE/gDW), and species spectral absorptions ranged from 1.650 to 1.660. The very high coefficient of determination of phenol concentration with spectral data (R2 = 0.94) confirmed the use of these data in phenol mapping at the landscape scale. The maximum estimated phenol concentration by GLM, GAM, RF, BRNN, and GPR models was 36.40, 26.76, 18.07, 22.74, and 32.92 GAE/g DW, respectively; which RF model had the highest (R2 = 0.955) and GLM had the lowest accuracy (R2 = 0.816). The findings show that hyperspectral data can estimate secondary metabolites at the landscape scale and can supplement laboratory methods in managing rangeland plants.

PARAFAC under non-negativity constraint is adapted to recover the underlying Beer-Lambert law of the excitation-emission fluorescence matrix measurements acquired from analyte-triggered semiconductor QDs photoluminescence modulation. When and why?

BackgroundAnalyte-triggered semiconductor quantum dots (QDs) modulation in the presence of non-consistently responsive fluorescent species represents a challenging analytical issue in concrete multi-way data handling. QDs with heterogeneous sizes and/or uneven distribution of functional moieties on their surfaces exhibit significant fluctuations in the fluorescent response components, known as chemical rank, across different excitation/emission modes. This phenomenon may lead to a substantial deviation from the proportionality prescribed by Beer-Lambert law. Nonetheless, even in the presence of such deviation, a multi-way model may be successfully selected after determining a proper chemical rank in a QDs system. ResultsWe show that in a valid PARAllel FACtor (PARAFAC) model under properly determined chemical rank, meaningfully resolved pure spectral profiles can be reached for each fluorescent responsive constituent in the original excitation-emission fluorescence matrix (EEFM) measurements. This was thoroughly illustrated by applying PARAFAC trilinear decomposition of a three-way data array of two distinct datasets acquired from semiconductor QDs sensing systems with low-rank trilinear assumption. The first dataset, presented here for the first time, comprises EEFM measurements of the ligand-driven quenching of thiomalic acid (TMA)-capped AgInS2 (AIS) QDs by vomitoxin. The second dataset, employed for illustrative purposes, comprises EEFM measurements of the quenching, via cation bridging, of glutathione (GSH)-capped CdTe QDs by Pb(II). The results of this study enabled the determination of vomitoxin at a ppb level in real samples of fish feeds, showcasing the efficacy of the PARAFAC model in resolving spectral signatures (loadings) and pure concentration profiles (scores). SignificancePARAFAC under a properly examined chemical rank can be easily adapted for retrieval the underlying Beer-Lambert law of the original EEFM measurements with a low-rank trilinear structure through the chemically meaningful information either when (i) no deviation of Beer-Lambert law was observed as deeply discussed in connection with the dataset acquired from vomitoxin-driven molecular sensing through TMA-capped AIS QDs, or when (ii) substantial deviations of the Beer-Lambert law are evident, as discussed in connection with the dataset collected from sensing ionic species through Pb(II) bridging of GSH-capped CdTe QDs.

First observation results of Macao Science Satellite 1 on lightning-induced electron precipitation

AbstractThe mid-energy electron detector (MEED) is a space-borne instrument onboard Macao Science Satellite 1 (MSS-1) dedicated to monitoring the typical charged particle radiation characteristics in the satellite orbit and the process of their occurrence and development, including short bursts of lightning-induced electron precipitation (LEP). This paper presents the first results of the LEP measurements by the MSS-1. 47 LEP events are identified with the routine operation for 3 months since satellite launch, all within the range of 1.5<L<3.0 (where L represents the McIlwain L-parameter), and the causative lightning discharges are definitively geo-located for these LEP events. The LEP events occur within <1 s of the causative lightning and consist of 40–300 keV electrons. A preliminary observation result indicates that, with medium-energy electron detectors, MSS-1 can present in-situ observations of large regions of enhanced background precipitation and reveal their fine spatiotemporal characteristics and spectral signatures. The collaborative VLF ground-based measurements at the Great Wall Station, Antarctica also have a good correspondence with LEP measurements of MSS-1. The observations also imply that lightning activity has a modulation effect on the energetic electron energy-spatial structure.

Monitoring wheat area using sentinel-2 imagery and In-situ spectroradiometer data in heterogeneous field conditions

Crop statistics are crucial for developing a demand-based export and import strategy to ensure a country’s sustainable food security. Remote sensing efficiently generates essential crop statistics, while ground-based supplementary sensor data offers sufficient information for crop delineation. This study explored the multispectral satellite imagery using in-situ ground-based hyperspectral reflectance phenology information as training data to delineate wheat from other competitive winter crops in Northwestern Bangladesh as a case study. Wheat spectral signatures were primarily obtained through a hand-held Spectroradiometer at various phenological stages, aligned with Sentinel-2 data availability. Five vegetation indices (VIs), namely Normalized Difference Vegetation Index (NDVI), Red-edge NDVI (RENDVI), Enhanced Vegetation Index (EVI), Greenness Chromatic Coordinate (GCC) and Soil-Adjusted Vegetation Index (SAVI), were derived from Spectroradiometer-data across six wheat growth stages: seedling, tillering, booting, flowering, grain development, and maturity. Maximum and minimum threshold values for the VIs at those six growth stages were determined from regression analysis of the values collected from Spectroradiometer and Sentinel-2. A rule-based classification technique was then used to categorize Sentinel-2 for wheat crop delineation based on those threshold values. The results revealed that maps based on NDVI, EVI, and SAVI showed overall accuracies of 83.33%, 85.18%, and 81.48%, respectively. These accuracies were found to be statistically acceptable (p < 0.05) outcomes. A positive agreement was observed when comparing the remotely sensed area at the union (4th tier administrative level) with the officially reported data of Bangladesh. This innovative method has the potential to be extended for developing phenology and area delineation for other major crops locally and globally.

Hyperspectral Imaging and Machine Learning: A Promising Tool for the Early Detection of Tetranychus urticae Koch Infestation in Cotton

Monitoring Tetranychus urticae Koch in cotton crops is challenging due to the vast crop areas and clustered mite attacks, hindering early infestation detection. Hyperspectral imaging offers a solution to such a challenge by capturing detailed spectral information for more accurate pest detection. This study evaluated machine learning models for classifying T. urticae infestation levels in cotton using proximal hyperspectral remote sensing. Leaf reflection data were collected over 21 days, covering various infestation levels: no infestation (0 mites/leaf), low (1–10), medium (11–30), and high (&gt;30). Data were preprocessed, and spectral bands were selected to train six machine learning models, including Random Forest (RF), Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), Feedforward Neural Network (FNN), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), and Partial Least Squares (PLS). Our analysis identified 31 out of 281 wavelengths in the near-infrared (NIR) region (817–941 nm) that achieved accuracies between 80% and 100% across 21 assessment days using Random Forest and Feedforward Neural Network models to distinguish infestation levels. The PCA loadings highlighted 907.69 nm as the most significant wavelength for differentiating levels of two-spotted mite infestation. These findings are significant for developing novel monitoring methodologies for T. urticae in cotton, offering insights for early detection, potential cost savings in cotton production, and the validation of the spectral signature of T. urticae damage, thus enabling more efficient monitoring methods.

Infrared Spectroscopy of Live Cells Using High-Aspect-Ratio Metal-on-Dielectric Metasurfaces.

Fourier transform infrared (FTIR) spectroscopy is widely used for molecular analysis. However, for the materials situated in an aqueous environment, a precondition for live biological objects such as cells, transmission-based FTIR is prevented by strong water absorption of mid-infrared (MIR) light. Reflection-based cellular assays using internal reflection elements (IREs) such as high-index prisms or flat plasmonic metasurfaces mitigate these issues but suffer from a shallow probing volume localized near the plasma membrane. Inspired by the recent introduction of high-aspect-ratio nanostructures as a novel platform for manipulating cellular behavior, we demonstrate that the integration of plasmonic metasurfaces with tall dielectric nanostructures dramatically enhances the sensing capabilities of FTIR spectroscopy. We also demonstrate the ability of a metal-on-dielectric metasurface to transduce intracellular processes, such as protein translocation to high-curvature membrane regions during cell adhesion, into interpretable spectral signatures of the reflected light.

Impact of inorganic mercury on carotenoids in freshwater algae: Insights from single-cell resonance Raman spectroscopy

The influence of inorganic mercury (Hg(II)) exposure on photosynthetic microorganisms and their pigments remains understudied. Here, we employed resonance Raman (RR) spectroscopy to investigate the responses of two freshwater phytoplankton species, the green alga Chlamydomonas reinhardtii and the diatom Cyclotella meneghiniana to Hg(II) exposure. We selectively recorded the spectral RR signature of carotenoids in intact cells exposed to concentrations of 10 nM and 100 nM of Hg(II), representative for contaminated environment and unexposed control cells. A two-hour exposure of C. reinhardtii resulted in a slight decrease in lutein and β-carotene levels, while total carotenoids RR band broadening, as revealed by the FWHM of the υ1(C=C) stretching mode from averaged RR spectra, suggested conformational changes in pigments. Higher Hg(II) concentration induced more pronounced conformational changes. Similarly, a two-hour exposure of C. meneghiniana resulted in slight decreased level of the fucoxanthin, while diadinoxanthin showed an opposite trend compared to control: when fucoxanthin decreased, diadinoxanthin increased under 10 nM Hg (II) exposure. At higher concentrations, the decrease in fucoxanthin was less pronounced, accompanied by a broadening of the band area, (with FHHM increased), indicating possible conformer occurrence in response to Hg-induced stress. The changes in the main carotenoid species of the two algae are species-specific, Hg(II) concentration-specific, and dependent on exposure time. The calculated spectral differences in absorbances from UV–VIS spectra of methanol extracts from each group supported the main findings obtained by RR, though with caution due to the selective extraction efficiency of the respective carotenoids. This study highlighted for a first time the capability of single-cell RR spectroscopy as a valuable tool for toxicity assessment and for comprehending early-stage alterations in carotenoid metabolism due to toxic metal exposure in vivo.

Predicting plants in the wild: Mapping arctic and boreal plants with UAS-based visible and near infrared reflectance spectra

Biophysical changes in the Arctic and boreal zones drive shifts in vegetation, such as increasing shrub cover from warming soil or loss of living mat species due to fire. Understanding current and future responses to these factors requires mapping vegetation at a fine taxonomic resolution and landscape scale. Plants vary in size and spectral signatures, which hampers mapping of meaningful functional groups at coarse spatial resolution. Fine spatial grain of remotely sensed data (<10 cm pixels) is often necessary to resolve patches of many Arctic and boreal plant groups, such as bryophytes and lichens, which are significant components of terrestrial vegetation cover. Separation of co-occurring small vegetation patches in images also requires high spectral resolution. Our goal here was to test the capabilities of UAS-based imaging spectroscopy for mapping plant functional types (PFT) using high spatial and spectral resolution data over Arctic and boreal vegetation at four sites in central Alaska. We then tested several Machine and Deep learning models of PFT cover using the reflectance spectra. The best models were very simple, balancing both bias (overfitting caused by imbalance sample sizes) and variance (fit to the independent validation data), explaining > 50 % of the independent ground cover estimation and > 84 % accuracy in estimating validation pixels. We explored the impact of spectral resolution on PFT mapping by including vegetation indices and a gradient of narrow (5 nm) to wide (50 nm) band features in our classification models across. Vegetation indices were the most important predictors for classifying PFTs, while including band features improved models, with narrow and wide bandwidths having similar importance but models with wide bandwidths performing slightly better. We conclude that Arctic and boreal PFT reflectance can be pooled across sites for mapping with relatively few labeled pixels. Underfit, simple algorithms outperformed deep learning, at least with these small sample sizes, in classifying PFTs by balancing bias and variance. Future work should aim to increase the number of labeled pixels and the detail of labels to further improve mapping taxonomic precision.

Satellite Imagery Analysis and Plane Detection

Monitoring natural vegetation and detecting planes in satellite imagery are critical tasks in remote sensing, with applications ranging from environmental conservation to security and surveillance. This project aims to develop an effective method for analyzing natural vegetation and detecting planes by utilizing satellite imagery and applying advanced image processing and machine learning techniques. By leveraging various features such as vegetation indices, texture, shape, and spectral signatures, we aim to build predictive models that accurately classify vegetation types and identify planes. This paper discusses the methodology, including data collection, preprocessing, feature extraction, model training, and evaluation, ultimately highlighting the models' performance and potential applications in real-world scenarios. Keywords— Vegetation Analysis,Deep learning ,Plane Detection.

Koopman analysis of the singularly perturbed van der Pol oscillator.

The Koopman operator framework holds promise for spectral analysis of nonlinear dynamical systems based on linear operators. Eigenvalues and eigenfunctions of the Koopman operator, the so-called Koopman eigenvalues and Koopman eigenfunctions, respectively, mirror global properties of the system's flow. In this paper, we perform the Koopman analysis of the singularly perturbed van der Pol system. First, we show the spectral signature depending on singular perturbation: how two Koopman principal eigenvalues are ordered and what distinct shapes emerge in their associated Koopman eigenfunctions. Second, we discuss the singular limit of the Koopman operator, which is derived through the concatenation of Koopman operators for the fast and slow subsystems. From the spectral properties of the Koopman operator for the singularly perturbed system and the singular limit, we suggest that the Koopman eigenfunctions inherit geometric properties of the singularly perturbed system. These results are applicable to general planar singularly perturbed systems with stable limit cycles.

Hierarchical homogeneity-based superpixel segmentation: application to hyperspectral image analysis

ABSTRACT Hyperspectral image (HI) analysis approaches have recently become increasingly complex and sophisticated. Recently, the combination of spectral-spatial information and superpixel techniques have addressed some hyperspectral data issues, such as the higher spatial variability of spectral signatures and dimensionality of the data. However, most existing superpixel approaches do not account for specific HI characteristics resulting from its high spectral dimension. In this work, we propose a multiscale superpixel method that is computationally efficient for processing hyperspectral data. The Simple Linear Iterative Clustering (SLIC) oversegmentation algorithm, on which the technique is based, has been extended hierarchically. Using a novel robust homogeneity testing, the proposed hierarchical approach leads to superpixels of variable sizes but with higher spectral homogeneity when compared to the classical SLIC segmentation. For validation, the proposed homogeneity-based hierarchical method was applied as a preprocessing step in the spectral unmixing and classification tasks carried out using, respectively, the Multiscale sparse Unmixing Algorithm (MUA) and the CNN-Enhanced Graph Convolutional Network (CEGCN) methods. Simulation results with both synthetic and real data show that the technique is competitive with state-of-the-art solutions.

Leaf spectrum analysis of three tropical timber species: Diomate (Astronium graveolens), Choibá (Dipteryx oleifera), and Algarrobo (Hymenaea courbaril)

This study analyzed the leaf spectral response of three native timber forest species in the tropical dry forest: Diomate (Astronium graveolens Jacq.), Choibá (Dipteryx oleifera Benth.), and Algarrobo (Hymenaea courbaril L.). The study was conducted at the León Morales Soto Arboretum and Palmetum, at the Universidad Nacional de Colombia in Medellín, Antioquia, Colombia. Spectral data from the leaves were collected in situ using the portable spectroradiometer ASD FieldSpec HandHeld-2, which operates with a spectral resolution of 1 nm (resampled to 10 nm) and covers a spectral range between 325 and 1,075 nm (limited to 400-900 nm). Based on the measurements, the behavior and spectral variability of the species were evaluated. One-factor Analysis of Variance and Mann Whitney-Wilcoxon U-test were implemented in reflectance spectra to select the optimal narrow bands for species discrimination. The classification capacity of the selected narrow bands was assessed using the K-nearest neighbors’ algorithm. It was found that A. graveolens and H. courbaril exhibited spectral signatures typical of healthy vegetation, while D. oleifera showed spectral changes during the early stages of senescence. Regarding spectral separability, 23 narrow bands in the visible region and near-infrared region were identified as optimal for distinguishing the plant species. The supervised classification algorithm applied to these 23 narrow bands achieved an overall accuracy of 95.8%. In conclusion, these findings provide valuable insights into the spectral response of important tropical species and contribute to their conservation efforts by enhancing understanding of their unique spectral characteristics in diverse and heterogeneous ecosystems like tropical forests.

Monitoring Heavy Metals and Metalloids in Soils and Vegetation by Remote Sensing: A Review

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

Magma fizz: Tremor during the Kīlauea summit reservoir decompression

Typical eruptions at Kīlauea volcano involve the evacuation of magma from the summit and/or south caldera reservoirs towards the East or Southwest rift zones. The reservoir drainage provokes the summit deflation, and on extreme occasions, such as in 2018, the summit caldera collapse. Systematically, seismic tremor, often with a particular multichromatic spectral signature characterized by frequency gliding, accompanies summit deflation episodes. In 2018, this type of continuous tremor accompanied the steady subsidence stage, whereas discrete earthquakes dominated the collapse stage. In this work, we aim to understand the source mechanism of the syn-deflation tremor of 2018. To locate the seismic source, we develop a novel machine-learning-based algorithm as an alternative to the amplitude source location technique. We use a large high-resolution catalog to resolve a composite amplitude decay function. Under these conditions, our method outperforms the traditional technique. We locate the tremor source 1 km below the eastern perimeter of the Halema‘uma‘u crater, which coincides with the position of the summit magma reservoir, as determined in many other studies. Furthermore, we model the seismic source as pressure oscillations driven by gas porous flow at the roof of the reservoir. In this model, gas accumulates temporarily in many gas pockets between the magma and the roof. Our modeling shows that the gas flux is responsible for the tremor amplitude modulations, whereas the gas pocket thickness controls the frequency variations. Beyond a critical point of depressurization, the magma cannot contribute further to the tremor oscillations via decompression-driven degassing, nor support the roof above it, resulting in rock failure. This work advances our understanding of magma-degassing dynamics and tremor generation at Kilauea volcano, and provides novel seismological techniques for volcano seismology monitoring and research.

Iron distribution and speciation in a 3D-printed hybrid food using synchrotron X-ray fluorescence and X-ray absorption spectroscopies

The bioavailability of iron from a food depends on its concentration and chemical form but also on dietary factors and nutrient interactions, which are affected by storage conditions and time. Here we investigated the time-course profile of iron in a hybrid 3D-printed food composed of alternating layers of liver and lentils after 0, 5, 7, 14 and 21 days of storage at 4 °C under oxygen or nitrogen packaging. Synchrotron X-ray fluorescence highlighted major variations in iron distribution in both the animal and plant parts of the food as a function of storage conditions. FeP and FeS positive spatial correlations pointed to iron-associated compounds. X-ray absorption near-edge structure spectroscopy showed spectral signatures specific to the animal and plant mixtures, and then highlighted interactions between animal and plant parts during food storage, with a change in iron forms in the plant part.