Search Results (427)

Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation. Therefore, in this study, we obtained spectral images of the grassland in April 2022 using a Soc710 VP imaging spectrometer (Surface Optics Corporation, San Diego, CA, USA), which were classified into three levels (low, medium, and high) based on the level of participation of Seriphidium transiliense (Poljakov) Poljakov and Ceratocarpus arenarius L. in the community. The optimal index factor (OIF) was employed to synthesize feature band images, which were subsequently used as input for the DeepLabv3p, PSPNet, and UNet deep learning models in order to assess the influence of species participation on classification accuracy. The results indicated that species participation significantly impacted spectral information extraction and model classification performance. Higher participation enhanced the scattering of reflectivity in the canopy structure of S. transiliense, while the light saturation effect of C. arenarius was induced by its short stature. Band combinations—such as Blue, Red Edge, and NIR (BREN) and Red, Red Edge, and NIR (RREN)—exhibited strong capabilities in capturing structural vegetation information. The identification model performances were optimal, with a high level of S. transiliense participation and with DeepLabv3p, PSPNet, and UNet achieving an overall accuracy (OA) of 97.86%, 96.51%, and 98.20%. Among the tested models, UNet exhibited the highest classification accuracy and robustness with small sample datasets, effectively differentiating between S. transiliense, C. arenarius, and bare ground. However, when C. arenarius was the primary target species, the model’s performance declined as its participation levels increased, exhibiting significant omission errors for S. transiliense, whose producer’s accuracy (PA) decreased by 45.91%. The findings of this study provide effective technical means and theoretical support for the identification of plant species and ecological monitoring in sericite–Artemisia desert grasslands. Full article

The intensification of olive growing has raised environmental concerns, particularly regarding nutrient loss from excessive fertiliser use. In line with the European Union’s Farm to Fork strategy, which aims to halve the soil nutrient losses by 2030, this study evaluates the effectiveness of two sensor-based approaches—proximal sensing with a FLAME spectrometer and remote sensing via UAV-mounted multispectral imaging—compared with foliar chemical analyses as the reference standard, for diagnosing the nutritional status of olive trees. The research was conducted in Elvas, Portugal, between 2022 and 2023, across three olive cultivars (‘Azeiteira’, ‘Arbequina’, and ‘Koroneiki’) subjected to different fertilisation regimes. Machine learning (ML) models showed strong correlations between sensor data and nutrient levels: the multispectral sensor performed best for phosphorus (P) (determination coefficient [$R^2$] = 0.75) and potassium (K) ($R^2$ = 0.73), while the FLAME spectrometer was more accurate for nitrogen (N) ($R^2$ = 0.64). These findings underscore the potential of sensor-based technologies for non-destructive, real-time nutrient monitoring, with each sensor offering specific strengths depending on the target nutrient. This work contributes to more sustainable and data-driven fertilisation strategies in precision agriculture. Full article

To determine the long-term effect of Australian bushfires on the upper tropospheric composition in the South Pacific, we investigated the variation in CO and hydrocarbon species in the South Pacific according to the extent of Australian bushfires (2004–2020). We conducted analyses using satellite data on hydrocarbon and CO from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and on fire (fire count, burned area, and fire radiative power) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, we compared the effects of bushfires between Northern and Southeastern Australia (N_Aus and SE_Aus, respectively). Our analyses show that Australian bushfires in austral spring (September to November) result in the largest increase in CO and hydrocarbon species in the South Pacific and even in the west of South America, indicating the trans-Pacific transport of smoke plumes. In addition to HCN (a well-known wildfire indicator), CO and other hydrocarbon species (C₂H₂, C₂H₆, CH₃OH, HCOOH) are also considerably increased by Australian bushfires. A unique finding in this study is that the hydrocarbon increase in the South Pacific mostly relates to the bushfires in N_Aus, implying that we need to be more vigilant of bushfires in N_Aus, although the severe Australian bushfire in 2019–2020 occurred in SE_Aus. Due to the surface conditions in springtime, bushfires on grassland in N_Aus during this time account for most Australian bushfires. All results show that satellite data enables us to assess the long-term effect of bushfires on the air composition over remote areas not having surface monitoring platforms. Full article

The fuel produced through water electrolysis is non-toxic and clean, and the water propulsion system offers low cost and easy integration with other systems. This study investigates the pulse operating characteristics of a water electrolytic chemical propulsion engine using microwave ignition technology. A high-speed camera captured flame images, while a spectrometer and pressure sensor were used for data quantification. Three peak gas pressure points were selected for data analysis. The experimental results revealed that the flame color changes at different combustion stages, starting white and turning blue at the flame tip during stable combustion. Combustion pressure fluctuated between −0.53 kPa and 765 kPa, with an average of ≈32 kPa, showing a rapid pressure rise followed by smooth decay. At all three operating points, the thrust was small (0.38 N, 0.37 N, and 0.35 N), but after the third operating point, thrust increased significantly to 2.25 N, an enhancement of 508.1%. Spectral data indicated that the combustion products included H, O, and N atoms. This study is the first to investigate the pulsed conditions of a direct microwave ignition system and provide insights into its operating characteristics. The system will be optimized in the future. Full article

Sub-kilometer spatial resolution humidity profiles from the stratosphere to the mesosphere are essential for investigating the function of atmospheric water vapor in the global water and energy cycles as well as in radiation transport. The significant variations in atmospheric radiation at low altitudes and the gradual changes at high altitudes pose challenges to the data acquisition and processing methods of limb imaging spectrometers that rely on atmospheric scattering and absorption mechanisms. In this paper, the effects of two binning techniques—interferogram binning and recovered spectrum binning—on improving the spectral signal-to-noise ratio (SNR) are examined through theoretical analysis and simulations, exemplified by a one-dimensional (1D) imaging spatial heterodyne spectrometer designed for measuring atmospheric humidity profiles. Rician random variables are employed to characterize the amplitude of the recovered spectral points under varying signal conditions, from which spectral SNR expressions are derived for both binning methods. The difference in both methods is evaluated through numerical simulations and experiments. Simulation results demonstrate that, with an integration time of 0.3 s and a spectral resolution of 0.03 nm, the input signal below 50 km is strong, with photon noise being the dominant factor, and both binning methods improve SNR proportionally to the square root of the number of binned rows. As the signal weakens above 50 km, additive noise gradually becomes dominant with increasing tangent altitude, and spectrum binning yields a higher SNR than interferogram binning. Experimental data obtained from a similar type of spectrometer further validate these simulation findings. The results indicate that spectrum binning provides greater advantages in improving the SNR for detecting water vapor in the mesosphere, paving the way for achieving a higher vertical resolution in subsequent retrievals. Full article

The following study presents the results obtained from a comparative analysis of dry (powder and hard snow) and wet snow based on satellite data and in situ data methods for monitoring in the high mountain belt of Bulgaria. The aim of the study is to analyze the effectiveness of different spectral indices based on satellite data from Synthetic Aperture Radar (SAR), high-resolution (HR) imagery, and spectrometer data for assessing the state and dynamics of the snow cover. The methods studied and the results obtained were validated by instrument-based field observations, with instruments using thermal imaging cameras, spectrometer measurements, ground control points, and HR imagery. Satellite data offer an ever-widening view of trends in snow distribution over time. All these data combined provide a detailed picture of surface temperature and snow properties, which are crucial for understanding snowmelt processes and the energy balance in the high-altitude belt. The findings suggest that a multi-method approach, utilizing the combined advantages of SAR satellite data, offers the most comprehensive and accurate framework for satellite-based snow cover monitoring in the high mountain regions of Bulgaria, such as Rila Mountain. This integrative strategy not only improves the precision of snow cover estimates but can also support many water resource-related studies, such as snowmelt runoff studies, snow avalanche modeling, and better-informed decisions in the management and maintenance of winter tourism resorts. Full article

Controlling forage quality and grazing are crucial for sustainable livestock production, health, productivity, and animal performance. However, the limited availability of reliable handheld sensors for timely pasture quality prediction hinders farmers’ ability to make informed decisions. This study investigates the in-field dynamics of Mombasa grass (Megathyrsus maximus) forage biomass production and quality using optical techniques such as visible imaging and near-infrared (VIS-NIR) hyperspectral proximal sensing combined with machine learning models enhanced by covariance-based error reduction strategies. Data collection was conducted using a cellphone camera and a handheld VIS-NIR spectrometer. Feature extraction to build the dataset involved image segmentation, performed using the Mahalanobis distance algorithm, as well as spectral processing to calculate multiple vegetation indices. Machine learning models, including linear regression, LASSO, Ridge, ElasticNet, k-nearest neighbors, and decision tree algorithms, were employed for predictive analysis, achieving high accuracy with R² values ranging from 0.938 to 0.998 in predicting biomass and quality traits. A strategy to achieve high performance was implemented by using four spectral captures and computing the reflectance covariance at NIR wavelengths, accounting for the three-dimensional characteristics of the forage. These findings are expected to advance the development of AI-based tools and handheld sensors particularly suited for silvopastoral systems. Full article

Lunar swirls are enigmatic features on the Moon’s surface, and their formation remains debated. Previous studies suggest that the distinctive spectral characteristics of lunar swirls result from the asymmetric space weathering between their bright markings (on-swirl) and dark surrounding background (off-swirl) regions. Nanophase iron (npFe⁰), as the product of space weathering, directly reflects this varying degree of space weathering. In this study, we investigated the formation of lunar swirls from the perspective of the npFe⁰ distribution across five lunar swirls using Chang’e-1 (CE-1) Interference Imaging Spectrometer (IIM) data. Our results show that (1) on-swirl regions exhibit an obvious lower npFe⁰ abundance compared to their backgrounds; (2) the relationship between the npFe⁰ abundance in swirl dark lanes and the off-swirl regions is associated with different stages of space weathering; (3) the difference in the npFe⁰ abundance between on-swirl regions and off-swirl fresh craters could be due to their different weathering processes; and (4) there is a correlation between npFe⁰, water content, and the strength of magnetic anomalies related to lunar swirls. These findings support the view that the process of solar wind deflection leads to the preservation of swirl surfaces with reduced space weathering and provide a new perspective for comparing different swirl formation models. Full article

This study addresses the critical need for radiometrically accurate and consistent hyperspectral data as the remote sensing community moves towards a hyperspectral world. Previous calibration efforts on Hyperion, the first on-orbit hyperspectral sensors, have exhibited temporal stability and absolute accuracy limitations. This work has developed and validated a novel cross-calibration methodology to address these challenges. Also, this work adds two other hyperspectral sensors, DLR Earth Sensing Imaging Spectrometer (DESIS) and Earth Surface mineral Dust Source Investigation instrument (EMIT), to maintain temporal continuity and enhance spatial coverage along with spectral resolution. The study established a robust approach for calibrating Hyperion using DESIS and EMIT. The methodology involves several key processes. First is a temporal stability assessment on Extended Pseudo Invariant Calibration Sites (EPICS) Cluster13–Global Temporal Stable (GTS) over North Africa (Cluster13–GTS) using Landsat Sensors Landsat 7 (ETM+), Landsat 8 (OLI). Second, a temporal trend correction model was developed for DESIS and Hyperion using statistically selected models. Third, absolute calibration was developed for DESIS and EMIT using multiple vicarious calibration sites, resulting in an overall absolute calibration uncertainty of 2.7–5.4% across the DESIS spectrum and 3.1–6% on non-absorption bands for EMIT. Finally, Hyperion was cross-calibrated using calibrated DESIS and EMIT as reference (with traceability to ground reference) with a calibration uncertainty within the range of 7.9–12.9% across non-absorption bands. The study also validates these calibration coefficients using OLI over Cluster13–GTS. The validation provided results suggesting a statistical similarity between the absolute calibrated hyperspectral sensors mean TOA (top-of-atmosphere) reflectance with that of OLI. This study offers a valuable contribution to the community by fulfilling the above-mentioned needs, enabling more reliable intercomparison, and combining multiple hyperspectral datasets for various applications. Full article

The Chinese Chang’E-7 (CE-7) mission is planned to land in the lunar south polar region, and then deploy a mini-flying probe to fly into the cold trap to detect the water ice. The selection of a landing site is crucial for ensuring both a safe landing and the successful achievement of its scientific objectives. This study presents a method for landing site selection in the challenging environment of the lunar south pole, utilizing multi-source remote sensing data. First, the likelihood of water ice in all cold traps within 85°S is assessed and prioritized using neutron spectrometer and hyperspectral data, with the most promising cold traps selected for sampling by CE-7’s mini-flying probe. Slope and illumination data are then used to screen feasible landing sites in the south polar region. Feasible landing sites near cold traps are aggregated into larger landing regions. Finally, high-resolution illumination maps, along with optical and radar images, are employed to refine the selection and identify the optimal landing sites. Six potential landing sites around the de Gerlache crater, an unnamed cold trap at (167.10°E, 88.71°S), Faustini crater, and Shackleton crater are proposed. It would be beneficial for CE-7 to prioritize mapping these sites post-launch using its high-resolution optical camera and radar for further detailed landing site investigation and evaluation. Full article

A wide band range can cover more of the characteristic spectral lines of substances, and thus analyze the structure and composition of substances more accurately. In order to broaden the band range of spectral instruments, an ultra-wide-band Fourier transform infrared spectrometer is designed. The incident light of the spectrometer is constrained by a secondary imaging scheme, and switchable light sources and detectors are set to achieve an ultra-wide band coverage. A compact and highly stable double-moving mirror swing interferometer is adopted to generate optical path difference, and a controller is used to stabilize the swing of the moving mirrors. A distributed design of digital system integration and analog system integration is adopted to achieve a lightweight and low-power-consumption spectrometer. High-speed data acquisition and a transmission interface are applied to improve the real-time performance. Further, a series of experiments are performed to test the performance of the spectrometer. Finally, the experimental results show that the spectral range of the ultra-wide-band Fourier transform infrared spectrometer covers 0.770–200 μm, with an accurate wave number, a spectral resolution of 0.25 cm⁻¹, and a signal-to-noise ratio better than 50,000:1. Full article

Remote sensing (RS) has been widely used to monitor cyanobacterial blooms in inland water bodies. However, the accuracy of RS-based monitoring varies significantly depending on factors such as waterbody type, sensor characteristics, and analytical methods. This study comprehensively evaluates the current capabilities and challenges of RS for cyanobacterial bloom monitoring, with a focus on achievable accuracy. We find that chlorophyll-a (Chl-a) and phycocyanin (PC) are the primary indicators used, with PC demonstrating greater accuracy and stability than Chl-a. Sentinel and Landsat satellites are the most frequently used RS data sources, while hyperspectral images, particularly from unmanned aerial vehicles (UAVs), have shown high accuracy in recent years. In contrast, the Medium-Resolution Imaging Spectrometer (MERIS) and Moderate-Resolution Imaging Spectroradiometer (MODIS) have exhibited lower performance. The choice of analytical methods is also essential for monitoring accuracy, with regression and machine learning models generally outperforming other approaches. Temporal analysis indicates a notable improvement in monitoring accuracy from 2021 to 2023, reflecting advances in RS technology and analytical techniques. Additionally, the findings suggest that a combined approach using Chl-a for large-scale preliminary screening, followed by PC for more precise detection, can enhance monitoring effectiveness. This integrated strategy, along with the careful selection of RS data sources and analytical models, is crucial for improving the accuracy and reliability of cyanobacterial bloom monitoring, ultimately contributing to better water management and public health protection. Full article

To achieve the rapid and accurate classification and identification of soybean components, this study selected soybeans harvested by the 4LZ-1.5 soybean combine harvester as the research subject. Hyperspectral images of soybean samples were collected using the Pika L spectrometer, and spectral information was extracted from the regions of interest (ROI) in the images. Eight preprocessing methods, including baseline correction (BC), moving average (MA), Savitzky–Golay derivative (SGD), normalization, standard normal variate transformation (SNV), multiplicative scatter correction (MSC), first derivative (DS), and Savitzky–Golay smoothing (SGS), were applied to the raw spectral data to eliminate irrelevant information. Feature wavelengths were selected using the successive projections algorithm (SPA) and the competitive adaptive reweighted sampling (CARS) algorithm to reduce spectral redundancy and enhance model detection performance, retaining eight and ten feature wavelengths, respectively. Subsequently, a random forest (RF) model was developed for soybean component classification. The model parameters were optimized using particle swarm optimization (PSO) and differential evolution (DE) algorithms to improve performance. Experimental results showed that the RF classification model based on SPA-BC preprocessed spectra and DE-tuned parameters achieved an optimal prediction accuracy of 1.0000 during training. This study demonstrates the feasibility of using hyperspectral imaging technology for the rapid and accurate detection of soybean components, providing technical support for the assessment of breakage and impurity levels during soybean harvesting and storage processes. It also offers a reference for the development of future machine-harvested soybean breakage and impurity detection systems. Full article

Raman spectroscopy is capable of precisely identifying and analyzing the composition and properties of samples collected from the lunar surface, providing crucial data support for lunar scientific research. However, in situ Raman spectroscopy on the lunar surface faces challenges such as weak Raman scattering from targets, alongside requirements for lightweight and long-distance detection. To address these challenges, time-gated Raman spectroscopy (TG-LRS) based on a passively Q-switched pulsed laser and a linear intensified charge-coupled device (ICCD), which enable simultaneous signal amplification and background suppression, has been developed to evaluate the impact of key operational parameters on Raman signal detection and to explore miniaturization optimization. The TG-LRS system includes a 40 mm zoom telescope, a passively Q-switched 532 nm pulsed laser, a fiber optic delay line, a miniature spectrometer, and a linear ICCD detector. It achieves an electronic gating width under 20 ns. Within a detection range of 1.1–3.0 m, the optimal delay time varies linearly from 20 to 33 ns. Raman signal intensity increases with image intensifier gain, while the signal-to-noise ratio peaks at a gain range of 800–900 V before declining. Furthermore, the effects of focal depth, telescope aperture, laser energy, and integration time were studied. The Raman spectra of lunar minerals were successfully obtained in the lab, confirming the system’s ability to suppress solar background light. This demonstrates the feasibility of in situ Raman spectroscopy on the lunar surface and offers strong technical support for future missions. Full article

Amyloid-β (Aβ) plaque deposits in the brain are a hallmark of Alzheimer’s disease (AD) neuropathology. Plaques consist of complex mixtures of peptides like Aβ_1–42 and characteristic lipids such as gangliosides, and they are targeted by reactive microglia and astrocytes. Background: In pharmaceutical research and development, it is a formidable challenge to contextualize the different biomolecular classes and cell types of the Aβ plaque microenvironment in a coherent experimental workflow on a single tissue section and on a benchtop imaging reader. Methods: Here, we developed a workflow that combines lipid MALDI mass spectrometry imaging using a vacuum-stable matrix with histopathology stains and with the MALDI HiPLEX immunohistochemistry of plaques and multiple protein markers on a benchtop imaging mass spectrometer. The three data layers consisting of lipids, protein markers, and histology could be co-registered and evaluated together. Results: Multimodal data analysis suggested the extensive co-localization of Aβ plaques with the peptide precursor protein, with a defined subset of lipids and with reactive glia cells on a single brain section in APPPS1 mice. Plaque-associated lipids like ganglioside GM2 and phosphatidylinositol PI38:4 isoforms were readily identified using the tandem MS capabilities of the mass spectrometer. Conclusions: Altogether, our data suggests that complex pathology involving multiple lipids, proteins and cell types can be interrogated by this spatial multiomics workflow on a user-friendly benchtop mass spectrometer. Full article