Search Results (244)

This study introduces a controlled laboratory setup for hyperspectral longwave infrared (LWIR) imaging of soils, designed to bridge the gap between laboratory measurements and remote sensing observations. A Fourier-transform hyperspectral LWIR imaging spectrometer (Telops Hyper-Cam LW) was employed, together with a specialized heating plate, rigorous calibration protocols, and a Spatial Averaging Before Blackbody Fitting (SABBF) method to enable accurate LWIR indoor measurements. Unlike established laboratory techniques that measure reflectance and calculate emissivity indirectly, this setup enables direct passive measurement of soil emissivity, replicating airborne and spaceborne LWIR measurements of the surface. The emissivity spectra of 12 variable soil samples obtained with the lab setup were compared and validated based on LWIR Hyper-Cam LW spectra acquired under outdoor conditions, then were subsequently analyzed to determine the mineral composition of each sample. Spectral features and indices were used to estimate the relative content of quartz, clay minerals, and carbonates, from the most to least abundant. The results demonstrate that the laboratory-based setup preserves spectral fidelity while offering improved repeatability, scheduling flexibility, and reduced dependence on weather. Beyond replicating outdoor measurements, this controlled setup is easy to install and provides a reproducible framework for LWIR soil spectroscopy that could be considered for standard laboratory protocols, enabling reliable mineral identification, calibration/validation of airborne and satellite LWIR data, and broader applications in soil monitoring and environmental remote sensing. Full article

Space-based imaging spectrometers that monitor the Earth’s surface generate vast amounts of data, the processing of which requires fast and accurate retrieval algorithms. Estimating scientifically relevant surface properties from remotely measured radiance data typically involves first inferring spectral surface reflectance from the observed radiance, followed by discipline-specific algorithms to derive scientifically relevant properties. Probabilistic reflectance retrieval algorithms, such as the commonly used optimal estimation (OE), are computationally expensive. Furthermore, the Gaussian assumptions associated with OE have not been fully validated in the context of hyperspectral retrievals. To address these challenges, we introduce accelerated optimal estimation (AOE), a Bayesian algorithm that speeds up the OE reflectance inversion process by up to two orders of magnitude compared to a reference OE implementation (ROE), while also providing improved convergence over a number of selected test targets. We also demonstrate that, under given atmospheric conditions, Gaussian uncertainty estimates from OE-type algorithms are accurate. This is achieved by comparing the OE-type posterior distributions to non-Gaussian ones obtained with Markov chain Monte Carlo (MCMC). Finally, we demonstrate how AOE scales to a larger AVIRIS-NG scene, showcasing its ability to handle complex, large-scale data. Full article

Methane (CH₄) emissions are a major contributor to greenhouse gases and pose significant challenges to global climate mitigation efforts. The accurate determination of CH₄ concentrations via remote sensing is crucial for emission monitoring but remains impeded by surface spectral heterogeneity—notably albedo variations and land cover diversity. This study systematically assessed the sensitivity of three mainstream algorithms, namely, matched filter (MF), albedo-corrected reweighted-L1-matched filter (ACRWL1MF), and differential optical absorption spectroscopy (DOAS), to surface type, albedo, and emission rate through high-fidelity simulation experiments, and proposed a dynamic regularized adaptive matched filter (DRAMF) algorithm. The experiments simulated airborne hyperspectral imagery from the Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) with known CH₄ concentrations over diverse surfaces (including vegetation, soil, and water) and controlled variations in albedo through the large-eddy simulation (LES) mode of the Weather Research and Forecasting (WRF) model and the MODTRAN radiative transfer model. The results show the following: (1) MF and DOAS have higher true positive rates (TP > 90%) in high-reflectivity scenarios, but the problem of false positives is prominent (TN < 52%); ACRWL1MF significantly improves the true negative rate (TN = 95.9%) through albedo correction but lacks the ability to detect low concentrations of CH₄ (TP = 63.8%). (2) All algorithms perform better at high emission rates (1000 kg/h) than at low emission rates (500 kg/h), but ACRWL1MF performs more robustly in low-albedo scenarios. (3) The proposed DRAMF algorithm improves the F1 score (0.129) by about 180% compared to the MF and DOAS algorithms and improves TP value (81.4%) by about 128% compared to the ACRWL1MF algorithm through dynamic background updates and an iterative reweighting mechanism. In practical applications, the DRAMF algorithm can also effectively monitor plumes. This research indicates that algorithms should be selected considering the specific application scenario and provides a direction for technical improvements (e.g., deep learning model) for monitoring gas emission. Full article

Hyperspectral (HS) remote sensing is a valuable tool for geological surveys and mineral classification. However, mineral maps derived from HS data can exhibit inconsistencies across different imaging times or sensors due to complex factors. In this study, we propose a novel method to enhance the robustness and temporal consistency of mineral mapping. The method combines the spectral identification capabilities of the Tetracorder expert system, developed by United States Geological Survey (USGS), with a data-driven classification model, involving the application of Tetracorder to high-purity pixels identified through the pixel purity index (PPI) analysis to generate reliable training labels. These labels, along with hyperspectral bands transformed by the minimum noise fraction (MNF), are used to train a random forest classifier. The methodology was evaluated using multi-temporal images of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), acquired over Cuprite, Nevada, between 2011 and 2013. The results demonstrate that the proposed method achieves accuracy comparable to Tetracorder while improving map consistency and reducing inter-annual mapping errors by approximately 30%. Full article

An advanced Dyson spectrometer is proposed that redesigns the Dyson prism to make the entire system coaxial and easy to implement for each subassembly, thus greatly enhancing optical design, optical processing, and system alignment. The design concept and fabrication methods, as well as the results of imaging evaluations of the proposed spectrometer, are described in detail. At present, the advanced Dyson spectrometer has been in orbit for more than a year, serving smart agriculture and marine applications. The advanced imaging spectrometer achieves high resolution in both the spectral and spatial directions and low spectral distortion at a high numerical aperture in the working waveband. On the basis of the above research, we have developed three other imaging spectrometers with different performance indicators, including a space-based instrument, an airborne instrument and a ground-based instrument, thus verifying the progress and versatility of advanced Dyson spectrometer technology. Full article

Computational imaging spectrometers using broad-bandpass filter arrays with distinct transmission functions are promising implementations of miniaturization. The number of filters is limited by the practical factors. Compressed sensing is used to model the system as linear underdetermined equations for hyperspectral imaging. This paper proposes the following method: parallel dictionary reconstruction and fusion for spectral recovery in computational imaging spectrometers. Orthogonal systems are the dictionary candidates for reconstruction. According to observation of ground objects, the dictionaries are selected from the candidates using the criterion of incoherence. Parallel computations are performed with the selected dictionaries, and spectral recovery is achieved by fusion of the computational results. The method is verified by simulating visible-NIR spectral recovery of typical ground objects. The proposed method has a mean square recovery error of ≤1.73 × 10⁻⁴ and recovery accuracy of ≥0.98 and is both more universal and more stable than those of traditional sparse representation methods. Full article

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

Reconstructing hyperspectral images (HSIs) from RGB images is an effective technique to overcome the high cost of spectrometers. Recently, Transformers have shown potential in capturing long-range dependencies for spectral reconstruction. However, few Transformer models attempt to simultaneously capture both spatial and spectral correlations in HSIs. Within this study, we introduce an integrated spatial–spectral hybrid Transformer (SSHFormer) framework designed to capture the interplay between spatial and spectral features in HSIs, with the aim of incrementally enhancing the fidelity of the reconstructed HSIs. In SSHFormer, we propose a spatial–spectral multi-head self-attention (SSMA) mechanism, which utilizes dilated convolution to extract non-local spatial features while maintaining parameter efficiency and applies the attention mechanism to the channel dimension to model inter-spectral correlations. Additionally, a 3D feedforward network (3DFFN) is proposed for SSHFormer, which leverages 3D convolution to fuse the spatial and spectral information, enabling more comprehensive feature extraction. Experimental results demonstrate that our SSHFormer achieves state-of-the-art (SOTA) performance on public datasets. 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

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

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

The nitrogen nutrition index (NNI) is a significant agronomic statistic used to assess the nitrogen nutrition status of crops. The use of remote sensing to invert it is crucial for accurately diagnosing and managing nitrogen nutrition in crops during critical periods. This study utilizes the UHD185 airborne hyperspectral imager and the ASD Field Spec3 portable spectrometer to acquire hyperspectral remote sensing data and agronomic parameters of the winter wheat canopy during the nodulation and flowering stages. The objective is to estimate the NNI of winter wheat through a winter wheat nitrogen gradient experiment conducted in Leling, Shandong Province. The ASD spectral reflectance data of the winter wheat canopy were selected as the reference standard and compared with the UHD185 hyperspectral data obtained from an unmanned aerial vehicle (UAV). The comparison focused on analyzing the trends in the spectral curve changes and the spectral correlation between the two datasets. The findings indicated a strong agreement between the UHD185 hyperspectral data and the spectral data obtained by ASD in the range of 450–830 nm. A spectrum index was developed to estimate the nitrogen nutritional index utilizing the bands within this range. The linear model, based on the first-order derivative ratio spectral index (RSI) (FD₆₆₆, FD₈₂₆), demonstrated the highest accuracy in estimating the nitrogen nutrient index in winter wheat. The model yielded R² values of 0.85 and 0.75, respectively, and may be represented by the equation y = −2.0655x + 0.156. The results serve as a benchmark for future utilization of the UHD185 hyperspectral data in estimating agronomic characteristics of winter wheat. Full article

To achieve large-scale stitching of the hyperspectral remote sensing images obtained by unmanned aerial vehicles (UAVs) equipped with an acousto-optic tunable filter spectrometer, this study proposes a method based on a feature fusion strategy and a seam-finding strategy using hyperspectral image classification. In the feature extraction stage, SuperPoint deep features from images in different spectral segments of the data cube were extracted and fused. The feature depth matcher, LightGlue, was employed for feature matching. During the data cube fusion stage, unsupervised K-means spectral classification was performed separately on the two hyperspectral data cubes. Subsequently, grayscale transformations were applied to the classified images. A dynamic programming method, based on a grayscale loss function, was then used to identify seams in the transformed images. Finally, the identified splicing seam was applied across all bands to produce a unified hyperspectral data cube. The proposed method was applied to hyperspectral data cubes acquired at specific waypoints by UAVs using an acousto-optic tunable filter spectral imager. Experimental results demonstrated that the proposed method outperformed both single-spectral-segment feature extraction methods and stitching methods that rely on seam identification from a single spectral segment. The improvement was evident in both the spatial and spectral dimensions. Full article