Search Results (4,690)

The investigation of exoplanetary habitability is integral to advancing our knowledge of extraterrestrial life potential and detailing the environmental conditions of distant worlds. In this analysis, we explore the properties of exoplanets situated with respect to circumstellar habitable zones by implementing a sophisticated filtering methodology on data from the NASA Exoplanet Archive. This research encompasses a thorough examination of 5595 confirmed exoplanets listed in the Archive as of 10 March 2024, systematically evaluated according to their calculated average surface temperatures and stellar classifications of their host stars, taking into account the biases implicit in the methodologies used for their discovery. Machine learning, in the form of a Random Forest classifier and an XGBoost classifier, is applied in the classification with high accuracies. The feature importance analysis indicates that our approach captures the most important parameters for habitability classification. Our findings elucidate distinctive patterns in exoplanetary attributes, which are significantly shaped by the spectral classifications and mass of the host stars. The insights garnered from our study both inform refinement of existing models for managing burgeoning exoplanetary datasets, and lay foundational groundwork for more in-depth explorations of the dynamic relationships between exoplanets and their stellar environments. Full article

This study innovatively leveraged proximal remote sensing to address the challenge of mineral exploration in vegetation-covered regions. Remote and proximal sensing has proven to be highly effective in pinpointing surface-exposed alteration minerals and detecting potential mining sites in previously unproductive areas. However, in regions where vegetation is abundant, the presence of foliage poses a significant challenge to mineral exploration efforts, creating a natural barrier that hinders the search for valuable minerals. In this study, we explored the linear relationship between the spectral changes induced by metals (specifically Fe and Mo) in wheat plants and the concentrations of these metal elements in different parts of the plant canopy at various growth stages. This investigation was conducted through meticulously designed controlled experiments to understand the interaction between metal elements in the soil and wheat plants. We have established linear models linking wheat biochemistry, vegetation spectroscopy, and soil concentration gradients of Fe and Mo. The analysis of Fe and Mo concentrations in leaves and wheat spikes across varying soil concentration gradients revealed significant positive correlations between the canopy accumulation sites and soil element concentrations (p < 0.05), with a correlation coefficient (R) exceeding 0.85, affirming the representativeness of these two canopy sites for subsequent spectral analysis and modeling. Regarding the wheat spectral analysis, the absorption features at specified wavelengths were identified as significant for creating valid linear models to analyze the effect of Fe and Mo in wheat leaf and spike spectra. Comparing the univariate (URL) and multivariate (MLR) models demonstrated that MLR modeling, incorporating multiple absorption feature parameters, provided more accurate results compared to scenarios with only one absorption feature in the modeling process (MLR: Fe-leaf: R² = 0.941, RMSE = 1.171; Mo-spike: R² = 0.934, RMSE = 0.042). To conclude, this study introduces a novel method that exploits the wheat spectral properties observed across different canopy sections during various growth stages of vegetation and under varying concentrations of Fe and Mo gradients. The methodology elucidated in this research provides technical support and lays the theoretical foundation for evaluating mineral resources in vegetated areas. Full article

Most deep learning-based hyperspectral anomaly detection (HAD) methods focus on modeling or reconstructing the hyperspectral background to obtain residual maps from the original hyperspectral images. However, these methods typically do not pay enough attention to the spectral similarity in the complex environment, resulting in inadequate distinction between background and anomalies. Moreover, some anomalies and background are different objects, but they are sometimes recognized as the objects with the same spectrum. To address the issues mentioned above, this paper proposes a Spectrum Difference Enhanced Network (SDENet) for HAD, which employs variational mapping and Transformer to amplify spectrum differences. The proposed network is based on the encoder–decoder structure, which contains a CSWin-Transformer encoder, Variational Mapping Module (VMModule), and CSWin-Transformer decoder. First, the CSWin-Transformer encoder and decoder are designed to supplement image information by extracting deep and semantic features, where a cross-shaped window self-attention mechanism is designed to provide strong modeling capability with minimal computational cost. Second, in order to enhance the spectral difference characteristics between anomalies and background, a randomly sampling VMModule is presented for feature space transformation. Finally, all fully connected mapping operations are replaced with convolutional layers to reduce the model parameters and computational load. The effectiveness of the proposed SDENet is verified on three datasets, and experimental results show that it achieves better detection accuracy and lower model complexity compared with existing methods. Full article

Accurate estimation of forest aboveground carbon stock (AGC) is essential for understanding carbon accounting and climate change. In previous studies, the extracted factors, such as spectral textures, vegetation indices, and textural features, were used to estimate the AGC. However, few studies examined how different factors affect estimation accuracy in detail. Meanwhile, there are also many uncertainties in the collection and processing of the field data. To quantify the various uncertainties in the process of AGC estimation, we used the random forest (RF) to establish estimation models based on field data and Sentinel-1/2 images in Shangri-La. The models included the band information model (BIM), the vegetation index model (VIM), the texture information model (TIM), the Sentinel-2 factor model (S-2M), and the Sentinel-1/2 factor model (S-1/2M). Then, uncertainties resulting from the plot scale and estimation models were calculated using error equations. Our goal is to analyze the influence of different factors on AGC estimation and to assess the uncertainty of plot scale and estimation models quantitatively. The results showed that (1) the uncertainty of the measurement was 3.02%, while the error of the monocarbon stock model was the main uncertainty at the plot scale, which was 9.09%; (2) the BIM had the lowest accuracy (R² = 0.551) and the highest total uncertainty (22.29%); by gradually introducing different factors in the process of modeling, the accuracies improved significantly (VIM: R² = 0.688, TIM: R² = 0.715, S-2M: R² = 0.826), and the total uncertainty decreased to some extent (VIM: 14.12%, TIM: 12.56%, S-2M: 10.79%); (3) the S-1/2M with the introduction of Sentinel-1 synthetic aperture radar (SAR) data has the highest accuracy (R² = 0.872) and the lowest total uncertainty (8.43%). The inaccuracy of spectral features is highest, followed by vegetation indices, while textural features have the lowest inaccuracy. Uncertainty in the remote-sensing-based estimation model remains a significant source of uncertainty compared to the plot scale. Even though the uncertainty at the plot scale is relatively small, this error should not be ignored. The uncertainty in the estimation process could be further reduced by improving the precision of the measurement and the fitting of the monocarbon stock estimation model. Full article

The Leaf Area Index (LAI) is a key indicator for assessing fruit tree growth and productivity, and accurate estimation using hyperspectral technology is essential for monitoring plant health. This study aimed to improve LAI estimation accuracy in apricot, jujube, and walnut trees in Xinjiang, China. Canopy hyperspectral data were processed using fractional-order differentiation (FOD) from 0 to 2.0 orders to extract spectral features. Three feature selection methods—Competitive Adaptive Reweighted Sampling (CARS), Successive Projections Algorithm (SPA), and their combination (CARS-SPA)—were applied to identify sensitive spectral bands. Various band combinations were used to construct three-band indices (TBIs) for optimal LAI estimation. Random forest (RF) models were developed and validated for LAI prediction. The results showed that (1) the reflectance spectra of jujube and walnut trees were similar, while apricot spectra differed. (2) The correlation between fractional-order differential spectra and LAI was highest at orders 1.4 and 1.7, outperforming integer-order spectra. (3) The CARS-SPA selected a smaller set of feature bands in the 1100~2500 nm, reducing collinearity and improving spectral index construction. (4) The RF model using TBI4 demonstrated high R², low RMSE, and an RPD value > 2, indicating optimal prediction accuracy. This approach holds promise for hyperspectral LAI monitoring in fruit trees. Full article

Proximal sensing has become increasingly popular due to developments in soil observation technologies and the demands of timely information gathering through contemporary methods. By utilizing the morphological, physical, and chemical characteristics of representative pedogenetic profiles established in various soils of the Sohag governorate, Egypt, the current research addresses the characterization of surface reflectance spectra and links them with the corresponding soil classification. Three primary areas were identified: recently cultivated, old cultivated, and bare soils. For morphological analysis, a total of 25 soil profiles were chosen and made visible. In the dark room, an ASD Fieldspec portable spectroradiometer (350–2500 nm) was used to measure the spectrum. Based on how similar their surface spectra were, related soils were categorized. Ward’s method served as the basis for the grouping. Despite the fact that the VIS–NIR spectra of the surface soils from various land uses have a similar reflectance shape, it is still possible to compare the soil reflectance curves and the effects of the surface soils. As a result, three groups of soil curves representing various land uses were observed. Cluster analysis was performed on the reflectance data in four ranges (350–750, 751–1150, 1151–1850, and 1851–2500 nm). The groups derived from the soil surface ranges of 350–750 nm and 751–1150 nm were not the same as those derived from the ranges of 1151–1850 nm and 1851–2500 nm. The last two categories are strikingly comparable to various land uses with marginally similar features. Based on the ranges of 1151–1850 nm and 1851–2500 nm in surface spectral data, the dendrogram effectively separated and combined the profiles into two separate clusters. These clusters matched different land uses exactly. The results can be used to promote the widespread usage of in situ hyperspectral data sets for the investigation of various soil characteristics. Full article

Chip-scale optical waveguide-assisted surface-enhanced Raman spectroscopy (SERS) that used nanoparticles (NPs) was demonstrated. The Raman signals from Raman reporter (RR) molecules on NPs can be efficiently excited by the waveguide evanescent field when the molecules are in proximity to the waveguide surface. The Raman signal was enhanced by plasmon resonance due to the NPs close to the waveguide surface. The optical waveguide mode and the NP-induced field enhancement were calculated using a finite difference method (FDM). The sensing performance of the waveguide-assisted SERS device was experimentally characterized by measuring the Raman scattering from various RRs, including 4-mercaptobenzoic acid (4-MBA), 5,5′-dithio-bis-(2-nitrobenzoic acid) (DTNB), and malachite green isothiocyanate (MGITC). The observed Raman spectral features were identified and assigned to the complex vibrational modes associated with different reporters. A low detection limit of 1 nM was achieved. In addition, the device sensing method was applied to the detection of the biomarker cardiac troponin I (cTnI) using an aptamer sandwich assay immobilized on the device surface. Overall, the optical waveguides integrated with SERS show a miniaturized sensing platform for the detection of small molecules and large proteins, potentially enabling multiplexed detection for clinically relevant applications. Full article

The combination of fractional derivatives (due to their global behavior) and the challenges related to hyperbolic PDEs pose formidable obstacles in solving fractional hyperbolic equations. Due to the importance and applications of the fractional telegraph equation, solving it and presenting accurate solutions via a novel and effective method can be useful. This work introduces and implements a method based on the spectral element method (SEM) that relies on interpolating scaling functions (ISFs). Through the use of an orthonormal projection, the method maps the equation to scaling spaces raised from multi-resolution analysis (MRA). To achieve this, the Caputo fractional derivative (CFD) is represented by ISFs as a square matrix. Remarkable efficiency, ease of implementation, and precision are the distinguishing features of the presented method. An analysis is provided to demonstrate the convergence of the scheme, and illustrative examples validate our method. Full article

Rolling bearings, as critical components of rotating machinery, significantly influence equipment reliability and operational efficiency. Accurate fault diagnosis is therefore crucial for maintaining industrial production safety and continuity. This paper presents a new fault diagnosis method based on FCEEMD multi-complexity low-dimensional features and directed acyclic graph LSTSVM. The Fast Complementary Ensemble Empirical Mode Decomposition (FCEEMD) method is applied to decompose vibration signals, effectively reducing background noise. Nonlinear complexity features are then extracted, including sample entropy (SE), permutation entropy (PE), dispersion entropy (DE), Gini coefficient, the square envelope Gini coefficient (SEGI), and the square envelope spectral Gini coefficient (SESGI), enhancing the capture of the signal complexity. In addition, 16 time-domain and 13 frequency-domain features are used to characterize the signal, forming a high-dimensional feature matrix. Robust unsupervised feature selection with local preservation (RULSP) is employed to identify low-dimensional sensitive features. Finally, a multi-classifier based on DAG LSTSVM is constructed using the directed acyclic graph (DAG) strategy, improving fault diagnosis precision. Experiments on both laboratory bearing faults and industrial check valve faults demonstrate nearly 100% diagnostic accuracy, highlighting the method’s effectiveness and potential. Full article

Hyperspectral object-detection algorithms based on deep learning have been receiving increasing attention due to their ability to operate without relying on prior spectral information about the target and their strong real-time inference performance. However, current methods are unable to efficiently extract both spatial and spectral information from hyperspectral image data simultaneously. In this study, an innovative hyperspectral object-detection algorithm is proposed that improves the detection accuracy compared to benchmark algorithms and state-of-the-art hyperspectral object-detection algorithms. Specifically, to achieve the integration of spectral and spatial information, we propose an innovative edge-preserving dimensionality reduction (EPDR) module. This module applies edge-preserving dimensionality reduction, based on spatial texture-weighted fusion, to the raw hyperspectral data, producing hyperspectral data that integrate both spectral and spatial information. Subsequently, to enhance the network’s perception of aggregated spatial and spectral data, we integrate a CNN with Visual Mamba to construct a spatial feature enhancement module (SFEM) with linear complexity. The experimental results demonstrate the effectiveness of our method. Full article

China is a major cotton-growing country with numerous cotton varieties, each exhibiting significant differences in yield and fiber quality. However, the current management of cottonseed varieties is disorganized, resulting in severe homogenization and the presence of counterfeit and mislabeled varieties. The detection of cottonseed variety information has become a critical issue for the Chinese cotton industry. In this study, we collected near-infrared (NIR) spectral data from six cottonseed varieties and constructed a GAN for cottonseed NIR data (GAN-CNIRD) model to generate additional cottonseed NIR data. The Euclidean distance method was used to label the generated NIR data according to the characteristics of the true NIR data. We then applied Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Normalization algorithms to preprocess the combined dataset of generated and real cottonseed NIR data. Feature wavelengths were extracted using Bootstrap Soft Shrinkage (BOSS) and Competitive Adaptive Reweighted Sampling (CARS) algorithms. Subsequently, we developed Linear Discriminant Analysis (LDA), Random subspace method (RSM), and convolutional neural network (CNN) models to classify the cottonseed varieties. The results showed that for the LDA model, the use of feature wavelengths extracted after Normalization-BOSS processing achieved the best performance with an accuracy of 97.00%. For the RSM model, the use of feature wavelengths extracted after SNV-CARS processing achieved the best performance with an accuracy of 98.00%. For the CNN model, the use of feature wavelengths extracted after MSC-CARS processing achieved the best performance with an accuracy of 100.00%. Data augmentation using GAN-CNIRD-generated cottonseed data improved the accuracy of the three optimal models by 6%, 5%, and 6%, respectively. This study provides a crucial reference for the rapid detection of cottonseed variety information and has significant implications for the standardized management of cottonseed varieties. Full article

Timely and accurate monitoring of above-ground biomass (AGB) is of great significance for indicating crop growth status, predicting yield, and assessing carbon dynamics. Compared with the traditional time-consuming and laborious method through destructive sampling, UAV remote sensing provides a timely and efficient strategy for estimating biomass. However, the universality of remote sensing retrieval models with multi-feature fusion under different management practices and cultivars are unknown. The spectral, textural, and structural features extracted by UAV multispectral and RGB imaging, coupled with agricultural meteorological parameters, were integrated to estimate the AGB in potato during the whole growth period. Six advanced modeling algorithms, including random forest (RF), partial least squares regression (PLSR), multiple linear regression (MLR), simple linear regression (SLR), ridge regression (RR), and lasso regression (LR) models, were adopted to evaluate the ability of estimating AGB by single feature and multi-feature information fusion. The results indicate the following: (1) The newly proposed variety-dependent indicator growth process ratio (GPR) can improve the model accuracy by over 20%. (2) The fusion of vegetation indices, canopy cover, growing degree days, and GPR achieved higher accuracy to estimate AGB at all growth stages compared with single feature model. (3) RF model performed best for the estimation of AGB during the whole growth period with R² 0.79 and rRMSE 0.24 ton/ha. The study demonstrated that the fusion of multi-feature coupled with the machine learning algorithm achieved the best performance for estimating potato AGB under different management practices and cultivars, which can be a potential and useful phenotyping strategy for estimating AGB at refined plot scale during the whole growth period. Full article

Nitrogen is the main nutrient element in the growth process of white radish, and accurate monitoring of radish leaf nitrogen content (LNC) is an important guide for precise fertilization decisions for radish in the field. Using white radish LNC monitoring as an object, research on radish nitrogen hyperspectral estimation methods was carried out based on leaf hyperspectral and field sample nitrogen data at multiple growth stages using feature selection and integrated learning algorithm models. First, the Vegetation Index (VI) was constructed from hyperspectral data. We extracted sensitive features of hyperspectral data and VI response to radish LNC based on Pearson’s feature-selection approach. Second, a stacking-integrated learning approach is proposed using machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Ridge and K-Nearest Neighbor (KNN) as the base model in the first layer of the architecture, and the Lasso algorithm as the meta-model in the second layer of the architecture, to realize the hyperspectral estimation of radish LNC. The analysis results show the following: (1) The sensitive bands of the radish LNC are mainly centered around 600–700 nm and 1950 nm, and the constructed sensitive VIs are also concentrated in this band range. (2) The Stacking model with spectral features as inputs achieved good prediction accuracy at the radish spectral leaf, with $R^2$ = 0.7, MAE = 0.16, MSE = 0.05 estimated over the whole growth stage of radish. (3) The Lasso algorithm with variable filtering function was chosen as the meta-model, which has a redundant model-selection effect on the base model and helps to improve the quality of the integrated learning framework. This study demonstrates the potential of the stacking-integrated learning method based on hyperspectral data for spectral estimation of nitrogen content in radish at multiple growth stages. Full article

Small lakes play an essential role in maintaining regional ecosystem stability and water quality. However, turbidity in these lakes is increasingly influenced by anthropogenic activities, which presents a challenge for traditional monitoring methods. This study explores the feasibility of using consumer-grade UAVs equipped with RGB cameras to monitor water turbidity in small lakes within the Taihu Lake Basin of eastern China. By collecting RGB imagery and in situ turbidity measurements, we developed and validated models for turbidity prediction. RGB band indices were used in combination with three machine learning models, namely Interpretable Feature Transformation Regression (IFTR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). Results showed that models utilizing combinations of the R, G, B, and ln(R) bands achieved the highest accuracy, with the IFTR model demonstrating the best performance (R² = 0.816, RMSE = 3.617, MAE = 2.997). The study confirms that consumer-grade UAVs can be an effective, low-cost tool for high-resolution turbidity monitoring in small lakes, providing valuable insights for sustainable water quality management. Future research should investigate advanced algorithms and additional spectral features to further enhance prediction accuracy and adaptability. Full article

Micro- and nanoplastics (MNPs) pose a significant threat to marine and human life due to their immense toxicity. To protect these ecosystems, the development of reliable technologies for MNP detection, characterisation, and removal is vital. While FTIR and Raman spectroscopy are established methods for MNP analysis, fluorescence (FL) spectroscopy has recently emerged as a promising alternative. However, most prior research relies on FL emission probing with a single excitation wavelength for MNP detection. In this study, we introduce a two-dimensional (2D) fluorescence excitation–emission (FLE) mapping method for the detection of commonly found microplastics, namely polystyrene (PS), polyethylene terephthalate (PET), and polypropylene (PP). The FLE mapping technique enables the collective recording of emission spectra across a range of excitation wavelengths, revealing the dominant excitation–emission features of different microplastics. This research advances the field by offering a non-destructive and label-free identification of MNP contamination through the use of FL spectral fingerprints. Full article