Spectral Space Research Articles

Exploring mid‐infrared spectral transfer functions for the prediction of multiple soil properties using a global dataset

AbstractInfrared spectroscopy is increasingly being adopted as a technology for soil analysis. However, laboratories worldwide are equipped with different infrared spectrometers, leading to variations that hinder the global application of soil spectroscopy. This study evaluates the transferability of soil spectra from a global dataset collected using four mid‐infrared spectrometers. To evaluate the efficacy of five spectral transfer functions (direct standardization, piecewise direct standardization, spectral space transformation [SST], principal components‐canonical correlation analysis [PC‐CCA], and domain‐invariant partial least square [DIPLS] regression), two datasets were used: dataset A (n = 224; standardized samples) was scanned using one primary spectrometer and three secondary spectrometers; dataset B (n = 1904; legacy samples) was scanned only using the primary spectrometer. The first set of chemometrics models was developed using dataset A to compare the performance of different spectrometers. The second set of models was developed using dataset B to evaluate the effectiveness of spectral transfer functions. Both models were developed using partial least squares regression. Spectral transfer functions developed using dataset A indicate that the PC‐CCA method was the best in converging spectra collected from four instruments into a similar space projected using Uniform Manifold Approximation and Projection. Spectral transfer did not result in consistent improvement in the prediction of soil properties compared to the direct use of spectra collected from different spectrometers. These findings carry significant implications for the utilization of legacy models, enabling laboratories to concentrate on acquiring new samples and spectral measurements using established protocols without the need for spectral transfer.

Discriminating Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Review.

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral-spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral-spatial representations for classification. In this article, the crucial factors to discriminate spectral-spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral-spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.

Autonomous large-eddy simulations of turbulence using eddy viscosity derived from the subgrid-scale similarity stress tensor

A previously developed method for large-eddy simulations (LES), based on spectral eddy-viscosity models, is generalised to the physical space representation. The method estimates the subgrid-scale (SGS) energy transfer using a similarity-type model expression for the SGS tensor obtained using Gaussian filtering of velocity fields advanced in the simulations. Following steps for the spectral space representation, the SGS transfer in the physical space is used to obtain a spatially varying eddy viscosity at each time step in LES. The computed eddy viscosity is employed to model the SGS stress tensor in the familiar Boussinesq form for use in LES. The method is tested in LES of isotropic turbulence at high Reynolds numbers where the inertial range dynamics is expected and for lower Reynolds number decaying turbulence under conditions of the classical Comte-Bellot and Corrsin experiments. In both cases the agreement with reference data is very good and the SGS transfer computed for the proposed eddy-viscosity model is highly correlated with the transfer computed for the similarity-type stress tensor.

Locality Preserving Refinement for Shape Matching with Functional Maps

In this paper, we address the nonrigid shape matching with outliers by a novel and effective pointwise map refinement method, termed Locality Preserving Refinement. For accurate pointwise conversion from a given functional map, our method formulates a two-step procedure. Firstly, starting with noisy point-to-point correspondences, we identify inliers by leveraging the neighborhood support, which yields a closed-form solution with linear time complexity. After obtained the reliable correspondences of inliers, we refine the pointwise correspondences for outliers using local linear embedding, which operates in an adaptive spectral similarity space to further eliminate the ambiguities that are difficult to handle in the functional space. By refining pointwise correspondences with local consistency thus embedding geometric constraints into functional spaces, our method achieves considerable improvement in accuracy with linearithmic time and space cost. Extensive experiments on public benchmarks demonstrate the superiority of our method over the state-of-the-art methods. Our code is publicly available at https://github.com/XiaYifan1999/LOPR.

Natural Gas Induced Vegetation Stress Identification and Discrimination from Hyperspectral Imaging for Pipeline Leakage Detection

Remote sensing detection of natural gas leaks remains challenging when using ground vegetation stress to detect underground pipeline leaks. Other natural stressors may co-present and complicate gas leak detection. This study explores the feasibility of identifying and distinguishing gas-induced stress from other natural stresses by analyzing the hyperspectral reflectance of vegetation. The effectiveness of this discrimination is assessed across three distinct spectral ranges (VNIR, SWIR, and Full spectra). Greenhouse experiments subjected three plant species to controlled environmental stressors, including gas leakage, salinity impact, heavy-metal contamination, and drought exposure. Spectral curves obtained from the experiments underwent preprocessing techniques such as standard normal variate, first-order derivative, and second-order derivative. Principal component analysis was then employed to reduce dimensionality in the spectral feature space, facilitating input for linear/quadratic discriminant analysis (LDA/QDA) to identify and discriminate gas leaks. Results demonstrate an average accuracy of 80% in identifying gas-stressed plants from unstressed ones using LDA. Gas leakage can be discriminated from scenarios involving a single distracting stressor with an accuracy ranging from 76.4% to 84.6%, with drought treatment proving the most successful. Notably, first-order derivative processing of VNIR spectra yields the highest accuracy in gas leakage detection.

Plant spectral diversity from high‐resolution multispectral imagery detects functional diversity patterns in coastal dune communities

AbstractQuestionRemote sensing is a fundamental tool to monitor biodiversity and spectral diversity may represent a proxy for different biodiversity facets such as taxonomic (TD) and functional diversity (FD). We used fine‐resolution multispectral imagery to explore spectral diversity (SD) patterns across spatial scales (i.e., plot, transect, area), and assess SD relationships with TD and FD along an environmental gradient.LocationCoastal sand dune, Viareggio, Italy (“Migliarino–San Rossore–Massaciuccoli” Regional Park, 43°83′ N, 10°25′ E).MethodsWe measured TD as species richness, while SD and FD were computed using probability density functions based on pixel and species position in multivariate spaces. We compared SD and FD patterns in space occupation, and we tested whether these patterns are coordinated along the sea–inland gradient. We also assessed univariate relationships between SD and biodiversity facets, and we tested how these facets were partitioned across scales.ResultsWe found a strong correspondence between functional and spectral patterns in space occupation and along the environmental gradient, with a lack of significance when considering TD. However, TD and SD showed higher variation at broader scale while most FD variation occurred at plot level.ConclusionBy measuring FD and SD with a common methodological framework, we demonstrate that SD approximates functional patterns in plant communities. Moreover, we show that SD retrieved using high‐resolution images can capture different aspects of FD, and that the occupation of the spectral space is analogous to the occupation of the functional space.

Soil Salinity Estimation by 3D Spectral Space Optimization and Deep Soil Investigation in the Songnen Plain, Northeast China

Saline–alkaline soil is a severe threat to Sustainable Development Goals (SDGs), but it can also be a precious land resource if properly utilized according to its properties. This research takes the Songnen Plain as the study area. The aim is to figure out the saline–alkaline status and mechanisms for its scientific utilization. Sentinel-2 multispectral imagery is used, and a 3D spectral space optimization method is proposed according to the restrictive relationships among the surface soil salinity index (SSSI), vegetation index (VI), and surface soil wetness index (SSWI) to construct a surface soil salinization–alkalization index (SSSAI) for estimation of the surface soil salinity (SSS). It is testified that SSS can be precisely estimated using the SSSAI (R2 = 0.74) with field verification of 50 surface salinized soil samples. Surface water and groundwater investigations, as well as deep soil exploration, indicate that the salt ions come from groundwater, and alkalinization is a primary problem in the deep soils. Fine-textured clay soils act as interrupted aquifers to prevent salt ions from penetrating and diluting downward with water, which is the cause of the salinization–alkalization problem in the study area. Finally, a sustainable solution for the saline–alkaline land resource is proposed according to the deep soil properties.

Historical change and variability of spectral wind wave climate in the New York Bight

AbstractThe sea surface is a complex superposition of various wave systems, thus, analysing their characteristics provides more meaningful and accurate information compared to conventional integrated wave parameter approaches. The concept of wave family—a population of wave systems within a specific region in spectral space—provides the foundation for investigating the long‐term climatology of wave systems. This study adopts a wave family approach to investigate the climatology of wave systems and their long‐term climate controllers at the location of a coastal buoy in the western North Atlantic Ocean. The frequency‐direction wave spectrum is constructed for a historical 20‐year period by applying the Maximum Entropy Method on directional buoy measurements acquired from the National Data Buoy Center station 44025, located in the New York Bight. The two‐dimensional wave spectrum is partitioned, using the watershed partitioning algorithm, and the occurrence distribution of spectral partitions is calculated to identify predominant wave families. Each of the four identified wave families carries unique characteristics based on spatial formation and duration of propagation prior to arrival at the study site. The study quantifies long‐term trends and variability in wave height of each wave family on a monthly, seasonal and annual level, and their relative importance to the bulk sea‐state climatology. The nexus between the bulk or wave family seasonal wave heights and the climate indices is quantified to identify the wave climate controllers in the study area.

Conservative correction procedures utilizing artificial dissipation operators

The conservative correction procedure of Abgrall [1] is studied from the perspective of filter-based artificial dissipation methods, which motivates the ability to tailor the behavior of the method in both physical and spectral space. Compared to the original formulation, employing diffusion operators biases the correction towards smaller scales and better controls discretization errors when seeking to enforce auxiliary conservation relations. Effective entropy-stable regularization of sharp gradients is furthermore shown to be attainable. Calculations of the Sod shock tube problem as governed by the one-dimensional Euler equations are used to highlight the utility of considering alternate filters within the original correction framework, where the notion of entropy conservation/stability is leveraged for improving non-linear scheme robustness.

Efficient Near-Infrared Spectrum Detection in Nondestructive Wood Testing via Transfer Network Redesign.

This study systematically developed a deep transfer network for near-infrared spectrum detection using convolutional neural network modules as key components. Through meticulous evaluation, specific modules and structures suitable for constructing the near-infrared spectrum detection model were identified, ensuring its effectiveness. This study extensively analyzed the basic network components and explored three unsupervised domain adaptation structures, highlighting their applications in the nondestructive testing of wood. Additionally, five transfer networks were strategically redesigned to substantially enhance their performance. The experimental results showed that the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network outperformed the Direct Standardization, Piecewise Direct Standardization, and Spectral Space Transformation models. The coefficients of determination for the Conditional Domain Adversarial Network and Globalized Loss Optimization Transfer network are 82.11% and 83.59%, respectively, with root mean square error prediction values of 12.237 and 11.582, respectively. These achievements represent considerable advancements toward the practical implementation of an efficient and reliable near-infrared spectrum detection system using a deep transfer network.

Electrically tunable topological phase transition in non-Hermitian optical MEMS metasurfaces.

Exceptional points (EPs), unique junctures in non-Hermitian open systems where eigenvalues and eigenstates simultaneously coalesce, have gained notable attention in photonics because of their enthralling physical principles and unique properties. Nonetheless, the experimental observation of EPs, particularly within the optical domain, has proven rather challenging because of the grueling demand for precise and comprehensive control over the parameter space, further compounded by the necessity for dynamic tunability. Here, we demonstrate the occurrence of optical EPs when operating with an electrically tunable non-Hermitian metasurface platform that synergizes chiral metasurfaces with piezoelectric MEMS mirrors. Moreover, we show that, with a carefully constructed metasurface, a voltage-controlled spectral space can be finely tuned to access not only the chiral EP but also the diabolic point characterized by degenerate eigenvalues and orthogonal eigenstates, thereby allowing for dynamic topological phase transition. Our work paves the way for developing cutting-edge optical devices rooted in EP physics and opening uncharted vistas in dynamic topological photonics.

Riemannian Manifold-Based Feature Space and Corresponding Image Clustering Algorithms.

Image feature representation is a key factor influencing the accuracy of clustering. Traditional point-based feature spaces represent spectral features of an image independently and introduce spatial relationships of pixels in the image domain to enhance the contextual information expression ability. Mapping-based feature spaces aim to preserve the structure information, but the complex computation and the unexplainability of image features have a great impact on their applications. To this end, we propose an explicit feature space called Riemannian manifold feature space (RMFS) to present the contextual information in a unified way. First, the Gaussian probability distribution function (pdf) is introduced to characterize the features of a pixel in its neighborhood system in the image domain. Then, the feature-related pdfs are mapped to a Riemannian manifold, which constructs the proposed RMFS. In RMFS, a point can express the complex contextual information of corresponding pixel in the image domain, and pixels representing the same object are linearly distributed. This gives us a chance to convert nonlinear image segmentation problems to linear computation. To verify the superiority of the expression ability of the proposed RMFS, a linear clustering algorithm and a fuzzy linear clustering algorithm are proposed. Experimental results show that the proposed RMFS-based algorithms outperform their counterparts in the spectral feature space and the RMFS-based ones without the linear distribution characteristics. This indicates that the RMFS can better express features of an image than spectral feature space, and the expressed features can be easily used to construct linear segmentation models.

Variational calculus in hybrid turbulence transport models with passive scalar

Non-zonal methods of hybrid Reynolds Averaged Navier–Stokes (RANS) equations-Large Eddy Simulation (LES) with subfilter transport equations enable to simulate turbulent flows out of spectral equilibrium on relatively coarse grid resolution. They can operate from RANS to LES depending on a control parameter linked to the grid step size. Variational analysis gives a useful framework to specify the functional dependence of this parameter to the grid size. This is the case for the partially integrated transport modelling method originally established in spectral space for homogeneous turbulence. The partitioning control function then monitors the ratio of the subfilter part to the total turbulent energy and the same process is developed in the present work for the thermal or transported scalar variance as well. So, we demonstrate here that this control function mechanism can be derived by variational calculus from a mathematical physics formalism developed both for first-order and second-order moment closures. We show that the result is entirely consistent with the spectral model derivation made in previous papers and that this approach can be transposed to almost any subfilter transport model developed in hybrid RANS-LES methodology in full generality. As a result, it will be evidenced also that resolved turbulent diffusion terms play a significant role in the acting mechanisms of turbulence and cannot be therefore neglected, even if these terms do not explicitly appear in LES subfilter closure because they are computed by the simulation itself and not modelled by the subfilter model. In addition, numerical simulations have been performed for illustrating the theoretical results of the variational analysis.

Optimizing Sentinel-2 feature space for improved crop biophysical and biochemical variables retrieval using the novel spectral triad feature selection algorithm

This study presents a novel Spectral Triad feature selection (STfs) technique based on music theory and compares it to the entire Sentinel-2 feature space and Random Forest-Recursive Feature Elimination (RF-RFE). The optimal subsets were evaluated with Random Forest for retrieving Leaf Area Index (LAI), Leaf Chlorophyll Content (LC ab ), and Canopy Chlorophyll Content (CCC) in a semi-arid agricultural landscape. The results indicated that the proposed STfs algorithm obtained equivalent or better (i.e. by 1 – 3%) retrieval accuracies for LAI (R 2 cv of 66%, root mean squared error of cross-validation [RMSEcv] of 0.53 m2 m−2), LC ab (R 2 cv: 74%, RMSEcv: 7.09 µg cm−2) and CCC (R 2 cv: 77%, RMSEcv: 33.69 µg cm−2), using only 5, 7 and 7 variables, respectively, when compared to RF-RFE and entire Sentinel-2 feature space. Overall, the proposed STfs algorithm has great potential to optimize the spectral feature space of quasi-hyperspectral sensors for rapid crop biophysical and biochemical parameter retrieval.

T-HyperGNNs: Hypergraph Neural Networks via Tensor Representations.

Hypergraph neural networks (HyperGNNs) are a family of deep neural networks designed to perform inference on hypergraphs. HyperGNNs follow either a spectral or a spatial approach, in which a convolution or message-passing operation is conducted based on a hypergraph algebraic descriptor. While many HyperGNNs have been proposed and achieved state-of-the-art performance on broad applications, there have been limited attempts at exploring high-dimensional hypergraph descriptors (tensors) and joint node interactions carried by hyperedges. In this article, we depart from hypergraph matrix representations and present a new tensor-HyperGNN (T-HyperGNN) framework with cross-node interactions (CNIs). The T-HyperGNN framework consists of T-spectral convolution, T-spatial convolution, and T-message-passing HyperGNNs (T-MPHN). The T-spectral convolution HyperGNN is defined under the t-product algebra that closely connects to the spectral space. To improve computational efficiency for large hypergraphs, we localize the T-spectral convolution approach to formulate the T-spatial convolution and further devise a novel tensor-message-passing algorithm for practical implementation by studying a compressed adjacency tensor representation. Compared to the state-of-the-art approaches, our T-HyperGNNs preserve intrinsic high-order network structures without any hypergraph reduction and model the joint effects of nodes through a CNI layer. These advantages of our T-HyperGNNs are demonstrated in a wide range of real-world hypergraph datasets. The implementation code is available at https://github.com/wangfuli/T-HyperGNNs.git.

On Zariski spaces of free acts over semigroups

This study is concerned with the collections of prime and radical subacts of free acts over a semigroup [Formula: see text] The collection of all prime subacts of any free act is topologized with two topologies as generalizations of the notion of the Zariski topology in rings and semigroups. Moreover, the interplay between some algebraic properties and some topological properties of the spectrum of free acts is investigated. In particular, the conditions under which the set of all prime subacts of free acts is a spectral space are studied. Also, it is shown that a certain type of Zariski space of free acts is isomorphic to a lattice of radical subacts.

Percepts, affects, clinamen: Alienation and entanglement in What Time Is It There? (2001)

This article takes as its starting point an uncanny dialectics of alienation, desire and connection that permeates Tsai Ming-liang’s oeuvre. Through approaching the non-intersecting parallel narratives in What Time Is It There? (2001) with the Epicurean and Lucretian notion of clinamen and the Deleuzian concepts of any-space-whatevers, percepts and affects, this article explores the relation between non-intersecting parallel spaces and times and a liminal, spectral cinematic space and time that is created by moving images themselves, which makes the dialectics of alienation and connection possible. This article argues that What Time involves Tsai’s meta-cinematic approach to cinema’s potentiality that enables both characters and spectators to engage directly with moving images and reimagine the world through its spectrality and non-human perception.

A colorful look at climate sensitivity

Abstract. The radiative response to warming and to changing concentrations of CO2 is studied in spectral space. If, at a particular wavenumber, the emission temperature of the constituent controlling the emission to space does not change its emission temperature, as is the case when water vapor adopts a fixed relative humidity in the troposphere or for CO2 emissions in the stratosphere, spectral emissions become independent of surface temperature, giving rise to the idea of spectral masking. This way of thinking allows one to derive simple, physically informative, and surprisingly accurate expressions for the clear-sky radiative forcing, the radiative response to warming, and hence climate sensitivity. Extending these concepts to include the effects of clouds leads to the expectation that (i) clouds dampen the clear-sky response to forcing; (ii) diminutive clouds near the surface, which are often thought to be unimportant, may be effective at enhancing the clear-sky sensitivity over deep moist tropical boundary layers; (iii) even small changes in high clouds over deep moist regions in the tropics make these regions radiatively more responsive to warming than previously believed; and (iv) spectral masking by clouds may contribute substantially to polar amplification. The analysis demonstrates that the net effect of clouds on warming is ambiguous, if not moderating, justifying the assertion that the clear-sky (fixed relative humidity) climate sensitivity – which, after accounting for surface albedo feedbacks, we estimate to be about 3 K – provides a reasonable prior for Bayesian updates accounting for how clouds are distributed, how they might change, and deviations associated with changes in relative humidity with temperature. These effects are best assessed by quantifying the distribution of clouds and water vapor and how they change in temperature rather than geographic space.

WAVEx: Stabilized finite elements for spectral wind wave models using FEniCSx

The prediction of the wind wave spectrum of the ocean using numerical models are an important tool for researchers, engineers, and communities living in coastal areas. The governing equation of the wind wave models, the Wave Action Balance Equation, presents unique challenges for implementing reliable numerical models because it is highly advective, highly nonlinear and high dimensional. Historically, most operational models have utilized finite difference methods, others have used finite volume methods but relatively few attempts at using finite element methods. In this work, we seek to fill this gap by investigating several different finite element discretizations of the Wave Action Balance Equation. The methods, which include streamline upwind Petrov–Galerkin (SUPG), least squares, and discontinuous Galerkin, are implemented and convergence properties are examined for some simplified 2-D test cases. Then, a new spectral wind wave model, WAVEx, is formulated and implemented for the full problem setting. WAVEx uses continuous finite elements along with SUPG stabilization in geographic/spectral space that allows for fully unstructured triangular meshes in both geographic and spectral space. For propagation in time, a second order fully implicit finite difference method is used. When source terms are active, a second order operator splitting scheme is used to linearize the problem. In the splitting scheme, propagation is solved using the implicit method and the nonlinear source terms are treated explicitly. Several test cases, including analytic tests and laboratory experiments, are demonstrated and results are compared to analytic solutions, observations, as well as output from another model that is used operationally.

Direct numerical evidence of the Phillips initial stage and its antecedent during wind-wave generation

How wind generates ocean surface waves is a classic fluid mechanics problem, and it is commonly believed that the resonance mechanism between wind and surface waves, first proposed by Phillips in 1957, is responsible for the early stages of wind-wave generation. However, there has not been any conclusive study to fully validate this theory. We present the results of a combined theoretical and computational study of the initial response of a calm water surface to turbulent wind and an analysis in terms of the Phillips theory on wind-wave generation. We address a nascent stage of wind-wave generation after the sudden impact of a turbulent wind on a calm water surface but before the initial stage described by Phillips. We show that in such nascent stage, the wave energy grows over time following a quartic law. We provide direct numerical evidence of the resonance mechanism during the initial stage and clarify its role in the formation of the heterogeneous wave energy distribution in the spectral space.