Search Results (56)

Methane is the second most important contributor to global warming, and monitoring super-emitters from space is critical for climate mitigation. Despite the advancements in hyperspectral remote sensing, comparing methane observations across diverse imaging spectrometers remains a challenging task. Different retrieval algorithms, plume segmentation techniques and uncertainty treatments make it very hard to perform fair comparisons between different products. To overcome these difficulties, this study presents HyGAS (Hyperspectral Gas Analysis Suite), a unified, open-source framework for sensor-agnostic methane retrieval and flux estimation. Starting from the established clutter-matched-filter (CMF) formalism and a physical calibration in concentration–path-length units (ppm·m), we propagate both instrument noise and surface-driven background variability consistently from methane enhancement to Integrated Mass Enhancement (IME) and flux. The framework further includes a spectrally matched background-selection strategy, scale-aware segmentation with fixed physical criteria across resolutions, and emission-rate estimation via an IME–$U_{eff}$ approach informed by Large Eddy Simulation (LES). We demonstrate the framework on near-simultaneous observations of landfills and gas infrastructure in Argentina, Turkmenistan, and Pakistan, spanning Level-1 radiance workflows (PRISMA, EnMAP, Tanager-1) and Level-2 methane products (EMIT, GHGSat). The standardised chain enables systematic inter-comparison of methane enhancement products and reduces methodological bias, supporting robust multi-mission assessment and future global monitoring. Full article

This paper is a result of the search for design assumptions for a sensor to detect oil dispersed in the sea waters (oil-in-water emulsions). Our approach is based on analyzing changes in the underwater solar radiance (L) field caused by the presence of oil droplets in the water column. This method would enable the sensor to respond to the presence of oil contaminants dispersed in the surrounding environment, even if they are not located directly at the measurement point. This study draws on both literature sources and the results of current numerical modeling of the spread of solar light in the water column to account for both downward and upward irradiance (Es). The core principle of the analysis involves simulating the paths of a large number of virtual solar photons in a seawater model defined by spatially distributed Inherent Optical Properties (IOPs). The IOPs data were taken from the literature and pertain to the waters of the southern Baltic Sea. The optical properties of the oil used in the model correspond to crude oil extracted from the Baltic shelf. The obtained results were compared with previously published spectral analyses of an analogous polluted sea model, considering vertical downward radiance, vertical upward radiance, and downward and upward irradiance. It was found that the optimal wavelength ratio of 555/412, identified for these quantities, is also applicable to scalar irradiance. The findings indicate that the most effective way to determine this index is by measuring it using a sensor with its window oriented in the direction of upward-traveling light. Full article

Conventional radiative transfer (RT) models often adopt the plane-parallel (PP) approximation, which neglects Earth’s curvature and leads to significant optical path errors under large solar or sensor zenith angles, particularly for high-latitude regions and twilight conditions. The spherical Monte Carlo method offers high accuracy but is computationally expensive, and the commonly used pseudo-spherical (PSS) approximation fails when the viewing zenith angle exceeds 80°. With the increasing application of machine learning in atmospheric science, the efficiency and angular limitations of spherical RT simulations may be overcome. This study provides a physical and quantitative foundation for developing a hybrid RT framework that integrates physical modeling with machine learning. By systematically quantifying the discrepancies between PP and spherical RT models under diverse atmospheric scenarios, key influencing factors—including wavelength, solar and viewing zenith angles, aerosol properties (e.g., single scattering albedo and asymmetry factor), and PP-derived radiance—were identified. These variables significantly affect spherical radiative transfer and serve as effective input features for data-driven models. Using the corresponding spherical radiance as the target variable, the proposed framework enables rapid and accurate inference of spherical radiative outputs based on computationally efficient PP simulations. Full article

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4B satellite plays a critical role in numerical weather prediction and extreme weather monitoring. To meet the requirements of quantitative remote sensing and high-precision operational applications for radiometric calibration accuracy, this study, based on pre-launch calibration experiments, conducts a novel modeling analysis of the coupling between stray radiation at the input side and the system’s nonlinearity, and proposes a correction method for nonlinear coupling errors. This method explicitly models and physically traces the calibration residuals caused by stray radiation introduced via non-nominal optical paths under the effect of system nonlinearity, which are related to the radiance of the observed target. Experimental results show that, within the brightness temperature range of 200–320 K, the calibration bias is reduced from approximately 0.7 to 0.3–0.4 K, with good consistency and stability observed across channels and pixels. Full article

Volumetric Path Tracing (VPT) based on Monte Carlo (MC) sampling often requires numerous samples for high-quality images, but real-time applications limit samples to maintain interaction rates, leading to significant noise. Traditional real-time denoising methods use radiance and geometric features as neural network inputs, but lightweight networks struggle with temporal stability and complex mapping relationships, causing blurry results. To address these issues, a spatiotemporal lightweight neural network is proposed to enhance the denoising performance of VPT-rendered images with low samples per pixel. First, the reprojection technique was employed to obtain features from historical frames. Next, a dual-input convolutional neural network architecture was designed to predict filtering kernels. Radiance and geometric features were encoded independently. The encoding of geometric features guided the pixel-wise fitting of radiance feature filters. Finally, learned weight filtering kernels were applied to images’ spatiotemporal filtering to produce denoised results. The experimental results across multiple denoising datasets demonstrate that this approach outperformed the baseline models in terms of feature extraction and detail representation capabilities while effectively suppressing noise with superior performance and enhanced temporal stability. Full article

This research presents an intelligent, environment-aware navigation framework for smart electric vehicles (EVs), focusing on multi-agent mapping, real-time obstacle recognition, and adaptive route optimization. Unlike traditional navigation systems that primarily minimize cost and distance, this research emphasizes how EVs perceive, map, and interact with their surroundings. Using a distributed mapping approach, multiple EVs collaboratively construct a topological representation of their environment, enhancing spatial awareness and adaptive path planning. Neural Radiance Fields (NeRFs) and machine learning models are employed to improve situational awareness, reduce positional tracking errors, and increase mapping accuracy by integrating real-time traffic conditions, battery levels, and environmental constraints. The system intelligently balances delivery speed and energy efficiency by dynamically adjusting routes based on urgency, congestion, and battery constraints. When rapid deliveries are required, the algorithm prioritizes faster routes, whereas, for flexible schedules, it optimizes energy conservation. This dynamic decision making ensures optimal fleet performance by minimizing energy waste and reducing emissions. The framework further enhances sustainability by integrating an adaptive optimization model that continuously refines EV paths in response to real-time changes in traffic flow and charging station availability. By seamlessly combining real-time route adaptation with energy-efficient decision making, the proposed system supports scalable and sustainable EV fleet operations. The ability to dynamically optimize travel paths ensures minimal energy consumption while maintaining high operational efficiency. Experimental validation confirms that this approach not only improves EV navigation and obstacle avoidance but also significantly contributes to reducing emissions and enhancing the long-term viability of smart EV fleets in rapidly changing environments. Full article

This study examines how different filming techniques can enhance the quality of 3D reconstructions with a particular focus on their use in indoor crime scene investigations. Using Neural Radiance Fields (NeRF) and Gaussian Splatting, we explored how factors like camera orientation, filming speed, data layering, and scanning path affect the detail and clarity of 3D reconstructions. Through experiments in a mock crime scene apartment, we identified optimal filming methods that reduce noise and artifacts, delivering clearer and more accurate reconstructions. Filming in landscape mode, at a slower speed, with at least three layers and focused on key objects produced the most effective results. These insights provide valuable guidelines for professionals in forensics, architecture, and cultural heritage preservation, helping them capture realistic high-quality 3D representations. This study also highlights the potential for future research to expand on these findings by exploring other algorithms, camera parameters, and real-time adjustment techniques. Full article

The term pansharpening denotes the process by which the geometric resolution of a multiband image is increased by means of a co-registered broadband panchromatic observation of the same scene having greater spatial resolution. Over time, the benchmarking of pansharpening methods has revealed itself to be more challenging than the development of new methods. Their recent proliferation in the literature is mostly due to the lack of a standardized assessment. In this paper, we draw guidelines for correct and fair comparative evaluation of pansharpening methods, focusing on the reproducibility of results and resorting to concepts of meta-analysis. As a major outcome of this study, an improved version of the additive wavelet luminance proportional (AWLP) pansharpening algorithm offers all of the favorable characteristics of an ideal benchmark, namely, performance, speed, absence of adjustable running parameters, reproducibility of results with varying datasets and landscapes, and automatic correction of the path radiance term introduced by the atmosphere. The proposed benchmarking protocol employs the haze-corrected AWLP-H and exploits meta-analysis for cross-comparisons among different experiments. After assessment on five different datasets, it was found to provide reliable and consistent results in ranking different fusion methods. Full article

Precise acquisition of potted plant traits has great theoretical significance and practical value for variety selection and guiding scientific cultivation practices. Although phenotypic analysis using two dimensional(2D) digital images is simple and efficient, leaf occlusion reduces the available phenotype information. To address the current challenge of acquiring sufficient non-destructive information from living potted plants, we proposed a three dimensional (3D) phenotyping pipeline that combines neural radiation field reconstruction with path analysis. An indoor collection system was constructed to obtain multi-view image sequences of potted plants. The structure from motion and neural radiance fields (SFM-NeRF) algorithm was then utilized to reconstruct 3D point clouds, which were subsequently denoised and calibrated. Geometric-feature-based path analysis was employed to separate stems from leaves, and density clustering methods were applied to segment the canopy leaves. Phenotypic parameters of potted plant organs were extracted, including height, stem thickness, leaf length, leaf width, and leaf area, and they were manually measured to obtain the true values. The results showed that the coefficient of determination (R²) values, indicating the correlation between the model traits and the true traits, ranged from 0.89 to 0.98, indicating a strong correlation. The reconstruction quality was good. Additionally, 22 potted plants were selected for exploratory experiments. The results indicated that the method was capable of reconstructing plants of various varieties, and the experiments identified key conditions essential for successful reconstruction. In summary, this study developed a low-cost and robust 3D phenotyping pipeline for the phenotype analysis of potted plants. This proposed pipeline not only meets daily production requirements but also advances the field of phenotype calculation for potted plants. Full article

In the context of autonomous driving, the augmentation of existing data through simulations provides an elegant solution to the challenge of capturing the full range of adverse weather conditions in training datasets. However, existing physics-based augmentation models typically rely on single scattering approximations to predict light propagation under unfavorable conditions, such as fog. This can prevent the reproduction of important signal characteristics encountered in a real-world environment. Consequently, in this work, Monte Carlo simulations are employed to assess the relevance of multiple-scattered light to the detected LiDAR signal in different types of fog, with scattering phase functions calculated from Mie theory considering real particle size distributions. Bidirectional path tracing is used within the self-developed GPU-accelerated Monte Carlo software to compensate for the unfavorable photon statistics associated with the limited detection aperture of the LiDAR geometry. To validate the Monte Carlo software, an analytical solution of the radiative transfer equation for the time-resolved radiance in terms of scattering orders is derived, thereby providing an explicit representation of the double-scattered contributions. The results of the simulations demonstrate that the shape of the detected signal can be significantly impacted by multiple-scattered light, depending on LiDAR geometry and visibility. In particular, double-scattered light can dominate the overall signal at low visibilities. This indicates that considering higher scattering orders is essential for improving AI-based perception models. Full article

In order to meet the high uniformity calibration requirements for scientific-grade, large-size space detectors used in the CHES Extrasolar Planet Exploration Mission, this paper presents the design of a wide dynamic range, high uniformity spectral irradiance source (WHUIS). Utilizing a cascade integrating sphere design, and optimizing the overlapping area radiant flux adjustment structure and illumination light path, we achieve a wide dynamic range and high uniformity irradiance output. We established an irradiance transmission model based on the new assumption and analyzed the influence of factors such as illumination distance, stray light, and non-uniform radiance on the uniformity of irradiance output. The model is then validated by building experimental equipment. The findings show that in a circular area of 40 mm, the irradiance uniformity of our light source system exceeds 99.9%, and constant color temperature is adjustable within six orders of magnitude, consistent with the uniformity level predicted by the model. Full article

As on-orbit space cameras evolve toward larger apertures, wider fields of view, and deeper cryogenic environments, achieving absolute radiometric calibration using an all-optical path blackbody reference source in orbit becomes increasingly challenging. Consequently, stars have emerged as a novel in-orbit standard source. However, due to differences in camera bands, directly obtaining the stellar radiance flux corresponding to specific camera bands is not feasible. In order to address this challenge, we propose a method for estimating radiance flux based on the MSX star catalog, which integrates a dual-band thermometry method with an improved grey wolf optimization (I-GWO) algorithm. In an experiment, we analyzed 351 stars with temperatures ranging from 4000 to 7000 K. The results indicate that our method achieved a temperature estimation accuracy of less than 10% for 83.5% of the stars, with an average estimation error of 5.82%. Compared with previous methods based on star catalogs, our approach significantly enhanced the estimation accuracy by 75.4%, improved algorithm stability by 91.3%, and reduced the computation time to only 3% of that required by other methods. Moreover, the on-orbit star calibration error using our stellar radiance flux estimation method remained within 5%. This study effectively leveraged the extensive data available in star catalogs, providing substantial support for the development of an infrared star calibration network, which holds significant value for the in-orbit calibration of large-aperture cameras. Future research will explore the potential applicability of this method across different spectral bands. Full article

Artificial intelligence emerges as a powerful catalyst poised to reshape the global sustainability landscape by facilitating the attainment of Sustainable Development Goals (SDGs). This comprehensive Delphi study meticulously probes the insights of domain experts, shedding light on the strategic utilization of AI to advance these critical sustainability objectives. Employing rigorous statistical techniques, encompassing measures of central tendency and interquartile deviation, this research scrutinizes consensus dynamics among experts and elucidates potential variations in their viewpoints. The findings resoundingly convey experts’ collective positive perspective regarding AI’s pivotal role in propelling the SDGs forward. Through two iterative rounds of extensive discussions, a compelling consensus crystallizes—AI indeed exerts an overall positive impact, exemplified by a robust mean score of 78.8%. Intriguingly, distinct SDGs manifest varied propensities toward AI intervention, with Goals 6, 7, 8, 9, 11, 13, 14, and 15 basking in the radiance of highly positive impacts. Goals 1, 2, 3, 4, 5, 10, and 12 exhibit positive impact scores, indicating a juncture ripe for positive advancements. Meanwhile, Goal 16 and Goal 17 languish with neutral scores, signifying a juncture demanding nuanced deliberations about AI’s impact on peace, justice, and strong institutions as well as on partnerships for the goals, respectively. This paper underscores AI as a formidable instrument poised to address humanity’s most pressing challenges while harmonizing seamlessly with the overarching SDG objectives. It gracefully dovetails into established practices across pivotal domains such as health, education, and resilient infrastructures, amplifying the collective global endeavor to navigate the path toward a more sustainable future. Full article

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching $R^2$ = 0.92 (LAI), $R^2$ = 0.72 (CCC) and 0.68 (CWC), $R^2$ = 0.94 (FAPAR), and $R^2$ = 0.95 (FVC). To demonstrate the models’ applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between $4\%$ and $16\%$ ($R^2$ between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level. Full article

The design and operation of quantum networks are both decisive in the current push towards a global quantum internet. Although space-enabled quantum connectivity has already been identified as a beneficial candidate for long-range quantum channels for over two decades, the architecture of a hybrid space–ground network is still a work in progress. Here, we propose an analysis of such a network based on a best-path approach, where either fiber- or satellite-based elementary links can be concatenated to form a repeater chain. The network consisting of quantum information processing nodes, equipped with both ground and space connections, is mapped into a graph structure, where edge weights represent the achievable secret key rates, chosen as the figure of merit for the network analysis. A weight minimization algorithm allows for identifying the best path dynamically, i.e., as the weather conditions, stray light radiance, and satellite orbital position change. From the results, we conclude that satellite links will play a significant role in the future large-scale quantum internet, in particular when node distances exceed 500 km, and both a constellation of satellites—spanning 20 or more satellites—and significant advances in filtering technology are required to achieve continuous coverage. Full article