Search Results (463)

The proliferation of Unmanned Aerial Vehicles (UAVs) equipped with multispectral imaging sensors has revolutionized data collection in precision agriculture. These platforms provide high-resolution, temporally dense data crucial for monitoring crop health, optimizing resource management, and predicting yield. However, the development and validation of robust data-driven algorithms, from vegetation index analysis to complex deep learning models, are contingent upon the availability of high-quality, standardized, and publicly accessible datasets. This review systematically surveys and characterizes the current landscape of available datasets containing multispectral imagery acquired by UAVs in agricultural contexts. Following guidelines for reporting systematic reviews and meta-analyses (PRISMA methodology), 39 studies were selected and analyzed, categorizing them based on key attributes including spectral bands (e.g., RGB, Red Edge, Near-Infrared), spatial and temporal resolution, types of crops studied, presence of complementary ground-truth data (e.g., biomass, nitrogen content, yield maps), and the specific agricultural tasks they support (e.g., disease detection, weed mapping, water stress assessment). However, the review underscores a critical gap in standardization, with significant variability in data formats, annotation quality, and metadata completeness, which hampers reproducibility and comparative analysis. Furthermore, we identify a need for more datasets targeting specific challenges like early-stage disease identification and anomaly detection in complex crop canopies. Finally, we discuss future directions for the creation of more comprehensive, benchmark-ready open datasets that will be instrumental in accelerating research, fostering collaboration, and bridging the gap between algorithmic innovation and practical agricultural deployment. This work serves as a foundational guide for researchers and practitioners seeking suitable data for their work and contributes to the ongoing effort of standardizing open data practices in agricultural remote sensing. Full article

In this study, we present the design and development of a mobile, multi-modal electroencephalography and functional near-infrared spectroscopy (EEG/fNIRS) device for wireless neurophysiological monitoring. The system was engineered to achieve high signal fidelity, low power consumption, and a fully untethered operation suitable for ambulatory brain research. The device integrates four Texas Instruments ADS1299 24-bit biopotential amplifiers, providing up to 32 simultaneous acquisition channels. Signal control, processing, and local storage via an SD card are managed by an STM32H7 microcontroller, while an ESP32-S2 module handles Wi-Fi communication. Dual-wavelength light-emitting diodes and OPT101 photodiodes form the optical front-end, driven by digitally controlled constant-current sources for stable illumination. The design employs galvanic isolation, multi-rail power management, and a four-layer PCB layout to minimise interference between analogue, power, and digital domains. Data are captured by a deterministic, clock-driven STM32 acquisition loop and forwarded to the ESP32, which operates under an RTOS and streams packets over Wi-Fi for collection on a mobile phone or PC using the Lab Streaming Layer (LSL) framework. The STM32H7 architecture was chosen for its capability to support future embedded edge-machine-learning functions, enabling on-device signal quality assessment and artefact rejection. Validation demonstrations include 32-channel synchronised acquisition using the ADS1299 internal test signal, eyes-open/eyes-closed alpha modulation visualised in EEGLAB, a forehead fNIRS breath-hold response with physiological spectral content, and real-time ECG/optical pulse streaming via LSL. The resulting system provides a compact platform with explicitly defined acquisition and data interfaces for synchronised EEG/fNIRS acquisition, enabling scalable, low-cost mobile neuroimaging research. Full article

Monitoring suspended-sediment concentration (SSC) is essential to better understand how sediment transport could adversely affect water availability for human communities and ecosystems. Aquatic remote sensing methods are increasingly utilized to estimate SSC and turbidity in rivers; however, an evaluation of their quantitative performance is limited. This study evaluates the performance of three multispectral sensors, which vary in resolution and ease of deployment, to estimate turbidity in the Colorado River: the Multispectral Instrument (MSI) on board the European Space Agency’s Sentinel-2 satellite, an industrial-grade 10-band dual camera system mounted on a cable car, and a consumer-grade 6-band dual camera system positioned on the riverbank. We use multivariate linear regression to compare in situ turbidity measurements with concurrent spectral reflectance data from each sensor. Models for all three sensors selected similar spectral information and resulted in mean errors <35% in predicting turbidity. A cross-sensor comparison showed that little accuracy is lost when applying models developed for satellite-based systems to ground-based systems, and vice versa. Transferability of satellite-based models to ground-based systems could support continuous water-quality monitoring between satellite overpasses and avoid issues associated with cloud interference. Conversely, continuously operating ground-based systems could be used to rapidly establish datasets and models for application in satellite imagery, thus accelerating remote sensing applications. The encouraging performance of the consumer-grade system indicates that SSC could be monitored for low cost. Full article

This study analyses the potential of a change detection method—the near-real-time cumulative sum change point detection method (CuSum-NRT)—applied to Sentinel-1 C-band synthetic aperture radar (SAR) data to monitor crops and identify field work based on a monthly number of changes. The temporal evolution of the number of changes occurring on Sentinel-1 backscatter at both VV and VH polarisations averaged at field scale was analysed over five years (2017–2021) and compared with NDVI derived from the Sentinel-2 multispectral instrument (MSI) sensor over more than 1000 fields in the southwest of France. The monthly number of changes detected did not show a significant difference between months with soil work and months with no soil work, so further analysis on the dates of changes should be conducted. The number of changes based on VV was found to poorly correlate with the VV backscatter (R_global = 0.25), and that based on VH was found to moderately correlate with the VH backscatter (R_global = 0.61): CuSum provides additional information compared to backscatter alone. The results also showed that the number of changes detected using CuSum-NRT is correlated to NDVI, mostly positively for the VH polarisation (R_max = 0.73, p-value < 0.05) and negatively for the VV polarisation (R_min = −0.69, p-value < 0.005). Furthermore, the analysis of crop groups (cereals, oilseeds, protein crops, fodder, vegetables, others) displayed statistically significant differences in terms of the annual number of changes occurring on both VH and VV polarisations, which has potential applications in crop classification. Full article

The Probe Of Extreme Multi-Messenger Astrophysics (POEMMA) is a proposed dual-satellite mission to observe Ultra-High-Energy Cosmic Rays (UHECRs), increase the statistics at the highest energies, and observe Very-High-Energy Neutrinos (VHENs) following multi-messenger alerts of astrophysical transient events, such as gamma-ray bursts and gravitational wave events, throughout the universe. POEMMA–Balloon with radio (PBR) is a small-scale version of the POEMMA design, adapted to be flown as a payload on one of NASA’s suborbital Super Pressure Balloons (SPBs) circling over the Southern Ocean for more than 20 days after a launch from Wanaka, New Zealand. The main science objectives of PBR are: (1) to observe UHECRs via the fluorescence technique from suborbital space; (2) to observe horizontal high-altitude air showers (HAHAs) with energies above the cosmic ray knee (E > 3PeV) using optical and radio detection for the first time; and (3) to follow astrophysical event alerts in the search of VHENs. The PBR instrument consists of a 1.1 m aperture Schmidt telescope similar to the POEMMA design, with two cameras on its focal surface: a Fluorescence Camera (FC) and a Cherenkov Camera (CC). In addition, PBR has a Radio Instrument (RI) optimized for detecting EASs (covering the 60–660 Mhz range). The FC observes UHECR-induced EASs in the ultraviolet (UV) spectrum using an array of 9216-pixel Multi-Anode Photo-Multiplier Tubes (MAPMTs) imaged every 1 $\mathsf{\mu }$s. The CC uses a 2048-pixel Silicon Photo-Multiplier (SiPM) imager to observe cosmic-ray-induced HAHAs and search for neutrino-induced upward-going EASs. The CC covers a spectral range of 320–900 nm, with an integration time of 10 ns. This contribution provides an overview of PBR instruments and their current status. Full article

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

Accurate and rapid prediction of sensor responses is critical for real-time measurement systems, digital twin implementations, and sensor design optimization. Traditional numerical methods such as Finite Element Method (FEM) and Computational Fluid Dynamics (CFD) provide high-fidelity solutions but suffer from prohibitive computational costs, limiting their applicability in time-sensitive applications. This paper presents a novel framework utilizing Fourier Neural Operators (FNO) as surrogate models for fast multi-physics sensor response prediction across thermal, acoustic, and flow measurement domains. Unlike conventional neural networks that learn finite-dimensional mappings, FNO learns operators between infinite-dimensional function spaces by parameterizing the integral kernel in Fourier space, enabling resolution-invariant predictions with remarkable computational efficiency. We demonstrate the framework’s efficacy through three comprehensive case studies: (1) thermal sensor response prediction achieving $R^2>0.98$ with 8300× speedup over FEM, (2) acoustic sensor array modeling with mean absolute error below 0.5 dB and 4000× speedup over BEM, and (3) flow sensor characterization with velocity field prediction accuracy exceeding 97% and 31,000× speedup over CFD. The proposed FNO-based surrogate models are trained on simulation datasets generated from high-fidelity numerical solvers and validated against simulation holdout data for all three case studies, with additional experimental validation conducted for the thermal sensor case. Results indicate that FNO architectures effectively capture the underlying physics governing sensor behavior while reducing inference time from minutes to milliseconds. The framework enables real-time sensor calibration, uncertainty quantification, and design optimization, opening new possibilities for intelligent measurement systems and Industry 4.0 applications. We also investigate the spectral characteristics of FNO predictions, addressing the inherent low-frequency bias through a hybrid architecture combining FNO with local convolutional layers. The primary contributions of this work include: (1) the first systematic application of FNO-based surrogate modeling specifically tailored for sensor response prediction across multiple physics domains, (2) a novel H-FNO architecture that combines spectral operators with local convolutions to mitigate spectral bias in sensor applications, and (3) comprehensive validation including both simulation and experimental data for practical deployment. This work establishes FNO as a powerful tool for accelerating sensor simulation and advancing the field of AI-enhanced instrumentation and measurement. Full article

Using a quantile–frequency connectedness framework, this study analyzes the regime-contingent and horizon-specific transmission of shocks among AI assets, FinTech markets, and Blue Economy financial instruments. The empirical results reveal a distinctly asymmetric connectedness structure, whereby high-frequency spillovers intensify in upper-quantile states associated with liquidity stress and sentiment-driven trading, while low-frequency connectedness remains comparatively muted, thereby preserving cross-segment diversification potential. AI assets emerge as dominant net transmitters in short-horizon dynamics, reflecting rapid innovation cycles and speculative adjustments. FinTech markets exhibit stabilizing properties under median regimes but transition into net propagation roles when risk conditions escalate. Blue finance instruments act as conditional net absorbers, attenuating volatility originating from digital innovation-driven markets, particularly during adverse market states. By decomposing spillover intensities across quantiles and spectral bands, the analysis highlights a structural differentiation between innovation-sensitive digital assets and the comparatively stable behavior of blue-themed financial assets. These findings advance the understanding of nonlinear dependence, asymmetric contagion, and state-dependent co-movements in emerging financial ecosystems. The results provide actionable insights for systemic-risk measurement, cross-market shock diagnostics, and multi-asset portfolio construction in an increasingly interconnected global financial system. Full article

The absolute radiometric calibration of a satellite sensor is an essential process that determines the coefficients required to convert the radiometric quantities of satellite images. This procedure is crucial for ensuring the applicability and enhancing the reliability of optical sensors onboard satellites. This study performs the absolute radiometric calibration of the Compact Advanced Satellite 500-1 (CAS500-1) Advanced Earth Imaging Sensor System-C (AEISS-C), a low Earth orbit satellite developed independently by Republic of Korea for precise ground observation. Field campaign using a tarp, an Analytical Spectral Devices FieldSpecIII spectroradiometer, and a MicrotopsII sunphotometer was conducted. Additionally, reflectance-based vicarious calibration was performed using observational data and the MODerate resolution atmospheric TRANsmission model (version 6) radiative transfer model (RTM). Cross-calibration was also performed using data from the Sentinel-2 MultiSpectral Instrument, RadCalNet observations, and MODIS Bidirectional nReflectance Distribution Function (BRDF) products (MCD43A1) to account for differences in spectral response functions, viewing/solar geometry, and atmospheric conditions between the two satellites. From these datasets, two correction factors were derived: the Spectral Band Adjustment Factor and the BRDF Correction Factor. CAS500-1/AEISS-C acquires satellite imagery using two Time Delay Integration (TDI) modes, and the absolute radiometric calibration coefficients were derived considering these TDI modes. The coefficient of determination (R²) ranged from 0.70 to 0.97 for the reflectance-based vicarious calibration and from 0.90 to 0.99 for the cross-calibration. For reflectance-based vicarious calibration, aerosol optical depth was identified as the primary source of uncertainty among atmospheric factors. For cross-calibration, the reference satellite and RTMs were the primary sources of uncertainty. The results of this study will support the monitoring of CAS500-1/AEISS-C, which produces high-resolution imagery with a spatial resolution of 2 m, and can serve as foundational material for absolute radiometric calibration procedures for other CAS500 satellites. Full article

Magnetic Barkhausen noise (MBN) represents a powerful non-destructive testing and material characterization methodology enabling quantitative assessment of microstructural features, mechanical properties, and stress states in ferromagnetic materials. This comprehensive review synthesizes recent advances spanning theoretical foundations, sensor design, signal processing methodologies, and industrial applications. The physical basis rooted in domain wall dynamics and statistical mechanics provides rigorous frameworks for interpreting MBN signals in terms of grain structure, dislocation density, phase composition, and residual stress. Contemporary instrumentation innovations including miniaturized sensors, multi-parameter systems, and high-entropy alloy cores enable measurements in challenging environments. Advanced signal processing techniques—encompassing time-domain analysis, frequency-domain spectral methods, time–frequency transforms, and machine learning algorithms—extract comprehensive material information from raw Barkhausen signals. Deep learning approaches demonstrate superior performance for automated material classification and property prediction compared to traditional statistical methods. Industrial applications span manufacturing quality control, structural health monitoring, railway infrastructure assessment, and predictive maintenance strategies. Key achievements include establishing quantitative correlations between material properties and stress states, with measurement uncertainties of ±15–20 MPa for stress and ±20 HV for hardness. Emerging challenges include standardization imperatives, characterization of advanced materials, machine learning robustness, and autonomous system integration. Future developments prioritizing international standards, physics-informed neural networks, multimodal sensor fusion, and wireless monitoring networks will accelerate industrial adoption supporting safe, efficient engineering practice across diverse sectors. Full article

Coastal bathymetric mapping is essential for marine conservation, navigation, and environmental management. Satellite-derived bathymetry (SDB) is a cost-effective solution to mapping bathymetry over large shallow areas. However, traditional multispectral instruments can produce poor depth estimates for several reasons, including image noise, atmospheric interference, waves and white caps, and where the seafloor-reflected signal is weak, e.g., in areas with deep water or a low-albedo seafloor. This study investigates the potential of PRISMA hyperspectral imagery to improve SDB performance. Through an iterative process, hyperspectral bands were added to a base Random Forest model, and model performance was assessed across different water pixel classes, including bright shallow substrates, seagrass, and deep water. The model’s performance was then compared to that of multispectral Landsat 8 imagery. The results demonstrated that adding hyperspectral bands to the base model improved bathymetric accuracy, particularly in deeper waters (25 m–30 m), where Mean Absolute Error decreased by 2.51 m from a 3-band to a 24-band model. However, the best-performing model was achieved using Landsat 8, resulting in a lower Mean Absolute Error (1.88 m) than the optimized 24-band PRISMA model (2.01 m). Our findings suggest that although additional hyperspectral bands can improve bathymetry estimation, multispectral imagery may still be more effective for general coastal bathymetry mapping despite its lower spectral resolution. Full article

Spectroscopic technology offers the advantage of rapid online monitoring and has attracted significant attention in molecular detection. However, the complex optical spectroscopic structure results in a relatively complex structure for spectral detection systems, limiting their widespread application. In water spectral detection, in addition to ensuring the stability of the optical system, waterproofing is also crucial. Therefore, developing miniaturized spectral detection modules in water spectral detection can improve system stability and reduce the complexity of developing and maintaining underwater hardware. This work develops a compact multispectral detection system centered on a miniature multispectral sensor. The system, controlled by a microcontroller, detects eight spectral channels within the 400–700 nm range and transmits data via the I²C bus. The sensitivity and stability of the detection are sufficient for water reflectance spectral detection. Based on the reflectance spectrum obtained by the above module, this work develops a regression algorithm to estimate the chlorophyll concentration in water. By comparing with standard chlorophyll concentration detection instruments, the results demonstrate the effectiveness of the proposed system in accurately estimating chlorophyll concentration. Full article

The mineral deposits in Namibia’s Erongo region are renowned and frequently associated with complex geological environments, including calcrete-hosted paleochannels and hydrothermal alteration zones. Mineral extraction is hindered by high operational costs, restricted accessibility and stringent environmental regulations. To address these challenges, this study proposes an integrated approach that combines satellite remote sensing and machine learning to map and identify mineralisation-indicative zones. Sentinel 2 Multispectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI) multispectral data were employed due to their global coverage, spectral fidelity and suitability for geological investigations. Normalized Difference Vegetation Index (NDVI) masking was applied to minimise vegetation interference. Spectral indices—the Clay Index, Carbonate Index, Iron Oxide Index and Ferrous Iron Index—were developed and enhanced using false-colour composites. Principal Component Analysis (PCA) was used to reduce redundancy and extract significant spectral patterns. Supervised classification was performed using Support Vector Machine (SVM), Random Forest (RF) and Maximum Likelihood Classification (MLC), with validation through confusion matrices and metrics such as Overall Accuracy, User’s Accuracy, Producer’s Accuracy and the Kappa coefficient. The results showed that RF achieved the highest accuracy on Landsat 8 and MLC outperformed others on Sentinel 2, while SVM showed balanced performance. Sentinel 2’s higher spatial resolution enabled improved delineation of alteration zones. This approach supports efficient and low-impact mineral prospecting in remote environments. Full article

Tidal inundation is a key factor determining the structure and function of estuarine salt marsh ecosystems. However, due to the influence of microtopography (small-scale topographic variations), the fine-scale spatial variations in tidal inundation have not been fully studied. To fill this research gap, this study focuses on the Luanhe Estuary—a region highly sensitive to topographic changes—and explores in depth the physical mechanisms regulating tidal inundation in this area. The study integrates long-term data from the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Instrument (MSI), spanning the period from 2016 to 2025, to construct a high-resolution time series dataset of Apparent Inundation Frequency (AIF). Subsequently, this dataset is correlated with a high-precision microtopographic Digital Elevation Model (DEM) obtained through Unmanned Aerial Vehicle (UAV) surveys. The analysis reveals a strong nonlinear relationship between AIF and topographic elevation, which is best described by an exponential decay model (R² = 0.903). The results show that the average inundation probability in the study area has shown a fluctuating but overall upward trend, increasing from 16.74% in 2016 to 29.02% in 2025 (peaking at 31.39% in 2024). Quantitative modeling confirms that microtopography is the primary controlling factor for fine-scale variations in tidal inundation levels. The integrated research approach proposed in this study provides a reliable framework for coastal vulnerability assessment. Against the backdrop of increasingly severe impacts from climate change and human activities, the high-resolution quantitative data generated by this study provides scientific support for formulating disaster mitigation and geomorphological management strategies. Full article

Unmanned Aerial Vehicles (UAVs) have become essential instruments for precision agriculture and forest monitoring, offering rapid, high-resolution data collection over wide areas. This review synthesizes global advances (2015–2024) in UAV-derived vegetation indices (VIs), combining bibliometric and content analyses of 472 peer-reviewed publications. The study identifies key research trends, dominant indices, and technical progress achieved through RGB, multispectral, hyperspectral, and thermal sensors. Results show an exponential growth of scientific output, led by China, the USA, and Europe, with NDVI, NDRE, and GNDVI remaining the most widely applied indices. New indices such as GSI, RBI, and MVI demonstrate enhanced sensitivity for stress and disease detection in both crops and forests. UAV-based monitoring has proven effective for yield prediction, water-stress evaluation, pest identification, and biomass estimation. Despite significant advances, challenges persist regarding illumination correction, soil background influence, and limited forestry applications. The paper concludes that UAV-derived vegetation indices—when integrated with machine learning and multi-sensor data—represent a transformative approach for the sustainable management of agricultural and forest ecosystems. Full article