Search Results (1,905)

This study evaluates the capabilities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion remote sensing sensors for mapping ophiolitic sequences and identifying manganese mineralization in the Bela Ophiolite region, located along the axial fold–thrust belt northwest of Karachi, Pakistan. The study area comprises tholeiitic basalts, gabbros, mafic and ultramafic rocks, and sedimentary formations where manganese occurrences are associated with jasperitic chert and shale. To delineate lithological units and Mn mineralization, advanced image processing techniques were applied, including band ratio (BR), Principal Component Analysis (PCA), and Spectral Angle Mapper (SAM) on visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Using these methods, gabbros, basalts, and mafic-ultramafic rocks were effectively mapped, and previously unrecognized basaltic outcrops and gabbroic outcrops were also discovered. The ENVI Spectral Hourglass Wizard was used to analyze the hyperspectral data, integrating the Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and N-Dimensional Visualizer to extract the spectra of end-members associated with Mn-bearing host rocks. In addition, the Hyperspectral Material Identification (HMI) tool was tested to recognize Mn minerals. The remote sensing results were validated by petrographic analysis and ground-truth data, confirming the effectiveness of these techniques in ophiolite mapping and mineral exploration. This study shows that ASTER band combinations (3-6-7, 3-7-9) and band ratios (1/4, 4/9, 9/1 and 3/4, 4/9, 9/1) provide optimal results for lithological discrimination. The results show that remote sensing-based image processing is a powerful tool for mapping ophiolites on a regional scale and can help geologists identify potential mineralization zones in ophiolitic sequences. Full article

The Haiyang-2B (HY-2B) satellite, launched on 25 October 2018, carries both active and passive microwave sensors, including a scanning microwave Radiometer (SMR), to deliver high-precision, all-weather global observations. Sea surface temperature (SST) is among its key products. We evaluated the HY-2B SMR Level-4A (L4A) SST (25 km resolution) over the North Pacific (0–60°N, 120°E–100°W) for the period 1 October 2023 to 31 March 2025 using the extended triple collocation (ETC) and dual-pairing methods. These comparisons were made against the Remote Sensing System (RSS) microwave and infrared (MWIR) fused SST product and the National Oceanic and Atmospheric Administration (NOAA) in situ SST Quality Monitor (iQuam) observations. Relative to iQuam, HY-2B SST has a mean bias of –0.002 °C and a root mean square error (RMSE) of 0.279 °C. Compared to the MWIR product, the mean bias is 0.009 °C with an RMSE of 0.270 °C, indicating high accuracy. ETC yields an equivalent standard deviation (ESD) of 0.163 °C for HY-2B, compared to 0.157 °C for iQuam and 0.196 °C for MWIR. Platform-specific ESDs are lowest for drifters (0.124 °C) and tropical moored buoys (0.088 °C) and highest for ship and coastal moored buoys (both 0.238 °C). Both the HY-2B and MWIR products exhibit increasing ESD and RMSE toward higher latitudes, primarily driven by stronger winds, higher columnar water vapor, and elevated cloud liquid water. Overall, HY-2B SST performs reliably under most conditions, but incurs larger errors under extreme environments. This analysis provides a robust basis for its application and future refinement. Full article

Multispectral remote sensing offers powerful capabilities for mineral exploration, particularly in regions with complex geological settings. This study investigates the mineralization potential of the Tidili region in Morocco, located between the South Atlasic and Anti-Atlas Major Faults, using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery to extract hydrothermal alteration zones. Key techniques include band ratio analysis and Principal Components Analysis (PCA), supported by the Crósta method, to identify spectral anomalies associated with alteration minerals such as Alunite, Kaolinite, and Illite. To validate the remote sensing results, field-based geological mapping and mineralogical analysis using X-ray diffraction (XRD) were conducted. The integration of satellite data with ground-truth and laboratory results confirmed the presence of argillic and phyllic alteration patterns consistent with porphyry-style mineralization. This integrated approach reveals spatial correlations between alteration zones and structural features linked to Pan-African and Hercynian deformation events. The findings demonstrate the effectiveness of combining multispectral remote sensing images analysis with field validation to improve mineral targeting, and the proposed methodology provides a transferable framework for exploration in similar tectonic environments. Full article

This study investigates the impact of assimilating FY-3D Microwave Radiation Imager (MWRI) radiance data into the Weather Research and Forecasting (WRF) model, utilizing a 3D-Var data assimilation system, on the forecast accuracy of Typhoon Muifa (2022). The research focuses on the selection of data from different channels, land/ocean coverage, and orbits of the MWRI, along with the synergistic assimilation strategy with MWHS-2 data. Ten assimilation experiments were conducted, starting from 0600 UTC on 14 September 2022, covering a 42 h forecast period. The results show that after assimilating the microwave radiometer data, the brightness temperature deviation in the ocean area was significantly reduced compared to the simulation without data assimilation. This led to an improvement in the accuracy of typhoon track and intensity predictions, particularly for predictions beyond 24 h. Furthermore, the assimilation of land data and single-orbit data (particularly from the western orbit) further enhanced forecast accuracy, while the joint assimilation of MWHS-2 and MWRI data yielded additional error reductions. These findings underscore the potential of satellite data assimilation in improving typhoon forecasting and highlight the need for optimal land observation and channel selection techniques. Full article

Urban parks often represent the last viable habitats for wildlife in city centres, functioning as crucial refuges and biodiversity hotspots for a wide array of plant and animal species. This study investigates the issue of light pollution in urban parks in selected Polish cities from the perspective of sustainable urban development and dark-sky friendly ordinances. Field data conducted in 2024 and 2025 include measurements of Upward Light Output Ratio (ULOR), illuminance, luminance, correlated colour temperature (CCT), and spectral characteristics of light sources. In addition, an analysis of changes in the level of light pollution in the studied parks and their surroundings between 2012 and 2025 was performed using data from the VIIRS (Visible Infrared Imaging Radiometer Suite) located on the Suomi NPP satellite. Results highlight the mismatch between sustainable development objectives and the current practice of lighting in most of the analysed parks. The study emphasises the need for better integration of light pollution mitigation in urban spatial policies and provides recommendations for environmentally and socially responsible lighting design in urban parks. Full article

This study presents the results of a spatiotemporal analysis of indoor radon concentration dynamics at the Al-Farabi Kazakh National University (Almaty, Republic of Kazakhstan), located near the Almaty tectonic fault. The research is based on a 2.5-year monitoring campaign of radon levels using the RAMON-02A radiometer. The radon activity concentration ranged from 1.29 ± 0.19 to 149 ± 22 Bq/m³. The distribution of radon concentrations was found to follow a lognormal law, with a skewness coefficient of 1.55 and kurtosis of 4.7. The mean values were 28.7 ± 4.2 Bq/m³ (arithmetic mean) and 24.5 ± 3.6 Bq/m³ (geometric mean). Distinct seasonal and monthly variations were observed: the lowest concentrations were recorded during the summer months (August—20.8 ± 3.1 Bq/m³), while the highest were observed in spring and winter (May—34.0 ± 4.9 Bq/m³, December—34.2 ± 4.9 Bq/m³). The springtime increase in radon levels is attributed to thermobaric effects, limited ventilation, and precipitation, which contributes to soil sealing. Autocorrelation analysis revealed diurnal, seasonal, and annual fluctuations, as well as quasi-periodic variations of approximately 150 days, presumably linked to geophysical processes. Correlation analysis indicated a weak positive relationship between radon concentration and air temperature during winter and spring (≈0.2), and a pronounced negative correlation with atmospheric pressure in winter (−0.57). The influence of humidity was found to be minor and seasonally variable. Full article

In this study, we propose a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in InSAR images through an enhanced sparse representation model with dynamically filtered coefficients. This design simultaneously preserves fine details and suppresses noise interference. Furthermore, an iterative refinement mechanism incorporates raw sampled data fidelity constraints, enhancing reconstruction accuracy. Simulation and physical experiments demonstrate that the proposed InSAR-PNS method significantly outperforms conventional techniques: it achieves a 1.93 dB average peak signal-to-noise ratio (PSNR) improvement over CS-based reconstruction while operating at reduced sampling ratios compared to Nyquist-rate fast fourier transform (FFT) methods. The framework provides a practical and efficient solution for high-fidelity millimeter-wave InSAR imaging under sub-Nyquist sampling conditions. Full article

Extreme operating conditions in solar receivers of concentrated solar thermal power plants, such as high temperatures, intense irradiance, and thermal cycling, pose significant challenges for conventional sensors. Optical fibers offer a promising alternative for flux measurement in such environments, but their long-term performance and degradation mechanisms require detailed investigation and characterization. This work presents a proof of concept for high solar flux measurement by using optical fibers as photon-capturing elements and showcases the behavior and damage that these optical fibers undergo when exposed to relevant conditions, including temperatures over 600 °C and flux levels exceeding 400 kW/m². Three fiber configurations, including polyimide and gold-coated fibers, were tested at a high-flux solar simulator and analyzed via scanning electron microscopy to assess structural integrity and material degradation. Results reveal significant coating deterioration, fiber retraction, and thermal-induced stress effects, which impact measurement reliability. These findings provide essential insights for improving the durability and accuracy of optical fiber-based sensing technologies in concentrating solar energy. Full article

Surface soil moisture (SSM) is a critical land surface parameter affecting a wide variety of economically and environmentally important processes. Spaceborne microwave remote sensing has been extensively employed for monitoring SSM. Active microwave sensors offering high spatial resolution are typically utilized to capture dynamic fluctuations in soil moisture, albeit with low temporal resolution, whereas passive sensors are typically used to monitor the absolute values of large-scale soil moisture, but offer coarser spatial resolutions (~10 km). In this study, a passive microwave observation system using an X-band microwave radiometer mounted on a drone was established to obtain high-resolution (~1 m) radiative brightness temperature within the observation region. The region was a control experimental field established to validate the proposed approach. Additionally, machine learning models were employed to invert the soil moisture. Based on the site-based validation the trained inversion model performed well, with estimation accuracies of 0.74 and 2.47% in terms of the coefficient of determination and the root mean square error, respectively. This study introduces a methodology for generating high-spatial resolution and high-accuracy soil moisture maps in the context of precision agriculture at the field scale. Full article

In satellite altimetry calibration, the atmosphere’s integrated water vapor content has been customarily derived through the Global Navigation Satellite Systems (GNSS), principally over land where the satellite radiometer is not operational. Progressively, several alternative methods have emerged to estimate this wet troposphere component with ground instruments, alternative satellite sensors, and global models. For any ground calibration facility, integration of various approaches is required to arrive at an optimum value of a calibration constituent and in accordance with the strategy of Fiducial Reference Measurements (FRM). In this work, different estimation methods and instruments are evaluated for wet troposphere delays, especially when transponder and corner reflectors are employed at the Permanent Facility for Altimetry Calibration of the European Space Agency. Evaluation includes, first, ground instruments with microwave radiometers and radiosondes; second, satellite sensors with the Ocean Land Color Instrument (OLCI) and the Sea Land Surface Temperature Radiometer (SLSTR) of the Copernicus Sentinel-3 altimeter, as well as the TROPOMI spectrometer on the Sentinel-5P satellite; and finally with global atmospheric models, such as the European Center for Medium-Range Weather Forecasts. Along these lines, multi-sensor and redundant values for the troposphere delays are thus integrated and used for the calibration of Sentinel-6 MF and Sentinel-3A/B satellite altimeters. All in all, the integrated water vapor value of the troposphere is estimated with an FRM uncertainty of ±15 mm. In the absence of GNSS stations, it is recommended that the OLCI and SLSTR measurements be used for determining tropospheric delays in daylight and night operations, respectively. Ground microwave radiometers can also be used to retrieve tropospheric data with high temporal resolution and accuracy, provided that they are properly installed and calibrated and operated with site-specific parameters. Finally, the synergy of ground radiometers with instruments on board other Copernicus satellites should be further investigated to ensure redundancy and diversity of the produced values for the integrated water vapor. Full article

Climate change has made weather more erratic and unpredictable. As a result, a growing need to develop more reliable short-term weather prediction models paved the way for a new era in satellite instrumentation technology, where radar systems for meteorological applications became critically important. This paper presents a comprehensive review of the evolution of weather forecasting satellites. We trace the technological development from the early weather and climate monitoring systems of the 1960s. Since the use of stabilized TV camera platforms on satellites aimed at capturing cloud cover data and storing it on magnetic tape for later readout and transmission back to ground stations, satellite sensor instrument technologies took great strides in the following decades, incorporating advancements in image and signal processing into satellite imagery methodologies. As innovative as they were, these technologies still lacked the capabilities needed to allow for practical use cases other than scientific research. The paper further examines how the next phase of satellite platforms is aimed at addressing this technological gap by leveraging the advantages of low Earth orbit (LEO) based satellite constellation deployments for near-real-time tracking of atmospheric hydrometers and precipitation profiles through innovative methods. These methods involve combining the collected data into big-data lakes on internet cloud platforms and constructing innovative AI-based multi-layered weather prediction models specifically tailored to remote sensing. Finally, we discuss how these recent advancements form the basis for new applications in aviation, severe weather readiness, energy, agriculture, and beyond. Full article

This study assessed the effectiveness of stress-based cognitive-behavioral training compared to standard training in firefighters, emphasizing their ability to distribute extinguishing water and cool environments evenly during enclosure fires. Experiments took place at the Zbiroh training facility with two firefighter teams (Team A with stress-based training and Team B with standard training) under realistic conditions. Using 58 thermocouples and 4 radiometers, temperature distribution and radiant heat flux were measured to evaluate water distribution efficiency and cooling performance during interventions. Team A consistently achieved temperature reductions of approximately 320 °C in the upper layers and 250–400 °C in the middle layers, maintaining stable conditions, whereas Team B only achieved partial cooling, with upper-layer temperatures remaining at 750–800 °C. Additionally, Team A recorded lower radiant heat flux densities (e.g., 20.74 kW/m² at 0°) compared to Team B (21.81 kW/m²), indicating more effective water application and adaptability. The findings confirm that stress-based training enhances firefighters’ operational readiness and their ability to distribute water effectively during interventions. This skill is essential for safer and effective management of indoor fires under extreme conditions. This study supports the inclusion of stress-based and scenario-based training in firefighter education to enhance safety and operational performance. Full article

Background: Core laboratory chemistry analyzers typically use plasma and serum samples, while blood gas instruments use whole blood for electrolyte and metabolite tests. Due to high costs to back up the core lab chemistry analyzers, especially in the remote small community hospitals, we have verified the interchangeability of serum/plasma electrolytes and metabolites on blood gas instruments (GEM4000 and Radiometer ABL90) vs. chemistry analyzers. In this study, we sought to extend the investigation to another blood gas device—Radiometer ABL837. Methods: One plasma separator tube and one serum separator tube were drawn from 20 apparently healthy individuals and outpatients and 20 intensive care unit patients. All the samples were run on Roche Cobas8000, and then were run on three Radiometer ABL837 analyzers for sodium (Na⁺), potassium (K⁺), chloride (Cl⁻), glucose, lactate (plasma only), and creatinine parameters. Paired measurements between the ABL837 and Cobas8000 were compared, and their difference were assessed for statistical and clinical significance. Results: ABL837 demonstrated statistical significance (p < 0.05) vs. Cobas8000 on all the plasma and serum parameters. However, no parameter differences were found when comparing the plasma/serum results on ABL837 to those on Cobas8000, indicating that none were clinically significant. ABL837 also demonstrated good–excellent correlations with Cobas8000 on all the parameters. Conclusions: When comparing metabolite and electrolyte values with plasma and serum sample types, the ABL837 blood gas instruments and Cobas 8000 chemistry analyzer are interchangeable. These data proves that ABL837 can be used as a backup for a chemistry analyzer in measuring plasma and serum electrolyte and metabolite concentrations. Full article

This study evaluates machine learning-based methods for retrieving thin Arctic sea ice thickness (SIT) from L-band radiometry, using data from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite. In addition to the operational ESA product, three alternative approaches are assessed: a Random Forest (RF) algorithm, a Convolutional Neural Network (CNN) that incorporates spatial coherence, and a Long Short-Term Memory (LSTM) neural network designed to capture temporal coherence. Validation against in situ data from the Beaufort Gyre Exploration Project (BGEP) moorings and the ESA SMOSice campaign demonstrates that the RF algorithm achieves robust performance comparable to the ESA product, despite its simplicity and lack of explicit spatial or temporal modeling. The CNN exhibits a tendency to overestimate SIT and shows higher dispersion, suggesting limited added value when spatial coherence is already present in the input data. The LSTM approach does not improve retrieval accuracy, likely due to the mismatch between satellite resolution and the temporal variability of sea ice conditions. These results highlight the importance of L-band sea ice emission modeling over increasing algorithm complexity and suggest that simpler, adaptable methods such as RF offer a promising foundation for future SIT retrieval efforts. The findings are relevant for refining current methods used with SMOS and for developing upcoming satellite missions, such as ESA’s Copernicus Imaging Microwave Radiometer (CIMR). Full article

This paper presents a near real-time implementation of the Karhunen–Loève Transform (KLT) for Radio Frequency Interference (RFI) mitigation in microwave radiometry. KLT is a powerful, data-adaptive technique capable of adjusting to various signal types by estimating the covariance matrix of the incoming signal and segmenting its eigenvectors to form an effective RFI basis. In this paper, the KLT is evaluated with real signals in laboratory conditions, aiming to characterize its performance in realistic conditions. To that effect, the dual Rx/Tx capability of a Pluto SDR is used to generate and capture RFI. The main mitigation metrics are computed for the KLT and other commonly used mitigation methods. In addition, while previous studies have shown the effectiveness of offline processing of recorded I/Q data, real-time mitigation is often necessary. Given the computational cost of eigendecomposition, this work introduces a low-complexity solution using the “economy covariance” approach alongside asynchronous covariance decomposition. The proposed implementation, realized within the GNU Radio framework, demonstrates the practical feasibility of real-time KLT-based mitigation and underscores its potential for improving signal integrity in digital radiometers operating under dynamic RFI conditions. Full article