Search Results (2,061)

During the suspension flight of high-altitude scientific balloons in near-space, they are highly vulnerable to time-varying wind field disturbances, which tend to excite multiple distinctive torsional modes of the balloons themselves, thereby interfering with the observations of balloon-borne equipment. Focusing on the azimuth control of the balloon-borne gondola, this paper designs a simplified decoupling mechanism and a reaction wheel as actuators. Specifically, the reaction wheel achieves azimuth tracking through angular momentum exchange, while the simplified decoupling mechanism performs the functions of decoupling and unloading. To fully utilize the control performance of the actuating structure, this paper further proposes a control algorithm based on a nonlinear differential tracker and neural network PID. Simulation results demonstrate that under typical wind disturbances and sensor noise conditions, the proposed system exhibits excellent smoothness and high-precision and stable control performance. This research provides a significant basis for stable observation platforms in precise near-space observation missions. Full article

Extreme climate events such as heatwaves are becoming more frequent and pose serious challenges in cities. Urban areas are particularly vulnerable because built surfaces absorb and release heat, while human activities generate additional greenhouse gases. This increases health risks, making it crucial to study population exposure to heat stress. This research focuses on Naples, Italy’s most densely populated city, where intense human activity and unique geomorphological conditions influence local temperatures. The presence of a Surface Urban Heat Island (SUHI) is assessed by deriving high-resolution Land Surface Temperature (LST) in a time series ranging from 2013 to 2023, processed with the Statistical Mono Window (SMW) algorithm in the Google Earth Engine (GEE) environment. SMW needs brightness temperature (Tb) extracted from a Landsat 8 (L8) Thermal InfraRed Sensor (TIRS), emissivity from Advanced Spaceborne and Thermal Emission Radiometer Global Emissivity Database (ASTERGED), and atmospheric correction coefficients from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR). A total of 64 nighttime images were processed and analyzed to assess long-term trends and identify the main heat islands in Naples. The hottest image was compared with population data, including demographic categories such as children, elderly people, and pregnant women. A risk index was calculated by combining temperature values, exposure levels, and the vulnerability of each group. Results identified three major heat islands, showing that risk is strongly linked to both population density and heat island distribution. Incorporating Local Climate Zone (LCZ) classification further highlighted the urban areas most prone to extreme heat based on morphology. Full article

Spaceborne Synthetic Aperture Radar (SAR) has become an indispensable tool for environmental monitoring, offering all-weather, day-and-night imaging capabilities. Before the launch, accurately analyzing the imaging characteristics of spaceborne SAR systems on the ground is crucial, and the antenna system is a very important part of SAR system simulation. This paper investigates the impact of antenna configuration on SAR imaging characteristics by using 3D antenna pattern, focusing on resolution consistency, coverage uniformity, and system adaptability under varying observation geometries. Different from the traditional SAR simulation with 2D antenna pattern (range direction and azimuth direction antenna pattern), we provide a novel simulation method by using 3D antenna pattern, which increases the simulation accuracy and realism. The two mainstream spaceborne SAR antennas (phased array antenna (PAA) and reflector antenna (RA)) are used to illustrate the differences between 2D antenna pattern and 3D antenna pattern. We provide a comparative analysis in the context of high-resolution and wide-swath imaging missions. Additionally, the importance of integrating 3D antenna pattern into SAR system simulation is emphasized, as it improves simulation fidelity, reduces development risk, and supports design validation. This study provides insights for the design and optimization of future SAR system simulation. Full article

This paper introduces a novel 3D simulation framework that integrates the Pipe-in-Pipe (PiP) model with Finite Element Analysis (FEA) using Ansys Parametric Design Language (APDL). This framework is designed to incorporate a 3D building model directly, assessing ground-borne vibrations from metro tunnels and their impact on surrounding structures. The PiP model efficiently calculates displacement fields around tunnels in full-space, applying the resulting fictitious forces to the FEA model, which includes a directly coupled 3D building model. This integration allows for precise simulation of vibration propagation through soil into buildings. A comprehensive verification test confirmed the model’s accuracy and reliability, demonstrating that the hybrid PiP-FEA model achieves significant computational savings-approximately 40% in time and 65% in memory usage-compared to the traditional full 3D FEA model. The results exhibit strong agreement between the PiP-FEA and full FEA models across a frequency range of 1–250 Hz, with less than 1% deviation, highlighting the effectiveness of the PiP-FEA approach in capturing the dynamic behavior of ground-borne vibrations. Additionally, the methodology developed in this paper extends beyond the specific case study presented and shows potential for application to various urban vibration scenarios. While the current validation is limited to numerical comparisons, future work will incorporate field data to further support the framework’s applicability under real metro-induced vibration conditions. Full article

Electrification introduces additional NVH (noise, vibration and harshness) challenges during the development of powertrain mounting systems due to high-frequency excitations from the powertrain and the absence of masking effects from the combustion engine. In these frequency ranges, engine mounts can stiffen up to a factor of five due to continuum resonances, reducing their structure-borne sound isolation properties and negatively impacting the customer’s NVH perception. Common hardening factors used during elastomer mount development are therefore limited in terms of their applicable validation frequency range. This study presents a methodology for determining decoupled permissible stiffness ranges for a double-isolated mounting system up to 1500 Hz, based on solution space engineering. Instead of optimizing for a single best design, we seek to maximize solution boxes, resulting in robust stiffness ranges that ensure the fulfillment of the formulated system requirements. These ranges serve as NVH requirements at the component level, derived from the sound pressure level at the seat location. They provide tailored guidelines for mount development, such as geometric design or optimal resonance placement, while simultaneously offering maximum flexibility by spanning the solution space. The integration of machine learning approaches enables the application of large-scale finite-element models within the framework of solution space analysis by reducing the computational time by a factor of $7.19·{10}^3$. From a design process standpoint, this facilitates frontloading by accelerating the evaluation phase as suppliers can directly benchmark their mounting concepts against the permissible ranges and immediately verify compliance with the defined targets. Full article

The Surface Extraction by TIN-based Search-space Minimization (SETSM) algorithm provides automatic generation of stereo-photogrammetric Digital Surface Models (DSMs) from single stereopairs of stereoscopic images (i.e., stereopairs), eliminating the need for terrain-dependent parameters. SETSM has been extensively validated through the ArcticDEM and Reference Elevation Models for Antarctica (REMA) DSM mapping projects. To enhance DSM coverage, quality, and accuracy by addressing stereopair occlusions, we expand the capabilities of the SETSM algorithm from single stereopair to multiple-pair matching. Building on SETSM’s essential components, we present a SETSM multiple-pair matching procedure (SETSM MMP) that modifies 3D voxel construction, similarity measurement, and blunder detection, among other components. A novel Three-Dimensional Kernel-based Weighted Height Estimation (3D KWHE) algorithm specialized for SETSM accurately determines optimal heights and reduces surface noise. Additionally, an adaptive pixel-to-pixel matching strategy mitigates the effect of differences in ground sample distance (GSD) between images. Validation using space-borne Worldview-2 and air-borne DMC multiple images over urban landscapes, compared to USGS lidar DSM, confirms improved height accuracy and matching success rates. The results from the DMC air-borne images demonstrate efficient elimination of occlusions. SETSM MMP enables high-quality DSM generation in urban environments while retaining the original, single-stereopair SETSM’s high performance. Full article

This study investigates multi-level coupled dynamic issues in near-space balloon-borne astronomical observation platforms subjected to multi-source disturbances, proposing an integrated azimuth pointing control scheme combining unified modeling with composite control strategies. A nonlinear dynamic model is established to characterize inertial coupling effects between the gondola system and secondary gimbal platform. The velocity-loop feedback mechanism utilizing fiber-optic gyroscopes achieves base disturbance decoupling, while an adaptive fuzzy PID controller enhances position-loop disturbance rejection capabilities. A gain adaptation strategy coordinates hierarchical control dynamics, complemented by anti-windup constraints safeguarding actuator operational boundaries. Simulation verifications confirm the exceptional high-precision pointing capability and robust stability under representative wind disturbances and sensor noise conditions. The system maintains a superior control performance across parameter perturbation scenarios, demonstrating consistent operational reliability. This study provides an innovative technical paradigm for precision observation missions in near space. Full article

Sea surface wind has been listed as an essential climate variable, playing crucial roles in regulating the global and regional weather and climate. Spaceborne synthetic aperture radar (SAR) has demonstrated the advantages in observing the wind field given its all-weather measurement capability. In this study, we present a convolutional neural network (CNN)-based framework for retrieving 10 m wind speed ($U_{10}$) from Sentinel-1 SAR wave mode (WV) imagery. The model is trained on SAR data acquired in 2017 using collocated ERA5 reanalysis wind vectors as the reference, with final performance evaluated against a temporally independent dataset from 2016 and in situ wind measurements. The CNN approach demonstrates improved retrieval accuracy compared to the conventional CMOD5.N-based result, achieving lower root mean square error (RMSE) and bias across both WV1 and WV2 incidence angle modes. Residual diagnostics show a systematic overestimation at low wind speeds and a slight underestimation at higher wind speeds. Spatial analyses of retrieval bias reveal regional variations, particularly in areas characterized by ocean swell or convective atmospheric activity, highlighting the importance of geophysical features in retrieval accuracy. These results support the viability of deep learning approaches for SAR-based ocean surface wind estimation and suggest a path forward for the development of more accurate, data-driven wind products suitable for both scientific research and operational marine forecasting. Full article

This study provides a comprehensive and systematic review of hyperspectral remote sensing in urban areas, with a focus on the evolving roles of airborne and spaceborne platforms. The main objective is to assess the state of the art and identify current trends, challenges, and opportunities arising from the scientific literature (the gray literature was intentionally not included). Despite the proven potential of hyperspectral imaging to discriminate between urban materials with high spectral similarity, its application in urban environments remains underexplored compared to natural settings. A systematic review of 1081 peer-reviewed articles published between 1993 and 2024 was conducted using the Scopus database, resulting in 113 selected publications. Articles were categorized by scope (application, method development, review), sensor type, image processing technique, and target application. Key methods include Spectral Unmixing, Machine Learning (ML) approaches such as Support Vector Machines and Random Forests, and Deep Learning (DL) models like Convolutional Neural Networks. The review reveals a historical reliance on airborne data due to their higher spatial resolution and the availability of benchmark datasets, while the use of spaceborne data has increased notably in recent years. Major urban applications identified include land cover classification, impervious surface detection, urban vegetation mapping, and Local Climate Zone analysis. However, limitations such as lack of training data and underutilization of data fusion techniques persist. ML methods currently dominate due to their robustness with small datasets, while DL adoption is growing but remains constrained by data and computational demands. This review highlights the growing maturity of hyperspectral remote sensing in urban studies and its potential for sustainable urban planning, environmental monitoring, and climate adaptation. Continued improvements in satellite missions and data accessibility will be key to transitioning from theoretical research to operational applications. Full article

Ultra-high-resolution synthetic aperture radar (SAR) has important applications in military and civilian fields. However, the acquisition of high-resolution SAR imagery poses considerable processing challenges, including limitations in traditional slant range model precision, the spatial variation in equivalent velocity, spectral aliasing, and non-negligible error introduced by stop-and-go assumption. To this end, this paper proposes an improved extended wavenumber domain imaging algorithm for ultra-high-resolution SAR to systematically address the imaging quality degradation caused by these challenges. In the proposed algorithm, the one-step motion compensation method is employed to compensate for the errors caused by orbital curvature through range-dependent envelope shift interpolation and phase function correction. Then, the interpolation based on modified Stolt mapping is performed, thereby facilitating effective separation of the range and azimuth focusing. Finally, the residual range cell migration correction is applied to eliminate range position errors, followed by azimuth compression to achieve high-precision focusing. Both simulation and spaceborne data experiments are performed to verify the effectiveness of the proposed algorithm. Full article

Accurate observations of Ocean Surface Wind Speed (OSWS) are vital for predicting extreme weather and understanding ocean–atmosphere interactions. However, spaceborne sensors (e.g., ASCAT, SMAP) often experience data loss due to harsh weather and instrument malfunctions. Existing inpainting methods often rely on reanalysis data that is released with delays, which restricts their real-time capability. Additionally, deep-learning-based methods, such as Transformers, face challenges due to their high computational complexity. To address these challenges, we present the Multimodal Wind Speed Inpainting Dataset (MWSID), which integrates 12 auxiliary forecasting variables to support real-time OSWS inpainting. Based on MWSID, we propose the MMamba framework, combining the Multimodal Feature Extraction module, which uses mutual information (MI) theory to optimize feature selection, and the OSWS Reconstruction module, which employs Attention-Mamba-2 within a Residual-in-Residual-Dense architecture for efficient OSWS inpainting. Experiments show that MMamba outperforms MambaIR (state-of-the-art) with an RMSE of 0.5481 m/s and an SSIM of 0.9820, significantly reducing RMSE by 21.10% over Kriging and 8.22% over MambaIR in high-winds (>15 m/s). We further introduce MMamba-L, a lightweight 0.22M-parameter variant suitable for resource-limited devices. These contributions make MMamba and MWSID powerful tools for OSWS inpainting, benefiting extreme weather prediction and oceanographic research. Full article

Large-scene space-borne SAR images with a high resolution are particularly effective for monitoring vast oceanic areas globally. However, ships are easily overlooked in such large scenes due to their small size and cluttered backgrounds, making SAR ship detection challenging for the existing methods. To address this challenge, we propose a progressive saliency-guided (PSG) method, which uses saliency-derived positional priors to guide the model in focusing on small targets and extracting their features. Specifically, a dual-guided perception enhancement (DGPE) module is developed, which introduces additional target saliency maps as prior information to cross-guide and highlight key regions in SAR images at the feature level, enhancing small object feature representation. Additionally, a saliency confidence aware assessment (SCAA) mechanism is designed to strengthen small object proposal learning at the proposal level, guided by classification and localization scores at key locations. The DGPE and SCAA modules jointly enhance small object learning across different network levels. Extensive experiments demonstrate that the PSG method significantly improves the detection performance (+4.38% AP on LS-SSDD and +4.35% on HRSID) for small ships in large-scene SAR images compared to that of the baseline, providing an effective solution for small ship detection in large scenes. Full article

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

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

Grassland aboveground biomass (AGB) serves as a critical indicator of ecosystem productivity and carbon cycling, playing a pivotal role in ecosystem functioning. The advances in hyperspectral and terrestrial Light Detection and Ranging (LiDAR) data have provided new opportunities for grassland AGB monitoring, but current research remains predominantly focused on data-driven machine learning models. The black-box nature of such approaches resulted in a lack of clear interpretation regarding the coupling relationships between these two data types in grassland AGB estimation. For grassland aboveground fresh biomass, the theoretical estimation can be decomposed into either the product of planar density (PD) and plot area or the product of stereoscopic density (SD) and grassland community volume. Based on this theory, our study developed a semi-mechanistic remote sensing model for grassland AGB estimation by integrating hyperspectral-derived biomass density with extracted structural parameters from terrestrial LiDAR. Initially, we built hyperspectral estimation models for both PD and SD of grassland fresh AGB using PLSR. Subsequently, by integrating the inversion results with grassland quadrat area and community volume measurements, respectively, we achieved quadrat-scale remote sensing estimation of grassland AGB. Finally, we conducted comparative accuracy assessments of both methods across different phenological stages to evaluate their performance differences. Our results demonstrated that SD, which incorporated structural features, could be more precisely estimated (R² = 0.90, nRMSE = 7.92%, Bias% = 0.01%) based on hyperspectral data compared to PD (R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%), with significant differences observed in their respective responsive spectral bands. PD showed greater sensitivity to shortwave infrared regions, while SD exhibited stronger associations with visible, red-edge, and near-infrared bands. Although both methods achieved comparable overall AGB estimation accuracy (PD-based: R² = 0.79, nRMSE = 10.19%, Bias% = −7.25%; SD-based: R² = 0.82, nRMSE = 10.58%, Bias% = 1.86%), the SD-based approach effectively mitigated the underestimation of high biomass values caused by spectral saturation effects and also demonstrated superior and more stable performance across different growth periods (R² > 0.6). This work provided concrete physical meaning to the integration of hyperspectral and LiDAR data for grassland AGB monitoring and further suggested the potential of multi-source remote sensing data fusion in estimating grassland AGB. The findings offered theoretical foundations for developing large-scale grassland AGB monitoring models using airborne and spaceborne remote sensing platforms. Full article