Search Results (415)

Accurate absolute radiometric calibration is critical for ensuring the data quality of spaceborne Synthetic Aperture Radar (SAR) systems and supporting quantitative remote sensing applications. Absolute radiometric calibration generally relies on ground reference targets with known radar cross-section (RCS) deployed at dedicated calibration sites. Such ground-based calibration methods are costly and time-consuming, and calibration frequency is constrained by the distribution of calibration sites and the satellite revisit cycles. Additionally, for specialized SAR missions, such as deep space exploration, deploying calibration equipment on the observed extraterrestrial surface is infeasible. This study proposes a space-based absolute calibration concept using a small calibration satellite carrying a well-characterized reference (e.g., a passive reflector or an active transponder) and flying in formation with the SAR satellite. The relative motion ensures a side-looking acquisition geometry, enabling the SAR to image the accompanying target and derive calibration factors. The overall calibration process is divided into two stages: determination of an in-orbit calibration factor using the calibration satellite, followed by its transformation to accommodate ground imaging conditions. This method effectively isolates the radar system gain to characterize the intrinsic hardware response. Furthermore, by operating entirely in space, it avoids atmospheric and ground-clutter distortions, ensuring a fully space-based, end-to-end calibration process dominated primarily by sensor systematic errors. Moreover, it allows for more frequent and flexible calibration, eliminating reliance on ground calibration sites and infrastructure. The feasibility and advantages of the proposed concept are demonstrated through comprehensive simulations, covering orbit analysis, echo simulation, and image processing. Full article

Urban agriculture plays a critical yet increasingly complex role in sustainable urban development, especially in high-density regions undergoing rapid transformation. Accurate mapping of its spatial distribution and functional composition remains a methodological challenge due to its fragmented landscape, small plot sizes, and multifunctional nature. This study addresses this gap by developing and applying a novel hierarchical classification framework that integrates agricultural land cover types with key socio-economic functions to map urban agriculture in the Pearl River Delta (PRD), China. This framework is structured around agricultural land categories (i.e., cropland, garden, forest, grass, and water body) and further delineated by two primary production functions, planting and breeding, with a third functional dimension, leisure activities, proposed as a conceptual extension for future research. Using unmanned aerial vehicle (UAV) imagery and high-resolution satellite data, we constructed a spatial sample database for urban agriculture. The random forest algorithm was applied to classify urban agriculture with Gaofen-2 imagery, generating detailed spatial distribution maps across the study area, with consistently reliable overall accuracy (79.07–81.82%), though this may be slightly optimistic due to potential spatial autocorrelation between training and testing samples. While the framework performed exceptionally well for spectrally and spatially distinct classes such as water bodies and perennial plantations, challenges remained in discriminating among annual field crops due to spectral similarity. These findings underscore the potential of integrating multi-temporal remote sensing data to capture phenological variations for improved classification. This study provides a replicable, functionally informed mapping approach that not only advances the methodological toolkit for urban agriculture characterization but also offers a valuable evidence base for land use planning, agricultural policy, and sustainable urban development in rapidly urbanizing regions. Full article

Low-Earth orbit satellites are gradually becoming the core infrastructure of integrated aerospace communication networks, with their significant advantages of high communication rates, small transmission delay, and wide coverage. Interference with military communications in response to their security and protection needs is a current research challenge. Consequently, this paper introduces an interference technique optimized for low-Earth orbit satellite signals using a multimodal learning transformer model (OI-MLT). The proposed method incorporates symmetry-aware design by exploiting the inherent time–frequency structural characteristics of LEO satellite signals and the spatially distributed topology of interference sources. An optimized model for distributed interference sources is developed, and multimodal information of spectra and numerical values is processed in parallel through the self-attention mechanism. This approach effectively addresses the problem of dynamic matching between the interference signal and target signal in high-speed LEO scenarios, as well as high-precision interference synchronization under time-varying channels. Experimental results demonstrate that this technique enhances the precision of frequency tracking, reduces the time required for synchronization establishment, and improves the interference success rate by 27.52% on average compared with existing methods. Full article

Arctic sea ice is a critical indicator of global climate dynamics, directly influencing maritime navigation, polar biodiversity, and offshore engineering safety. The precise mapping of diverse ice types, such as frazil ice, slush, melt ponds, and open water, is essential for environmental monitoring; however, it remains a formidable challenge in satellite remote sensing. These difficulties arise from low-contrast imagery, overlapping spectral signatures, and the subtle textural nuances characteristic of polar regions. Traditional entropy-based thresholding techniques often falter when segmenting these complex scenes, as they typically rely on Gaussian distribution assumptions that do not align with the stochastic nature of Arctic data. To address these limitations, this paper presents a novel unsupervised segmentation framework based on symmetric cross-entropy (SCE). Unlike standard directional measures, SCE provides a more robust objective function for multi-level thresholding by simultaneously maximizing intra-class cohesion and minimizing inter-class ambiguity. The proposed method uses an optimized search strategy to identify intensity levels that best delineate complex Arctic features. We conducted an extensive entropy-based comparative study that benchmarked SCE against 25 state-of-the-art entropy measures, including Shannon, Kapur, Rényi, Tsallis, and Masi entropies. Our experimental results demonstrate that the SCE method: (i) achieves superior accuracy by consistently outperforming established models in segmentation precision and boundary definition; (ii) provides visual clarity by producing segments with significantly reduced noise, making them ideal for identifying small-scale melt ponds and slush zones; and (iii) demonstrates computational robustness by providing stable threshold values even in datasets with non-Gaussian class distributions and poor illumination. Ultimately, these improvements deliver high-quality ice feature data that enhance risk assessment, operational planning, and predictive modeling. This research marks a major step forward in Arctic sea studies and introduces a valuable new tool for wider image processing and computer vision communities. Full article

This paper presents a modular Power Conditioning and Distribution Unit (PCDU) designed for small satellites. The proposed system features a highly adaptable architecture capable of managing a total power throughput of up to 100 W, with specific limits defined by mission-dependent thermal and redundancy configurations. Aligned with the New Space paradigm, the implementation relies on Commercial Off-The-Shelf (COTS) components, a strategy that drastically reduces development and manufacturing costs without compromising reliability. The system has been optimized for operation in harsh environments, including high vacuum, ionizing radiation, and extreme thermal gradients. The design incorporates strict redundancy and fault-tolerance criteria to provide a robust solution for critical subsystems. Comprehensive validation was performed through functional testing, Total Ionizing Dose (TID) radiation campaigns, and Thermal Vacuum (TVAC) cycles. Experimental results demonstrate that the PCDU withstands high-vacuum thermal cycling and cumulative radiation doses exceeding 75 kRad. These findings confirm that the developed unit is a cost-effective, high-reliability solution suitable for both Low Earth Orbit (LEO) and deep-space missions. Full article

This study examines land surface temperature (LST) variability over Malta, a small island in the central Mediterranean, using satellite observations compared with in situ near-surface air temperature (NSAT) measurements. The analysis focuses on the comparison between satellite-derived LST and local atmospheric thermal conditions for urban and rural land cover types. LST data from Landsat-8, MODIS (Terra and Aqua), and Sentinel-3A and 3B were analysed over a six-month period (September 2024 to February 2025). Monthly morning and evening field campaigns were conducted at 19 monitoring sites distributed across the island, during which NSAT, relative humidity, wind speed, and wind direction were recorded. Morning comparisons showed strong correlations between satellite-derived LST and in situ NSAT, i.e., Pearson’s correlation coefficient, r, in the range of 0.82–0.85. Landsat-8 exhibited a slight positive bias (+1.04 °C), while MODIS and Sentinel-3 Level-2 products showed negative biases (−3.82 °C and −1.89 °C, respectively). Nighttime comparisons revealed larger negative biases for MODIS (−6.91 °C) and Sentinel-3 (−6.89 °C). After empirical-based harmonisation, these discrepancies were reduced to near-zero mean bias, maintaining strong correlations. Spatial analysis indicated a persistent nocturnal urban heat island (UHI) effect, with urban areas retaining more heat than rural zones. Morning patterns showed seasonal modulation: during late summer and early autumn, rural areas exhibited higher surface temperatures due to sparse vegetation and exposed soils, whereas during cooler months the urban signal became more pronounced as vegetation recovery enhanced rural cooling. Overall, the results demonstrate the usefulness of multi-sensor satellite observations, interpreted alongside ground-based measurements for characterising thermal behaviour in small island environments. Full article

Satellite remote sensing plays an essential role in wildfire monitoring due to its large-scale observation capability. However, fire targets in satellite imagery are typically extremely small, sparsely distributed, and embedded in complex backgrounds, making accurate segmentation highly challenging for existing methods. To address these challenges, this paper proposes a multi-scale Mamba-based network for tiny fire segmentation, named FireMambaNet. The network adopts a nested U-shaped encoder-decoder architecture, primarily consisting of three modules: the Cross-layer Gated Residual U-shaped module (CG-RSU), the Fire-aware Directional Context Modulation module (FDCM), and the Multi-scale Mamba Attention Module (M2AM). The CG-RSU, as the core building block, adaptively suppresses background redundancy and enhances weak fire responses by extracting multi-scale features through cross-layer gating. The FDCM explicitly enhances the network’s ability to perceive anisotropic expansion features of fire points, such as those along the wind direction and terrain orientation, by modeling multi-directional context. The M2AM model employs a Mamba state-space model to suppress background interference through global context modeling during cross-scale feature fusion, while enhancing consistency among sparsely distributed tiny fire targets. In addition, experimental validation is conducted using two subsets from the Active Fire dataset, which have significant pixel-level sparse features: Oceania and Asia4. The results show that the proposed method significantly outperforms various mainstream CNN, Transformer, and Mamba baseline models on both datasets. It achieves an IoU of 88.51% and F1 score of 93.76% on the Oceania dataset, and an IoU of 85.65% and F1 score of 92.26% on the Asia4 dataset. Compared to the best-performing CNN baseline model, the IoU is improved by 1.81% and 2.07%, respectively. Overall, the FireMambaNet demonstrates significant advantages in detecting tiny fire points in complex backgrounds. Full article

The breakup of Intelsat 33E on 19 October 2024 posed a potential risk to satellites in the Geostationary Earth Orbit (GEO). This study analyzes the evolution and distribution of these fragments using a probabilistic approach. The initial distribution of the fragments, derived from the NASA Standard Breakup Model, indicates the generation of 4393 fragments larger than 1 cm. The spatial propagation of these fragments is modeled analytically in the Earth-Centered Earth-Fixed reference frame, showing the formation of high-density ring structures in the equatorial plane from 24 h to 28 days after the breakup. The orbits of 36 cataloged fragments are retrieved and compared with the probability density. Furthermore, Monte Carlo simulations validate the probabilistic model and highlight its efficiency in capturing low-probability events. Collision risks to other GEO satellites are assessed, showing that the top 10% of satellites encounter a collision probability of up to ${10}^{-8}$ after 28 days. Satellites near the equatorial plane are at higher risk, whereas those with higher inclinations are less affected. These findings underscore the need for enhanced monitoring and mitigation strategies for GEO breakup events, given the challenges in detecting small fragments. Full article

Global Positioning System (GPS)-derived ionospheric electron concentration measurements provide a powerful observational framework for seismo-electromagnetic studies, enabling quantitative investigation of lithosphere–atmosphere–ionosphere coupling processes through statistically detectable perturbations in ionospheric electron concentration. We analyze GPS-derived Vertical Total Electron Content (VTEC) variations associated with the 14 August 2021 Haiti earthquake ($M_w$ 7.2) and the 11 January 2022 Cyprus earthquake ($M_w$ 6.6) using data from nearby International GNSS (Global Navigation Satellite System) Service (IGS) stations located within their respective earthquake preparation zones. VTEC time series spanning 45 days before and 7 days after each event are processed to remove the diurnal component, yielding residuals that isolate short-term ionospheric variability. Anomaly detection is performed using three statistical frameworks: a Gaussian mean, standard deviation model, a robust median/median absolute deviation (MAD) model, and a distribution-free quantile-based model. Daily “occurrence” and “energy” indices are constructed to quantify the frequency and cumulative strength of detected anomalies, respectively. While the indices exhibit similar temporal patterns across all methods, they indicate frequent anomaly detection, limiting statistical selectivity. To address this, both indices are normalized by their median values and filtered using a 95% quantile threshold, retaining only extreme deviations. This procedure substantially reduces background fluctuations and isolates a small number of statistically significant anomaly peaks. For both earthquakes, enhanced anomaly activity is identified in the weeks preceding the events, whereas post-event peaks coincide with periods of elevated meteorological and geomagnetic activity. The results demonstrate that normalization combined with robust statistical methods is essential for discriminating significant ionospheric TEC anomalies from background variability. Full article

Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region. Full article

The development and execution of prospective inner and outer space missions require focusing on the use of many small space vehicles operating in swarms with multiple informational, navigational, and mission-oriented interactions among themselves. Such missions involve providing communication and surveillance services, facilitating distributed material production in space, and conducting research expeditions to explore the resources and environments of new worlds. The cornerstone technology for operating distributed space systems is propulsion. Among a range of propulsion technologies—from using pressurized cold gases to implementing laser beams to generate thrust—certain methods stand out for application in small spacecraft. This paper provides a summary of space-operated propulsion, emphasizing the reasons for the more frequent adoption of one technology over another. The discussion on propulsion trends is complemented by examining the physical, engineering, production, operational, and societal rationale behind these choices. The findings reinforce the trend toward transitioning to fully electric satellites. This review serves as a means for reevaluating global propulsion trends and guiding the future development of inner and outer space propulsion-assisted economies effectively. Full article

Polarimetric information is essential for scattering interpretation and target characterization in synthetic aperture radar (SAR) remote sensing, yet many resource-constrained platforms (e.g., small satellites and unmanned aerial vehicles (UAVs)) operate with limited polarization modes or even a single radio frequency (RF) chain, which limits full polarimetric scattering acquisition. To address this limitation, this paper proposes a single-channel framework for estimating the full polarization scattering matrix (PSM) enabled by time-varying polarization modulation. The transmit/receive polarization states are steered along predefined trajectories on the Poincaré sphere to generate time-varying polarization tags that are encoded into the received echoes through the target’s polarization-varying response. A compact observation model is then derived to relate the single-channel echoes, the known polarization tags, and the unknown PSM; based on this, the PSM is then estimated via a least squares formulation with a low-rank approximation. Simulation results demonstrate the robust reconstruction of the full polarimetric scattering matrix under diverse modulation trajectories. For arbitrarily chosen random point targets, when the signal-to-noise ratio (SNR) exceeds −20 dB, the polarimetric similarity coefficient approaches 1, and the estimation errors of Pauli power components converge toward zero. Furthermore, the method’s reliability is validated on distributed vegetation clutter. Quantitative metrics demonstrate near-perfect statistical consistency, with polarimetric entropy and alpha angle errors within 0.14%. Overall, the proposed approach provides a practical pathway to enhance the availability of full polarimetric scattering information under limited-observation conditions, confirming its feasibility for downstream analysis in complex natural scenes while maintaining a single radio frequency (RF) chain architecture augmented by a polarization modulator. Full article

To address the stringent cost and performance requirements of commercial Satellite-on-the-Move (SOTM) terminals, we propose a Genetic Algorithm (GA)-based design for a millimeter-wave Phased-Array-Fed Lens (PAFL). This antenna is specifically intended to be the electronic scanning module within a hybrid mechanical–electronic steering architecture. In this hybrid configuration, wide-angle coverage is handled by mechanical positioning, while the PAFL is responsible for high-precision fine tracking and jitter compensation within a critical ±15° field of view. By utilizing a small-scale active array to illuminate a large passive planar lens, this design significantly reduces hardware costs compared to full phased arrays. To mitigate phase aberrations and gain loss inherent in such compact focal-to-diameter (F/D) systems, a two-stage co-optimization strategy is introduced. It globally optimizes the lens phase distribution and subsequently synthesizes feed excitation codebooks to dynamically correct residual errors. A Ka-band prototype comprising an 8 × 8 active feed and a 28 × 28 transmitarray lens was fabricated. Measurements demonstrated stable scanning within the required ±15° range with a gain variation of less than 1.5 dB, achieving a peak directivity of 28.9 dBi and sidelobe levels below −12 dB. Full article

We investigate the potential of frequent repeat imagery acquired by the PlanetScope Dove small satellite constellation to overcome temporal and spatial limitations in automated surface topography mapping. While individual PlanetScope Dove stereo pairs produce low-quality Digital Surface Models (DSMs) with large height uncertainties, the high temporal frequency enables multiple DSMs to enhance accuracy through multiple-pair image matching. We present a fully automated SETSM framework by improving the quality of PlanetScope Dove DSMs based on SETSM Multi-Pair Matching Procedure (SETSM MMP). This framework enhances stereo pair quality through an optimized stereo pair selection by sequential conditional filtering and a Weighted Stereo Pair Index (WSPI). A novel inter-plane vertical coregistration, which minimizes scaling errors between single stereo pair DSMs, was developed to improve consistency and accuracy in DSM quality without reference surfaces. Applied to the cloud-obscured Pantasma crater region in Nicaragua, the optimized stereo pair selection automatically selects well-defined stereo pairs. The inter-plane vertical coregistration without existing reference surfaces achieves up to a 43% Root Mean Square Error (RMSE) reduction and 26% improvement in distribution within a 5 m vertical error. DSM quality correlated strongly with tile size, stereo pair convergence angle, asymmetric angle and terrain-dependent scale variability. The proposed framework provides fully automatic, high quality PlanetScope Dove DSMs without Ground Control Points (GCPs). Full article

Populations in areas with limited hydrological data face ongoing challenges related to water supply and management, with climate change increasing the risks of floods and droughts. New remote sensing and modeling tools can improve land and water management in these regions, especially when combined with limited ground measurements and local knowledge of extreme events. This study examined hydrological extremes and land cover change impacts in the Grande Rivière du Nord watershed, Haiti, using satellite and model-based data. Precipitation extremes were obtained from the Global Precipitation Measurement Integrated Multi-satellite Retrievals for GPM (GPM IMERG; 2000–2025), and streamflow data were sourced from the Group on Earth Observation Global Water Sustainability (GEOGLOWS) system and bias-corrected with a small historical hydrologic database. Annual maximum series were created and fitted with Gumbel, Lognormal, and Generalized Extreme Value (GEV) distributions using the L-moment method. Goodness-of-fit tests identified the best models, and precipitation amounts for return periods of 2–100 years were estimated. The precipitation maxima aligned with locally reported extreme events, and GEV provided the best overall fit. Using the bias-corrected streamflow, a hydrologic model was calibrated and validated and then applied to land cover change scenarios. Simulations suggest that moderate land-use change can increase peak flows beyond channel capacity, raising flood risk and informing adaptation planning in northern Haiti, which has limited data. Full article