Search Results (2,001)

The Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) technique enables cislunar spacecraft to obtain accurate position and velocity information, allowing full state estimation of two vehicles using only inter-satellite range (ISR) measurements when both their dynamical states are unknown. However, its stand-alone use leads to significantly increased orbit determination errors when the orbital planes of the two spacecraft are nearly coplanar, and is characterized by long initial convergence times and slow recovery following dynamical disturbances. To mitigate these issues, this study introduces an integrated navigation method that augments inter-satellite range measurements with line-of-sight vector angles relative to background stars. Additionally, an enhanced Adaptive Robust Cubature Kalman Filter (ARCKF) incorporating a chi-square test-based adaptive forgetting factor (AFF-ARCKF) is developed. This algorithm performs adaptive estimation of both process and measurement noise covariance matrices, improving convergence speed and accuracy while effectively suppressing the influence of measurement outliers. Numerical simulations involving spacecraft in Earth–Moon L4 planar orbits and distant retrograde orbits (DRO) confirm that the proposed method significantly enhances system observability under near-coplanar conditions. Comparative evaluations demonstrate that AFF-ARCKF achieves faster convergence compared to the standard ARCKF. Further analysis examining the effects of initial state errors and varying initial forgetting factors clarifies the operational boundaries and practical applicability of the proposed algorithm. Full article

This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Floods are among the most frequent and devastating natural hazards, particularly in developing countries such as Myanmar, where monsoon-driven rainfall and inadequate flood-control infrastructure exacerbate disaster impacts. This study presents a satellite-driven and interpretable framework for high-resolution flood susceptibility and risk assessment by integrating multi-source remote sensing and geospatial data with ensemble machine-learning models—Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)—implemented on the Google Earth Engine (GEE) platform. Eleven satellite- and GIS-derived predictors were used, including the Digital Elevation Model (DEM), slope, curvature, precipitation frequency, the Normalized Difference Vegetation Index (NDVI), land-use type, and distance to rivers, to develop flood susceptibility models. The Jenks natural breaks method was applied to classify flood susceptibility into five categories across Myanmar. Both models achieved excellent predictive performance, with area under the receiver operating characteristic curve (AUC) values of 0.943 for XGBoost and 0.936 for LightGBM, effectively distinguishing flood-prone from non-prone areas. XGBoost estimated that 26.1% of Myanmar’s territory falls within medium- to high-susceptibility zones, while LightGBM yielded a similar estimate of 25.3%. High-susceptibility regions were concentrated in the Ayeyarwady Delta, Rakhine coastal plains, and the Yangon region. SHapley Additive exPlanations (SHAP) analysis identified precipitation frequency, NDVI, and DEM as dominant factors, highlighting the ability of satellite-observed environmental indicators to capture flood-relevant surface processes. To incorporate exposure, population density and nighttime-light intensity were integrated with the susceptibility results to construct a natural–social flood risk framework. This observation-based and explainable approach demonstrates the applicability of multi-source remote sensing for flood assessment in data-scarce regions, offering a robust scientific basis for flood management and spatial planning in monsoon-affected areas. Full article

Despite a growing number of Earth Observation satellites, a critical observational gap persists for timely, high-resolution flood mapping, primarily due to infrequent satellite revisits and persistent cloud cover. To address this issue, we propose a novel framework that synergistically fuses complementary data from three public sensor types. Our methodology harmonizes these disparate data sources by using surface water fraction as a common variable and downscaling them with flood susceptibility and topography information. This allows for the integration of sub-daily observations from the Visible Infrared Imaging Radiometer Suite and the Advanced Himawari Imager with the cloud-penetrating capabilities of the Advanced Microwave Scanning Radiometer 2. We evaluated this approach on the February 2022 flood in Brisbane, Australia using an independent ground truth dataset. The framework successfully compensates for the limitations of individual sensors, enabling the consistent generation of detailed, high-resolution flood maps. The proposed method outperformed the flood extent derived from commercial high-resolution optical imagery, scoring 77% higher than the Copernicus Emergency Management Service (CEMS) map in the Critical Success Index. Furthermore, the True Positive Rate was twice as high as the CEMS map, confirming that the proposed method successfully overcame the cloud cover issue. This approach provides valuable, actionable insights into inundation dynamics, particularly when other public data sources are unavailable. Full article

With the rapid growth of multimodal remote sensing (RS) data, there is an increasing demand for intelligent onboard computing to alleviate the transmission and latency bottlenecks of traditional orbit-to-ground downlinking workflows. While many lightweight AI algorithms have been widely developed and deployed for onboard inference, their limited generalization capability restricts performance under the diverse and dynamic conditions of advanced Earth observation. Recent advances in remote sensing foundation models (RSFMs) offer a promising solution by providing pretrained representations with strong adaptability across diverse tasks and modalities. However, the deployment of RSFMs onboard resource-constrained devices such as nano satellites remains a significant challenge due to strict limitations in memory, energy, computation, and radiation tolerance. To this end, this review proposes the first comprehensive survey of onboard RSFMs deployment, where a unified deployment pipeline including RSFMs development, model compression techniques, and hardware optimization is introduced and surveyed in detail. Available hardware platforms are also discussed and compared, based on which some typical case studies for low Earth orbit (LEO) CubeSats are presented to analyze the feasibility of onboard RSFMs’ deployment. To conclude, this review aims to serve as a practical roadmap for future research on the deployment of RSFMs on edge devices, bridging the gap between the large-scale RSFMs and the resource constraints of spaceborne platforms for onboard computing. Full article

Incremental computing is becoming increasingly important for processing large-scale datasets. In satellite imagery, spatial resolution, temporal depth, and large files pose significant computational challenges, requiring efficient architectures to manage processing time and resource usage. Accordingly, in this study, we propose a dynamic architecture, termed Multi-Cube, for optical satellite time series. The framework introduces a modular and baseline-aware approach that enables scalable subdivision, incremental growth, and consistent management of spatiotemporal data. Built on NetCDF, xarray, and Zarr, Multi-Cube automatically constructs stable multidimensional data cubes while minimizing redundant reprocessing, formalizing automated internal decisions governing cube subdivision, baseline reuse, and incremental updates to support recurrent monitoring workflows. Its performance was evaluated using more than 83,000 Sentinel-2 images (covering 2016–2024) across multiple areas of interest. The proposed approach achieved a 5.4× reduction in end-to-end runtime, decreasing execution time from 53 h to 9 h, while disk I/O requirements were reduced by more than two orders of magnitude compared with a traditional sequential reprocessing pipeline. The framework supports parallel execution and on-demand sub-cube extraction for responsive large-area monitoring while internally handling incremental updates and adaptive cube management without requiring manual intervention. The results demonstrate that the Multi-Cube architecture provides a decision-driven foundation for integrating dynamic Earth observation workflows with analytical modules. Full article

Understanding how strain is transferred across the interior of tectonic plates is fundamental to quantifying lithospheric deformation. The Central Anatolia Transition Zone (CATZ), situated between the North and East Anatolian fault systems, provides a unique natural laboratory for investigating how continental deformation evolves from localized faulting to distributed shear. In this study, we integrate InSAR analysis with Global Navigation Satellite System (GNSS) velocity data, and stress tensor inversion with supporting gravity and seismic datasets to characterize the geometry, kinematics, and geodynamic significance of the CATZ. The combined geodetic and geophysical observations reveal that the CATZ is a persistent, left-lateral deformation corridor (i.e., elongated zone of Earth’s crust that accommodates movement where the landmass on the opposite side of a fault system moves to the left relative to an observer) accommodating ~4 mm/yr of shear between the oppositely moving eastern and western sectors of the Anatolian Plate. Spatial coherence among LiCSAR-derived shear patterns, GNSS velocity gradients, and regional stress-field rotations defines the CATZ as a crustal- to lithospheric-scale transition zone linking the strike-slip domains of central Anatolia with the subduction zones of the Hellenic and Cyprus arcs. Stress inversion analyses delineate four subzones with systematic kinematic transitions: compressional regimes in the north, extensional fields in the central domain, and complex compressional–transtensional deformation toward the south. The CATZ coincides with zones of variable Moho depth, crustal thickness, and inferred lithospheric tearing within the retreating African slab, indicating a deep-seated origin. Its S-shaped curvature and long-term evolution since the late Miocene reflect progressive coupling between upper-crustal faulting and deeper lithospheric reorganization. Recognition of the CATZ as a lithospheric-scale transition zone, rather than a discrete active fault, refines the current understanding of Anatolia’s kinematic framework. This study demonstrates the capability of integrated satellite geodesy and stress modeling to resolve diffuse intra-plate deformation, offering a transferable approach for delineating similar transition zones in other continental regions. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

Reusable launch vehicles are a key category of next-gen European launchers, as multiple companies are in advanced stages of study and are shifting towards the development of first demonstrators. A worldwide tendency to reduce the costs associated with satellite insertion into low Earth orbits can be observed, together with the existence of a niche in the future European launcher family for reusable small launch vehicles, known as microlaunchers. Multiple recovery methods exist for space launch vehicles; in this study, a return to launch site (RTLS) vertical-landing approach is being prioritized for the recovery of the first stage of a two-stage LOX/methane microlauncher. In 2023, INCAS, with support from the Romanian Nucleu Program, initiated a large study to address the prospect of developing a partially reusable microlauncher. A multidisciplinary optimization (MDO) environment has been developed, which is used in this paper to assess the implications of stage recovery versus the landing location (return to launch site versus downrange recovery) and state whether an RTLS can be feasible for small launchers. The paper will also present some key results from previous studies, such that a clear solution trade-off can be made, together with the quantitative assessment of how different vertical-landing techniques affect the microlauncher specifications. Full article

The increasing availability of satellite data at different spatial resolutions offers new opportunities for environmental monitoring, highlighting the limitations of medium-resolution products for fine-scale territorial analysis. However, it also raises the need to enhance the resolution of low-quality imagery to enable more detailed spatial assessments. This study investigates the effectiveness of different super-resolution techniques applied to low-resolution (LR) multispectral Sentinel-2 satellite imagery to generate high-resolution (HR) data capable of supporting advanced knowledge extraction. Three main methodologies are compared: traditional bicubic interpolation, a generic Artificial Neural Network (ANN) approach, and a Convolutional Neural Network (CNN) model specifically designed for super-resolution tasks. Model performances are evaluated in terms of their ability to reconstruct fine spatial details, while the implications of these methods for subsequent visualization and environmental analysis are critically discussed. The evaluation protocol relies on RMSE, PSNR, SSIM, and spectral-faithfulness metrics (SAM, ERGAS), showing that the CNN consistently outperforms ANN and bicubic interpolation in reconstructing geometrically coherent structures. The results confirm that super-resolution improves the apparent spatial detail of existing spectral information, thus clarifying both the practical advantages and inherent limitations of learning-based super-resolution in Earth observation workflows. Full article

Climate change is impacting rivers worldwide, with a reduction in normal flow conditions in temperate regions like Poland. Such changes might have a significant influence on riparian vegetation and channel planform dynamics. To better understand how these changes impact the river morphology, this research focuses on a 250 km-long reach of the Polish Vistula River and investigates variations of monthly maximum discharges and vegetation conditions over the period 1984–2023 by means of Landsat satellite images. These satellite data were handled via Google Earth Engine, looking at a common index such as the Normalized Difference Vegetation Index, considered as a proxy of vegetation coverage variations. Results point out an increase in the median NDVI over the study area from 0.2 in 1984 to 0.3 in 2023, connected with a reduction of monthly discharge from around 920 m³/s to 880 m³/s. This suggests that changes in flow discharge are associated with a process of riparian vegetation growth, leading to a reduction of planform and bars dynamics and closure of secondary channels (i.e., oversimplification). This is particularly evident over the last couple of decades, during which water availability has decreased significantly, as more humid years in the middle of the study period are now no longer existing, with an observed decrease in the maximum monthly discharge during the last 20 years, likely connected with a more severe impact of climate change. This reduction in flooding events allows more time for vegetation to establish on river bars and banks, eventually creating new islands and causing the observed oversimplification of the active channel. In summary, using the Vistula River as an exemplary case study, this research suggests that prolonged dry periods, more common in recent decades due to climate change, might impact large rivers located in temperate climates, favouring the development of vegetation on exposed sandbars, eventually resulting in a less dynamic active channel. 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

Quantifying forest patch dynamics is essential for understanding how forest patch characteristics vary in relation to ancient tree locations. This study developed a satellite-based framework to analyze the differences among forest patches associated with natural and planted ancient trees across the Sichuan–Chongqing region, China. Using monthly LandTrendr on Google Earth Engine, we analyzed long-term (1990–2024) and high-frequency observations of forest dynamics at a 180 m × 180 m (6 × 6 pixels) spatial scale. Disturbance and recovery events were characterized by their magnitude, rate, timing, and duration. Patches were classified into six categories based on ancient tree type and proximity and further subdivided by land use type. The results show that in natural forests, patches with natural ancient trees are associated with more stable change signatures, whereas in planted forests, patches containing planted ancient trees are associated with stronger recovery-related change patterns. Over 60% of detected changes were short-lived (≤5 years), indicating that most disturbances and recovery processes were transient rather than persistent. These findings show that the presence and spatial context of ancient trees are associated with differences in patch change patterns. The proposed workflow provides a scalable approach for integrating multi-temporal remote sensing into large-scale monitoring and management of ancient trees and their associated forest patches. Full article

Accurate and timely crop yield prediction is crucial for global food security and modern agricultural management. Traditional methods often lack the scalability and granularity required for precision farming. This paper introduces FARM (Fine-tuning Agricultural Regression Models), a deep learning framework designed for high-resolution, intra-field canola yield prediction. FARM leverages a pre-trained, large-scale geospatial foundation model (Prithvi-EO-2.0-600M) and adapts it for a continuous regression task, transforming multi-temporal satellite imagery into dense, pixel-level (30 m) yield maps. Evaluated on a comprehensive dataset from the Canadian Prairies, FARM achieves a Root Mean Squared Error (RMSE) of $0.44$ and an $R^2$ of $0.81$. Using an independent high-resolution yield monitor dataset, we further show that fine-tuning FARM on limited ground-truth labels outperforms training the same architecture from scratch, confirming the benefit of pre-training on large, upsampled county-level data for data-scarce precision agriculture. These results represent improvement over baseline architectures like 3D-CNN and DeepYield, which highlight the effectiveness of fine-tuning foundation models for specialized agricultural applications. By providing a continuous, high-resolution output, FARM offers a more actionable tool for precision agriculture than conventional classification or county-level aggregation methods. This work validates a novel approach that bridges the gap between large-scale Earth observation and on-farm decision-making, offering a scalable solution for detailed agricultural monitoring. Full article

In January 2025, multiple wildfires erupted across the Los Angeles region, fueled by prolonged dry conditions and intense Santa Ana winds. Southern California has faced increasingly frequent and severe wildfires in recent years, driven by prolonged drought, high temperatures, and the expanding wildland–urban interface. These fires have caused major loss of life, extensive property damage, mass evacuations, and severe air-quality decline in this densely populated, high-risk region. This study integrates passive and active satellite observations to characterize the spatiotemporal and vertical distribution of wildfire emissions and assesses their impact on air quality. TROPOMI (Sentinel-5P) and the recently launched TEMPO geostationary instrument provide hourly high temporal-resolution mapping of trace gases, including nitrogen dioxide (NO₂), carbon monoxide (CO), formaldehyde (HCHO), and aerosols. Vertical column densities of NO₂ and HCHO reached 40 and 25 Pmolec/cm², respectively, representing more than a 250% increase compared to background climatological levels in fire-affected zones. TEMPO’s unique high-frequency observations captured strong diurnal variability and secondary photochemical production, offering unprecedented insights into plume evolution on sub-daily scales. ATLID (EarthCARE) lidar profiling identified smoke layers concentrated between 1 and 3 km altitude, with optical properties characteristic of fresh biomass burning and depolarization ratios indicating mixed particle morphology. Vertical profiling capability was critical for distinguishing transported smoke from boundary-layer pollution and assessing radiative impacts. These findings highlight the value of combined passive–active satellite measurements in capturing wildfire plumes and the need for integrated monitoring as wildfire risk grows under climate change. Full article

This study introduces SquareSwish, a smooth, self-gated activation $f\left(x\right)=x\sigma {\left(x\right)}^2$, and benchmarks it against ten established activations (ReLU, LeakyReLU, ELU, SELU, GELU, Snake, LearnSnake, Swish, Mish, Hard-Swish) across six CNN architectures (EfficientNet-B1/B4, EfficientNet-V2-M/S, ResNet-50, and Xception) under a uniform transfer-learning protocol. Two geographically grounded datasets are used in this study. FuelRiskMap-TR comprises 7686 satellite images of urban fuel stations in Türkiye, which is semantically enriched with the OpenStreetMap context and YOLOv8-Small rooftop segmentation (mAP@0.50 = 0.724) to support AI-enabled, ICT-integrated risk screening. In a similar fashion, FuelRiskMap-UK is collected, comprising 2374 images. Risk scores are normalized and thresholded to form balanced High/Low-Risk labels for supervised training. Across identical training settings, SquareSwish achieves a top-1 validation accuracy of 0.909 on EfficientNet-B1 for FuelRiskMap-TR and reaches 0.920 when combined with SELU in a simple softmax-probability ensemble, outperforming the other activations under the same protocol. By squaring the sigmoid gate, SquareSwish more strongly attenuates mildly negative activations while preserving smooth, non-vanishing gradients, tightening decision boundaries in noisy, semantically enriched Earth-observation settings. Beyond classification, the resulting city-scale risk layers provide actionable geospatial outputs that can support inspection prioritization and integration with municipal GIS, offering a reproducible and low-cost safety-planning approach built on openly available imagery and volunteered geographic information. Full article