Search Results (5,344)

Precise characterization of the thermospheric neutral wind is essential for comprehending the dynamic interactions within the ionosphere-thermosphere system, as evidenced by the development of models like HWM and the need for localized data. However, numerical models often suffer from biases due to uncertainties in external forcing and the scarcity of direct wind observations. This study examines the influence of incorporating actual neutral wind profiles from the Michelson Interferometer for Global High-resolution Thermospheric Imaging (MIGHTI) on the Ionospheric Connection Explorer (ICON) satellite into the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE-GCM) via an ensemble-based data assimilation framework. To address the challenges of assimilating real observational data, a robust background check Quality Control (QC) scheme with dynamic thresholds based on ensemble spread was implemented. The assimilation performance was evaluated by comparing the analysis results against independent, unassimilated observations and a free-running model Control Run. The findings demonstrate a substantial improvement in the precision of the thermospheric wind field. This enhancement is reflected in a 45–50% reduction in Root Mean Square Error (RMSE) for both zonal and meridional components. For zonal winds, the system demonstrated effective bias removal and sustained forecast skill, indicating a strong model memory of the large-scale mean flow. In contrast, while the assimilation exceptionally corrected the meridional circulation by refining the spatial structures and reshaping cross-equatorial flows, the forecast skill for this component dissipated rapidly. This characteristic of “short memory” underscores the highly dynamic nature of thermospheric winds and emphasizes the need for high-frequency assimilation cycles. The system required a spin-up period of approximately 8 h to achieve statistical stability. These findings demonstrate that the assimilation of data from ICON/MIGHTI satellites not only diminishes numerical inaccuracies but also improves the representation of instantaneous thermospheric wind distributions. Providing a high-fidelity dataset is crucial for advancing the modeling and understanding of the complex interactions within the Earth’s ionosphere-thermosphere system. Full article

The transient electromagnetic (TEM) method is a key detection and monitoring technology for safe coal-mine production. Surface TEM depth penetration is limited by real geological conditions and transmitter–receiver hardware performance. Compared with the surface TEM method, the tunnel TEM method can enhance the depth of exploration to some extent, but it is constrained by the limited working space of the roadway, which makes it difficult to perform the area-wide and multi-line data acquisition, and thus the lateral detection resolution is directly compromised. Consequently, either surface or tunnel TEM alone suffers inherent limitations. The multidimensional surface and surface-to-tunnel TEM method employs a single large-loop transmitter and records electromagnetic (EM) signals both on the surface and in the tunnel, enabling joint data interpretation. The joint TEM observation method effectively addresses the limitations by using a single observation mode, with the goal of achieving high-precision detection. To investigate the detection capabilities of the joint surface and surface-to-tunnel TEM method, we propose a three-dimensional (3D) joint surface and surface-to-tunnel TEM forward modeling method based on the spectral element method (SEM). The SEM, using high-order vector basis functions, enables high-precision modeling of TEM responses with complex geo-electric earth models. The accuracy of the SEM is validated through comparisons with one-dimensional (1D) TEM semi-analytical solutions. To further reveal TEM response characteristics and multi-dimensional resolution under joint surface and tunnel detection modes, we construct several typical 3D geo-electric earth models and apply the SEM algorithm to simulate the TEM responses. We systematically analyze the horizontal and vertical resolution of 3D earth model targets at different decay times. The numerical results demonstrate that surface multi-line TEM surveying can accurately delineate the lateral extent of the target body, while vertical in-tunnel measurements are crucial for identifying the top and bottom interfaces of geological targets adjacent to the tunnel. Finally, the theoretical modeling results demonstrate that compared to individual TEM methods, the multi-dimensional joint surface and tunnel TEM observation yields superior target spatial information and markedly improves TEM detection efficacy under complex conditions. The 3D TEM forward modeling based on the SEM provides the theoretical foundation for subsequent 3D inversion and interpretation of surface-to-surface and surface-to-tunnel joint TEM data. Full article

Recurrent flooding poses a persistent and growing threat to West African watersheds facing rapid urbanization and climate change. Despite advances in machine learning and geospatial datasets, urban planning and flood prevention often rely on limited datasets and traditional analysis. This study addresses this research gap in the Bonoumin-Palmeraie watershed (Abidjan, Côte d’Ivoire) by developing an integrated approach leveraging remote sensing, Geographic Information Systems (GIS), and the Random Forest algorithm to assess and map flood susceptibility. Twelve conditioning factors related to topography, hydrology, land use, and climate were derived from Sentinel-1, ALOS PALSAR, and multi-source earth observation datasets. Historical flood extents were mapped in Google Earth Engine to train the Random Forest model in a Google Colab environment. The model demonstrated high discriminatory power, yielding an Area Under the Curve of 0.94 and Overall Accuracy of 0.83. Drainage density, rainfall, and altitude were identified as the primary explanatory drivers. The resulting flood susceptibility map indicates that 39% of the watershed exhibits medium to very high susceptibility, with critical hotspots in the neighborhoods of Palmeraie, Attoban, Akouedo, Djorogobité, and Riviera-Sogefiha. While limited by the exclusion of certain anthropogenic variables and ground truth constraints, the study provides a reproducible, data-driven framework for flood risk assessment in tropical urban environments. These findings offer essential scientific support for urban planners and decision-makers to enhance territorial planning and sustainable flood management in Abidjan. Full article

The snowpack in the European Alps is declining due to global warming, which affects both the amount of seasonal snow and the timing of accumulation and melt. As the European Alps is the largest winter tourism destination in the world by revenue, this decline in natural snow poses an existential threat to the sector. Several smaller ski areas have closed permanently since 1980, and all Alpine regions face rising costs due to an increasing reliance on snowmaking. Professional winter sports are also affected, with several canceled events in recent years due to unsuitable snow conditions. In this study, we present the first remote sensing-based assessment of long-term snow reliability for winter tourism in the European Alps. Using snowline elevation (SLE) data derived from Landsat observations from 1985 to 2024, combined with OpenStreetMap ski infrastructure data and digital elevation models, we quantified the monthly snow coverage of ski area segments across 43 Alpine basins. Theil–Sen trends and Mann–Kendall significances were calculated for the full season and for three subseasons, with quality checks applied to guarantee sufficient data coverage. The results show predominantly negative trends across all seasons, with the strongest declines occurring in the late season. In this period, 97.8% of all downhill ski areas and 99.5% of the cross-country ski areas for which a trend was derived exhibited negative trends. For the full season, the corresponding shares were 94% for downhill ski areas and 99.2% for cross-country ski areas. In addition, areas located at the geographical edges of the European Alps showed more pronounced negative trends compared with the core regions. These findings align with previous studies on the subject and highlight the ongoing shortening of natural snow seasons and thus the increased challenges for the winter tourism sector in the Alps. Full article

This study aims to assess how climate change will affect the intensity of soil erosion in the Vistula River basin by the mid-21st century. A simulation framework based on the SWAT–MUSLE model was applied, calibrated, and validated against observed streamflow data and driven by climatic forcings from the EURO-CORDEX ensemble (the RACMO22E, HIRHAM5, and RCA4 models forced by EC-EARTH GCM) under the RCP 4.5 and RCP 8.5 scenarios. Simulations were conducted at a daily time step for the years 2021–2050 and compared to the reference period 2013–2018. The analysis included the decadal and seasonal aggregation of the sediment yield (SYLD, t ha⁻¹ yr⁻¹). The results indicate that, relative to the baseline value (~1.84 t ha⁻¹ yr⁻¹), the SYLD increases under both scenarios. In RCP 4.5, the rise culminates during 2031–2040 and then stabilizes in 2041–2050. Under RCP 8.5, a continuous upward trend is observed, with the highest values projected for 2041–2050, particularly for the HIRHAM5 realization. The largest relative increases occur in summer (JJA) and, in the final decade, also in autumn (SON); in the early horizon, autumn may locally exhibit declines that later shift to increases. The spread among RCM realizations remains significant and should be interpreted as an expression of projection uncertainty. The practical implications include prioritizing soil protection measures in sub-catchments with high LS factors and soils susceptible to water erosion, strengthening runoff and sediment control in summer, and planning maintenance of small-scale retention infrastructure. Study limitations arise from the inherent structure of the MUSLE model, bias correction procedures for climate data, and the representation of extreme events. Therefore, greater emphasis is placed on the direction and seasonality of changes rather than absolute numerical values. Full article

In the last decades, satellite remote sensing has played a key role in Earth Observation, as an effective monitoring tool applied to geo-hazard identification and mitigation. In particular, the differential synthetic aperture radar interferometry technique provides incomparable information on ground movements related to volcanic unrest, co-eruptive deformation, and volcano flank motion. In this work, ground deformation data derived from Sentinel-1 satellites were analyzed over the Cumbre Vieja volcano, located in the southern part of La Palma Island, Canary archipelago. The volcano started to erupt on 19 September 2021, after a seismic swarm. The eruption buried hundreds of buildings and properties, causing severe economic losses. Analyzing the vertical ground displacement of the volcano in the year preceding the eruption, the results show that ground deformation can be considered a precursor of the eruption, which allows us to identify the phases of the magmatic ascent up to the opening of the eruptive vent. Interestingly, after a subsidence phase lasting 4 months, the ground displacement rate reverted and an uplift was observed, lasting 9 months, marking an uplift on the Cumbre Vieja volcano related to volcanic activity. This can be interpreted as the effect of the magma rising from the deeper chamber (15–25 km) to an intermediate stagnation zone (5 km) that provided a measurable anticipation of the eruption by 9 months. In the future, regular monitoring of Cumbre Vieja could adopt uplift detection as an indicator for shallow magma activity and as a possible eruption precursor. Full article

Google Earth Engine (GEE) has revolutionised remote sensing. The GEE cloud platform lets users quickly analyse large satellite imagery datasets with custom programmes, enhancing global-scale analysis. Crop condition monitoring using GEE would greatly help in decision-making and precision agriculture. Estimating canopy chlorophyll content (CCC) is an effective way to monitor crops using remote sensing because leaf chlorophyll is a key indicator. A hybrid model that combines radiative transfer models (RTMs), such as PROSAIL, with Gaussian Process Regression (GPR) can effectively estimate crop biophysical parameters using remote sensing images. GPR has proven to be one of the best methods for this purpose. This study aimed to develop a hybrid model to estimate CCC from S2 imagery and transfer it to the GEE platform for efficient data processing. In this work, the CCC (g/cm²) data from the S2 biophysical processor toolbox for the S2 imagery of the ICAR-Indian Agricultural Research Institute (IARI) on 23 February 2023 were used as observation data to train the hybrid algorithm. The hybrid model was successfully validated against the 155 input data with an R2 of 0.94, RMSE of 10.02, and NRMSE of 5.04%. The model was integrated into GEE to successfully generate a CCC-estimated map of IARI using S2 imagery from 23 February 2023. An R2 value of 0.96 was observed when GEE-estimated CCC values were compared against CCC values estimated locally. This establishes that the GEE-based CCC estimation with the PROSAIL + GPR hybrid model is an effective and accurate method for monitoring vegetation and crop conditions over large areas and extended periods. Full article

Earth Observation (EO) and Remote Sensing (RS) data are widely used in various fields, including weather, environment, and natural disaster modeling and prediction. EO and RS done through geostationary satellite constellations in fields such as these are limited to a smaller region, while sun synchronous satellite constellations have discontinuous spatial and temporal coverage. This limits the ability of EO and RS data for near-real-time weather, environment, and natural disaster applications. To address these limitations, we introduce Now Observation Assemble Horizon (NOAH), a multi-modal, sensor fusion dataset that combines Ground-Based Sensors (GBS) of weather stations with topography, vegetation (land cover, biomass, and crown cover), and fuel types data from RS data sources. NOAH is collated using publicly available data from Environment and Climate Change Canada (ECCC), Spatialized CAnadian National Forest Inventory (SCANFI) and United States Geological Survey (USGS), which are well-maintained, documented, and reliable. Applications of the NOAH dataset include, but are not limited to, expanding RS data tiles, filling in missing data, and super-resolution of existing data sources. Additionally, Generative Artificial Intelligence (GenAI) or Generative Modeling (GM) can be applied for near-real-time model-generated or synthetic estimate data for disaster modeling in remote locations. This can complement the use of existing observations by field instruments, rather than replacing them. UNet backbone with Feature-wise Linear Modulation (FiLM) injection of GBS data was used to demonstrate the initial proof-of-concept modeling in this research. This research also lists ideal characteristics for GM or GenAI datasets for RS. The code and a subset of the NOAH dataset (NOAH mini) are made open-sourced. Full article

Classical earth pressure theories often struggle to account for the complex coupling effects of wall displacement and spatial non-uniformity under non-limit states. This study presents an interpretable machine learning framework designed to extract universal mechanical laws from heterogeneous experimental datasets. Using a multi-source database of rigid retaining walls with sandy backfill, a three-stage feature refinement strategy is proposed that incorporates Recursive Feature Elimination, Collinearity Analysis, and Interpretability Comparison to identify a parsimonious set of five fundamental physical parameters. A SHapley Additive exPlanations-Categorical Boosting (CatBoost-SHAP) framework is established to predict the active earth pressure coefficient (K) and interpret the underlying mechanisms across various movement modes (RB, RT, and T). Results demonstrate that the model effectively captures the progressive evolution of shear bands and the soil arching effect. Specifically, a critical displacement threshold of Δ/H ≈ 0.006 is identified, marking the transition from mode-dominated stress non-uniformity to magnitude-driven limit states. Leave-One-Dataset-Out Cross-Validation (LODOCV) confirms the model’s ability to maintain physical consistency over purely statistical fitting despite significant inter-literature heterogeneity. Finally, a Graphical User Interface (GUI) is developed to facilitate rapid, displacement-based design in engineering practice. This research bridges the gap between empirical laboratory observations and generalized mechanical logic, providing a data-driven foundation for refined geotechnical design. Full article

Remote sensing plays a vital role in monitoring environmental change in Antarctica, offering non-invasive insights into ice dynamics, biodiversity, and fragile ecosystems. Harsh conditions, limited field access, and logistical challenges result in sparse, noisy, and often unlabelled datasets, posing major obstacles for machine learning (ML) approaches. Data scarcity remains a fundamental challenge for uncrewed aerial vehicle (UAV)-based ecological monitoring. While ML models in other Earth observation domains demonstrate state-of-the-art performance, their applicability in Antarctic and polar regions’ settings is limited. This paper reviews the intersection of ML and UAV-based remote sensing in Antarctica under extreme data constraints. We surveyed recent strategies designed to overcome these limitations, including self-supervised learning, physics-informed modelling, and foundation models. Results highlight a notable gap, as polar environments remain excluded from global datasets and benchmarks due to the extensive data requirements of large-scale models. Opportunities exist where multimodal and multi-scale generalisation can enhance cross-domain adaption to data-scarce use cases. Unlike prior reviews on general remote sensing or task-specific polar studies, this work uniquely underscores the need for Antarctic representation in global ML advances, positioning Antarctica as a frontier testbed for machine learning in extreme, inaccessible, and under-resourced fields. Full article

Parabolic cylindrical antennas are characterized by their structural simplicity, high radiation efficiency, and low manufacturing costs. Consequently, they are widely used in Earth observation and serve as a viable option for spaceborne Synthetic Aperture Radar (SAR) systems. However, structural errors in the phased array feed and the parabolic cylindrical reflector are inevitable during manufacturing, assembly, and operation. These errors significantly degrade the accuracy of antenna pattern models. To address this issue, this paper proposes a comprehensive radiation pattern model that accounts for structural errors in both the linear feed and the reflector. This approach enables precise pattern prediction and efficient in-orbit calibration. Specifically, the reflected far-field pattern is first calculated using the field superposition principle and the Physical Optics (PO) method. Specifically, the combined phase effects resulting from feed and reflector structural errors are superimposed to establish a direct integration pattern model for the parabolic cylindrical antenna. Given the high computational complexity of the direct integration model, a simplified model based on Fresnel approximation is proposed. This approach significantly reduces integration complexity while preserving the quadratic phase characteristics of the main lobe, thereby substantially improving computational efficiency. Simulation results verify that the simplified model maintains high accuracy in both normalized amplitude and phase. Furthermore, a partitioned calibration method is proposed to compensate for the absolute gain deviation inherent in the simplified model. By integrating weighting relationships derived from sensitivity analysis of individual errors, an empirical parameter is defined to quantify the correlation between total structural errors, antenna performance, and the prediction accuracy of the simplified model. The results indicate that reflector structural errors are the dominant factor affecting the overall performance of the antenna. In contrast, the prediction accuracy of the simplified model is found to be more sensitive to feed structural errors. The simplified model exhibits tolerance to structural errors far exceeding the wavelength, enabling it to effectively replace the direct integration model. This work provides new theoretical foundations and technical methods for tolerance design, performance assurance, in-orbit testing, and calibration of parabolic cylindrical antennas. Full article

The increased mobilization of Rare Earth Elements (REEs), due to emerging technologies, could impact human health. The study assessed the effects of CeO₂ nanopowder (100 μg/mL) in human intestinal cells (HT-29) following both acute (24 h) and, a novelty for in vitro study, sub-chronic exposure, treating subcultures of exposed cells to CeO₂ NP up to 35 days. Recovery was also examined in exposed cells’ progeny. CeO₂ NP internalization and acute cytotoxicity were dose and time dependent. A significant pro-oxidant effect was observed for up to 14 days. The highest mitochondrial impairment was detected after 7 days, but in post-exposure experiments the recovery was observed. Conversely, genotoxicity highlighted the saturation of the DNA repair mechanisms. The irreversible cell damage of sub-chronic exposure was highlighted by the percentage of death cells (p = 0.011) and by the weekly cell replication index (5.68 vs. 7.41). The homeostatic mitophagy pathway was able to counteract ROS-induced mitochondrial dysfunction, as shown by overexpression of ATG5, LC3, and BECN1 genes throughout the examined times. Instead, the overexpression of the pro-apoptotic gene Bax was very brief, highlighting that prolonged exposure might cause more widespread adverse effects, also involving cells that are not directly exposed to nanoceria. Full article

We employ a two-dimensional fluid simulation approach to study the nonlinear turbulent dynamics of internal gravity waves (IGWs) in the weakly ionized Earth’s ionospheric F-layer with the effects of Pedersen conductivity. We observe that the presence of Pedersen conductivity leads to the formation of intermediate-scale structures in the velocity potential, along with the development of small-scale density fluctuations. The characteristic turbulent energy spectrum exhibits a non-Kolmogorov scaling of $k^{-2.40}$ in the presence of Pedersen conductivity, while a Kolmogorov-like $k^{-5/3}$ scaling is observed when it is absent, where k denotes the wave number. Due to energy loss caused by Pedersen conductivity, the wave’s amplitude reduces gradually with time. The cross-field diffusion coefficient related to the velocity potential also reduces as Pedersen conductivity increases. The results in the F-layer are compared with those in the literature, where the Ampère force and hence the Pedersen conductivity effect were ignored compared to the pressure gradient and gravity forces, as relevant in the Earth’s D-layer. Full article

Current satellite-based active microwave observations lack the temporal resolution needed to accurately capture rapid Earth system dynamics such as soil–plant–atmosphere interactions, rainfall interception, snowfall and rain-on-snow events. Ground-based radar systems can resolve these processes but typically rely on high-end VNAs, limiting their affordability and deployment scale. This work presents a low-cost SFCW radar system built around a compact, SDR-based VNA with an enhanced RF front end supported by remote-access firmware and a cloud-based back end with automatic backup. Calibration experiments and preliminary measurements demonstrate that the system achieves stable performance and is capable of capturing high-temporal-resolution microwave signatures relevant for climate monitoring. Full article

Rock glaciers are key indicators of mountain permafrost and act as climatically resilient water reservoirs in arid mountains. This study presents the first inventory and kinematic classification of rock glaciers in Western Tien Shan (Kazakhstan and Kyrgyzstan), combining geomorphological mapping with InSAR time-series analysis. Using high-resolution optical imagery (Google Earth Pro (version 7.3.6.10441), Bing Maps, SAS Planet (version 200606.10075), digital elevation models, and Small Baseline Subset InSAR processing, 741 rock glaciers covering more than 70.5 km² were identified. Activity classification revealed 232 transitional and 509 active forms, with mean seasonal displacement rates of ~15 cm yr⁻¹ calculated based on August and September observations. Spatial analysis showed a strong rock glacier concentration on north-facing slopes (>66% of total area) with reduced potential incoming solar radiation. Rock glaciers mainly occur between 2800 and 3800 m a.s.l., with a mean elevation of 3340 m a.s.l. However, their kinematic activity varies across mid-altitudinal ranges, underscoring the influence of slope, aspect, shading, and local topography. Integration with the Global Permafrost Zonation Index (PZI) indicated a lower permafrost boundary at ~1922 m a.s.l., with the largest and most active glaciers occurring at intermediate PZI values (0.5–0.7). This first rock glacier inventory for the Western Tien Shan establishes a benchmark dataset that supports the validation and refinement of global models at a regional scale, guides priorities for permafrost monitoring, and provides a replicable framework for inventory development in other data-scarce mountain regions. Full article