Search Results (18,151)

Accurate and spatially representative estimation of Global Horizontal Irradiance (GHI) is critical for solar energy planning in tropical regions characterized by strong atmospheric variability and complex topography. This study aims to evaluate the performance and robustness of four semi-empirical satellite-derived GHI models, Beyer, Perez, Hammer, and Rigollier, under heterogeneous tropical conditions in West Java, Indonesia. Hourly GHI data for 2022 were derived from GK2A satellite observations and validated against ground measurements from eight stations representing coastal, lowland, and mountainous areas. Model performance was assessed at annual and seasonal scales using relative Root Mean Square Error (rRMSE) and relative Mean Bias Error (rMBE). The results show significant variability in model performance across locations, with the average annual rRMSE computed per model and averaged over the eight stations being similar among models: 41.10% (Perez), 41.18% (Beyer), 42.44% (Hammer), and 42.49% (Rigollier). Perez showed the most consistent performance, with station-level rRMSE values ranging from 35.36% to 43.32% and rMBE ranging from −18.20% to 22.09%. Seasonal analysis indicates higher errors during the rainy season, 41.16% (Perez), 45.23% (Beyer), 42.74% (Hammer), and 46.34% (Rigollier), while lower errors were observed during the dry season, particularly for Beyer (36.16%) and Rigollier (36.29%). Spatial analysis indicates higher irradiance in coastal and lowland areas compared to mountainous regions. These findings emphasize the importance of climate- and topography-aware model selection for reliable solar resource assessment in tropical environments. Full article

Generating satellite-based deforestation alerts with actionable latency requires frequent imaging, creating an imperative to use different sensors together. We introduce a simple and open-source framework called the Disturbance Index Alert System (DIAS), which is based upon transformation of imagery from different sources into an interoperable stream of Disturbance Index (DI) values. Whereas most alert systems target divergence of forested pixels from historical states, DIAS targets movement of a pixel’s Z-score position relative to the image-wide population of forest pixels along a forest-sensitive axis. This strategy provides the following practical benefits: (1) it reduces the need to process the historical archive; (2) it reduces dependence upon stable sensor calibration; (3) it allows Z-score-based DI values to be combined across sensors; and (4) it accommodates changes to the group of sensors providing measurements. We demonstrated in Madagascar that sensor integration through DIAS can provide more timely alerts than both conventional individual-sensor systems and additive combination of such systems. Across our study sites, using a commercial source of daily imaging (PlanetScope) in conjunction with imagery from public sources (Landsat, Sentinels-1 and -2) allowed high-confidence detection (false alert rate of approximately 20%) of two-thirds of deforestation occurring at 10 m reference pixels within one month; 40% were detected in that timeframe with public data alone. As commercial options for Earth observation proliferate, flexible and computationally lightweight approaches such as DIAS are needed to accommodate diverse and sometimes only loosely calibrated instruments in support of timely forest monitoring. Full article

Preventing large-scale illegal migration is one of the EU’s highest priorities. In this study, we analyze the potential for integrating and fusing remote sensor data with a wider range of data streams to enhance border security situational awareness, specifically targeting illegal migration. The aim was to develop a dynamic predictive risk analysis model to identify high-risk zones for illegal border crossings at Croatia’s external EU borders. The model’s methodological framework is based on the integration of Geographic Information Systems (GISs), Multi-Criteria Analysis (MCA), and the Analytic Hierarchy Process (AHP). The model utilizes various environmental and infrastructure variables derived from the open-source databases ESA WorldCover and OpenStreetMap to generate a categorized risk map showing areas of lowest, moderate, and highest risk for illegal border crossing. The model was quantitatively verified using a weighted detection-versus-background design against 7481 geocoded border crossing incidents, demonstrating high predictive skill and robust calibration (Continuous Boyce Index up to 0.97) when controlling for patrol effort bias and spatial autocorrelation. High-resolution historical satellite imagery showing activities related to illegal migration was used for the generation of labeled datasets for AI training. Features such as suspicious vans, river boats, tire tracks, tents, illegal campsites, and clusters of individuals were observed in high-resolution Airbus and Maxar historical satellite images. The model can be used for various practical applications, including the strategic allocation of surveillance resources and the enhancement of frontier and pre-frontier intelligence, enabling more informed actions and optimized operations. Full article

Accurate field-scale surface soil moisture (SSM) measures are important for agricultural water management. Conventional satellite SSM products remain too coarse for within-field applications. Here, we developed sequence-aware deep learning models for growing-season SSM estimation by fusing data from Sentinel-1 C-band SAR, harmonized Landsat-8/Sentinel-2 (HLS), and local ancillary datasets. We assembled a multi-source dataset on Sentinel-1 overpass time for 2016–2024 (May–September), yielding 1469 samples and 65 features per sample, including SAR and optical features, meteorological data, soil texture and bulk density, topography, crop labels, irrigation-likelihood flag, and irregular-time-step encoding. We compared long short-term memory (LSTM) and temporal convolutional neural network (TCN) architectures together with attention-augmented variants, including feature attention (FA), temporal attention (TA), and the combined feature–temporal attention (FTA). Models were trained and tested on seven years of data and were validated based on a temporal generalization using combined data of a wet year (2016) and a dry year (2023). The best model, FTA-TCN, achieved R² = 0.851, RMSE = 0.024 m³.m⁻³, and MAE = 0.020 m³.m⁻³ on the withheld validation years, outperforming the base LSTM (R² = 0.422; RMSE = 0.053 m³.m⁻³; MAE = 0.043 m³.m⁻³) and the base TCN (R² = 0.746; RMSE = 0.034 m³.m⁻³; MAE = 0.022 m³.m⁻³). Shapley additive explanations (SHAP) analysis indicated that antecedent precipitation and short-term rainfall accumulations were dominant forcings, while soil texture, elevation, incidence angle, and vegetation indices modulated SSM variability. Satellite-derived features accounted for ~28.5% of aggregated SHAP importance. Overall, the results show that dual-attention temporal convolution can capture field-scale SSM dynamics across wet and dry seasons when satellite signals are coupled with local soil-meteorological-management context. Full article

Spatial and temporal patterns of terrestrial water storage (TWS), and their relationship with groundwater levels, were investigated with the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Data Assimilation System (GLDAS) land surface model results, and climate observations for the Murray–Darling Basin (MDB). The results show that: (1) TWS displays a clear temporal variability: a negative TWS anomaly with a declining trend during 2002–2009, a positive TWS anomaly with a decreasing trend during 2010–2017, and a period of mixed positive and negative TWS anomalies being accompanied by an increasing trend from 2018 to 2025; (2) five dominant cluster patterns were identified that explain the spatial variability of temporal TWS across the MDB; (3) overall, TWS temporal variability is strongly correlated with rainfall, although it is weak at certain locations; (4) TWS is also influenced by evaporation (both actual and potential evapotranspiration, AET and PET) and runoff, and a combined model significantly improves the overall performance in explaining TWS temporal variability; and (5) TWS-derived groundwater storage changes show both similarities and differences in comparison with groundwater level observation changes, reflecting complex hydrogeological processes and the influence of human activities such as groundwater extraction. These findings provide valuable insights to support improved groundwater resource management with GRACE satellite information and land surface models. Full article

Forecasting fine particulate matter with a diameter of 2.5 μm (PM_2.5) is critically important due to its adverse effects on human health and environmental sustainability. Although ground-based monitoring stations provide accurate measurements, their limited spatial coverage restricts large-scale PM_2.5 assessment, especially in complex urban regions. Consequently, aerosol optical depth (AOD) derived from satellite imagery, combined with advanced deep learning (DL) techniques, has emerged as an effective alternative by offering wide spatial coverage and rich spatio-temporal information. This paper proposed an optimization-driven fuzzy transformer–deep belief network (ODFT-DBN) for accurate PM_2.5 air pollution prediction. The proposed framework integrates a fuzzy inference module to model uncertainty and nonlinear environmental relationships, a transformer encoder to capture long-range spatio-temporal dependencies, and a DBN to extract hierarchical features and improve prediction robustness. In addition, a novel multi-objective gray wolf optimizer (NMOGWO) is employed to jointly optimize the model hyper-parameters and fuzzy membership functions. The proposed approach is implemented for the city of Tehran, Iran, using meteorological variables, topographical features, ground-based PM_2.5 measurements, and satellite-derived AOD data. The ODFT-DBN model is compared with several benchmark methods, including bidirectional encoder representations from transformers (BERT), transformer, long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), DBN, and extreme gradient boosting (XGBoost). Experimental results demonstrate that the proposed framework achieves superior predictive performance, attaining an R² value of 0.94 and root mean square error (RMSE) of 0.8 μg/m³. Scatter plot analyses indicate a strong agreement between predicted and observed PM_2.5 values, while the proposed model exhibits low variance, stable convergence behavior, and acceptable computational time. Overall, the results confirm the effectiveness, robustness, and practical applicability of the proposed ODFT-DBN framework for spatio-temporal PM_2.5 forecasting. Full article

The 8.025–8.400 GHz band is one of the key X-band downlink ranges for modern Earth observation satellites, enabling high-rate transmission of imagery and sensor data for agriculture, environmental monitoring, greenhouse gas assessment, disaster response and security-related applications. The potential introduction of 5G networks into this band raises serious concerns about harmful interference to Earth observation ground stations cand, consequently, about the continuity and growth of the global Earth observation data chain. This paper investigates the feasibility of sharing this downlink band between Earth observation systems and 5G networks using a Monte Carlo simulation framework. The model includes a low-Earth-orbit Earth observation satellite with dynamically tracking ground stations and dense urban, suburban and rural deployments of 5G base stations and user devices, together with established radio-propagation and clutter models and representative protection objectives for satellite downlinks. The results suggest that, to keep interference at acceptable levels, ground stations would need to be located far from 5G deployments, which is difficult to achieve in practice and could seriously limit the future expansion of Earth observation infrastructure. Full article

The Sea and Land Surface Temperature Radiometer (SLSTR) is a dual-view scanning radiometer onboard the Sentinel-3A and Sentinel-3B satellites. This sensor provides data from the visible to the thermal infrared, with a temporal resolution of approximately 12 h. In this work, we present an automated system using shortwave infrared (SWIR) bands at 500 m spatial resolution to monitor active volcanoes in near real time. The system implements a normalized hotspot index (NHI) to detect and characterize high-temperature volcanic features in daylight and nighttime conditions. During the first three months of operation (i.e., August–October 2025), the system successfully identified several eruptive activities, with a false positive rate around 2.0%. The latter includes also true hot pixels associated with vegetation fires and other high-temperature sources. Results were assessed through comparison with the Fire Information for Resource Management System (FIRMS), the Middle Infrared Observations of Volcanic Activity (MIROVA), MODVOLC, and the S3-L2 FRP product. The preliminary comparison with the MIROVA-MODIS dataset reveals a good correlation in the estimates of fire radiative power over Etna (Italy) and Kilauea (Hawaii, USA), although discrepancies in the magnitude of this parameter remain significant also because of the SWIR retrieval method, which was optimized for gas flares. Despite the impact of snow-covered surfaces and band co-registration on the accuracy of hotspot detection, this study shows that the NHI-SLSTR system may provide a relevant contribution to the surveillance of active volcanoes from space, integrating information from other systems performing globally. Full article

High-temporal-resolution vertical total electron content (VTEC) inversion is important for ionospheric delay correction in maritime GNSS applications, but offshore single-station observations often suffer from limited satellite geometry, clustered ionospheric pierce points, and noise-sensitive least-squares (LSs) solutions. This study proposes a Virtual-Observation-Based Tikhonov Regularization (TVO) method for stabilizing ill-conditioned least-square VTEC inversion. TVO links the regularization factor to the condition number of the normal-equation matrix and selectively constrains higher-order spatial-gradient parameters while preserving background VTEC and receiver-bias terms. Experiments using the European mid-latitude station OBE4 and 17 surrounding stations on 1 July 2021 show that short epoch intervals and increased model complexity aggravate ill-conditioning, especially for the full quadratic model at 30 s. Compared with LS, TVO reduces the average RMS difference relative to the GIM-interpolated VTEC reference by 56.30% across the four VTEC models for the 17 stations. Maritime validation using South China Sea buoy data collected from 19 to 25 May 2025 further shows that TVO suppresses local discontinuities and amplitude anomalies, reducing the overall RMS difference relative to the GIM-interpolated VTEC reference from 26.07 TECU to 14.74 TECU. These results suggest that TVO can improve the numerical stability of maritime single-station VTEC inversion under constrained observation geometry. Full article

Over the past decade, drone-based aerial inspection of overhead power lines has proven superior to traditional ground-based methods. However, in flatland areas, it remains costlier, as total expenses include not only flights but also extensive mission planning. Operators must select takeoff zones and conduct flights in compliance with weather conditions and numerous regulations. Automating mission planning can reduce operator workload, lower the risk of rule violations, and boost inspection efficiency. This paper introduces a framework for automating power line inspection route planning. It selects takeoff areas and generates drone routes for specified line segments, which meet all regulatory requirements. The framework incorporates a novel method for automatic pole-type identification using satellite imagery. The approach combines a YOLO detector, trained on synthetic data, with an expert system, resulting in a 36.9% improvement in performance (on the tested dataset) compared to prior solutions. The final solution was implemented as an open-source QGIS plugin. The experimental results demonstrate that the automated path-planning approach successfully generates inspection routes for line segments exceeding 50 km (135 poles) and increases the number of inspected poles by 58.7%, enabling the capture of power line insulators, which can then be automatically segmented and analyzed using machine learning algorithms. Full article

Post-disaster scenarios such as tsunamis require rapid terrain assessment that cannot wait for the next satellite synthetic aperture radar (SAR) revisit, yet a readily deployable system remains lacking. We present an off-the-shelf K-band drone-borne dual circular polarimetric (DCP) SAR and a processing pipeline for on-demand terrain classification. Compared with fully polarimetric (FP) SAR, DCP requires only a single transmit polarization and two receive channels, providing a wider swath than FP for the same acquisition, while still separating odd-bounce and even-bounce scattering mechanisms, which dual linear polarimetric modes with the same channel count provide with greater ambiguity due to their sensitivity to target orientation angle. To compensate for platform motion, we implemented RTK global navigation satellite system (GNSS) guided time-domain backprojection (TDBP) with phase gradient autofocus (PGA), yielding an 11.98 dB improvement in peak amplitude. We then applied single-target wire calibration to correct a measured 8.91 dB inter-channel complex gain difference between co-polarization and cross-polarization. As a result, H/α decomposition of the calibrated DCP data classifies canonical reflectors, artificial structures, gravel roads, vegetation, and a pond surface. These field experiments extend compact polarimetric H/α decomposition to drone-borne SAR data for terrain discrimination, establishing a practical pathway toward rapid post-disaster terrain assessment. Full article

Understanding turbidity in coastal systems is essential to ensure the sustainable management of these ecosystems, which are increasingly under pressure from natural factors and human activities. Thus, this study aims to develop a local Sentinel-2-based turbidity model for the Aveiro lagoon (Portugal) by combining Sentinel-2 records with in situ measurements. A field campaign synchronized with a Sentinel-2 overpass was conducted across the lagoon channels on 28 May 2025, to capture spatial variability by measuring near-surface turbidity and Secchi depth, for correspondence with the spectral records of satellite. Remote Sensing Reflectance (Rrs) and turbidity were derived using various algorithms integrated within the ACOLITE software (v20250114.0). Additionally, new turbidity models were developed and empirically adjusted based on the Rrs data, with their performance quantified through the coefficient of determination (R²) and Root Mean Square Error (RMSE). The results showed that the existing algorithms are not directly suitable for the Aveiro lagoon, as they underestimate the highest turbidity values. The ratio between 665 and 560 nm bands (RG_ratio) proved to be the most suitable spectral index, performing best in estimating turbidity (R² = 0.822 and RMSE = 1.77 NTU). This study highlights the importance of locally calibrated models over standard ACOLITE algorithms for turbidity retrieval in shallow coastal lagoons, while emphasizing that the proposed model was calibrated for the tidal, wind, and river discharge conditions sampled during the campaign and has not yet been independently validated. Full article

Numerous attempts have been made to detect signs of ozone layer recovery over Antarctica, which has been expected since the beginning of the 21st century as a result of the reduction in concentrations of ozone-depleting substances in the Antarctic stratosphere, in accordance with the provisions of the 1987 Montreal Protocol and subsequent amendments aimed at protecting the ozone layer. Large year-to-year variability in the Antarctic ozone, driven by changes in atmospheric dynamics, has made it difficult to draw definitive conclusions about the rate of Antarctic ozone recovery. In this paper, we present an alternative approach to analyse ozone recovery by examining patterns in blue–red heatmaps of total ozone column (TOC) trends during the winter–spring period from 1980 to 2025. Three annual TOC time series (winter average, 15 September value, and spring minimum) were analysed to monitor the ozone hole development over the Syowa and Amundsen–Scott stations. Various sources of the daily TOC data were examined, including reanalysis data, ground-based measurements, and satellite observations. Regardless of the data source, we found that, for both stations, blue cells (negative trends) dominated in the areas of the heatmap where the TOC trends ended before 2000, while red cells (positive trends) appeared mostly afterwards. These results confirm the hypothesis of a trend reversal, i.e., a recovery beginning in the early 2000s, which was obscured in the original, noisy TOC time series. Full article

Clouds frequently degrade optical satellite imagery, limiting the reliability of remote sensing models. However, in the literature, cloud-free analyses are often performed by excluding cloudy images from machine learning datasets and methods. This restricts their usefulness in time-critical scenarios such as disaster response, where waiting for cloud-free imagery is impractical. Cloud removal can mitigate this issue, but methods remain imperfect and may introduce visual artifacts. Therefore, it is desirable to develop cloud-robust methods by combining optical imagery with radar data, a modality unaffected by clouds. While datasets for machine learning combine optical and radar data, most researchers exclude cloudy images from training and evaluation. We identify this exclusion as a limitation that reduces applicability to cloudy scenarios and address it by introducing CloudyBigEarthNet (CBEN), a dataset of paired optical and radar images containing cloud occlusions for land-use and land-cover classification. Using average precision (AP), we show that state-of-the-art methods trained on clear-sky optical and radar data suffer performance drops of between 23.8 and 33.4 AP points when tested on cloudy imagery. We adapt these methods using cloudy images during training and improve AP on cloudy test cases by 17.2 to 28.7 AP points. Code and dataset have been published. Full article

We develop a geometric thermodynamic framework for the analysis of sectoral economic dynamics grounded in statistical physics principles. By constructing a Legendre-invariant thermodynamic metric within the formalism of geometrothermodynamics (GTD), we establish a minimal effective structure consistent with extensivity and entropy-based representations of macroscopic economic systems. The resulting thermodynamic curvature provides a coordinate-independent measure of structural interactions and equilibrium stability across economic sectors. Applying this framework to satellite account data, we find that the thermodynamic curvature of the equilibrium manifold remains finite and regular across the empirically relevant range, with no curvature singularity in the period studied. In particular, the 2020 contraction—the most pronounced macroeconomic disruption in the sample—is not reflected as a curvature singularity in the equilibrium geometry. We read this regularity as a diagnostic of structural stability: the sectoral system absorbs such disruptions without an abrupt reorganisation of its equilibrium geometry. The geometric invariants thus capture stability properties not directly accessible through standard entropic indicators alone, offering a complementary statistical description of economic dynamics. Our results demonstrate that thermodynamic geometry furnishes a consistent bridge between entropy-based macroeconomic modelling and coordinate-invariant measures of equilibrium stability, extending the applicability of geometric methods in statistical physics to complex economic systems. Full article