Search Results (1,696)

Anomaly detection using multivariate time-series data remains a significant challenge for complex industrial systems, such as Cyber–Physical Systems (CPSs), Industrial Control Systems (ICSs), Intrusion Detection Systems (IDSs), the Internet of Things (IoT), and Remote Sensing Monitoring Platforms, including satellite Earth observation systems and Mars Rovers. In these systems, sensors are highly interconnected, and local anomalies frequently affect multiple components. Because these interconnections are often implicit and involve complex interactions, systematic characterization is required. To address this, our study employs graph neural networks with a structure-entropy-based attention mechanism, which models multi-element relationships and formally represents implicit relationships within complex industrial systems using a network-based structural model. Specifically, our method distinguishes the weights of different high-order neighbor nodes based on their locations, rather than treating all nodes equally. Through this formalization, we identify and represent key adjacent elements by analyzing system entropy. We validate our method on SMAT, MSL, SWaT, and WADI datasets, and experimental results demonstrate improved detection performance compared to baseline approaches. Full article

Monitoring surface water quality is essential for assessing water resources and identifying their quality patterns. Traditional monitoring methods, based on conventional point-sampling stations, are reliable but costly and limited in frequency and spatial coverage. These constraints hinder the ability to evaluate water quality parameters at the temporal and spatial scales required to detect the effects of extreme events on aquatic systems. Satellite imagery offers a viable complementary alternative to enhance the temporal and spatial monitoring scales of traditional assessment methods. However, limitations related to spectral, spatial, temporal, and/or radiometric resolution still pose significant challenges to prediction accuracy. This study aimed to propose a methodology for predicting optically active and inactive water quality parameters in lotic and lentic environments using remote-sensing data and machine-learning techniques. Three remote-sensing datasets were organized and evaluated: (i) data extracted from Sentinel-2 imagery; (ii) data obtained from raw PlanetScope (PS) imagery; and (iii) data from PS imagery normalized using the methodology developed by Dias. Data on water quality parameters were collected from 24 monitoring stations located along the Paraopeba River channel and the Três Marias Reservoir, covering the period from 2016 to 2023. Four machine-learning algorithms were applied to predict water quality parameters: Random Forest, k-Nearest Neighbors, Support Vector Machines with Radial Basis Function Kernel, and Cubist. Model performance was evaluated using four statistical metrics: root-mean-square error, mean absolute error, Lin′s concordance correlation coefficient, and the coefficient of determination. Models based on normalized PS data achieved the best performance in parameter estimation. Additionally, decision-tree-based algorithms showed superior generalization capability, outperforming the other models tested. The proposed methodology proved suitable for this type of analysis, confirming not only the applicability of PS data but also providing relevant insights for its use in diverse environmental-monitoring applications. Full article

Validating satellite-derived sea surface temperature (SST) requires resolving spatial and vertical mismatches between remotely sensed measurements and traditional in situ observations. This study evaluates the bias between infrared-based satellite SST and high-resolution in situ measurements collected in the North Sea using the autonomous surface vehicle (ASV) HALOBATES. The ASV enables the direct sampling of the ocean skin layer via a rotating glass disc system, alongside near-surface layer (NSL, 1 m depth) measurements using a flow-through system. Across 37 missions conducted between 2022 and 2023, we quantified biases in our approach and performed match-ups with a level-4 SST product for the North and Baltic Seas. Satellite SST showed strong correlations with in situ observations (r > 0.98), with Deming regression slopes approaching unity for all platforms. Despite this agreement, satellite SST exhibited a consistent cold bias. The mean differences were −0.44 ± 0.60 °C for the skin layer and −0.40 ± 0.52 °C for the NSL. The RMSE values were 0.75 °C for the skin layer and 0.66 °C for the NSL, indicating that satellite SST more closely reflects temperatures at 1 m than those at the skin layer. These findings highlight the importance of depth-resolved in situ measurements for improving remote SST validation. Full article

Precision agriculture will continue to heavily rely on data-driven models to enable more intensive crop monitoring and data-driven decisions. The available remote sensing techniques, particularly those based on multispectral Sentinel-2 data, still have major shortcomings due to cloud cover, low temporal resolution, and time lags in data availability. To address these shortcomings, this paper proposes a hybrid approach that combines Sentinel-2 satellite data with real-time data generated by low-cost ground-based single-pixel detectors (SPDs), such as the AS7263. This hybrid approach addresses key shortcomings in existing agricultural monitoring systems and offers a cost-effective, scalable solution for real-time monitoring and prediction of end-of-season yield, moisture, and plant height using simple PLRS models implemented directly in SPDs with an energy-efficient algorithm for deployment on the STM32G030 microcontroller. Full article

Many countries around the world repeatedly suffer from natural disasters such as earthquakes, tsunamis, floods, and hurricanes due to geographical factors, including plate boundaries, tropical cyclone zones, and coastal regions. Representative examples include Hurricane Katrina, which struck the United States in 2005, and the Great East Japan Earthquake in 2011. Both were large-scale disasters that occurred in developed countries and caused enormous human and economic losses regardless of disaster type or location. As the occurrence of such catastrophic events remains inevitable, establishing effective preparedness and rapid response systems for large-scale disasters has become an urgent global challenge. One of the critical issues in disaster response is the rapid estimation of the number of affected individuals required for effective rescue operations. During large-scale disasters, terrestrial communication infrastructure is often rendered unusable, which severely hampers the collection of situational information. If the population within a disaster-affected area can be estimated without relying on ground-based communication networks, rescue resources can be more appropriately allocated based on the estimated number of people in need, thereby accelerating rescue operations and potentially reducing casualties. In this study, we propose a population-estimation system that remotely senses radio signals emitted from smartphones in disaster areas using Low Earth Orbit (LEO) satellites. Through numerical analysis conducted in MATLAB R2023b, the feasibility of the proposed system is examined. The numerical results demonstrate that, under ideal conditions, the proposed system can estimate the number of smartphones within the observation area with an average error of 2.254 devices. Furthermore, an additional evaluation incorporating a 3D urban model demonstrates that the proposed system can estimate the number of smartphones with an average error of 19.03 devices. To the best of our knowledge, this is the first attempt to estimate post-disaster population using wireless LAN signals sensed by LEO satellites, offering a novel remote-sensing-based approach for rapid disaster response. Full article

Harmful Algal Blooms (HABs) in large river systems present significant challenges for water quality monitoring, with traditional in-situ sampling methods limited by spatial and temporal coverage. This study evaluates the effectiveness of machine learning techniques applied to Landsat spectral data for detecting and quantifying HABs in the Ohio River system, with particular focus on the unprecedented 2015 bloom event. Our methodology combines Google Earth Engine (GEE) for satellite data processing with an ensemble machine learning approach incorporating Support Vector Regression (SVR), Neural Networks (NN), and Extreme Gradient Boosting (XGB). Analysis of Landsat 7 and 8 data revealed that the 2015 HAB event had both broader spatial extent (636.5 river miles) and earlier onset (5–7 days) than detected through conventional monitoring. The ensemble model achieved a correlation coefficient of 0.85 with ground-truth measurements and demonstrated robust performance in detecting varying bloom intensities (R² = 0.82). Field validation using ORSANCO monitoring stations confirmed the model’s reliability (Nash-Sutcliffe Efficiency = 0.82). The integration of multispectral indices, particularly the Floating Algae Index (FAI) and Normalized Difference Chlorophyll Index (NDCI), enhanced detection accuracy by 23% compared to single-index approaches. The GEE-based framework enables near real-time processing and automated alert generation, making it suitable for operational deployment in water management systems. These findings demonstrate the potential for satellite-based HAB monitoring to complement existing ground-based systems and establish a foundation for improved early warning capabilities in large river systems through the integration of remote sensing and machine learning techniques. Full article

The use of satellite-based remote sensing imagery for water quality monitoring of inland and coastal waters has become widespread over the last few decades, with the expansion of, and investment in, operational Earth-observing missions. Satellite-based sensors are uniquely suited to provide synoptic, system-wide water quality parameter estimates that supplement traditional field-based sampling methods. The remote sensing of water quality parameter estimates is particularly valuable in systems with high temporal and spatial variability, as well as in areas that are difficult to access, or where agencies lack funding for routine monitoring. However, optically complex inland and coastal waters pose additional challenges for developing robust remote sensing retrieval models for optical properties and water quality parameters. One of the biggest challenges is collecting high quality field measurements that are used to calibrate and validate the retrieval algorithms. Here, we present the current status of satellite missions, field methods that include instruments used and commonly measured parameters, and repositories of historical field data that are relevant to inland and coastal water studies. We then present data requirements for model validation and highlight gaps in validation coverage. Finally, we provide considerations for future field campaigns to improve coordination with remote sensing data collection and ensure that field data is well suited for use in model or algorithm development. Full article

Groundwater depletion is among the most critical hydrological threats to sustainable agriculture and water security in semi-arid regions. This study presents a high-resolution, multi-sensor assessment of groundwater storage (GWS) dynamics across the Lower Chenab Canal (LCC) command area in Punjab, Pakistan—an intensively irrigated agro-hydrological system within the Indus Basin. We integrated downscaled GRACE/GRACE-FO-derived total water storage anomalies with CHIRPS precipitation, MODIS evapotranspiration (ET) and vegetation indices, TerraClimate soil moisture, land surface temperature (LST), land use/land cover (LULC), and population density using the Google Earth Engine (GEE) platform to reconstruct spatiotemporal GWS changes from 2002 to 2020. The results reveal a persistent and accelerating decline in groundwater levels, averaging 0.52 m yr⁻¹, which intensified to 0.73 m yr⁻¹ after 2014. Cumulative GWS losses exceeded 320 mm yr⁻¹, with severe depletion (up to −3800 mm) in northern districts such as Sheikhupura, Gujranwala, and Narowal. Validation with borewell data (R² = 0.87; NSE = 0.85) confirms the reliability of the remote sensing estimates. Statistical analysis indicates that anthropogenic drivers (population growth, urban expansion, and intensive irrigation) explain over two-thirds of the observed variability (R² = 0.67), whereas precipitation contributes only marginally (R² = 0.28), underscoring the dominance of human-induced stress over climatic variability. The synergistic rise in evapotranspiration, land surface temperature, and cultivation of high-water-demand crops such as rice and sugarcane has further amplified hydrological imbalance. This study establishes an operational framework for integrating satellite and ground-based observations to monitor aquifer stress at basin scale and highlights the urgent need for adaptive, data-driven groundwater governance in the Indus Basin. The approach is transferable to other data-scarce semi-arid regions facing rapid aquifer depletion, aligning with the global targets of Sustainable Development Goal 6 on water sustainability. Full article

The article presents an approach to designing a low-orbit remote Earth sensing spacecraft. The low operational orbit of the satellite is maintained using a corrective electric propulsion system. The comprises an optical imaging system based on the Richey-Cretien telescope design augmented with an additional swivel reflection mirror. The optical system’s layout was optimized to minimize the spacecraft’s midsection area. This reduction in the frontal cross-sectional area decreases the aerodynamic drag forces exerted by the upper atmosphere, thereby reducing the propellant mass required for orbit maintenance. The article presents a model of constraints imposed by the satellite’s power supply system on the operating modes of the electric propulsion system and the orbit correction modes. Finally, a preliminary design of a low-orbit satellite, derived from the proposed approach, is presented. Full article

Integrated Coastal Zone Management (ICZM) requires multi-scalar, high-resolution monitoring data to effectively address climate change impacts, particularly sea-level rise and accelerated erosion. This study presents an innovative Remote Sensing (RS) and Geoinformatics approach to precisely quantify and contextualize the exposure of sandy beaches. The research focuses on the highly dynamic insular tidal inlet margin of the Pontal Sul da Ilha de Itamaracá, located within a tropical estuarine system in Northeast Brazil that is subject to intense anthropogenic pressure. The methodology of this study integrates high-resolution GNSS-PPK surveys from two seasonal cycles (2017–2018) with a Difference of DEMs (DoD) analysis to precisely quantify seasonal sediment transport. Furthermore, a multi-temporal analysis leverages the Fort Orange Archaeological Site as a stable datum, combining colonial-era maps with modern satellite imagery to trace shoreline evolution. During the 2017–2018 period, maximum erosion (up to ~2.60 m in altimetric losses) affected the southern and central-northern shoreline, while accretion (up to ~2.25 m in altimetric gains) occurred between these erosional sectors and in the northeastern offshore area. This multi-scalar approach provides the robust data necessary for ICZM, effectively prioritizing sustainable, nature-based strategies that align with local administrative capacities. Full article

Video satellites feature agile attitude maneuverability and the capability for continuous target imaging, making them an effective complement to ground-based remote sensing technologies. Existing research on video satellite tracking methods generally assumes either accurately calibrated camera parameters or precisely known target positions. However, deviations in camera parameters and errors in target localization can significantly degrade the performance of current tracking approaches. This paper proposes a novel adaptive visual tracking method for video satellites to track near-circular space targets in the presence of simultaneous uncertainties in both camera parameters and target position. First, the parameters representing these two types of uncertainties are separated through linearization. Then, based on the real-time image tracking error and the current parameter estimates, an update law for the uncertain parameters and a visual tracking law are designed. The stability of the closed-loop system and the convergence of the tracking error are rigorously proven. Finally, quantitative comparisons are conducted using a defined image stability index against two conventional tracking methods. Simulation results demonstrate that under coexisting uncertainties, traditional control methods either fail to track the target or exhibit significant tracking precision degradation. In contrast, the average image error during the steady-state phase exhibits a reduction of approximately one order of magnitude with the proposed method compared to the traditional image-based approach, demonstrating its superior tracking precision under complex uncertainty conditions. Full article

Coastal landscapes are complex socio-ecological systems that undergo rapid transformations driven by both natural dynamics and human pressures. Their sustainable management depends on robust, cost-effective remote sensing methodologies for long-term monitoring and quantitative assessment of spatiotemporal change. In this study, we developed an integrated remote-sensing-based framework that combines historical aerial photograph interpretation, transition matrix analysis, and machine learning to assess coastal dune landscape dynamics over a seventy-year period. Georeferenced orthorectified and preprocessed aerial imagery freely available from the Italian Ministry of the Environment for the years 1954, 1986, and Google Satellite Images for 2022 were used to generate detailed land-cover maps, enabling the analysis of two temporal intervals (1954–1986 and 1986–2022). Transition matrices quantified land-cover conversions and identified sixteen dynamic processes, while a Random Forest (RF) classifier, optimized through parameter tuning and cross-validation, modeled and compared landscape dynamics within protected Long-Term Ecological Research (LTER) sites and adjacent unprotected areas. Model performance was evaluated using Balanced Accuracy (BA) to ensure robustness and to interpret the relative importance of change-driving variables. The RF model achieved high accuracy in distinguishing change processes inside and outside LTER sites, effectively capturing subtle yet ecologically relevant transitions. Results reveal non-random, contrasting landscape trajectories between management regimes: protected sites tend toward naturalization, whereas unprotected sites exhibit persistent urban influence. Overall, this research demonstrates the potential of integrating multi-temporal remote sensing, spatial statistics, and machine learning as a scalable and transferable framework for long-term coastal landscape monitoring and conservation planning. Full article

In Sicily, many natural water bodies were reclaimed over the last two centuries for malaria control and agricultural expansion, causing widespread habitat loss. Some of these former ponds (still locally called “lakes”) reappear occasionally after extreme rainfall, temporarily restoring aquatic habitats but remaining poorly documented. We confirm the occurrence of such episodic ponds in central Sicily (Sommatino–Riesi) and present one of these ponds (Lago Montagna) as a case study. Combining satellite observations with field surveys conducted during a spring 2025 inundation, we document repeated episodes of flooding and a remarkable aquatic flora, including charophytes and other taxa of conservation interest. Episodic inundation events, therefore, act as transient refugia and stepping stones for regional biodiversity within an otherwise dry landscape. Because these systems commonly escape routine monitoring and legal protection, we argue they should be explicitly recognized in regional conservation planning and long-term monitoring programs. Moreover, the integrated remote-sensing approach used here allows the detection of overlooked temporary wetland ecosystems and provides fine-scale hydrological insights often missed by sparse weather station networks or satellite-derived rainfall data. Full article

To meet the growing demand for quantitative remote sensing applications in GIIRS radiometric calibration, this paper proposes a temperature field-driven adaptive scan mirror thermal radiation interference correction method. Based on the on-orbit deep space observation data from the Fengyun-4B satellite, this paper systematically analyzes the thermal radiation interference characteristics caused by scan mirror deflection and constructs the first scan mirror thermal radiation response model suitable for GIIRS. On the basis of this model, this paper further introduces the dynamic variation characteristics of the internal thermal environment of the instrument, enabling adaptive response and compensation for radiation disturbances. This method overcomes the limitations of relying on static calibration parameters and improves the generality and robustness of the model. Independent validation results show that this method effectively suppresses the interference of scan mirror deflection on instrument background radiation and enhances the consistency of the deep space and blackbody spectral diurnal variation time series. After correction, the average system bias of the interference-sensitive channel decreased by 94%, and the standard deviation of radiance bias from 2.5 mW/m²·sr·cm⁻¹ to below 0.5 mW/m²·sr·cm⁻¹. In the O-B test, the maximum improvement in relative standard deviation reached 0.15 K. Full article

Classical instrumental technologies still remain important among the geodetic methods of dam monitoring, but periodic observations are often insufficient for timely detection of hazardous deformations. Therefore, the integration of continuous and remote sensing technologies into a multi-level system of observation improves the assessment of a structural condition. This research work evaluates the integrated approach that combines the GNSS data, robotic total station measurements, and satellite radar data processed by the PSInSAR technique for detecting the cyclic thermal deformations of the Dniester HPP concrete dam. The dataset includes 185 ascending and 184 descending Sentinel-1A SAR images (2019–2025, 12-day repeat cycle). PSInSAR processing was performed using StaMPS, with validation through comparison of InSAR-derived vertical displacements and GNSS data from the stationary monitoring system of the dam. The GNSS and InSAR time series have revealed consistent seasonal patterns and a common long-term trend. Harmonic components with amplitudes of 4–5 mm, peaking in late summer and declining in winter, confirm the dominant influence of thermal processes. In order to reduce noise, Fourier-based filtering and approximation were applied, thus ensuring balance between accuracy and data retention. The combined use of GNSS, robotic total station, and InSAR has increased the density of reliable control points and improved the thermal deformation model. Maximum vertical displacements of 6–13 mm were observed on the horizontal sections most exposed to solar radiation. Full article