Search Results (4,014)

The deployment of Wireless Sensor Networks (WSNs) remains challenging and time consuming due to the manual commissioning, configuration, and maintenance of resource-constrained Internet of Things (IoT) devices. Achieving precise network-wide time synchronization in such systems further increases this deployment complexity. This paper presents a novel Real-Time Operating System (RTOS)-integrated time synchronization method that distributes an absolute Coordinated Universal Time (UTC) reference across the network using a single Global Navigation Satellite System (GNSS)-enabled host. The method extends the semantics of the RTOS tick count by directly linking it to a global time reference. Consequently, sensor nodes obtain a notion of UTC time and can execute time-critical tasks at precisely defined moments without requiring a dedicated Real-Time Clock (RTC) or GNSS module on each sensor node. This design reduces both hardware cost and overall system complexity. Experimental results obtained on custom-developed hardware running FreeRTOS demonstrate a task synchronization error below ±30 μs between the GNSS reference and a sensor node operating at a clock frequency of 32 MHz. Such precise network-wide synchronization enables more efficient channel utilization, reduces power consumption, and improves the accuracy of both local and coordinated task execution across multiple devices in WSNs. It therefore serves as a key enabler for self-deployable WSNs. Full article

The Advanced Meteorological Imager (AMI) on GEO-KOMPSAT-2A (GK-2A) lacks a 2.1 μm shortwave infrared channel, precluding the dark target surface reflectance estimation that other geostationary aerosol retrievals rely on. We propose an improved land aerosol optical depth (AOD) retrieval in which background surface reflectance (BSR) is derived entirely from pixel-level bidirectional reflectance distribution function (BRDF) inversion using the scaled Ross-Thick Li-Sparse (sRTLS) kernel model fitted to geostationary time-series observations. Unlike existing approaches, the algorithm inverts the BRDF independently at each retrieval channel without relying on spectral reflectance relationships or external surface reflectance products; it assumes a low-background AOD during an initial accumulation period and then iteratively refines both BRDF coefficients and AOD. Two aerosol models—generic and dust—are supported, with a geographic dust-zone mask activating two-model selection during spring. Validation against 74 Aerosol Robotic Network sites over 2023 yields R = 0.86, RMSE = 0.15, and bias = −0.02, compared with R = 0.59, RMSE = 0.25, and bias = −0.04 for the National Meteorological Satellite Center (NMSC) GK-2A AOD product. The largest improvements appear at AOD ≤ 0.1 (bias: +0.03 versus +0.11) and AOD > 0.8 (bias: −0.12 versus −0.85). The full March–May (MAM) evaluation yields bias = −0.06 across all 74 sites. As a separate parallel retrieval restricted to matchups inside the geographic dust-zone mask, the proposed algorithm (dust model included) gives bias = −0.03, which worsens to −0.11 when only the generic model is applied—nearly a fourfold increase. A comparison against Himawari-9/Advanced Himawari Imager (AHI)—a co-located geostationary sensor carrying a 2.3 μm shortwave infrared (SWIR) channel—shows that the proposed algorithm (R = 0.897) outperforms Himawari-9/AHI (R = 0.855) across all metrics, demonstrating competitive accuracy without relying on a SWIR channel. Full article

Heterogeneous change detection (HCD) using satellite and Unmanned Aerial Vehicle (UAV) imagery is a pivotal task in remote sensing and Earth observation. However, the effective utilization of such multi-source data is significantly hindered by extreme spatial resolution disparities and distinct radiometric characteristics. Existing deep learning methods, often based on weight-sharing Siamese architectures, struggle to bridge these domain gaps, leading to spectral pseudo-changes and blurred detection boundaries. To address these challenges, we propose a novel Dual-Branch Cross-Scale Fusion Network (DBCF-Net) specifically tailored for heterogeneous satellite–UAV change detection. We introduce a Difference-Aware Attention Module (DAAM) to explicitly align cross-modal feature spaces and suppress domain-related noise through a hybrid local–global attention mechanism. Furthermore, an Adaptive Gated Fusion Module (AGFM) is designed to dynamically weight multi-scale interactions, ensuring the preservation of high-frequency spatial details from UAV imagery while maintaining the semantic consistency of satellite data. Extensive experiments on the Heterogeneous Satellite–UAV Dataset (HSUD) demonstrate that DBCF-Net achieves state-of-the-art performance, reaching an F1-score of 88.75% and an IoU of 80.58%. This study provides a robust technical framework for heterogeneous sensor fusion and high-precision monitoring in complex remote sensing scenarios. Full article

Although satellite sensors provide global observations, factors such as cloud interference and narrow swath widths frequently result in partial data gaps which constrain the continuous spatiotemporal analysis of the column-averaged dry air mole fraction of CO₂ (XCO₂). To address this challenge, this study develops a novel multi-stage fusion framework that integrates GOSAT and OCO-2 data using inverse error variance weighting and a dynamic bias correction technique, generating a seamless monthly XCO₂ dataset for East Asia (2016–2024). Validation against TCCON measurements (RMSE = 1.22 ppm; R² = 0.96) and WDCGG data (RMSE = 2.85 ppm; R² = 0.76) demonstrates the high accuracy of the product. The results show that the growth rate consistently exceeds 2.2 ppm/year, with clear seasonal patterns characterized by spring maxima and summer minima. Spatially, the locus of rapid growth has shifted toward central and western China, reflecting patterns of regional economic development, while substantial concentrations still persist in the industrialized regions of eastern China, Japan, and South Korea. This study provides new insights into regional atmospheric CO₂ dynamics and emphasizes the efficacy of dynamic bias correction in data fusion. Full article

Autonomous vehicles at level 3 and above must maintain high navigation accuracy, particularly in global navigation satellite system (GNSS)-denied environments. The main innovations of this work are threefold. First, we integrate visual inertial odometry (VIO) and light detection and ranging (LiDAR) inertial odometry (LIO) as external updates to mitigate the rapid drift of micro-electromechanical system (MEMS)-based industrial-grade inertial measurement units (IMUs) during long-term GNSS outages. Second, we adopt a redundant IMU (RIMU) approach that fuses multiple low-cost IMUs to reduce sensor noise and improve reliability. Third, we propose a system calibration methodology using both static and dynamic vehicle motion to estimate extrinsic parameters (boresight angles and lever arms) of the sensors, achieving an overall boresight angle root-mean-square error of 0.04 degrees in the simulation. Experiments were conducted under a 7 min GNSS-denied scenario in an underground parking lot, allowing for comparison of the error characteristics of multi-sensor fusion schemes against a navigation-grade reference. The INS/GNSS/LIO framework achieved a two-dimensional root-mean-square position error of 1.22 m (95% position error within 2.5 m), meeting the lane-level (1.5 m) accuracy requirement under a GNSS outage exceeding 7 min without prior maps. In contrast, the RINS/GNSS/VIO framework yielded a 4.71 m 2D mean position error under the same conditions. This paper provides a quantitative comparison of the baseline error characteristics of VIO-, LIO-, and RIMU-assisted INS/GNSS fusion under a GNSS-denied navigation scenario. Full article

In Canada, early fire detection is an important component of wildfire management, and it utilizes a combined effort approach including public reports, aviation patrols, and satellite observations. The role of ground-based continuous smoke sensors has not been formally assessed in Canadian wildfire management detection systems. Dense networks of ground-based, internet-enabled continuous smoke sensors were deployed at three locations across southern Canada during 2023 and 2024, in concert with planned prescribed fire in grass fuels as well as incidental wildfire ignitions. Smoke sensor detection of fires was compared to polar orbiting and geostationary fire detection. Large fire events (50–600 ha) with a ground smoke detector distance of 1–2 km were observed on most occasions (n = 7), but the detection rate dropped to 30% for fires 1 ha or smaller. Follow-up smoke monitoring after the initial detection offered valuable information on smoke production and dispersion across multiple sensors. This typically nighttime smoldering smoke production fell below the threshold for geostationary satellite fire observation and is otherwise only captured sparingly by polar orbiting satellites. Thus, ground-based smoke detection systems likely fit an important niche for monitoring low-energy (i.e., smoldering) smoke events from fully contained fires or to monitor fires considered recently extinguished. Full article

In wetland ecological monitoring, accurate acquisition of water bodies is particularly crucial, especially for hydrological monitoring and eutrophication control. Water bodies can be clearly delineated by using optical remote sensors. Optical sensors can clearly delineate water boundaries and features when extracting water bodies via remote sensing. Meanwhile, synthetic aperture radar (SAR), with its unique microwave capabilities, can easily penetrate vegetation and operate regardless of weather conditions, enabling all-weather monitoring. Each sensor type exhibits distinct advantages in water body monitoring and research. This study focuses on Caohai Wetland in Guizhou Province, utilizing data from the optical satellite Zhuhai-1 (launched by China in 2017) and the radar satellite RadarSat-2 (launched by Canada) at identical resolutions during the same period. Five supervised classification methods were applied to extract water bodies using optical imagery within the wetland area, with results evaluated against SAR data. Results indicate that the optimal water body extraction methods based on optical and SAR data are Random Forest Classification and Support Vector Machine classification, respectively, achieving an overall accuracy of 0.896 and 0.940, with Kappa coefficients of 0.791 and 0.879. The water area extracted using SAR was significantly larger than that based on optical data, thereby identifying areas within Caohai Wetland that were not fully submerged in vegetation during this period. This study holds significant implications for accurate water body extraction and analysis benefited an improved monitoring and conserving the wetland environment. Full article

The need for Structural Health Monitoring is evident in order to ensure the safety of civil infrastructure. The goal of vibration monitoring is to derive the eigenfrequencies, mode shapes and damping of a structure. A change in the eigenfrequency over time can indicate deterioration or damage in a structure. The amplitude can be used to calculate the damping ratio. As the damping ratio is amplitude-dependent, it is important to correctly determine the amplitude values. This study focuses on the amplitude correctness of high-rate Global Navigation Satellite System (GNSS) receiver data. In an experiment with controlled oscillations with a shaker and a Laser Triangulation Sensor (LTS) as a reference, the vibration amplitudes derived by GNSS measurements were analyzed, using time-frequency techniques like Short Time Fourier Transform (STFT) and Wavelet Transform (WT). We demonstrate that vibrations in the millimeter range can be derived from the measurements of satellites orbiting 20,000 km above Earth. However, the amplitudes of the determined frequencies show systematic errors up to 60% when compared to independent reference measurements. We introduce a correction method to reduce this error by applying a frequency-dependent correction function. Full article

The FOCUS mission is an integrative project developed at the Universidad Nacional de San Martín (UNSAM), Argentina, featuring a constellation of small satellites equipped with X-band Synthetic Aperture Radar (SAR) sensors. Designed with autonomous orbit control, the mission enables Interferometric SAR (InSAR) applications for critical infrastructure monitoring, providing scalable and cost-effective global observation capabilities. This paper presents the modeling, design, and numerical evaluation of the Attitude and Orbit Determination and Control System (AODCS) for the FOCUS mission. The analysis incorporates realistic constraints, including actuator saturation, sensor noise, underactuation effects, and hardware limitations—specifically regarding magnetorquer magnetic moments, reaction wheel capacities, and propulsion unit impulse bounds. Utilizing the NASA 42 attitude and orbit simulator, numerical simulations were conducted to assess stability, pointing accuracy, and agile maneuver tracking through specialized guidance laws. The results confirm that the proposed AODCS architecture achieves stable, responsive performance and supports continuous orbit maintenance, ensuring adequate target acquisition per orbit. Additionally, the selection of star trackers allows achieving a secondary objective through the detection of Resident Space Objects. Full article

Calving front position is a key indicator of glacier and ice-sheet dynamics and an important variable for assessing mass loss and sea-level rise. Rapid growth in satellite data availability and image analysis techniques has driven the development of numerous automated calving front detection algorithms; however, the methodological landscape remains fragmented. This scoping review aims to map the existing literature on automated calving front detection, characterize the types of algorithms and data sources used, and identify trends, gaps, and challenges in current approaches. A systematic search of major bibliographic databases and complementary sources was conducted to identify studies describing automated or semi-automated calving front detection from satellite imagery or derived datasets. Eligible studies included peer-reviewed articles and relevant grey literature using optical, synthetic aperture radar (SAR), or multi-sensor data. Data were charted using a predefined framework that captures the algorithmic approach, input data characteristics, spatial and temporal coverage, validation strategies, and reported performance metrics. The review identifies a wide range of methods, from early threshold- and edge-based techniques to recent machine learning and deep learning approaches, with a strong shift toward convolutional neural networks over the past few years. Despite methodological progress, validation practices and evaluation metrics remain heterogeneous, and standardized benchmark datasets are scarce. This scoping review provides a structured overview of the field and highlights priorities for future methodological development and benchmarking. Full article

Coastal seas around Aotearoa, New Zealand, are among the least observed parts of the global ocean, limiting our ability to monitor and forecast marine conditions. The Moana Project addresses this gap with a new observing system that includes temperature sensors mounted on commercial fishing gear—the Mangōpare fishing vessel network. This study presents the first evaluation of New Zealand’s operational ocean 4D-Var data assimilation system that incorporates these fishing vessel (FV) observations into a regional ROMS model. Using just over one year of operational forecasts, we show that FV temperature profiles significantly improve subsurface temperature representation, especially in coastal regions where satellite products have warm biases or miss key features such as upwelling and mesoscale variability. Assimilation of FV data reduces background temperature biases throughout the upper ocean and enhances forecast skill in areas influenced by major currents and dynamic coastal processes. We also identify sensitivity to periods of missing satellite sea surface temperature, which can lead to overfitting of the available observations. Overall, the results demonstrate that FV observations provide essential subsurface information and can substantially strengthen operational coastal ocean forecasting systems. Full article

Indonesia is among the most landslide-prone countries in the world, with thousands of fatalities and widespread infrastructure damage recorded over recent decades. Despite this high hazard level, regional-scale landslide monitoring remains constrained by the limitations of conventional bitemporal satellite imagery, which is susceptible to cloud contamination, dependent on precise acquisition timing, and unable to capture the full temporal dynamics of landslide occurrence and recovery. While the LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) algorithm has been widely applied for detecting vegetation disturbances such as forest loss and land-use change, its potential for landslide detection in tropical environments has not been sufficiently explored. This study aims to evaluate the applicability of LandTrendr applied to long-term Landsat time series imagery for automated regional-scale landslide detection and mapping in Indonesia. The method integrates temporal segmentation of the Normalized Difference Vegetation Index (NDVI) derived from Landsat imagery spanning 2000–2022 with slope information from the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) to identify the characteristic drop-recovery spectral signature associated with landslide events. The algorithm was applied and evaluated in two geologically distinct study areas: Lombok, West Nusa Tenggara, and Pasaman, West Sumatra. Detection accuracies of 25.9% by location and 20.3% by area were achieved in Lombok and 76.3% by location and 85.3% by area in Pasaman. The lower accuracy in Lombok is primarily attributed to the predominance of small landslides below the sensor’s spatial resolution and rapid vegetation recovery. The proposed approach demonstrates the unique capability of LandTrendr to model the entire life cycle of a mass movement event, from pre-event stability through abrupt disturbance to ecological recovery within a single unified framework, providing a scalable and cost-effective tool for long-term landslide monitoring applicable to other tropical, landslide-prone regions. Full article

Tropical cyclones rank among the most destructive natural hazards globally, posing significant threats to coastal ecosystems and communities. Mangrove forests, renowned for their ecological importance and coastal protection services, are vulnerable to these disturbances, suffering structural damage, habitat loss, and disruption of vital ecosystem functions. Conventional field-based assessment methods often fall short in capturing the rapid and widespread impacts of cyclones, particularly in remote or cloud-obscured regions. This review aims to provide a comprehensive synthesis of remote sensing applications for monitoring cyclone-induced impacts on mangrove and coastal ecosystems worldwide. Through a systematic literature review of 74 peer-reviewed articles from 1990 to 2025, the study evaluates the utility of optical sensors, radar systems, and multi-sensor platforms in assessing inundation, vegetation damage, and ecosystem service loss. Key methodological advances such as time-series analysis, machine learning, and UAV-based validation are highlighted, alongside critical gaps including limited geographic coverage, weak validation practices, and minimal socio-economic integration. Notably, 75.4% of reviewed studies are concentrated in Asia, with Bangladesh and India alone accounting for 44.6% of the total literature, underscoring a pronounced geographic bias. The findings underscore the need for robust, near-real-time monitoring frameworks that combine satellite technologies with ground data and community engagement. Ultimately, the review advocates for an integrated, multi-sensor, and participatory approach to cyclone resilience, offering valuable insights for future research, disaster response planning, and sustainable mangrove management. Full article

Hyperspectral remote sensing (HRS) is gaining widespread adoption within the geoscience and Earth observation communities. It fosters diverse applications, including precision agriculture, soil science, mineral exploration, and carbon detection, to name a few. Recent technological advancements facilitated a growing number of satellite missions as well as an increase in the availability of commercial sensors and platforms, such as drones. A significant challenge in deploying the varied platforms and sensors is the design and optimization of the hyperspectral surveys. Forward modelling simulators are valuable for optimizing mission parameters and estimating imaging performance. Limited accessibility of open-source simulators presents an obstacle for users who seek to benefit from such tools. To bridge this gap, HySIMU (Hyperspectral SIMUlator) was developed and described herein. It is an open-source, forward modelling toolkit that combines and integrates a primary processing pipeline with various open-source packages into a transparent and modular workflow. It offers a cost-effective approach to evaluating the performance of hyperspectral surveys. HySIMU is designed to simulate hyperspectral imagery based on user-defined targets, platforms, and sensor parameters. Features include (i) a ground truth data cube builder for customizable input parameters, (ii) a terrain-based solar and view geometry calculator for illumination modelling, (iii) integrated open-source radiative transfer models for incorporating atmospheric effects, and (iv) spatial resampling filters. In this manuscript, the initial framework for HySIMU is presented with some example applications, including two validation studies with real hyperspectral images. As remote sensing technologies advance, forward modelling toolkits such as HySIMU play a crucial role in refining mission designs and assessing survey feasibility. The scalability for arbitrary hyperspectral sensors, platforms, and spectral libraries ensures broad applicability. Of particular importance is support for parameter optimization for both scientific and commercial HRS campaigns. Full article

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