Search Results (16,652)

Accurate mapping of wetland vegetation is essential for ecological monitoring and conservation, yet it remains challenging due to the spatial heterogeneity of wetlands, the scarcity of ground-truth data, and the spread of invasive species. Invasive plants alter native vegetation patterns, making their early detection critical for preserving ecosystem integrity. This study proposes a novel framework that integrates self-supervised learning (SSL), supervised segmentation, and multi-sensor data fusion to enhance vegetation classification in the Bayinbuluke Alpine Wetland, China. High-resolution satellite imagery from PlanetScope-3 and Jilin-1 was fused, and SSL methods—including BYOL, DINO, and MoCo v3—were employed to learn transferable feature representations without extensive labeled data. The results show that SSL methods exhibit consistent variations in classification performance, while multi-sensor fusion significantly improves the detection of rare and fragmented vegetation patches and enables the early identification of invasive species. Overall, the proposed SSL–fusion strategy reduces reliance on labor-intensive field data collection and provides a scalable, high-precision solution for wetland monitoring and invasive species management. Full article

Nitrogen dioxide (NO₂) poses severe risks to human health and the environment, especially in densely populated megacities. Ground-based air quality monitoring stations provide high-temporal-resolution data but are spatially limited, while satellite observations offer broad coverage but measure column densities rather than surface concentrations. To overcome these limitations, this study integrates ground-based observations with satellite-derived NO₂ from Sentinel-5P TROPOMI and GEOS-CF products to estimate ground-level NO₂ in Istanbul using machine learning (ML) approaches. Three ML algorithms (RF, XGB, and CB) were tested on two datasets spanning 2019–2024 at ~1 km resolution, incorporating 20 features, including topographic, meteorological, environmental, and demographic variables. Among models, CB achieved the best performance (R: 0.686, RMSE: 16.23 µg/m³, and MAE: 11.75 µg/m³ in the test dataset) with the Sentinel-5P dataset, successfully capturing spatial and seasonal variations in ground-level NO₂ both quantitatively and qualitatively. SHAP analysis revealed that regarding satellite-derived NO₂, anthropogenic indicators such as population density, road length, and digital elevation model were the most influential features, while meteorological factors contributed secondarily. Despite the lower spatial resolution of GEOS-CF data, both Sentinel-5P and GEOS-CF datasets supported reliable model outputs. This study provides the first ML-based ground-level NO₂ estimation framework for the Istanbul Metropolitan City. Full article

Accurate and timely prediction of diseases in water-intensive crops is critical for sustainable agriculture and food security. AI-based crop disease management tools are essential for an optimized approach, as they offer significant potential for enhancing yield and sustainability. This study centers on maize, training deep learning models on UAV imagery and satellite remote-sensing data to detect and predict disease. The performance of multiple convolutional neural networks, such as ResNet-50, DenseNet-121, etc., is evaluated by their ability to classify maize diseases such as Northern Leaf Blight, Gray Leaf Spot, Common Rust, and Blight using UAV drone data. Remotely sensed MODIS satellite data was used to generate spatial severity maps over a uniform grid by implementing time-series modeling. Furthermore, reinforcement learning techniques were used to identify hotspots and prioritize the next locations for inspection by analyzing spatial and temporal patterns, identifying critical factors that affect disease progression, and enabling better decision-making. The integrated pipeline automates data ingestion and delivers farm-level condition views without manual uploads. The combination of multiple remotely sensed data sources leads to an efficient and scalable solution for early disease detection. Full article

Satellite remote sensing has emerged as a reliable and cost-effective approach for monitoring inland water quality, offering spatial and temporal advantages over traditional in situ methods. Lake Victoria, the largest tropical lake and a critical freshwater resource for East Africa, faces increasing eutrophication driven by nutrient inflows from agriculture, urbanization, and industrial activities. This study assessed the spatiotemporal dynamics of water quality along Uganda’s Lake Victoria coast by integrating field measurements (2014–2024) with Landsat 8/9 imagery. Chlorophyll-a, a proxy for algal blooms, and Secchi disk depth, an indicator of water clarity, were selected as key parameters. Cloud-free satellite images were processed using the Dark Object Subtraction method, and spectral reflectance values were correlated with field data. Linear regression models from single bands and band ratios showed strong performance, with adjusted R² values of up to 0.88. When tested on unseen data, the models achieved R² values above 0.70, confirming robust predictive ability. Results revealed high algal concentrations for nearshore and clearer offshore waters. These models provide an efficient framework for monitoring eutrophication, guiding restoration priorities, and supporting sustainable water management in Lake Victoria. Full article

Post-fire soil and vegetation changes can intensify erosion and sediment yield by altering the factors controlling the runoff–infiltration balance. Erosion barriers (EBs) are widely used in hydrological and forest restoration to mitigate erosion, reduce sediment transport, and promote vegetation recovery. However, precise spatial assessments of their effectiveness remain scarce, requiring validation through operational methodologies. This study evaluates the impact of EB on post-fire vegetation recovery at two temporal and spatial scales: (1) Remotely Piloted Aircraft System (RPAS) imagery, acquired at high spatial resolution but limited to a single acquisition date coinciding with the field flight. These data were captured using a MicaSense RedEdge-MX multispectral camera and an RGB optical sensor (SODA), from which NDVI and vegetation height were derived through aerial photogrammetry and digital surface models (DSMs). (2) Sentinel-2 satellite imagery, offering coarser spatial resolution but enabling multi-temporal analysis, through NDVI time series spanning four consecutive years. The study was conducted in the area of the Luna Fire (northern Spain), which burned in July 2015. A paired sampling design compared upstream and downstream areas of burned wood stacks and control sites using NDVI values and vegetation height. Results showed slightly higher NDVI values (0.45) upstream of the EB (p < 0.05), while vegetation height was, on average, ~8 cm lower than in control sites (p > 0.05). Sentinel-2 analysis revealed significant differences in NDVI distributions between treatments (p < 0.05), although mean values were similar (~0.32), both showing positive trends over four years. This study offers indirect insight into the functioning and effectiveness of EB in post-fire recovery. The findings highlight the need for continued monitoring of treated areas to better understand environmental responses over time and to inform more effective land management strategies. Full article

The China Seismo Electromagnetic Satellite (CSES) mission provides in situ measurements of the electromagnetic field, plasma, and charged particles in the topside ionosphere. Each CSES spacecraft carries several different scientific payloads delivering a wealth of information about the ionospheric plasma dynamics and properties, as well as measurement about energetic particles precipitating in the ionosphere. In this work, we introduce CSESpy, a Python package designed to provide an interface to CSES data products, with the aim of easing the pathway for scientists to carry out analyses of CSES data. Beyond simply being an interface to the data, CSESpy aims to provide higher-level analysis and visualization tools, as well as methods for combining concurrent measurements from different instruments, so as to allow multipayload studies in a unified framework. Moreover, CSESpy is designed to be highly flexible as such, it can be extended to interface with datasets from other sources and can be embedded in wider software ecosystems. We highlight some applications, also demonstrating that CSESpy is a powerful visualization tool for investigating complex events involving variations across multiple physical observables. Full article

From 2011 to 2021, marine and brackish water aquaculture production in the Mediterranean and Black Seas increased by 91.3% in volume and 74.5% in value, primarily due to the rise in finfish marine aquaculture. In the Aegean Sea, a significant aquaculture hotspot, Greece and Turkey lead in fish farm numbers and production volume. This study uses Google Earth satellite imagery to map and analyze fish farming cages along the Aegean Sea, comparing findings with the EMODnet dataset. By cataloging fish farm cages along the Greek and Turkish coastlines, we identified 4729 cages in Greece and 2349 in Turkey, with Turkey’s cages occupying a larger area (1.64 km²) than Greece (1.35 km²) due to their larger average size. The analysis revealed significant discrepancies between satellite-derived data and EMODnet records, particularly along the Greek coastline, highlighting gaps in existing datasets. Our findings underscore the need for improved marine spatial planning and management as well as for consistent data collection to support sustainable aquaculture. Full article

One of the leading causes of climate change and global warming is the rise in carbon dioxide (${\mathrm{CO}}_2$) levels. For a precise assessment of ${\mathrm{CO}}_2$’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing ${\mathrm{CO}}_2$ sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) ${\mathrm{XCO}}_2$ retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled ${\mathrm{CO}}_2$ concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 ${\mathrm{km}}^2$) ${\mathrm{XCO}}_2$. When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and ${\mathrm{R}}^2$ of 0.90. Full article

Agile satellites leverage rapid and flexible maneuvering to image more targets per orbital cycle, which is essential for time-sensitive emergency operations, particularly disaster assessment. Correspondingly, the increasing observation data volumes necessitate the use of on-orbit computing to bypass storage and transmission limitations. However, coordinating precedence-dependent observation, computation, and downlink operations within limited time windows presents key challenges for agile satellite service optimization. Therefore, this paper proposes a deep reinforcement learning (DRL) approach to solve the joint observation and on-orbit computation scheduling (JOOCS) problem for agile satellite constellations. First, the infrastructure under study consists of observation satellites, a GEO satellite (dedicated to computing), ground stations, and communication links interconnecting them. Next, the JOOCS problem is described using mathematical formulations, and then a partially observable Markov decision process model is established with the objective of maximizing task completion profits. Finally, we design a joint scheduling decision algorithm based on multiagent proximal policy optimization (JS-MAPPO). Concerning the policy network of agents, a problem-specific encoder–decoder architecture is developed to improve the learning efficiency of JS-MAPPO. Simulation results show that JS-MAPPO surpasses the genetic algorithm and state-of-the-art DRL methods across various problem scales while incurring lower computational costs. Compared to random scheduling, JOOCS achieves up to 82.67% higher average task profit, demonstrating enhanced operational performance in agile satellite constellations. Full article

As the world is technologically advancing, the integration of FSO communication in non-terrestrial platforms is transforming the landscape of global connectivity. By enabling high-data-rate inter-satellite links, secure UAV–ground channels, and efficient HAPS backhaul, FSO technology is paving the way for sustainable 6G non-terrestrial networks. However, the stringent requirement for precise line-of-sight (LoS) alignment between the optical transmitter and receivers poses a hindrance in practical deployment. As non-terrestrial missions require continuous movement across the mission area, the platform is subject to vibrations, dynamic motion, and environmental disturbances. This makes maintaining the LoS between the transceivers difficult. While fine-pointing mechanisms such as fast steering mirrors and adaptive optics are effective for microradian angular corrections, they rely heavily on an initial coarse alignment to maintain the LoS. Coarse pointing modules or gimbals serve as the primary mechanical interface for steering and stabilizing the optical beam over wide angular ranges. This survey presents a comprehensive analysis of coarse pointing and gimbal modules that are being used in FSO communication systems for non-terrestrial platforms. The paper classifies gimbal architectures based on actuation type, degrees of freedom, and stabilization strategies. Key design trade-offs are examined, including angular precision, mechanical inertia, bandwidth, and power consumption, which directly impact system responsiveness and tracking accuracy. This paper also highlights emerging trends such as AI-driven pointing prediction and lightweight gimbal design for SWap-constrained platforms. The final part of the paper discusses open challenges and research directions in developing scalable and resilient coarse pointing systems for aerial FSO networks. Full article

Real-time lossless image compression based on the JPEG-LS algorithm is in high demand for critical missions such as satellite remote sensing and space exploration due to its excellent balance between complexity and compression rate. However, few researchers have made appropriate modifications to the JPEG-LS algorithm to make it more suitable for high-speed hardware implementation and application to Bayer pattern data. This paper addresses the current limitations by proposing a real-time lossless compression system specifically tailored for Bayer pattern images from spaceborne cameras. The system integrates a hybrid encoding strategy modified from JPEG-LS, combining run-length encoding, predictive encoding, and a non-encoding mode to facilitate high-speed hardware implementation. Images are processed in tiles, with each tile’s color channels processed independently to preserve individual channel characteristics. Moreover, potential error propagation is confined within a single tile. To enhance throughput, the compression algorithm operates within a 20-stage pipeline architecture. Duplication of computation units and the introduction of key-value registers and a bypass mechanism resolve structural and data dependency hazards within the pipeline. A reorder architecture prevents pipeline blocking, further optimizing system throughput. The proposed architecture is implemented on a XILINX XC7Z045-2FFG900C SoC (Xilinx, Inc., San Jose, CA, USA) and achieves a maximum throughput of up to 346.41 MPixel/s, making it the fastest architecture reported in the literature. Full article

One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns over time. High-resolution, cloud-free satellite images, particularly from publicly available sources like Sentinel, are ideal for this analysis. However, such images are not always available due to cloud and shadow contamination. To address this limitation, we propose a model that integrates both the temporal and spatial aspects of the data to approximate the missing or contaminated regions. In this method, we separately approximate NDVI using spatial and temporal components of the time-varying satellite data. Spatial approximation near the boundary of the missing data is expected to be more accurate, while temporal approximation becomes more reliable for regions further from the boundary. Therefore, we propose a model that leverages the distance transform to combine these two methods into a single, weighted model, which is more accurate than either method alone. We introduce a new decay function to control this transition. We evaluate our spatiotemporal model for approximating NDVI across 16 farm fields in Western Canada from 2018 to 2023. We empirically determined the best parameters for the decay function and distance-transform-based model. The results show a significant improvement compared to using only spatial or temporal approximations alone (up to a 263% improvement as measured by RMSE relative to the baseline). Furthermore, our model demonstrates a notable improvement compared to simple combination (up to 51% improvement as measured by RMSE) and Spatiotemporal Kriging (up to 28% improvement as measured by RMSE). Finally, we apply our spatiotemporal model in a case study related to improving the specification of the peak green day for numerous fields. Full article

In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EOs), leveraging the Landsat archive and data from other sensors to detect disturbances in forest ecosystems. Despite the wide adoption of these methods, robust documentation, and a growing community of users, little research has systematically detailed their tuning process in mangrove environments. This work aims to identify the best practices for applying these models to monitor changes within mangrove forest cover, which has been declining gradually in Belize the last several decades. Partnering directly with the Belizean Forest Department, our team developed a replicable, efficient methodology to annually update the country’s mangrove extent, employing EO-based change detection. We ran a series of model variations in both CCDC-SMA and LandTrendr to identify the parameterizations best suited to identifying change in Belizean mangroves. Applying the best performing model run to the starting 2017 mangrove extent, we estimated a total loss of 540 hectares in mangrove coverage by 2024. Overall accuracy across thirty variations in model runs of LandTrendr and CCDC-SMA ranged from 0.67 to 0.75. While CCDC-SMA generally detected more disturbances and had higher precision for true changes, LandTrendr runs tended to have higher recall. Our results suggest LandTrendr offered more flexibility in balancing precision and recall for true changes compared to CCDC-SMA, due to its greater variety of adjustable parameters. Full article

When a navigation constellation depends exclusively on inter-satellite links for autonomous orbit determination, the absence of inertial frame orientation measurements can result in the accumulation of rotational errors across the entire constellation. To address these challenges, this study introduces inter-satellite orientation information in the inertial frame to provide the BeiDou satellite constellation with a stable inertial orientation reference. The results demonstrate that (1) incorporating space-based orientation observations with satellite-to-ground data significantly enhances orbit determination accuracy, reducing the three-dimensional orbit error from 2.604 m to 0.611 m. (2) Introducing a single orientation data point per epoch improves orbit determination accuracy from 2.604 m to 0.982 m. Compared to the scanning mode, the staring mode achieves higher performance. (3) When the error of space-based orientation data remains below 10 mas, the resulting spatial reference frame accuracy is better than 50 cm for the satellites. This research provides technical support for the construction of next-generation BDS. Full article

Etesian winds, the characteristic summer winds over large parts of Greece and the eastern Mediterranean, can cause coastal upwelling, especially over the eastern Aegean. The question that many studies address is whether these northern winds can cause upwelling processes that alter not only sea surface temperature but also chlorophyll concentrations, which are indicative of phytoplankton growth and overall ocean health. The present study is an effort to investigate the above matter over the eastern Aegean, from Lesvos to Ikaria and Samos islands, on a monthly basis, based on all the available satellite chlorophyll data up to now. Full article