Search Results (34)

Satellite videos are widely applied for large-scale surveillance. Existing low-rank matrix decomposition-based methods produce promising results under simple and stationary backgrounds. However, these methods suffer a severe performance drop on satellite videos with complex and dynamic backgrounds. To address these challenges, we propose a matrix-based total variation regularized robust PCA (TV-RPCA) approach for moving-vehicle detection. Specifically, our TV-RPCA uses the partial sum of singular values to model the low-rank background. Moreover, a ${\mathit{\ell }}_p$ norm and a spatial–temporal TV regularization are adopted to encourage the spatial–temporal continuity of foregrounds. The optimization of our TV-RPCA is carried out using the augmented Lagrangian multiplier framework combined with the alternating direction minimization approach. Comprehensive experiments conducted on SkySat and Jilin-1 video data verify the effectiveness of the proposed approach. Full article

Phased antenna arrays have revolutionized modern wireless systems by enabling dynamic beamforming, multibeam synthesis, and user tracking to enhance data rates and reduce interferences, yet their reliance on expensive active components (e.g., phase shifters, amplifiers) embedded in antenna array elements limits adoption in cost-sensitive sub-GHz applications. Therefore, the active phased antenna arrays are still considered as high-end technology and primarily designed only for high-frequency bands and demanding applications such as radars and mobile base stations in microwave bands. In contrast, various important radio communication services still operate in sub-GHz bands with no adequate solution for modern antenna systems with beamforming capability. This paper introduces a 3D antenna array with switched-beam or multibeam capability, designed to eliminate mechanical rotators and active circuitry while maintaining all-sky coverage. By integrating collinear radiating elements with a Butler matrix feed network, the proposed 3D array achieves transmit/receive multibeam operation in the 435 MHz amateur satellite band and adjacent 433 MHz ISM band. Simulations demonstrate a design that provides selectable eight beams, enabling horizontal 360° coverage with only one radio connected to the Butler matrix. If eight noncoherent radios are used simultaneously, the proposed antenna array acts as a multibeam all-sky coverage antenna. Innovations in our design include a 3D circular collinear topology combining the broad and adjustable elevation coverage of collinear antennas with azimuthal beam steering, a passive Butler matrix enabling bidirectional transmit/receive multibeam operation, and scalability across sub-GHz bands where collinear antennas dominate (e.g., Lora WAN, trunked radio). Results show sufficient gain, confirming feasibility for low-earth-orbit satellite tracking or long-range IoT backhaul, and maintenance-free beamforming solutions in sub-GHz bands. Given the absence of practical beamforming or multibeam-capable solutions in this frequency band, our novel concept—featuring non-coherent cooperation across multiple ground stations and/or beams—has the potential to fundamentally transform how the growing number of CubeSats in low Earth orbit can be efficiently supported from the ground segment perspective. Full article

This systematic review investigates recent advances and persistent challenges in Land Use and Land Cover (LULC) classification using Sentinel-2 imagery, emphasizing the gap between benchmark results and operational performance. Following PRISMA guidelines, we analyzed 89 peer-reviewed studies published between 2020–2025 to address the discrepancy between academic benchmarks and real-world deployment. While benchmark datasets such as EuroSAT routinely achieve accuracies above 98%, operational systems deployed at regional or global scales typically reach only 75–85%. Through systematic analysis and meta-analysis of reported results, we identify three main factors: (i) methodological issues, particularly the inflation of reported accuracies caused by spatial autocorrelation; (ii) domain adaptation limitations, where geographic and temporal transferability reduce accuracy by 15–25%; (iii) training data constraints, where geographic diversity proves more important than sample size. Multi-spectral approaches provide modest 5–8% gains over RGB at significantly higher computational costs. Foundation models (e.g., Prithvi, Sky Sense) and self-supervised learning show promise for reducing data requirements while maintaining performance. Comparisons with operational products such as ESA WorldCover and Google Dynamic World confirm the more modest performance achievable under real-world conditions. The findings emphasize the need for rigorous spatial validation protocols, standardized evaluation frameworks, and closer integration between research and operational development. Full article

In this study, the long-term (1961–2020) values of the summertime station-based surface air temperature (SAT) data at 151 qualified stations, alongside the corresponding ERA5 gridded data, were analyzed to investigate climate change over the Middle East and the west Indian subcontinent. Significant positive (negative) trends were observed at 134 (2) stations, while trends were insignificant at 15 stations. The positive (negative and insignificant) trends were mainly concentrated in the interior highlands (monsoon-dominated lowlands), where ERA5 exhibited from 10% to 70% overestimations (5% to 26% underestimations). These ERA5-related biases exhibited strong correlations with elevation. To assess the trends’ disparity reasons, we first showed that the outputs of SAT_+AMO − SAT_−AMO are highly positive (negative or near zero) over the overestimated (underestimated) regions. The study then demonstrated that cloudiness, atmospheric circulation, specific humidity, and convective activities above the monsoon-dominated areas differ between +AMO and −AMO. For these areas, the enhanced +AMO-related cloudiness suppresses positive SAT anomalies, while the increased −AMO-associated sunshine offsets negative SAT anomalies. Contrarily, for some areas such as northern Iran, the +AMO (−AMO)-associated cloudiness or clear sky can affect climate change by amplifying the warmness or coldness. In addition, +AMO (−AMO) has caused further convective activities over the Arabian Sea (Bengal Bay). Full article

Yield maps and in-season forecasts help optimize agricultural practices. The traditional approaches to predicting yield during the growing season often rely on ground-based observations, which are time-consuming and labor-intensive. Remote sensing offers a promising alternative by providing frequent and spatially extensive information on crop development. In this study, we evaluated the feasibility of high-resolution satellite imagery for the early yield prediction of an under-investigated crop, Japanese squash (Cucurbita maxima), in a small farm in Hollister, California, over the growing seasons of 2022 and 2023 using vegetation indices, including the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI). We identified the optimal time for yield prediction and compared the performances across satellite platforms (Sentinel-2: 10 m; PlanetScope: 3 m; SkySat: 0.5 m). Pearson’s correlation coefficient (r) was employed to determine the dependencies between the yield and vegetation indices measured at various stages throughout the squash growing season. The results showed that SkySat-derived vegetation indices outperformed those of Sentinel-2 and PlanetScope in explaining the squash yields (R² = 0.75–0.76; RMSE = 0.8–1.9 tons/ha). Remote sensing showed very strong correlations with yield as early as 29 days after planting in 2022 and 37 and 76 days in 2023 for the NDVI and the SAVI, respectively. These early dates corresponded with the vegetative stages when the crop canopy became denser before fruit development. These findings highlight the utility of high-resolution imagery for in-season yield estimation and within-field variability detection. Detecting yield variability early enables timely management interventions to optimize crop productivity and resource efficiency, a critical advantage for small-scale farms, where marginal yield changes impact economic outcomes. Full article

The accurate perception and prediction of traffic parameters like vehicles is essential to transportation land planning and management. Video satellites launched in recent years have brought promising opportunities into this field, providing a wide perspective and high frame frequency for extracting moving vehicles. However, detecting moving vehicles remains a challenge due to their small size, which diminishes shape and texture details, often causing them to blend with noise or other objects. To address this issue, we propose an effective method for moving vehicle detection in video satellites by integrating road maps. Experiments conducted on videos sampled from Jilin-1 and Skysat satellites show that our approach achieves F-scores of 0.98 and 0.87, respectively, which are superior to the three traditional methods, Gaussian mixture model (GMM), improved frame difference (IFD), and visual background extractor (ViBe). Our method can be used for accurate parameter estimation in real traffic, which paves the way for the application of video satellites in transportation land planning and management. Full article

Rice farming in Italy accounts for about 50% of the EU’s rice area and production. Precision agriculture has entered the scene to enhance sustainability, cut pollution, and ensure food security. Various studies have used remote sensing tools like satellites and drones for multispectral imaging. While Sentinel-2 is highly regarded for precision agriculture, it falls short for specific applications, like at the “Riserva San Massimo” (Gropello Cairoli, Lombardia, Northern Italy) rice farm, where irregularly shaped crops need higher resolution and frequent revisits to deal with cloud cover. A prior study that compared Sentinel-2 and the higher-resolution PlanetScope constellation for vegetative indices found a seasonal miscalibration in the Normalized Difference Vegetation Index (NDVI) and in the Normalized Difference Red Edge Index (NDRE). Dr. Agr. G.N. Rognoni, a seasoned agronomist working with this farm, stresses the importance of studying the radiometric intercalibration between the PlanetScope and Sentinel-2 vegetative indices to leverage the knowledge gained from Sentinel-2 for him to apply variable rate application (VRA). A high-resolution SkySat image, taken almost simultaneously with a pair of Sentinel-2 and PlanetScope images, offered a chance to examine if the irregular distribution of vegetation and barren land within rice fields might be a factor in the observed miscalibration. Using an unsupervised pixel-based image classification technique on SkySat imagery, it is feasible to split rice into two subclasses and intercalibrate them separately. The results indicated that combining histograms and agronomists’ expertise could confirm SkySat classification. Moreover, the uneven spatial distribution of rice does not affect the seasonal miscalibration object of past studies, which can be adjusted using the methods described here, even with images taken four days apart: the first method emphasizes accuracy using linear regression, histogram shifting, and histogram matching; whereas the second method is faster and utilizes only histogram matching. Full article

Gamma-ray bursts (GRBs) are intense and short-lived cosmic explosions. Miniaturised CubeSat-compatible instruments for the study of GRBs are being developed to help bridge the gap in large missions and assist in achieving full sky coverage. CubeSats are small, compact satellites conforming to a design standard and have transformed the space industry. They are relatively low-cost and are developed on fast timescales, which has provided unparalleled access to space. This paper focuses on GMOD, the gamma-ray module, onboard the 2U CubeSat EIRSAT-1, launched on December 1st 2023. GMOD is a scintillation-based instrument with a cerium bromide crystal coupled to an array of sixteen silicon photomultipliers, designed for the detection of GRBs. The characterisation of GMOD in the spacecraft, along with the validation of an updated spacecraft MEGAlib model is presented and this approach can be followed by other CubeSats with similar science goals. The energy resolution of the flight model is 7.07% at 662 keV and the effective area peaks in the tens to hundreds of keV, making it a suitable instrument for the detection of GRBs. An investigation into the instrument’s angular response is also detailed. The results from this characterisation campaign are a benchmark for the instrument’s performance pre-launch and will be used to compare with the detector’s performance in orbit. Full article

Controls on Arctic vegetation physiology have been linked to microscale (1–100 m) topography and landscape position, yet drivers may change under future climates as temperature, active-layer thickness, and nutrient limitations are removed or altered. Focusing on the cosmopolitan dwarf birch (Betula glandusa), physiological metrics were measured over two field campaigns at Trail Valley Creek, NWT, Canada, and linked to tasked and archived multispectral imagery to investigate drivers. Relative humidity was ~31.1% on 25 June 2023, and increased to 45.6% on 29 June 2023, which corresponded to heightened physiological activity of stomatal conductance and light-adapted fluorescence (gsm: 0.118 vs. 0.165 μmol m⁻² s⁻¹, Fs: 129.29 vs. 178.42). Normalized difference vegetation index of AVIRIS, Sentinel 2, and SkySat were negligibly correlated to dwarf birch physiological activity, but moderately correlated to dwarf birch height and active-layer thickness. Random forest variable importance revealed that environmental factors and field-measured active-layer thickness ranked higher than remote sensing metrics in explaining physiological activity regardless of the field campaign. Overall, these findings suggest that microscale variation can influence dwarf birch physiological activity, yet microscale effects are overwritten by environmental conditions that may hinder fine-scale space-based monitoring of Arctic vegetation physiological dynamics. Full article

TanSat-2, the next-generation Chinese greenhouse gas monitoring satellite for measuring carbon dioxide (CO${}_2$), has a new city-scale observing mode. We assess the theoretical capability of TanSat-2 to quantify integrated urban CO${}_2$ emissions over the cities of Beijing, Jinan, Los Angeles, and Paris. A high-resolution emission inventory and a column-averaged CO${}_2$ (${\mathrm{X}}_{{\mathrm{CO}}_2}$) transport model are used to build an urban CO${}_2$ inversion system. We design a series of numerical experiments describing this observing system to evaluate the impacts of sampling patterns and ${\mathrm{X}}_{{\mathrm{CO}}_2}$ measurement errors on inferring urban CO${}_2$ emissions. We find that the correction in systematic and random flux errors is correlated with the signal-to-noise ratio of satellite measurements. The reduction in systematic flux errors for the four cities are sizable, but are subject to unbiased satellite sampling and favorable meteorological conditions (i.e., less cloud cover and lower wind speed). The corresponding correction to the random flux error is 19–28%. Even though clear-sky satellite data from TanSat-2 have the potential to reduce flux errors for cities with high CO${}_2$ emissions, quantifying urban emissions by satellite-based measurements is subject to additional limitations and uncertainties. Full article

It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat from June to September 2018 are investigated. Based on sample sets matched by two types of satellite data, a random forest (RF) algorithm was applied to train a model, and a retrieval method was developed for cloud classification. With the use of this method, the sample sets were inverted and classified as clear sky, low clouds, middle clouds, thin cirrus, thick cirrus, multi-layer clouds and deep convection (cumulonimbus) clouds. The results indicate that the average accuracy for all cloud types during the day is 88.4%, and misclassifications mainly occur between low and middle clouds, thick cirrus clouds and cumulonimbus clouds. The average accuracy is 79.1% at night, with more misclassifications occurring between middle clouds, multi-layer clouds and cumulonimbus clouds. Moreover, Typhoon Muifa from 2022 was selected as a sample case, and the cloud type (CLT) product of an FY-4A satellite was used to examine the classification method. In the cloud system of Typhoon Muifa, a cumulonimbus area classified using the method corresponded well with a mesoscale convective system (MCS). Compared to the FY-4A CLT product, the classifications of ice-type (thick cirrus) and multi-layer clouds are effective, and the location, shape and size of these two varieties of cloud are similar. Full article

Aiming at the problem of misdetection caused by the traditional texture characteristic extraction model, which does not describe the correlation among multiple bands, an object-oriented remote sensing image change detection method based on a color co-occurrence matrix is proposed. First, the image is divided into multi-scale objects by graph-based superpixel segmentation, and the optimal scale is determined by the overall goodness F-measure (OGF). Then, except for the extraction of the spectral features, the multi-channel texture features based on the color co-occurrence matrix (CCM) are extracted to consider the correlation among multiple bands. To accurately find the representative features to overcome the impact of feature redundancy, a cumulative backward search strategy (CBSS) is further designed. Finally, the change detection is completed by inputting the difference image of dual time points to the trained random forest model. Taking Shenzhen and Dapeng as the study areas, with Sentinel-2 and Skysat images under different spatial resolutions, and the forest–bareland change type as an example, the effectiveness of the proposed algorithm is verified by qualitative and quantitative analyses. They show that the proposed algorithm can obtain higher detection accuracy than the texture features without band correlation. Full article

The geolocation accuracy of Synthetic Aperture Radar (SAR) images is crucial for their application in various industries. Five high-resolution SAR satellites, namely ALOS, TerraSAR-X, Cosmo-SkyMed, RadarSat-2, and Chinese YG-3, provide a vast amount of image data for research purposes, although their geometric accuracies differ despite similar resolutions. To evaluate and compare the geometric accuracy of these satellites under the same ground control reference, a validation field was established in Xianning, China. The rational function model (RFM) was used to analyze the geometric performance of the five satellites based on the Xianning validation field. The study showed that each image could achieve sub-pixel positioning accuracy in range and azimuth direction when four ground control points (GCPs) were placed in the corners, resulting in a root mean square error (RMSE) of 1.5 pixels. The study also highlighted the effectiveness of an automated GCP-matching approach to mitigate manual identification of points in SAR images, and results demonstrate that the five SAR satellite images can all achieve sub-pixel positioning accuracy in range and azimuth direction when four GCPs are used. Overall, the verification results provide a reference for SAR satellite systems’ designs, calibrations, and various remote sensing activities. Full article

The paper proposes a flexible and efficient wavenumber domain processing scheme suited for close formations of low earth orbiting (LEO) synthetic aperture radar (SAR) sensors hosted on micro-satellites or CubeSats. Such systems aim to generate a high-resolution image by combining data acquired by each sensor with a low pulse repetition frequency (PRF). This is usually performed by first merging the different channels in the wavenumber domain, followed by bulk focusing. In this paper, we reverse this paradigm by first upsampling and focusing each acquisition and then combining the focused images to form a high-resolution, unambiguous image. Such a procedure is suited to estimate and mitigate artifacts generated by incorrect positioning of the sensors. An efficient wave–number method is proposed to focus data by adequately coping with the orbit curvature. Two implementations are provided with different quality/efficiency. The image quality in phase preservation, resolution, sidelobes, and ambiguities suppression is evaluated by simulating both point and distributed scatterers. Finally, a demonstration of the capability to compensate for ambiguities due to a small across-track baseline between sensors is provided by simulating a realistic X-band multi-sensor acquisition starting from a stack of COSMO-SkyMed images. Full article

The rapid increase of space debris poses a risk to space activities, so it is vital to develop countermeasures in terms of space surveillance to prevent possible threats. The current Space Surveillance Network is majorly composed of radar and optical telescopes that regularly observe and track space objects. However, these measures are limited by size, being able to detect only a tiny amount of debris. Hence, alternative solutions are essential for securing the future of space activities. Therefore, this paper proposes the design of a payload camera breadboard for space surveillance to increase the information on debris, particularly for the under-catalogued ones. The device was designed with similar characteristics to star trackers of small satellites and CubeSats. Star trackers are attitude devices usually used in satellites for attitude determination and, therefore, have a wide potential role as a major tool for space debris detection. The breadboard was built with commercial off-the-shelf components, representing the current space-camera resolution and field of view. The image sensor was characterized to compute the sensitivity of the camera and evaluate the detectability performance in several simulated positions. Furthermore, the payload camera concept was tested by taking images of the night sky using satellites as proxies of space debris, and a photometric analysis was performed to validate the simulated detectability performance. Full article