Search Results (15,972)

Pine wilt disease (PWD) can spread rapidly after the disease occurs and often causes large-scale death of the pine. Therefore, the timely identification of infected trees is critical for forest conservation and effective disease management. However, early infected trees are difficult to distinguish in satellite remote sensing images. Their visual differences from healthy trees and complex background features are often subtle, and existing image-processing methods do not fully exploit heterogeneous information. To address this problem, we constructed the Naro dataset for satellite-based PWD detection and proposed SGM-RTDETR based on Real-Time Detection Transformer (RT-DETR). The proposed model consists of a Semantic–Visual Fusion Module (SVFM) and a Disease Feature Refinement Module (DFRM). In SVFM, ExG, VARI, and GLI are concatenated with RGB imagery to form a six-channel visual input, which enhances the spectral differences between diseased and non-diseased targets. In addition, textual prior knowledge is introduced into the decoder input through a Stackelberg game-based visual–text fusion strategy. This strategy helps the encoded memory features maintain clearer disease-related semantics in complex backgrounds. DFRM then performs channel recalibration, feature refinement, and residual enhancement on the fused memory features to better extract fine-grained disease cues in remote sensing scenes. Experiments on the Naro dataset show that SGM-RTDETR achieves 80.75% mAP@0.5 and 35.43% mAP@0.5:0.95, which is 2.74 percentage points higher than RT-DETR-L on mAP@0.5:0.95. Overall, the results indicate that the dual-module structure improves the precision and robustness of PWD detection in satellite remote sensing images. Full article

Long and narrow shielded confined spaces, represented by traffic tunnels and underground utility tunnels, constitute critical application scenarios for indoor and underground positioning services. Despite their relatively simple geometric configurations, such environments suffer from severe spatial distortion of geometric dilution of precision (GDOP). Coupled with pervasive low-elevation signal propagation and intensive multipath reflection effects, conventional BeiDou Navigation Satellite System (BDS) positioning services are unable to provide continuous and reliable coverage in these scenarios. To date, existing research on high-precision pseudolite positioning for narrow confined spaces remains largely confined to theoretical analysis and laboratory experimental verification, while systematic studies on application-oriented signal atlas feature network design are significantly insufficient, forming a prominent gap that restricts the practical engineering deployment of relevant technologies. To address the aforementioned technical bottlenecks, this paper proposes a novel BDS pseudolite signal atlas network design method to improve the continuity, stability and comprehensive positioning performance in spatially distorted narrow shielded environments. Field vehicular tests were carried out in actual engineering tunnels and underground utility tunnels to systematically analyze the variation characteristics of raw BDS pseudolite observation data, including pseudorange, carrier phase, carrier-to-noise ratio (C/N₀) and Doppler shift. The test results verified that kinematic Doppler parameters exhibited outstanding stability in complex shielded environments with strong multipath interference. On this basis, a spatial feature model based on kinematic Doppler measurements was constructed, and wavelet denoising technology was adopted to extract effective typical spatial feature parameters. Combined with the deterministic one-to-one mapping relationship between Doppler peak characteristics and spatial positions, a multi-peak kinematic Doppler atlas was established, which eliminates the dependence on pre-deployment data collection, dedicated database construction and offline model training. Furthermore, comprehensively considering multi-dimensional constraints such as spatial environment scale, carrier dynamic characteristics and terminal output rate, the atlas network scheme was optimized to achieve a balanced trade-off among positioning detection accuracy, absolute positioning precision and suppression of the pseudolite near-far effect. Comparative experimental results demonstrate that the proposed BDS pseudolite atlas network effectively resolves the inherent GNSS positioning difficulty in long and narrow shielded spaces. Benefiting from the rational spectral peak configuration strategy, the system can satisfy the continuous and stable positioning requirements of multiple carrier types including motor vehicles and railway locomotives under variable motion speeds and terminal output rates. This study provides a robust and feasible technical solution for high-precision BDS positioning services in long and narrow shielded confined spaces, and holds favorable engineering application prospects for underground navigation scenarios. Full article

Selective logging is an important component of forest degradation in the Brazilian Amazon. The detection and mapping of selective logging via satellite imagery remains challenging because spatial features associated with selective logging are generally small-scale, spatially heterogeneous, and short-lived disturbances in the forest. This study evaluated the potential of Sentinel-2 MSI imagery at 10 m and 20 m, and Landsat-8 OLI imagery at 30 m and pansharpened 15 m, to discriminate land-cover classes associated with selective logging in the state of Mato Grosso in the Brazilian Amazon for 2017 using the Random Forest algorithm. The resulting maps were used to characterize selective logging alerts from the Deter system and areas under Sustainable Forest Management Plans (SFMP). Sentinel-2 at 10 m achieved the highest overall accuracy, while Landsat-based products tended to estimate larger areas of exposed soil and, in some cases, regeneration. Deter polygons showed higher proportions of exposed soil and degradation and lower remaining forest cover than SFMP areas, suggesting that Deter alerts tend to capture more advanced stages of visible forest disturbance. Overall, the results indicate that differences in overall accuracy among the evaluated products were small, but class-specific performance and spatial representation patterns remain important for interpreting selective logging-related disturbance in the Amazon. Full article

Efficient task–channel allocation in satellite communication networks becomes particularly challenging when tasks are subject to both time and frequency constraints, and when resource failures or environmental changes invalidate an initially feasible allocation. Existing studies often treat static allocation and dynamic adaptation separately, lacking a unified framework that ensures both a low resource fragmentation rate and low reconfiguration cost. This paper proposes a two-stage approach that integrates static task–channel allocation with dynamic reconfiguration. In the static stage, a greedy algorithm is developed to assign tasks to channels under time-window, bandwidth, and conflict-free constraints, aiming to achieve as low a resource fragmentation rate as possible within the heuristic search. When channel failures occur, a heuristic search-based reconfiguration algorithm is proposed to generate a sequence of reconfiguration events that transitions the initial static allocation strategy step by step to a feasible target static allocation strategy, while maintaining constraint satisfaction and an acceptable resource fragmentation rate throughout the process. Comparative experiments on both small-scale and large-scale datasets demonstrate that the unified framework effectively balances allocation quality, low-cost and compact dynamic reconfiguration, and adaptability in dynamic network environments. Full article

Early fault detection in microsatellite thermal control systems (TCS) faces fundamental challenges: high-dimensional redundant telemetry channels, overlapping multi-scale periodicities that obscure anomaly signatures, and severely limited daily data downlink (1–2 passes per day) that restricts the temporal window for diagnosis. Existing data-driven approaches either rely on supervised learning, requiring labeled fault data that are scarce in practice, or employ univariate analysis that fails to capture inter-sensor spatial correlations. To address these limitations, this paper introduces a hybrid framework integrating information-theoretic feature selection and spatiotemporal deep learning. The Generalized Maximum Information Coefficient (GMIC) quantifies nonlinear dependencies between temperature channels for key channel selection, reducing dimensionality by 82% while preserving diagnostic information. A dual-level Seasonal Trend Decomposition (STL) method disentangles orbital-periodic dynamics from diurnal cycles, effectively isolating distinct thermal characteristics at multiple timescales. Each decomposed component is modeled using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) networks to capture spatiotemporal dependencies for accurate temperature prediction. An adaptive threshold-based weighted error fusion mechanism enables early fault detection within a single day of telemetry data. Experimental validation on real satellite telemetry data demonstrates that the proposed framework achieves high-precision fault detection across multiple fault types using a minimal set of temperature channels, significantly outperforming existing benchmarks in both prediction accuracy and detection reliability. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

This study assesses the spatio-temporal distribution and transition dynamics of land use and land cover (LULC) in Egypt using satellite-based MODIS observations (SAT-MODIS) and WRF static datasets (WRF-MODIS) from 2001 to 2020. Dominant LULC types, barren areas (BAs), cropland (CR), urban and built-up land (UBL), water bodies (WBs), grassland (GR), and open shrubland (OS), exhibited notable changes associated with agricultural expansion, urbanization, and land reclamation due to human-induced activities. BAs remained dominant, covering more than 94% of Egypt throughout the study period. Comparative analysis between the three WRF-MODIS options (WRF-Opt1, WRF-Opt2, and WRF-Opt3) and SAT-MODIS revealed LULC classification discrepancies, which may be due to differences in algorithms, temporal representation, and spatial resolution. WRF-Opt3 showed the highest spatial consistency with SAT-MODIS, particularly before and around 2010. The findings highlight limitations of static WRF land cover datasets and emphasize the need for higher-resolution and dynamically updated LULC datasets to improve regional climate and land–atmosphere modeling applications over Egypt. Full article

Distant Retrograde Orbits (DROs) and Near-Rectilinear Halo Orbits (NRHOs), as categories of Lagrange orbits, have been selected for the construction of future deep-space navigation constellations in the Earth-Moon space due to their unique orbital trajectories and dynamical characteristics. To obtain high-precision orbit and clock solutions, the orbit determination (OD) and time synchronization (TS) performance of DRO and NRHO based on Beidou Navigation Satellite System (BDS) L-band and Ka-band signals were analyzed. Considering the constraints of onboard resources and cost, it may be infeasible to establish Ka-band links with all BDS satellites. Therefore, multiple experiments with different link configuration schemes were designed. The results show that an orbit determination accuracy of about 500 m and the time synchronization accuracy of 50 ns can be achieved using only L-band observations. In contrast, much higher accuracy can be obtained with full Ka-band links, with orbit and clock accuracy reaching 80 m and 7 ns, respectively. Moreover, higher orbit and clock accuracies can be obtained with more Ka-band links based on L-band observations. Furthermore, with the addition of the DRO-NRHO links, the orbit determination and time synchronization performance of each scheme was further improved by 15%. And the orbit determination accuracy can be better than 65 m, while the time synchronization accuracy can be better than 5 ns. Although the analysis is based on BDS signals, the proposed framework is general in nature and can be extended to other GNSS-based or future space navigation systems, providing a reference for the design of high-precision cislunar navigation and timing architectures. Full article

Effective evaluation of the accuracy of multi-source NO₂ products from different satellites and reanalysis is of great significance for data fusion and application. Based on NO₂ observation data from Pandora stations worldwide, we verify and compare the accuracy of the total column density of NO₂ (TOTNO₂) from OMI, TROPOMI, GOME-2 satellites and CAMS reanalysis. The mean biases of the four TOTNO₂ datasets relative to the Pandora station observation data are all negative, indicating that all four TOTNO₂ products show systematic underestimation with respect to Pandora. Overall, TROPOMI has the highest correlation (R = 0.88) and the smallest root mean square error (RMSE = 4.83 Pmolec·cm⁻²), suggesting that among the four TOTNO₂ products, the accuracy of TROPOMI TOTNO₂ is higher compared with the other TOTNO₂ products. The accuracies of OMI and GOME-2 are in the middle, while the performance of CAMS is the poorest. The TOTNO₂ values and accuracies from the four TOTNO₂ products both show a seasonal characteristic. Among the four TOTNO₂ products, the accuracy is higher in summer, and the error increases in autumn and winter. After performing linear fitting correction on the four NO₂ products, the mean biases of each data are reduced by more than 79%, and the RMSE decreases by 4–28%. The consistency of the four TOTNO₂ products with the ground-based observation data is significantly improved. Full article

With the increasing availability of high-resolution (<50 m) spaceborne night time light imagery, it is now becoming more feasible to examine the correspondence between spaceborne and ground-based measurements of night lights. However, so far there have been very few studies that have conducted a ground-based campaign of night time brightness measurements during the overpass of a night light-sensitive satellite. Here we tested whether the correspondence between measurements is higher when ground-based measurements are conducted at the same time as the satellite overpass. We conducted measurements using a LANcube photometer along the same route on two consecutive nights (27–28 August 2025) in Brisbane, Australia, and compared them with an SDGSAT-1 (10–40 m) and Haishao-1 (10 m) images acquired concurrently in the evening and with an early morning ISS photo (8 m) acquired three months earlier. We found the correlation between ground-based and spaceborne measurements was not higher for simultaneous measurements, and the explanatory power of our model predicting night time brightness as measured from space increased when including horizontal and upwards ground-based brightness measurements alongside variables of canopy height, land use and road hierarchy. We confirmed the importance of multidirectional ground measurements and urban structure for understanding night time brightness levels measured from space. Full article

Recently, numerous studies have exploited satellite Aerosol Optical Depth (AOD) to estimate near-surface particulate matter (PM) concentrations, with the aim of overcoming the limited spatial and temporal coverage of ground-based air quality monitoring networks. Despite significant progress, the relationship between AOD and PM remains highly uncertain, mainly due to the inadequate representation of local aerosol microphysical properties and of hygroscopic growth effects. In particular, satellite AOD is retrieved at ambient relative humidity, whereas standard PM measurements are performed under dry conditions. This study proposes a physics-informed, semi-empirical approach that overcomes these limitations by directly relating satellite AOD to PM measured at ambient humidity. Co-located measurements, from a Light Optical Aerosol Counter (LOAC) in the urban area of Bologna (Po Valley, Italy) during 2023, are used. This study is designed as a pilot application to evaluate the physical consistency of the proposed framework under well-characterised observational conditions, including spatial co-location, temporal matching to satellite overpasses, and exclusion of precipitation and desert dust events. The LOAC provides particle number size distribution and particle-type classification, which are used to estimate key aerosol properties controlling the AOD–PM theoretical relationship, including the Effective Radius, Extinction Efficiency, and aerosol Mass Density. These quantities, together with Mixing Layer Height, are combined within a theoretical framework linking PM and AOD, allowing for the derivation of a physically based scaling coefficient without relying on empirical hygroscopic growth corrections. The results show that using ambient PM_2.5 alone already yields a moderate linear correlation with AOD normalized by Mixing Layer Height (Pearson’s R = 0.56) whereas no meaningful correlation is found when using standard dry PM_2.5. When aerosol microphysical properties derived from LOAC measurements are incorporated, the correlation substantially improves (R = 0.76), with regression slopes close to unity and reduced errors, independently of the season. These results demonstrate that explicitly accounting for aerosol size and optical properties enhances the physical consistency and robustness of satellite-based PM estimates. The proposed framework also provides a pathway to indirectly derive aerosol hygroscopic growth factors by coupling ambient PM estimates from satellite observations with conventional dry PM measurements. This opens new perspectives for characterizing aerosol–humidity interactions from space and for improving air quality monitoring in regions lacking of dense in situ networks. Full article

In satellite-denied environments such as urban canyons, tunnels, and underground parking facilities, achieving high-precision autonomous positioning for vehicles remains a critical challenge. Although high-precision inertial measurement units (IMUs) can provide accurate dead reckoning, their deployment is limited by cost, size, and power consumption, making low-cost, microelectromechanical systems IMUs (MIMUs) an attractive alternative solution. However, the single MIMU suffers from substantial measurement noise and bias instability, leading to rapid error divergence that cannot sustain long-term autonomous navigation. To address the above issues, this paper proposes an autonomous positioning system based on a wheel-mounted MIMU array (Wheel-AINS). The system adopts a differential layout in which multiple low-cost MIMU chips are installed at the center of each of the left and right rear wheels, forming redundant sensor arrays. By differentially fusing symmetrically mounted chips, common-mode noise and zero bias are effectively canceled while the wheel rotation provides natural rotational modulation. The fused gyroscope outputs and known wheel radius are then used to estimate the vehicle forward speed, replacing traditional odometers. The estimated wheel speed and vehicle kinematic constraints are then integrated within a Kalman filter framework to suppress the error divergence of the inertial navigation system. A dedicated embedded hardware prototype with multi-chip synchronous acquisition and wireless transmission was developed. Three groups of urban road tests with total distances of 0.85 km, 2.14 km, and 2.49 km were conducted. The results indicate that the average position drift rate of the Wheel-AINS is 0.50%, and the average heading RMSE is 12.2°. The closure error of the 2.49 km trajectory is 10.43 m, reduced by approximately 80% compared with a single MIMU. The ablation experiment reveals that the MIMU array fusion module is the primary source of accuracy improvement, reducing the position RMSE from 155.0 m to 10.1 m, while the dual-wheel distance constraint further optimizes the position RMSE to 8.2 m, but increases the heading RMSE from 13.3° to 13.6°. This demonstrates that the proposed method can substantially improve autonomous positioning accuracy while maintaining a notably low system cost, providing a viable technical pathway for long-endurance vehicle navigation in satellite-denied environments. Full article

Reliable outdoor localization is essential for autonomous mobile robots, where the Global Navigation Satellite System (GNSS) is widely used to provide global positioning information. However, GNSS signals are often degraded in real-world environments due to occlusions, multipath effects, and environmental interference, leading to unstable localization and degraded navigation performance. This paper proposes an adaptive multi-sensor fusion framework for robust outdoor localization and path tracking under weak GNSS conditions. The proposed system integrates GNSS, LiDAR, wheel odometry, and inertial measurement unit (IMU) measurements within an Extended Kalman Filter (EKF) framework. To address the limitations of GNSS, an adaptive weighting mechanism is introduced to dynamically adjust the influence of GNSS observations based on signal quality indicators. Furthermore, a GNSS quality-aware mode-switching strategy is developed, enabling seamless transition between GNSS-dominant localization and multi-sensor fusion-based localization. In the fusion mode, LiDAR, odometry, and IMU jointly provide robust pose estimation, while GNSS acts as a weak global constraint. The IMU further enhances heading estimation, improving orientation stability and path tracking performance. The estimated pose is then used for trajectory tracking using a path-following controller. Experimental results conducted in outdoor environments demonstrate that the proposed framework significantly improves localization robustness and path tracking performance under degraded GNSS conditions. Compared with raw GNSS localization, the proposed method reduces the mean localization error by 47.2% and decreases the root mean square localization error by 55.5%, while maintaining smoother and more continuous trajectory estimation in weak GNSS environments. Full article

The emerging potential of low-Earth-orbit (LEO) satellite-based Positioning, Navigation, and Timing services has increased the need for real-time, stable, and accurate orbit determination techniques. Here, we propose a method for estimating sub-meter-level LEO satellite orbits using Global Navigation Satellite System (GNSS) code pseudorange observations. To mitigate ionospheric delay, a dual-frequency ionosphere-free combination was applied, while code-carrier smoothing was employed to reduce code observation noise. A satellite weighting model based on Signal-in-Space Range Error was developed to reflect the orbit and clock error characteristics of different GNSS, and a robust weighting scheme was applied to alleviate the impact of observation outliers. Further, Galileo High Accuracy Service corrections compensated for orbit, clock and code bias errors. The algorithm was validated using the GNSS observation data collected from the Sentinel-6A satellite on 10 August 2023. Each successively applied technique gradually improved orbit determination accuracy, achieving up to a 51% reduction in 3D root mean square error (RMSE). The final RMSE values in the radial, along-track, cross-track, and 3D components were 39.4, 18.8, 23.5, and 49.6 cm, respectively. Temporal analysis showed no distinct periodicity in orbit errors and no significant correlation with satellite visibility or ground track. Full article

Coarsely gridded Land Surface Models (LSMs) often smooth over sub-grid spatial heterogeneity and non-linear surface soil moisture dynamics during extreme-precipitation events. This study introduces a clustering-based Soil Moisture Active Passive (SMAP) residual framework, evaluating the spatiotemporal discrepancies between 3 km SMAP Level 2 (SMAP-L2) retrievals and 9 km SMAP Level 4 (SMAP-L4) data-assimilation products within the Yanco study region during the extreme March 2021 floods in New South Wales, Australia. By applying k-means clustering to the residual time series, we partitioned the landscape into three distinct hydrological response patterns: a Low-Residual Baseline ($64.5\%$), a Persistent Positive Anomaly ($20.7\%$) indicative of unmodeled inundation, and a Transient Negative Anomaly ($14.8\%$) representing rapid drainage. Consequently, $35.5\%$ of the usable analysis area exhibited temporal trajectories that diverged significantly from model expectations, highlighting profound geographic heterogeneity in surface wetting and retention that cannot be captured by uniform precipitation inputs alone. Benchmarking the satellite-derived time series against the Yanco in situ network provided critical context for cross-scale variations, illustrating general agreement in overarching temporal trends despite the inherent scale mismatch. Ultimately, this approach leverages residual dynamics as a scalable spatial diagnostic, offering a robust, data-driven method to map localized flood responses that are typically obscured by broad-scale model parameters. Full article