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Journal Description
Remote Sensing
Remote Sensing
is an international, peer-reviewed, open access journal about the science and application of remote sensing technology, and is published semimonthly online by MDPI. The Remote Sensing Society of Japan (RSSJ) and the Japan Society of Photogrammetry and Remote Sensing (JSPRS) are affiliated with Remote Sensing, and their members receive a discount on the article processing charge.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SCIE (Web of Science), Ei Compendex, PubAg, GeoRef, Astrophysics Data System, Inspec, dblp, and other databases.
- Journal Rank: JCR - Q1 (Geosciences, Multidisciplinary) / CiteScore - Q1 (General Earth and Planetary Sciences)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 24.9 days after submission; acceptance to publication is undertaken in 2.5 days (median values for papers published in this journal in the first half of 2025).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
- Companion journal: Geomatics.
- Journal Cluster of Geospatial and Earth Sciences: Remote Sensing, Geosciences, Quaternary, Earth, Geographies, Geomatics and Fossil Studies.
Impact Factor:
4.1 (2024);
5-Year Impact Factor:
4.8 (2024)
Latest Articles
YOLO-CAM: A Lightweight UAV Object Detector with Combined Attention Mechanism for Small Targets
Remote Sens. 2025, 17(21), 3575; https://doi.org/10.3390/rs17213575 - 29 Oct 2025
Abstract
Object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging due to the prevalence of small targets, complex backgrounds, and the stringent requirement for real-time processing on computationally constrained platforms. Existing methods often struggle to balance detection accuracy, particularly for small objects, with
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Object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging due to the prevalence of small targets, complex backgrounds, and the stringent requirement for real-time processing on computationally constrained platforms. Existing methods often struggle to balance detection accuracy, particularly for small objects, with operational efficiency. To address these challenges, this paper proposes YOLO-CAM, an enhanced object detector based on YOLOv5n. First, a novel Combined Attention Mechanism (CAM) is integrated to synergistically recalibrate features across both channel and spatial dimensions, enhancing the network’s focus on small targets while suppressing background clutter. Second, the detection head is strategically optimized by introducing a dedicated high-resolution head for tiny targets and removing a redundant head, thereby expanding the detectable size spectrum down to small pixels with reduced parameters. Finally, the CIoU loss is replaced with the inner-Focal-EIoU loss to improve bounding box regression accuracy, especially for low-quality examples and small objects. Extensive experiments on the challenging VisDrone2019 benchmark demonstrate the effectiveness of our method. YOLO-CAM achieves a mean Average Precision (mAP0.5) of 31.0%, which represents a significant 7.5% improvement over the baseline YOLOv5n, while maintaining a real-time inference speed of 128 frames per second. Comparative studies show that our approach achieves a superior balance between accuracy and efficiency compared to other state-of-the-art detectors. The results indicate that the proposed YOLO-CAM establishes a new way for accuracy–efficiency trade-offs in UAV-based detection. Due to its lightweight design and high performance, it is particularly suitable for deployment on resource-limited UAV platforms for applications requiring reliable real-time small object detection.
Full article
(This article belongs to the Special Issue Target Detection, Recognition, Tracking, and Positioning Using Remote Sensing and AI Techniques)
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Open AccessArticle
A Comparative Analysis of CG Lightning Activities in the Hengduan Mountains and Its Surrounding Areas
by
Jingyue Zhao, Yinping Liu, Yuhui Jiang, Yongbo Tan, Zheng Shi, Yang Zhao and Junjian Liu
Remote Sens. 2025, 17(21), 3574; https://doi.org/10.3390/rs17213574 - 29 Oct 2025
Abstract
Based on five years of data (2017–2021) from the China National Lightning Detection Network (CNLDN), this study compares and analyzes the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activities in the Hengduan Mountain region and its surroundings. It explores the relationship
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Based on five years of data (2017–2021) from the China National Lightning Detection Network (CNLDN), this study compares and analyzes the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activities in the Hengduan Mountain region and its surroundings. It explores the relationship between CG lightning occurrences and altitude, topography, and various meteorological elements. Our findings reveal a stark east–west divide: high lightning density in the Sichuan Basin and the central Yungui Plateau contrasts sharply with lower densities over the eastern Tibetan Plateau and Hengduan Mountains. This geographical dichotomy extends to the diurnal cycle, where positive cloud-to-ground (PCG) lightning activities are more prevalent in the western part of the study area, while significant nocturnal activity defines the eastern basin and plateau. The study also finds that the relationship between CG lightning activities in the four sub-regions and 2 m temperature, precipitation, convective available potential energy, and Bowen ratio (the ratio of sensible heat flux to latent heat flux) exhibits similarities. Furthermore, we show that the relationship between lightning frequency and altitude is highly region-specific, with each area displaying a unique signature reflecting its underlying topography: a normal distribution over the eastern Tibetan Plateau, a bimodal pattern in the Hengduan Mountains, a sharp low-altitude peak in the Sichuan Basin, and a complex trimodal structure on the Yungui Plateau. These distinct regional patterns highlight the intricate interplay between large-scale circulation, complex terrain, and local meteorology in modulating lightning activity.
Full article
(This article belongs to the Special Issue Estimating Atmospheric Aerosols and Cloud Physics with Optical and Multispectral Sensors)
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Open AccessArticle
Coseismic and Postseismic Deformations of the 2023 Turkey Earthquake Doublet
by
Chaoya Liu, Hongru Li, Huili Zhan, Shaojun Wang and Ling Bai
Remote Sens. 2025, 17(21), 3573; https://doi.org/10.3390/rs17213573 - 29 Oct 2025
Abstract
On 6 February 2023, an earthquake doublet of Mw 7.8 and Mw 7.5 occurred in southeastern Turkey and caused surface ruptures over 350 km for the eastern Anatolian fault (EAF) and 150 km for the Surgu fault (SF), respectively. Over 3700 Mw >
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On 6 February 2023, an earthquake doublet of Mw 7.8 and Mw 7.5 occurred in southeastern Turkey and caused surface ruptures over 350 km for the eastern Anatolian fault (EAF) and 150 km for the Surgu fault (SF), respectively. Over 3700 Mw > 3.0 aftershocks occurred within 5 months following the earthquake doublet, indicating that postseismic stress adjustment is evident. Here, we utilize InSAR technology to investigate the earthquake doublet in terms of its coseismic and postseismic deformations and to estimate the changes in Coulomb stress. We found that the postseismic surface deformation is consistent with the coseismic rupture, characterized by left-lateral strike-slip movement. The coseismic deformations (>5 m) are concentrated in the central-eastern (Pazarcik and Erkenek) segments in the EAF and the central (Cardak) segment in the SF. Notably, the maximum coseismic slip (up to 10 m) and the largest postseismic slip (∼0.5 m) both occurred on the Cardak segment. Postseismic deformations (>0.05 m) are concentrated in the northeastern Erkenek segment and southwestern Amanos segment of the EAF, as well as the eastern Dogansehir segment of the SF. Compared with the coseismic deformation, the postseismic slip compensated for the insufficient deeper slip of the southwestern Amanos segment of the EAF and the central Cardak segment of the SF. Additionally, the postseismic slip extended the rupture area to both the northeast of the Dogansehir segment along the SF and the epicentral area of the 2020 Mw 6.7 earthquake along the EAF. The postseismic afterslip largely reduced the potential seismic hazard of the seismic gap between the eastern end of the coseismic rupture of the 2023 Mw 7.8 earthquake and the epicentral area of the 2020 Mw 6.7 earthquake, as well as the southwestern Amanos segment of the EAF and the eastern Dogansehir segment of the SF.
Full article
(This article belongs to the Special Issue Early Warning Systems and Real-Time Monitoring for Geohazards by Remote Sensing Techniques)
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Open AccessArticle
MMA-Net: A Semantic Segmentation Network for High-Resolution Remote Sensing Images Based on Multimodal Fusion and Multi-Scale Multi-Attention Mechanisms
by
Xuanxuan Huang, Xuejie Zhang, Longbao Wang, Dandan Yuan, Shufang Xu, Fengguang Zhou and Zhijun Zhou
Remote Sens. 2025, 17(21), 3572; https://doi.org/10.3390/rs17213572 - 28 Oct 2025
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Semantic segmentation of high-resolution remote sensing images is of great application value in fields like natural disaster monitoring. Current multimodal semantic segmentation methods have improved the model’s ability to recognize different ground objects and complex scenes by integrating multi-source remote sensing data. However,
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Semantic segmentation of high-resolution remote sensing images is of great application value in fields like natural disaster monitoring. Current multimodal semantic segmentation methods have improved the model’s ability to recognize different ground objects and complex scenes by integrating multi-source remote sensing data. However, these methods still face challenges such as blurred boundary segmentation and insufficient perception of multi-scale ground objects when achieving high-precision classification. To address these issues, this paper proposes MMA-Net, a semantic segmentation network enhanced by two key modules: cross-layer multimodal fusion module and multi-scale multi-attention module. These modules effectively improve the model’s ability to capture detailed features and model multi-scale ground objects, thereby enhancing boundary segmentation accuracy, detail feature preservation, and consistency in multi-scale object segmentation. Specifically, the cross-layer multimodal fusion module adopts a staged fusion strategy to integrate detailed information and multimodal features, realizing detail preservation and modal synergy enhancement. The multi-scale multi-attention module combines cross-attention and self-attention to leverage long-range dependencies and inter-modal complementary relationships, strengthening the model’s feature representation for multi-scale ground objects. Experimental results show that MMA-Net outperforms state-of-the-art methods on the Potsdam and Vaihingen datasets. Its mIoU reaches 88.74% and 84.92% on the two datasets, respectively. Ablation experiments further verify that each proposed module contributes to the final performance.
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Open AccessArticle
PLISA: An Optical–SAR Remote Sensing Image Registration Method Based on Pseudo-Label Learning and Interactive Spatial Attention
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Yixuan Zhang, Ruiqi Liu, Zeyu Zhang, Limin Shi, Lubin Weng and Lei Hu
Remote Sens. 2025, 17(21), 3571; https://doi.org/10.3390/rs17213571 - 28 Oct 2025
Abstract
Multimodal remote sensing image registration faces severe challenges due to geometric and radiometric differences, particularly between optical and synthetic aperture radar (SAR) images. These inherent disparities make extracting highly repeatable cross-modal feature points difficult. Current methods typically rely on image intensity extreme responses
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Multimodal remote sensing image registration faces severe challenges due to geometric and radiometric differences, particularly between optical and synthetic aperture radar (SAR) images. These inherent disparities make extracting highly repeatable cross-modal feature points difficult. Current methods typically rely on image intensity extreme responses or network regression without keypoint supervision for feature point detection. Moreover, they not only lack explicit keypoint annotations as supervision signals but also fail to establish a clear and consistent definition of what constitutes a reliable feature point in cross-modal scenarios. To overcome this limitation, we propose PLISA—a novel heterogeneous image registration method. PLISA integrates two core components: an automated pseudo-labeling module (APLM) and a pseudo-twin interaction network (PTIF). The APLM introduces an innovative labeling strategy that explicitly defines keypoints as corner points, thereby generating consistent pseudo-labels for dual-modality images and effectively mitigating the instability caused by the absence of supervised keypoint annotations. These pseudo-labels subsequently train the PTIF, which adopts a pseudo-twin architecture incorporating a cross-modal interactive attention (CIA) module to effectively reconcile cross-modal commonalities and distinctive characteristics. Evaluations on the SEN1-2 dataset and OSdataset demonstrate PLISA’s state-of-the-art cross-modal feature point repeatability while maintaining robust registration accuracy across a range of challenging conditions, including rotations, scale variations, and SAR-specific speckle noise.
Full article
(This article belongs to the Section Remote Sensing Image Processing)
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Open AccessArticle
Glacier Extraction from Cloudy Satellite Images Using a Multi-Task Generative Adversarial Network Leveraging Transformer-Based Backbones
by
Yuran Cui, Kun Jia, Haishuo Wei, Guofeng Tao, Fengcheng Ji, Jie Li, Shijiao Qiao, Linlin Zhao, Zihang Jiang, Xinyi Gao, Linyan Gan and Qiao Wang
Remote Sens. 2025, 17(21), 3570; https://doi.org/10.3390/rs17213570 - 28 Oct 2025
Abstract
Accurate delineation of glacier extent is crucial for monitoring glacier degradation in the context of global warming. Satellite remote sensing with high spatial and temporal resolution offers an effective approach for large-scale glacier mapping. However, persistent cloud cover limits its application on the
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Accurate delineation of glacier extent is crucial for monitoring glacier degradation in the context of global warming. Satellite remote sensing with high spatial and temporal resolution offers an effective approach for large-scale glacier mapping. However, persistent cloud cover limits its application on the Tibetan Plateau, leading to substantial omissions in glacier identification. Therefore, this study proposed a novel sub-cloudy glacier extraction model (SCGEM) designed to extract glacier boundaries from cloud-affected satellite images. First, the cloud-insensitive characteristics of topo-graphic (Topo.), synthetic aperture radar (SAR), and temporal (Tempo.) features were investigated for extracting glaciers under cloud conditions. Then, a transformer-based generative adversarial network (GAN) was proposed, which incorporates an image reconstruction and an adversarial branch to improve glacier extraction accuracy under cloud cover. Experimental results demonstrated that the proposed SCGEM achieved significant improvements with an IoU of 0.7700 and an F1 score of 0.8700. The Topo., SAR, and Tempo. features all contributed to glacier extraction in cloudy areas, with the Tempo. features contributing the most. Ablation studies further confirmed that both the adversarial training mechanism and the multi-task architecture contributed notably to improving the extraction accuracy. The proposed architecture serves both to data clean and enhance the extraction of glacier texture features.
Full article
(This article belongs to the Special Issue Earth Observation of Glacier and Snow Cover Mapping in Cold Regions)
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Open AccessArticle
Radiometric Cross-Calibration and Performance Analysis of HJ-2A/2B 16m-MSI Using Landsat-8/9 OLI with Spectral-Angle Difference Correction
by
Jian Zeng, Hang Zhao, Yongfang Su, Qiongqiong Lan, Qijin Han, Xuewen Zhang, Xinmeng Wang, Zhaopeng Xu, Zhiheng Hu, Xiaozheng Du and Bopeng Yang
Remote Sens. 2025, 17(21), 3569; https://doi.org/10.3390/rs17213569 - 28 Oct 2025
Abstract
The Huanjing-2A/2B (HJ-2A/2B) satellites are China’s next-generation environmental monitoring satellites, equipped with four visible light wide-swath charge-coupled device (CCD) sensors. These sensors enable the acquisition of 16-m multispectral imagery (16m-MSI) with a swath width of 800 km through field-of-view stitching. However, traditional vicarious
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The Huanjing-2A/2B (HJ-2A/2B) satellites are China’s next-generation environmental monitoring satellites, equipped with four visible light wide-swath charge-coupled device (CCD) sensors. These sensors enable the acquisition of 16-m multispectral imagery (16m-MSI) with a swath width of 800 km through field-of-view stitching. However, traditional vicarious calibration techniques are limited by their calibration frequency, making them insufficient for continuous monitoring requirements. To address this challenge, the present study proposes a spectral-angle difference correction-based cross-calibration approach, using the Landsat 8/9 Operational Land Imager (OLI) as the reference sensor to calibrate the HJ-2A/2B CCD sensors. This method improves both radiometric accuracy and temporal frequency. The study utilizes cloud-free image pairs of HJ-2A/2B CCD and Landsat 8/9 OLI, acquired simultaneously at the Dunhuang and Golmud calibration sites between 2021 and 2024, in combination with atmospheric parameters from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset and historical ground-measured spectral reflectance data for cross-calibration. The methodology includes spatial matching and resampling of the image pairs, along with the identification of radiometrically stable homogeneous regions. To account for sensor viewing geometry differences, an observation-angle linear correction model is introduced. Spectral band adjustment factors (SBAFs) are also applied to correct for discrepancies in spectral response functions (SRFs) across sensors. Experimental results demonstrate that the cross-calibration coefficients differ by less than 10% compared to vicarious calibration results from the China Centre for Resources Satellite Data and Application (CRESDA). Additionally, using Sentinel-2 MSI as the reference sensor, the cross-calibration coefficients were independently validated through cross-validation. The results indicate that the radiometrically corrected HJ-2A/2B 16m-MSI CCD data, based on these coefficients, exhibit improved radiometric consistency with Sentinel-2 MSI observations. Further analysis shows that the cross-calibration method significantly enhances radiometric consistency across the HJ-2A/2B 16m-MSI CCD sensors, with radiometric response differences between CCD1 and CCD4 maintained below 3%. Error analysis quantifies the impact of atmospheric parameters and surface reflectance on calibration accuracy, with total uncertainty calculated. The proposed spectral-angle correction-based cross-calibration method not only improves calibration accuracy but also offers reliable technical support for long-term radiometric performance monitoring of the HJ-2A/2B 16m-MSI CCD sensors.
Full article
(This article belongs to the Special Issue Remote Sensing Satellites Calibration and Validation: 2nd Edition)
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Open AccessArticle
Windthrow Mapping with Sentinel-2 and PlanetScope in Triglav National Park: A Regional Case Study
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Matej Zupan, Krištof Oštir and Ana Potočnik Buhvald
Remote Sens. 2025, 17(21), 3568; https://doi.org/10.3390/rs17213568 - 28 Oct 2025
Abstract
Extreme weather increasingly damages forest ecosystems, and affected areas are often remote or inaccessible, limiting field surveys. In such contexts, remote sensing can complement damage assessment. This study presents a regional case study evaluating established multi-temporal optical change detection for windthrow mapping in
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Extreme weather increasingly damages forest ecosystems, and affected areas are often remote or inaccessible, limiting field surveys. In such contexts, remote sensing can complement damage assessment. This study presents a regional case study evaluating established multi-temporal optical change detection for windthrow mapping in Triglav National Park (Slovenia) using Sentinel-2 and PlanetScope imagery. Bitemporal index differencing and fixed thresholds were applied, with accuracy quantified via a block bootstrap to account for spatial autocorrelation. Within-sample overall accuracy was 69.2% (95% CI: 67.4–71.2%) for Sentinel-2 and 72.9% (95% CI: 71.2–74.6%) for PlanetScope. Detection was strongly size-dependent: gaps greater than 0.5 ha were consistently detected, whereas gaps smaller than 0.1 ha were frequently omitted, particularly with Sentinel-2. Linking satellite-derived change maps with forest stand data enabled parcel-level estimates of damaged timber volume; this linkage was examined on a small, non-probability set of parcels and is therefore preliminary. We position the work strictly as a case study documenting within-sample performance in alpine terrain. Broader generalisation will require probability-based validation across additional events and forest types, and wider access to parcel-level official records.
Full article
(This article belongs to the Special Issue Forest Disturbance Monitoring with Optical Satellite Imagery)
Open AccessArticle
Deformation Pattern Classification of Sea-Crossing Bridge InSAR Time Series Based on a Transfer Learning Framework
by
Lichen Ren, Chengyin Liu and Jinping Ou
Remote Sens. 2025, 17(21), 3567; https://doi.org/10.3390/rs17213567 - 28 Oct 2025
Abstract
Interferometric Synthetic Aperture Radar (InSAR) provides unique advantages for sea-crossing bridge monitoring through continuous, large-scale deformation detection. Dividing monitoring data into specific deformation patterns helps establish the connection between bridge deformation and its underlying mechanisms. However, the classification of complex and nonlinear bridge
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Interferometric Synthetic Aperture Radar (InSAR) provides unique advantages for sea-crossing bridge monitoring through continuous, large-scale deformation detection. Dividing monitoring data into specific deformation patterns helps establish the connection between bridge deformation and its underlying mechanisms. However, the classification of complex and nonlinear bridge deformations often requires extensive manual labeling work. To achieve automatic classification of deformation patterns with minimal labeled data, this study introduces a transfer learning approach and proposes an InSAR-based method for deformation pattern recognition of cross-sea bridges. At first, deformation time series of the study area are acquired by PS-InSAR, with GNSS results confirming less than 10% error. Then, six types of deformation are identified, including stable, linear, step, piecewise linear, power law, and temperature-related types. Large amounts of simulated data with labels are generated based on these six types. Subsequently, four models—TCN, Transformer, TFT, and ROCKET—are trained using synthetic data and finely adjusted using few real data. Finally, the final classification results are weighted by the classification results of multiple models. Even though confidence and global consistency of each single model are also calculated, the final result is the combined result of a set of multi-type confidences. ROCKET achieved the highest accuracy on simulation data (96.27%) in these four representative models, while ensemble weighting improved robustness on real data. The methodology addresses supervised learning’s labeled data requirements through synthetic data generation and ensemble classification, producing probabilistic outputs that preserve uncertainty information rather than deterministic labels. The framework enables automatic classification of sea-crossing bridge deformation patterns with minimal labeled data, identifying patterns with distinct dominant factors and providing probabilistic information for engineering decision making.
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Open AccessArticle
Spatially Constrained Discontinuity Trace Extraction from 3D Point Clouds by Intersecting Boundaries Segmented
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Jingsong Sima, Qiang Xu, Xiujun Dong, Haoliang Li, Qiulin He and Bo Deng
Remote Sens. 2025, 17(21), 3566; https://doi.org/10.3390/rs17213566 - 28 Oct 2025
Abstract
Discontinuity trace provides critical geological data for engineering design and construction optimization. However, current extraction methods relying on discontinuity intersection fitting are highly sensitive to the segmentation accuracy of individual discontinuity, while trace segment connectivity remains suboptimal. To address these challenges, we propose
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Discontinuity trace provides critical geological data for engineering design and construction optimization. However, current extraction methods relying on discontinuity intersection fitting are highly sensitive to the segmentation accuracy of individual discontinuity, while trace segment connectivity remains suboptimal. To address these challenges, we propose an ARCG (Adaptive Region Contour Growing) method using 3D point clouds. By dynamically adjusting parameter thresholds, our approach simultaneously extracts both discontinuities and their boundaries. We then evaluate the fitting performance of different discontinuity models using area ratios, identifying the parallelogram as the most suitable representation. The method then detects intersection lines between paired discontinuities through spatial intersection analysis, with dynamic partitioning preserving original geometric properties. Finally, a bidirectional weighted graph-based growth algorithm connects intersection lines belonging to the same discontinuity, generating the final trace results. The proposed method was validated using slope data from two case studies. Results demonstrate that, compared to existing methods and point cloud processing software, our approach achieves robust extraction of complex traces while maintaining high connectivity. Moreover, it improves computational efficiency by 48.8% without compromising trace accuracy. Thus, this method offers a novel solution for the digital characterization of rock mass discontinuity parameters.
Full article
(This article belongs to the Special Issue Geological Hazard Monitoring, Identify, Predict, and Risk Assessment Using Geographic Information Science and Remote Sensing)
Open AccessArticle
Developing Computer Vision-Based Digital Twin for Vegetation Management near Power Distribution Networks
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Fardin Bahreini, Mazdak Nik-Bakht and Amin Hammad
Remote Sens. 2025, 17(21), 3565; https://doi.org/10.3390/rs17213565 - 28 Oct 2025
Abstract
The maintenance of power distribution lines is critically challenged by vegetation encroachment, posing significant risks to the reliability and safety of power utilities. Traditional manual inspection methods are resource-intensive and lack the precision required for effective and proactive maintenance. This paper presents an
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The maintenance of power distribution lines is critically challenged by vegetation encroachment, posing significant risks to the reliability and safety of power utilities. Traditional manual inspection methods are resource-intensive and lack the precision required for effective and proactive maintenance. This paper presents an automated, accurate, and efficient approach to vegetation management near power lines by leveraging advancements in LiDAR as a remote sensing technology and deep learning algorithms. The RandLA-Net model is employed for semantic segmentation of large-scale point clouds to accurately identify vegetation, poles, and power lines. A comprehensive sensitivity analysis is conducted to optimize the model’s hyperparameters, enhancing segmentation accuracy. Post-processing techniques, including clustering and rule-based thresholding, are applied to refine the semantic segmentation results. Proximity detection is applied using spatial queries based on a KDTree structure to assess potential risks of vegetation near power lines. Furthermore, a digital twin of the power distribution network and surrounding trees is developed by integrating 3D object registration and surface generation, enriching it with semantic attributes and incorporating it into City Information Modeling (CIM) systems. This framework demonstrates the potential of remote sensing data integration for efficient environmental monitoring in urban infrastructure. The results of the case study on the Toronto-3D dataset demonstrate the computational efficiency and accuracy of the proposed method, presenting a promising solution for power utilities in proactive vegetation management and infrastructure planning. The optimized full 9-class model achieved an overall accuracy of 96.90% and IoU scores of 97.05% for vegetation, 88.09% for power lines, and 82.33% for poles, supporting comprehensive digital twin creation. An auxiliary 4-class model further improved targeted performance, with IoUs of 99.55% for vegetation, 88.79% for poles, and 87.18% for power lines.
Full article
(This article belongs to the Section Environmental Remote Sensing)
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Open AccessTechnical Note
Ionospheric TEC Forecasting with ED-ConvLSTM-Res Integrating Multi-Channel Features
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Jiayue Yang, Wengeng Huang, Lei Zhang, Heng Xu, Hua Shen, Xin Wang and Ming Li
Remote Sens. 2025, 17(21), 3564; https://doi.org/10.3390/rs17213564 - 28 Oct 2025
Abstract
This paper proposes a convolutional Long Short-Term Memory (ConvLSTM) network integrated with multi-channel features dedicated to ionospheric total electron content (TEC) forecasting. To improve generalization, solar, and geomagnetic activity indices are added as auxiliary channel inputs. The model is built upon an Encoder–Decoder
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This paper proposes a convolutional Long Short-Term Memory (ConvLSTM) network integrated with multi-channel features dedicated to ionospheric total electron content (TEC) forecasting. To improve generalization, solar, and geomagnetic activity indices are added as auxiliary channel inputs. The model is built upon an Encoder–Decoder (ED) architecture enhanced with residual connections and convolutional channel projection, which collectively improve the synergy among its core components. Based on this framework, we developed ED-ConvLSTM-Res, a multi-channel feature-based global ionospheric TEC prediction model. Comprehensive accuracy evaluation and comparative tests were carried out using datasets from the solar minimum year of 2019 and the current solar maximum year of 2024. The results indicate that the proposed model consistently achieves strong predictive performance compared with other models, along with a significantly enhanced feature representation capability. Specifically, the Root Mean Square Errors (RMSE) of the ED-ConvLSTM-Res model’s predictions in 2019 and 2024 are 1.28 TECU and 5.28 TECU, respectively, while the corresponding Mean Absolute Errors (MAE) are 0.87 and 3.87, and the coefficients of determination (R2) are 0.95 and 0.94. In the current high solar activity year 2024, the proposed model achieves error reductions of 13.6% in MAE and 11.6% in RMSE compared with the Center for Orbit Determination in Europe (CODE)’s one-day-ahead forecast product, c1pg. These results confirm that the proposed model not only outperforms the ConvLSTM model without additional indices and c1pg but also exhibits strong generalization capability, maintaining stable performance with low errors under both high and low solar activity conditions.
Full article
(This article belongs to the Special Issue Ionosphere and Space Weather Based on Satellite Remote Sensing Observation)
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Open AccessArticle
Phenology-Guided Wheat and Corn Identification in Xinjiang: An Improved U-Net Semantic Segmentation Model Using PCA and CBAM-ASPP
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Yang Wei, Xian Guo, Yiling Lu, Hongjiang Hu, Fei Wang, Rongrong Li and Xiaojing Li
Remote Sens. 2025, 17(21), 3563; https://doi.org/10.3390/rs17213563 - 28 Oct 2025
Abstract
Wheat and corn are two major food crops in Xinjiang. However, the spectral similarity between these crop types and the complexity of their spatial distribution has posed significant challenges to accurate crop identification. To this end, the study aimed to improve the accuracy
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Wheat and corn are two major food crops in Xinjiang. However, the spectral similarity between these crop types and the complexity of their spatial distribution has posed significant challenges to accurate crop identification. To this end, the study aimed to improve the accuracy of crop distribution identification in complex environments in three ways. First, by analysing the kNDVI and EVI time series, the optimal identification window was determined to be days 156–176—a period when wheat is in the grain-filling to milk-ripening phase and maize is in the jointing to tillering phase—during which, the strongest spectral differences between the two crops occurs. Second, principal component analysis (PCA) was applied to Sentinel-2 data. The top three principal components were extracted to construct the input dataset, effectively integrating visible and near-infrared band information. This approach suppressed redundancy and noise while replacing traditional RGB datasets. Finally, the Convolutional Block Attention Module (CBAM) was integrated into the U-Net model to enhance feature focusing on key crop areas. An improved Atrous Spatial Pyramid Pooling (ASPP) module based on deep separable convolutions was adopted to reduce the computational load while boosting multi-scale context awareness. The experimental results showed the following: (1) Wheat and corn exhibit obvious phenological differences between the 156th and 176th days of the year, which can be used as the optimal time window for identifying their spatial distributions. (2) The method proposed by this research had the best performance, with its mIoU, mPA, F1-score, and overall accuracy (OA) reaching 83.03%, 91.34%, 90.73%, and 90.91%, respectively. Compared to DeeplabV3+, PSPnet, HRnet, Segformer, and U-Net, the OA improved by 5.97%, 4.55%, 2.03%, 8.99%, and 1.5%, respectively. The recognition accuracy of the PCA dataset improved by approximately 2% compared to the RGB dataset. (3) This strategy still had high accuracy when predicting wheat and corn yields in Qitai County, Xinjiang, and had a certain degree of generalisability. In summary, the improved strategy proposed in this study holds considerable application potential for identifying the spatial distribution of wheat and corn in arid regions.
Full article
(This article belongs to the Special Issue Advancements in Remote Sensing for Sustainable Agriculture (Second Edition))
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Open AccessArticle
Evaluation of CMORPH V1.0, IMERG V07A and MSWEP V2.8 Satellite Precipitation Products over Peninsular Spain and the Balearic Islands
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Alejandro García-Ten, Raquel Niclòs, Enric Valor, Vicente Caselles, María José Estrela, Juan Javier Miró, Yolanda Luna and Fernando Belda
Remote Sens. 2025, 17(21), 3562; https://doi.org/10.3390/rs17213562 - 28 Oct 2025
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Climate change is altering the global distribution of precipitation, especially in Mediterranean areas with heterogeneous climates. The spatiotemporal variability of precipitation complicates its monitoring. Satellite-derived precipitation products (SPPs) usually offer global continuous coverage at daily scale; however, their coarse spatial resolution and indirect
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Climate change is altering the global distribution of precipitation, especially in Mediterranean areas with heterogeneous climates. The spatiotemporal variability of precipitation complicates its monitoring. Satellite-derived precipitation products (SPPs) usually offer global continuous coverage at daily scale; however, their coarse spatial resolution and indirect measurement introduce relevant bias. We analysed the suitability of CMORPH V1.0, IMERG V07A and MSWEP V2.8 across Peninsular Spain and Balearic Islands using Agencia Estatal de Meteorología (AEMET) gauge data as reference, and investigated performance dependence on seasonality, precipitation intensity, altitude and orography. CMORPH is not recommended and MSWEP is preferable over IMERG, although MSWEP performs worse for lighter intensities and summer. IMERG and MSWEP show mainly Correlation Coefficient (CC) and Probability of Detection (POD) , and False Alarm Ratio (FAR) (vice versa for CMORPH). All products overestimate with lower frequency but greater magnitude (at least twice the reference value). Monthly performance is better than daily, but with increased underestimation. Performance for spring and autumn is similar to overall performance, while summer presents the most divergent patterns. For heavier intensities, all products improve their correlation with reference data and their detection capabilities, but also increase their underestimation rate and magnitude. Worst performance occurs in those regions with simultaneously higher orographical complexity, annual precipitation and altitude. These SPPs should be used with caution, and we recommend first analysing their performance on the specific application of interest.
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Open AccessArticle
Multi-Method and Multi-Depth Geophysical Data Integration for Archaeological Investigations: First Results from the Greek City of Gela (Sicily, Italy)
by
Luca Piroddi, Emanuele Colica, Sebastiano D’Amico, Luciano Galone, Caterina Ingoglia, Grazia Spagnolo, Antonella Santostefano, Lorenzo Zurla, Antonio Crupi, Stefania Lanza and Giovanni Randazzo
Remote Sens. 2025, 17(21), 3561; https://doi.org/10.3390/rs17213561 - 28 Oct 2025
Abstract
Geophysical techniques are a core toolkit of modern archeology, thanks to their effectiveness in reconstructing important pieces of evidence for buried ruins, which are relics of the past usage of an inspected site. Some methodological approaches and advancements are proposed for investigating the
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Geophysical techniques are a core toolkit of modern archeology, thanks to their effectiveness in reconstructing important pieces of evidence for buried ruins, which are relics of the past usage of an inspected site. Some methodological approaches and advancements are proposed for investigating the site of Gela, which was one of the most important western Greek colonies, founded in 689–688 BC on the southern coast of Sicily, Italy. The ancient settlement was developed on a hill, mostly flat on the top, and over its sides. The archeological evidence discovered so far in the acropolis of the city can be attributed to two main architectural typologies: urban blocks and archaic temples. Based on these targets, a geophysical protocol has been tested, utilizing passive seismic, electrical resistivity tomography (ERT), and ground-penetrating radar (GPR) methods. Where the lowest physical contrast was expected among possible archeological remains and burying soil (close to the urban blocks area), the three geophysical techniques have been jointly applied, while an innovative support-to-interpretation approach for GPR datasets is proposed and developed over both kinds of archeological targets. Our experimental outcomes underline the effectiveness (and possible weaknesses) of the two geophysical investigation strategies against various targets producing different signal-to-noise responses, thanks to the synergistic contributions from multi-method and multi-depth approaches. The integrated use of GPR, ERT, and passive seismic methods allowed the reconstruction of complementary information, with each method compensating for the limitations of the others. This combined approach provided a more robust and comprehensive understanding of the subsurface features than would have been achieved through the application of any single technique.
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(This article belongs to the Special Issue Ground Penetrating Radar (GPR) Applications in Earth, Moon and Planetary Exploration (Second Edition))
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Open AccessArticle
Optimizing Agricultural Drought Monitoring in East Africa: Evaluating Integrated Soil Moisture and Vegetation Health Index (SM-VHI)
by
Albert Poponi Maniraho, Jie Bai, Lanhai Li, Habimana Fabien, Patient Mindje Kayumba, Ogbue Chukwuka Prince, Muhirwa Fabien and Lingjie Bu
Remote Sens. 2025, 17(21), 3560; https://doi.org/10.3390/rs17213560 - 28 Oct 2025
Abstract
This study presents a comprehensive analysis of the integrated Soil Moisture–Vegetation Health Index (SM-VHI) as a robust tool for drought detection and agricultural monitoring across East Africa using data from 2000 to 2020. A sensitivity analysis within the SM-VHI algorithm identified an optimal
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This study presents a comprehensive analysis of the integrated Soil Moisture–Vegetation Health Index (SM-VHI) as a robust tool for drought detection and agricultural monitoring across East Africa using data from 2000 to 2020. A sensitivity analysis within the SM-VHI algorithm identified an optimal parameter weighting (α = 0.5), which improved detection accuracy, achieving a Critical Success Index (CSI) of 0.78. The SM-VHI exhibited strong correlations with independent drought indicators, including the Standardized Soil Moisture Index (SSMI), Vegetation Health Index (VHI), and one-month Standardized Precipitation-Evapotranspiration Index (SPEI-1), confirming its reliability in capturing agricultural drought dynamics and vegetation stress responses across diverse climatic conditions. Through spatial and temporal trend analyses, we identified patterns of drought severity and recovery, which emphasized the importance of tailored management strategies. Furthermore, the analysis incorporated historical maize yield data to evaluate the effectiveness of SM-VHI in representing agricultural drought conditions. A notable positive correlation (R = 0.45–0.72) was identified between SM-VHI anomalies and detrended maize yield throughout East Africa, suggesting that enhanced vegetation and soil moisture conditions are strongly linked to increased crop productivity. This validation demonstrates the capability of SM-VHI to effectively capture drought-induced yield variability. The findings confirm the effectiveness of SM-VHI as a reliable remote-sensing tool for monitoring drought conditions and have strong potential to inform agricultural practices and policy decisions aimed at enhancing food security in a changing climate.
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(This article belongs to the Topic Remote Sensing Research and Application of Agricultural Drought and Water Management)
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Open AccessTechnical Note
PolarFormer: A Registration-Free Fusion Transformer with Polar Coordinate Position Encoding for Multi-View SAR Target Recognition
by
Xiang Yu, Ying Qian, Guodong Jin, Zhe Geng and Daiyin Zhu
Remote Sens. 2025, 17(21), 3559; https://doi.org/10.3390/rs17213559 - 28 Oct 2025
Abstract
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Multi-view Synthetic Aperture Radar (SAR) provides rich information for target recognition. However, fusing features from unaligned multi-view images presents challenges for existing methods. Conventional early fusion methods often rely on image registration, a process that is computationally intensive and can introduce feature distortions.
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Multi-view Synthetic Aperture Radar (SAR) provides rich information for target recognition. However, fusing features from unaligned multi-view images presents challenges for existing methods. Conventional early fusion methods often rely on image registration, a process that is computationally intensive and can introduce feature distortions. More recent registration-free approaches based on the Transformer architecture are constrained by standard position encodings, which were not designed to represent the rotational relationships among multi-view SAR data and thus can cause spatial ambiguity. To address this specific limitation of position encodings, we propose a registration-free fusion framework based on a spatially aware Transformer. The framework includes two key components: (1) a multi-view polar coordinate position encoding that models the geometric relationships of patches both within and across views in a unified coordinate system; and (2) a spatially aware self-attention mechanism that injects this geometric information as a learnable inductive bias. Experiments were conducted on our self-developed FAST-Vehicle dataset, which provides full 360° azimuthal coverage. The results show that our method outperforms both registration-based strategies and Transformer baselines that use conventional position encodings. This work indicates that for multi-view SAR fusion, explicitly modeling the underlying geometric relationships with a suitable position encoding is an effective alternative to physical image registration or the use of generic, single-image position encodings.
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Open AccessTechnical Note
In-Flight Radiometric Calibration of Gas Absorption Bands for the Gaofen-5 (02) DPC Using Sunglint
by
Sifeng Zhu, Liguo Zhang, Yanqing Xie, Lili Qie, Zhengqiang Li, Miaomiao Zhang and Xiaochu Wang
Remote Sens. 2025, 17(21), 3558; https://doi.org/10.3390/rs17213558 - 28 Oct 2025
Abstract
The Directional Polarimetric Camera (DPC) onboard the Gaofen-5 (02) satellite includes gas absorption bands that are crucial for the quantitative retrieval of clouds, atmospheric aerosols, and surface parameters. However, in-flight radiometric calibration of these bands remains challenging due to strong absorption features and
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The Directional Polarimetric Camera (DPC) onboard the Gaofen-5 (02) satellite includes gas absorption bands that are crucial for the quantitative retrieval of clouds, atmospheric aerosols, and surface parameters. However, in-flight radiometric calibration of these bands remains challenging due to strong absorption features and the lack of onboard calibration devices. In this study, a calibration method that exploits functional relationships between the reflectance ratios of gas absorption and adjacent reference bands and key surface–atmosphere parameters over sunglint were presented. Radiative transfer simulations were combined with polynomial fitting to establish these relationships, and prior knowledge of surface pressure and water vapor column concentration was incorporated to achieve high-precision calibration. Results show that the calibration uncertainty of the oxygen absorption band is mainly driven by surface pressure, with a total uncertainty of 3.01%. For the water vapor absorption band, uncertainties are primarily associated with water vapor column concentration and surface reflectance, yielding total uncertainties of 3.45%. Validation demonstrates the robustness of the proposed method: (1) cross-calibration using desert samples confirms the stability of the results, and (2) the retrieved surface pressure agrees with the DEM-derived estimates, and the retrieved total column water vapor agrees with the MODIS products, confirming the calibration. Overall, the method provides reliable in-flight calibration of DPC gas absorption bands on Gaofen-5 (02) and can be adapted to similar sensors with comparable spectral configurations.
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(This article belongs to the Special Issue Advances in Calibration, Validation, and Quality Assurance for Optical Remote Sensors)
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Open AccessArticle
Comparative Accuracy Assessment of Unmanned and Terrestrial Laser Scanning Systems for Tree Attribute Estimation in an Urban Mediterranean Forest
by
Ante Šiljeg, Katarina Kolar, Ivan Marić, Fran Domazetović and Ivan Balenović
Remote Sens. 2025, 17(21), 3557; https://doi.org/10.3390/rs17213557 - 28 Oct 2025
Abstract
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Urban mediterranean forests are key components of urban ecosystems. Accurate, high-resolution data on forest structural attributes are essential for effective management. This study evaluates the efficiency of unmanned laser scanning systems (ULS) and terrestrial LiDAR (TLS) in deriving key tree attributes, diameter at
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Urban mediterranean forests are key components of urban ecosystems. Accurate, high-resolution data on forest structural attributes are essential for effective management. This study evaluates the efficiency of unmanned laser scanning systems (ULS) and terrestrial LiDAR (TLS) in deriving key tree attributes, diameter at breast height (DBH) and tree height, within a small urban park in Zadar, Croatia. Accuracy assessment of the ULS and TLS-derived DBH was conducted based on traditional ground-based measurement (TGBM) data. For ULS, an automatic Spatix workflow was applied that classified points into a Tree class, segmented trees using trunk-based logic, and estimated DBH by fitting a circle to a 1.3 m slice; tree height was computed from the ground-normalized cloud with the Output Tree Cells tool. A semi-automatic CloudCompare/ArcMap workflow used CSF ground filtering, Connected Components segmentation, extraction of a 10 cm slice, manual trunk vectorization, and DBH calculation via Minimum Bounding Geometry. TLS scans, processed in FARO SCENE, were then analyzed in Spatix using the same automatic trunk-fitting procedure to derive DBH and height. Accuracy for DBH was evaluated against TGBM; comparative performance was summarized with standard error metrics, while ULS and TLS tree heights were compared using Concordance Correlation Coefficient (CCC) and Bland–Altman statistics. Results indicate that the semi-automatic approach outperformed the automatic approach in deriving DBH. TLS-derived DBH values demonstrated higher consistency and agreement with TGBM, as evidenced by their strong linear correlation, minimal bias, and narrow residual spread, while ULS exhibited greater variability and systematic deviation. Tree height comparisons between ULS and TLS revealed that ULS consistently produced slightly higher and more uniform measurements. This study highlights limitations in the evaluated techniques and proposes a hybrid approach combining ULS scanning with personal laser scanning (PLS) systems to enhance data accuracy in urban forest assessments.
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Open AccessArticle
High-Precision BDS PPP Positioning Method Based on SSR Correction Prediction
by
Minghui Gao, Jian Cao, Mengyang Liu, Chuang Yang, Siyu Liu, Jinye Peng and Lin Wang
Remote Sens. 2025, 17(21), 3556; https://doi.org/10.3390/rs17213556 - 28 Oct 2025
Abstract
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The interruption of real-time state space representation (SSR) corrections significantly degrades the performance of precise point positioning (PPP). To address this challenge, we propose a novel residual-enhanced iTransformer model specifically designed for BeiDou navigation satellite system (BDS) SSR prediction. Unlike conventional approaches including
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The interruption of real-time state space representation (SSR) corrections significantly degrades the performance of precise point positioning (PPP). To address this challenge, we propose a novel residual-enhanced iTransformer model specifically designed for BeiDou navigation satellite system (BDS) SSR prediction. Unlike conventional approaches including polynomial fitting, harmonic modeling, and autoregressive moving average (ARMA) methods, our framework innovatively integrates residual networks with the iTransformer architecture to effectively capture the complex nonlinear characteristics and non-stationary patterns in satellite clock offsets. The model demonstrates remarkable performance improvements, achieving 72–85% reduction in prediction error compared with traditional ARMA models. Experimental results show that, within 2 h prediction windows, orbit corrections achieve better than 0.1 m (radial), 0.2 m (along-track), and 0.2 m (cross-track) accuracy, while clock corrections maintain sub-0.5 ns precision. Most importantly, during 30 min SSR outages, BDS real-time PPP utilizing our predicted corrections sustains positioning accuracy within 10 cm in all east, north, and up directions, representing over 80% improvement compared with traditional time-differenced carrier phase (TDCP) methods. This work establishes an effective solution for maintaining high-precision positioning services during SSR interruptions.
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