IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献

SADFF-Net: Scale-Aware Detection and Feature Fusion for Multiscale Remote Sensing Object Detection 基于尺度感知的多尺度遥感目标检测与特征融合

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-05 DOI: 10.1109/LGRS.2025.3606521

Runbo Yang;Huiyan Han;Shanyuan Bai;Yaming Cao

{"title":"SADFF-Net: Scale-Aware Detection and Feature Fusion for Multiscale Remote Sensing Object Detection","authors":"Runbo Yang;Huiyan Han;Shanyuan Bai;Yaming Cao","doi":"10.1109/LGRS.2025.3606521","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3606521","url":null,"abstract":"Multiscale object detection in remote sensing imagery poses significant challenges, including substantial variations in object size, diverse orientations, and interference from complex backgrounds. To address these issues, we propose a scale-aware detection and feature fusion network (SADFF-Net), a novel detection framework that incorporates a Multiscale contextual attention fusion (MCAF) module to enhance information exchange between feature layers and suppress irrelevant feature interference. In addition, SADFF-Net employs an adaptive spatial feature fusion (ASFF) module to improve semantic consistency across feature layers by assigning spatial weights at multiple scales. To enhance adaptability to scale variations, the regression head integrates a deformable convolution. In contrast, the classification head utilizes depth-wise separable convolutions to significantly reduce computational complexity without compromising detection accuracy. Extensive experiments on the DOTAv1 and DIOR_R datasets demonstrate that SADFF-Net outperforms current state-of-the-art methods in Multiscale object detection.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic Change Detection of Bitemporal Remote Sensing Images Using Frequency Feature Enhancement 基于频率特征增强的双时相遥感图像语义变化检测

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-04 DOI: 10.1109/LGRS.2025.3605910

Renfang Wang;Kun Yang;Feng Wang;Hong Qiu;Yingying Huang;Xiufeng Liu

引用次数: 0

A Unified Framework for Bridging the Data Gap Between GRACE/GRACE-FO for Both Greenland and Antarctica 弥合格陵兰和南极洲GRACE/GRACE- fo数据差距的统一框架

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-04 DOI: 10.1109/LGRS.2025.3605913

Zhuoya Shi;Zemin Wang;Baojun Zhang;Nicholas E. Barrand;Manman Luo;Shuang Wu;Jiachun An;Hong Geng;Haojian Wu

{"title":"A Unified Framework for Bridging the Data Gap Between GRACE/GRACE-FO for Both Greenland and Antarctica","authors":"Zhuoya Shi;Zemin Wang;Baojun Zhang;Nicholas E. Barrand;Manman Luo;Shuang Wu;Jiachun An;Hong Geng;Haojian Wu","doi":"10.1109/LGRS.2025.3605913","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3605913","url":null,"abstract":"The 11-month data gap between gravity recovery and climate experiment (GRACE) and GRACE Follow-On (GRACE-FO) hinders monitoring long-term ice mass change and its further analysis. While many attempts have been made to bridge water storage gaps, few unified frameworks exist to bridge the ice mass change gaps for both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS). This study combined partial least squares regression (PLSR) and the Sparrow Search Algorithm optimized back propagation (SSA-BP) to fill this gap in GrIS and AIS. During this process, seasonal autoregressive integrated moving average (MA) with exogenous variables (SARIMAX) and multiple linear regression (MLR) were introduced as comparison. PSLR is utilized to select key variables for constructing predictive models. We found SSA-BP outperformed SARIMAX and MLR, with correlation coefficients (CCs) and root mean square error (RMSE) at 0.99 and 39.22 Gt for GrIS, and 0.95 and 189.85 Gt for AIS within the testing period. SSA-BP demonstrated a reasonable mass change trend with less noise than other methods. SSA-BP reconstructed result shows superiority than other researches. Moreover, the reconstructed seasonal signals highlight the importance of filling the gap, showing decreased mass loss for GrIS and continuous mass loss acceleration for AIS post-2016.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

First Implementation of GPD+ Wet Tropospheric Correction on SWOT Side 1 and Side 2 Radiometer Tracks 在SWOT侧1和侧2辐射计轨道上首次实施GPD+湿对流层校正

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-03 DOI: 10.1109/LGRS.2025.3605854

Isabel Cardoso;Clara Lázaro;Telmo Vieira;M. Joana Fernandes

{"title":"First Implementation of GPD+ Wet Tropospheric Correction on SWOT Side 1 and Side 2 Radiometer Tracks","authors":"Isabel Cardoso;Clara Lázaro;Telmo Vieira;M. Joana Fernandes","doi":"10.1109/LGRS.2025.3605854","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3605854","url":null,"abstract":"The Surface Water and Ocean Topography (SWOT) satellite provides high-resolution observations of the ocean surface topography and elevation of inland waters. Measurements from the two onboard Advanced Microwave Radiometers (AMRs) are used to compute the wet tropospheric correction (WTC), accounting for the radar signal delay due to water vapor and cloud liquid water content in the troposphere. This study presents the first implementation of the Global Navigation Satellite System (GNSS)-derived Path Delay Plus (GPD+) algorithm for SWOT to estimate the WTC when AMR observations are absent or invalid. Using the first 15 science-phase cycles between 50°N and 50°S, GPD+ retrieves the WTC for approximately 7% of points per cycle that would otherwise be excluded. Retrieval rates per cycle range from less than 5% of the points in passes mostly over open ocean, where the WTC derived from the radiometers is usually preserved, to up to 15% in passes including coastal zones. These results indicate that GPD+ can recover WTC values otherwise unavailable from SWOT’s radiometers, increasing the availability of valid WTC for SWOT measurements, in particular over coastal regions. Further refinements will focus on improving the accuracy of the WTC along the KaRIn swath and the Poseidon-3C nadir track.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of Optical Multiangle Multispectral Reflectance in Land Cover Classification 光学多角度多光谱反射率在土地覆盖分类中的应用

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-02 DOI: 10.1109/LGRS.2025.3605331

Fan Ye;Xiaoning Zhang;Zhengjie Wang;Yifei Wang;Zhaoyang Peng;Tengying Fu;Ziti Jiao;Yanxuan Wu;Yue Wang

{"title":"Application of Optical Multiangle Multispectral Reflectance in Land Cover Classification","authors":"Fan Ye;Xiaoning Zhang;Zhengjie Wang;Yifei Wang;Zhaoyang Peng;Tengying Fu;Ziti Jiao;Yanxuan Wu;Yue Wang","doi":"10.1109/LGRS.2025.3605331","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3605331","url":null,"abstract":"Considering the simplicity of flight route planning, orthorectified images obtained from nadir observations are widely used in remote sensing. However, they are always insufficient to represent the anisotropic reflectance and 3-D structural information of objects. Therefore, multiangle observation information can enhance target information and potentially improve the accuracy of target classification and recognition. In this study, we investigated the potential of anisotropic reflectance information in land cover classification. By employing the DJI P4M multispectral observation system, multiangle multispectral reflectance images for five land cover types were captured at bare soil, concrete roads, grassland, apricot tree, and red broom cypress areas. Subsequently, the anisotropic flat index (AFX)-based bidirectional reflectance distribution function (BRDF) archetypes model and the kernel-driven model were used to reconstruct the BRDF. Finally, land cover classification was performed using three types of machine learning algorithm considering different BRDF features and band combinations. The results indicate that, compared to nadir directional reflectance, multiangle feature sets can improve the overall classification accuracy up to 24%. Compared to using single-band information, band combinations can also improve that up to 54%. The overall accuracy using the feature set of kernel-driven model parameters and nadir reflectance was also enhanced significantly, which can reach 86% using green-red-near infrared band combinations. This work demonstrates the contribution of multiangle multispectral information to natural and artificial land cover classification.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LMG-Net: A Lightweight Remote Sensing Change Detection Network With Multilevel Global Features LMG-Net：一种具有多层次全局特征的轻型遥感变化检测网络

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-09-01 DOI: 10.1109/LGRS.2025.3604651

Yutian Li;Wei Liu;Erzhu Li;Lianpeng Zhang;Xing Li

{"title":"LMG-Net: A Lightweight Remote Sensing Change Detection Network With Multilevel Global Features","authors":"Yutian Li;Wei Liu;Erzhu Li;Lianpeng Zhang;Xing Li","doi":"10.1109/LGRS.2025.3604651","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3604651","url":null,"abstract":"Remote sensing change detection (RSCD) is a key tool for environmental monitoring and resource management, playing a significant role in monitoring dynamic surface changes. In practical applications, RSCD often requires high precision and efficient detection methods. However, traditional methods tend to involve high technical complexity and a large number of parameters and are susceptible to interference from complex background noise, leading to poor performance in detecting change areas. To address these issues, this letter proposes a lightweight RSCD network, LMG-Net. The model uses a lightweight encoder and incorporates a hierarchical transformer module (HTF) to suppress background noise and minimize parameter increase, effectively extracting multilevel global features. Additionally, this letter introduces a multidimensional cooperative attention guidance (MAG) mechanism, further enhancing the ability to detect boundary changes. The model has only 3.29 M parameters and a computational load of 3.89G, demonstrating its high applicability, particularly for real-time applications in resource-constrained environments. Experimental results show that LMG-Net achieves the state-of-the-art (SOTA) <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula> scores and IoU values on the WHU-CD, SYSU-CD, and LEVIR-CD+ datasets: (94.79%, 90.09%), (82.29%, 69.90%), and (84.30%, 71.14%).","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Martian Regolith Permittivity Using Deep Learning Methods—Revisiting Southern Utopia Planitia 利用深度学习方法预测火星风化层介电常数——重访南部乌托邦平原

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-29 DOI: 10.1109/LGRS.2025.3604251

Qinfen Cai;Feng Zhou;Iraklis Giannakis;Sijing Liu;Xiangyun Hu

{"title":"Predicting Martian Regolith Permittivity Using Deep Learning Methods—Revisiting Southern Utopia Planitia","authors":"Qinfen Cai;Feng Zhou;Iraklis Giannakis;Sijing Liu;Xiangyun Hu","doi":"10.1109/LGRS.2025.3604251","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3604251","url":null,"abstract":"China’s first Mars mission [Tianwen-1 (TW-1)] successfully touched down in the Utopia Planitia of Mars with a rover subsurface penetrating radar (RoPeR) carried for exploring the regolith dielectric properties. Hyperbolic fitting is a conventional method to infer the subsurface material relative permittivity from ground penetrating radar (GPR) data. However, it is difficult to directly extract valid hyperbolas from the RoPeR data. Inspired by the recently developed deep learning-based geophysical inversion method to estimate the subsurface wave velocities through GPR data, an improved deep learning architecture is proposed to infer the Martian regolith relative permittivity from the RoPeR data, with self-attention (SA) and cascade modules are introduced into the network. The improved cascade and SA modules can improve the inversion efficiency and mitigate the scatter-diffraction effect of the predicted results. The inverted relative permittivity from the first 60 ns of the RoPeR data demonstrates an approximate line with a mean value of 4.73 in the regolith of interest. The very limited fluctuation of relative permittivity implies that no explicit stratification existing in the investigated regolith, agreeing with the previous studies.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bridging Temporal and Spatial–Spectral Features With Satellite Image Time Series: TAS2B-Net for Crop Semantic Segmentation 利用卫星影像时间序列桥接时空光谱特征：TAS2B-Net作物语义分割

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-28 DOI: 10.1109/LGRS.2025.3603294

Xiaohan Luo;Hangyu Dai;Vladimir Lysenko;Jinglu Tan;Ya Guo

{"title":"Bridging Temporal and Spatial–Spectral Features With Satellite Image Time Series: TAS2B-Net for Crop Semantic Segmentation","authors":"Xiaohan Luo;Hangyu Dai;Vladimir Lysenko;Jinglu Tan;Ya Guo","doi":"10.1109/LGRS.2025.3603294","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3603294","url":null,"abstract":"Semantic segmentation based on satellite image time series (SITS) is fundamental to a wide range of geospatial applications, including land cover mapping and urban development analysis. By integrating crop phenological dynamics over time, SITS provides richer spatiotemporal information than static satellite imagery. However, existing models fail to effectively process the temporal and spatial–spectral dimensions of SITS independently, leading to reduced segmentation accuracy. In this letter, we propose a temporal aggregation spatial–spectral bridge network (TAS2B-Net), a novel architecture designed to extract fine-grained crop features from SITS. The network consists of two key components: the pixel-aware grouping temporal integrator (PGTI), which captures temporal dependencies within pixel groups, and the edge-aware contextual fusion head (ECFH), which enhances spatial boundary and global structural representation. Additionally, we introduce a lightweight multiscale spectral decoder (LMSD) to aggregate contextual information across multiple spectral scales, further improving feature learning for semantic segmentation. Extensive experiments on the panoptic agricultural satellite time series (PASTIS) and MTLCC datasets show that the proposed network achieves mIoU scores of 68.91% and 84.59%, respectively, outperforming eight state-of-the-art (SOTA) methods and setting new benchmarks for SITS-based semantic segmentation.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual Collaborative Sparse and Total Variation Regularization for Unmixing-Based Change Detection 基于非混合变化检测的双协同稀疏和全变分正则化

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-28 DOI: 10.1109/LGRS.2025.3603339

Shile Zhang;Yuxing Zhao;Zhihan Liu;Xiangming Jiang;Maoguo Gong

{"title":"Dual Collaborative Sparse and Total Variation Regularization for Unmixing-Based Change Detection","authors":"Shile Zhang;Yuxing Zhao;Zhihan Liu;Xiangming Jiang;Maoguo Gong","doi":"10.1109/LGRS.2025.3603339","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3603339","url":null,"abstract":"Hyperspectral change detection is critical for analyzing the temporal evolution of the feature components in multitemporal hyperspectral images. However, existing methods often fall short of fully exploiting the spatiotemporal–spectral correlations within these images, thereby limiting their accuracy and robustness. This letter introduces a novel hyperspectral change detection method, termed dual collaborative sparse unmixing via variable splitting augmented Lagrangian and total variation (DCLSUnSAL-TV). By integrating dual collaborative sparsity and total variation (TV) regularizers, this method capitalizes on the local similarity of changes in the feature components, leveraging the low-rank property of hyperspectral difference images (HSDIs) and their inherent spatial–spectral correlations. A customized abundancewise truncation and ensemble strategy is designed to obtain the change map by aggregating the subpixel-level changes with respect to each endmember. Comprehensive comparison and ablation experiments demonstrate the effectiveness of the proposed method in improving the accuracy of change detection. The source code is available at: <uri>https://github.com/2alsbz/DCLSUnSAL_TV</uri>","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Super Equatorial Plasma Bubbles Observed Over South America During the October 10 and 11, 2024 Strong Geomagnetic Storm 2024年10月10日和11日强磁暴期间在南美洲观测到的超级赤道等离子体气泡

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-08-27 DOI: 10.1109/LGRS.2025.3603418

Yumei Li;Hong Zhang;Fan Xu;Qiong Ding;Long Tang

{"title":"Super Equatorial Plasma Bubbles Observed Over South America During the October 10 and 11, 2024 Strong Geomagnetic Storm","authors":"Yumei Li;Hong Zhang;Fan Xu;Qiong Ding;Long Tang","doi":"10.1109/LGRS.2025.3603418","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3603418","url":null,"abstract":"On October 10, 2024, the second most intense geomagnetic storm of solar cycle 25 to date took place. This storm was triggered by multiple coronal mass ejections (CMEs) that arrived at Earth from October 7 to 9, causing significant geomagnetic disturbances. The geomagnetic Kp index peaked at its highest level (Kp = 9), indicating a red alert status. This study investigated equatorial plasma bubbles (EPBs) over South America during this geomagnetic storm using ground-based Global Navigation Satellite System (GNSS) rate of total electron content index (ROTI) and Global-scale Observations of the Limb and Disk (GOLD) satellite oxygen atom (OI) 135.6-nm radiance wavelength data. The analysis revealed that the EPBs observed in South America lasted for an unusually long duration of approximately 14 h, from around 23:00 UT (18:00 LT) on October 10 to about 14:00 UT (9:00 LT) on October 11. In addition, these super EPBs extended over a wide latitude range, reaching approximately 35°N and down to 50°S, gradually forming an inverted C-shaped pattern. The observed characteristics of the EPBs are likely associated with changes in solar wind parameters and the effects of the prompt penetration electric field (PPEF).","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0