IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

{"title":"Estimating Seafloor Topography of the South China Sea Using SWOT Wide-Swath Altimetry Data","authors":"Fengshun Zhu;Jinbo Li;Yang Li;Jianqiao Xu;Jinyun Guo;Jiangcun Zhou;Heping Sun","doi":"10.1109/JSTARS.2025.3526683","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3526683","url":null,"abstract":"The surface water and ocean topography (SWOT) wide-swath altimetry satellite was launched in December 2022. The performance of novel wide-swath altimetry in seafloor topography modeling needs to be evaluated. This study utilized 15 cycles of SWOT Level-3 product to construct seafloor topography model of the South China Sea by linear regression analysis. The root mean square error of the difference between the model and shipborne bathymetry at checkpoints is about 120 m, which is 20 m better than topo_27.1 and DTU18BAT, and 40 m better than ETOPO1. First, the effects of the shipborne bathymetry at control points and priori bathymetry model in different topography-gravity scaling factor estimation strategies [A: using robust least squares (RBLSQ) to estimate regional scaling factor; B: using ratio method to calculate scaling factors at control points; C: using the moving window method and RBLSQ to obtain scaling factor grids.] on SWOT seafloor topography modeling are explored. We find that the control point number barely affects strategy A/C but significantly affects strategy B, while the priori bathymetry model mainly affects strategy C. Then, the three strategies are applied to the traditional radar altimetry gravity anomaly, and the results are compared with the SWOT-derived seafloor topography. The results show that incorporating SWOT data can improve the accuracy of seafloor topography estimation by about 7 m, and improve the power spectral density in the wavelength range about 10–20 km, which can help to reveal more detailed topography information.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3569-3580"},"PeriodicalIF":4.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829941","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Granularity Feature Alignment for Change Detection in Remote Sensing Images","authors":"Feng Zhou;Xinyu Zhang;Hui Shuai;Renlong Hang;Shanshan Zhu;Tianyu Geng","doi":"10.1109/JSTARS.2025.3526795","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3526795","url":null,"abstract":"Deep learning has emerged as the preferred method for remote sensing change detection owing to its ability to automatically extract discriminative features from bitemporal images. However, few methods simultaneously consider heterogeneous appearance of objects and affine geometric difference between bitemporal images, both of which contribute to pseudochange. In this article, dual-granularity feature alignment (DgFA) is proposed to deal with these two issues. Specifically, bitemporal features extracted by transformer, along with learnable class tokens, are input into the proposed semantic alignment module to adjust the appearance of separate instances from same-category objects to ensure a cohesive style. Then, a spatial alignment module is introduced to use the estimated transformation field to accomplish bitemporal feature registration. Finally, we develop a temporal contrast-based change detection head to infer the change map based on dual-granularity aligned bitemporal features and corresponding difference maps. To refine the change map, this head also constrains the feature similarity within changed and unchanged regions across bitemporal features via a contrastive loss. Experimental results demonstrate that DgFA outperforms several state-of-the-art methods on three public benchmark datasets, including LEVIR-CD, CDD, and SYSU-CD.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4487-4497"},"PeriodicalIF":4.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10830007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Apparent Thermal Inertia Based Trapezoid Model for Downscaling ESA CCI Soil Moisture Products","authors":"Shulin Li;Minfeng Xing;Taifeng Dong","doi":"10.1109/JSTARS.2024.3525305","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3525305","url":null,"abstract":"Existing long-term soil moisture (SM) products are relatively coarse in spatial resolution, limiting their applications in heterogeneous scales. Various spectral information derived from optical satellite data, such as the land surface temperature-vegetation parameter (LST-VP), have been widely employed to detect spatiotemporal variability of SM under different regional hydrological scales. In this study, inspired by the concept of LST-VI space, an ATI-VP (apparent thermal inertia-vegetation parameter) was proposed and assessed for downscaling the ESA CCI SM product from 25 to 1 km. Different vegetation indices (including NDVI, EVI, NIRv, and MSAVI) and biophysical variables (LAI and fPAR) derived from MODIS satellites were first assessed as inputs of the ATI-VP space to estimate AVDI (apparent thermal inertia/vegetation drought index). The AVDI was then applied to the weight decomposition model for SM downscaling. Overall, LAI for the ATI-VP space achieved the best AVDI performance. The accuracy of SM estimation was validated using in situ SM collected from the Murrumbidgee soil moisture monitoring network. The results showed that the accuracy of the downscaled 1 km SM (R = 0.637, bias = 0.038 m³/m³) was close to that of the CCI SM (R = 0.661, bias = 0.030 m³/m³). However, the downscaled SM data exhibited enhanced spatial detail compared to CCI SM data. Further analysis based on the time series SM indicated that both the CCI SM and the downscaled SM are in good agreement in terms of temporal evolution. The downscaling method shows high potential for application in SM mapping across semiarid regions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4473-4486"},"PeriodicalIF":4.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Heterogeneous Ensemble Learning Method Combining Spectral, Terrain, and Texture Features for Landslide Mapping","authors":"Yi He;Hesheng Chen;Qing Zhu;Qing Zhang;Lifeng Zhang;Tao Liu;Wende Li;Huaiyuan Chen","doi":"10.1109/JSTARS.2025.3525633","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3525633","url":null,"abstract":"The existing landslide recognition methods mainly focus on the use of spectral bands of optical remote sensing and machine learning base classifiers, which are insufficient in landslide characterization in complex scenes, resulting in a high missed and false detection of landslides. In this article, we develop a landslide recognition framework, which combines the multidimensional feature advantages of spectral, terrain, and texture of optical satellite images, and constructs a heterogeneous ensemble learning method for landslide mapping. First, we construct a landslide multidimensional feature dataset using Sentinel-2A and Advanced Land Observing Satellite digital elevation model data. Then, we construct a heterogeneous ensemble learning landslide recognition method, which combines the advantages of fully convolutional network, U-Net, and attention U-Net base classifiers to fully learn the multidimensional features of landslides. Finally, we evaluate the performance of the landslide recognition framework in the Bailongjiang River Basin complex scenes. The experimental results show that integrating the multidimensional features of spectral, terrain, and texture and using the heterogeneous ensemble learning method can reduce the missed and false detection of landslides in complex scenes. Specifically, compared with using only spectral bands, integrating spectral bands, spectral indexes, terrain factors, and texture indexes achieves the highest Recall, Kappa, F1-score, and MIoU in testing areas, and missed alarm (MA) is reduced by 15.56%. Compared with deep learning base classifiers, the constructed heterogeneous ensemble learning demonstrates improvements in Recall ranging from 41.67% to 69.89%, and MA is reduced from 52.17% to 30.11%. This study provides a new idea for high-precision landslide recognition in complex environments.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3746-3765"},"PeriodicalIF":4.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824934","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing the Spatial Resolution of Sentinel-2 Images Through Super-Resolution Using Transformer-Based Deep-Learning Models","authors":"Alireza Sharifi;Mohammad Mahdi Safari","doi":"10.1109/JSTARS.2025.3526260","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3526260","url":null,"abstract":"Satellite imagery plays a pivotal role in environmental monitoring, urban planning, and national security. However, spatial resolution limitations of current satellite sensors restrict the clarity and usability of captured images. This study introduces a novel transformer-based deep-learning model to enhance the spatial resolution of Sentinel-2 images. The proposed architecture leverages multihead attention and integrated spatial and channel attention mechanisms to effectively extract and reconstruct fine details from low-resolution inputs. The model's performance was evaluated on the Sentinel-2 dataset, along with benchmark datasets (AID and UC-Merced), and compared against state-of-the-art methods, including ResNet, Swin Transformer, and ViT. Experimental results demonstrate superior performance, achieving a peak signal-to-noise ratio (PSNR) of 33.52 dB, structural similarity index (SSIM) of 0.862, and signal-to-reconstruction error ratio (SRE) of 36.7 dB on Sentinel-2 RGB bands. The proposed method outperforms state-of-the-art approaches, including ResNet, Swin Transformer, and ViT, on benchmark datasets (Sentinel-2, AID, and UC-Merced). The results demonstrate that the proposed method achieves superior performance in terms of PSNR, SSIM, and SRE metrics, highlighting its effectiveness in revealing finer spatial details and improving image quality for practical remote sensing applications.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4805-4820"},"PeriodicalIF":4.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829708","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DGFEG: Dynamic Gate Fusion and Edge Graph Perception Network for Remote Sensing Change Detection","authors":"Shengning Zhou;Genji Yuan;Zhen Hua;Jinjiang Li","doi":"10.1109/JSTARS.2025.3526208","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3526208","url":null,"abstract":"Benefiting from continuous innovations in deep learning algorithms, the accuracy of building change detection (BCD) in remote sensing (RS) has significantly improved. Numerous networks combining CNN and transformer architectures have emerged, yet effectively balancing local detail and global context features remains a topic of ongoing discussion. Furthermore, accurately leveraging edge information within RS images to enhance the recognition of structural changes in buildings is another critical challenge. To address these issues, this article proposes a BCD network based on dynamic gate fusion and edge graph perception (DGFEG). First, a hybrid backbone, MCTrans, is employed as the encoder to extract multiscale detailed features and global positional information of buildings. Second, a dynamic gate fusion module is introduced to dynamically weight and fuse the concatenated and differential features obtained by the encoder, enhancing the semantic representation of actual building change regions. Finally, an edge graph perception module integrates edge information with the fused features, leveraging the spatial similarity of graph structures and the interaction of edge features to suppress irrelevant edge interference, thereby improving the model's sensitivity and accuracy in detecting subtle building changes. In experiments, DGFEG was tested on real-world change scenarios and multiple RSCD datasets. The results demonstrate its superior performance compared to existing state-of-the-art methods, proving its excellence and broad application potential in tackling complex BCD tasks.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3581-3598"},"PeriodicalIF":4.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Land Use Mapping of the Guangdong–Hong Kong Macao Greater Bay Area Based on a New Approach at 30 m Resolution for the Years 1976 to 2020","authors":"Yu Gu;Yangbo Chen;Jun Liu","doi":"10.1109/JSTARS.2024.3523707","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523707","url":null,"abstract":"Multicategory land use data of high spatiotemporal resolution and large scale are crucial for studying regional ecological and environmental changes and urbanization impacts as well as for sustainable development planning. Currently available public data products include those of high spatial resolution global land use temporally limited to a single or short period, or global annual land cover products in which only a single land use type is depicted, such that regional characteristics are overlooked. In either case, fine-scale annual variation over longer time spans may not be reflected. In this study, the Google Earth Engine platform, Landsat satellite imagery, and a substantial number of manually interpreted samples were used to develop a dataset of annual land use changes in the Guangdong–Hong Kong Macao Greater Bay Area (GBA) at a 30 m resolution for the years 1976 to 2020. This dataset, termed Annual Land Use/Cover of the Greater Bay Area (LUC-GBA), was used to analyze the annual land use variation in 11 cities within the GBA. The high level of accuracy achieved with the LUC-GBA dataset was evidenced by an overall accuracy (OA) of 93.9% in 2020. The OA of interannual classification models ranged from 83.9% to 93.9%, and the kappa coefficients from 0.805 to 0.923. These results indicate that the LUC-GBA dataset effectively reflects the surface cover distribution and interannual dynamic evolution of the land area in the GBA at a 30 m spatial resolution, thus providing reliable data support for land surface process research and related applications.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3943-3958"},"PeriodicalIF":4.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10827814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ER-GMMD: Cross-Scene Remote Sensing Classification Method of Tamarix chinensis in the Yellow River Estuary","authors":"Liying Zhu;Yabin Hu;Guangbo Ren;Na Qiao;Ziyue Meng;Jianbu Wang;Yajie Zhao;Shibao Li;Yi Ma","doi":"10.1109/JSTARS.2024.3523346","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523346","url":null,"abstract":"<italic>Tamarix chinensis</i> effectively prevents coastal erosion, stabilizes the surface of coastal wetlands, and improves the soil quality of saline-alkali land, playing a crucial role in coastal wetland ecosystem restoration. <italic>Tamarix chinensis</i> exhibits a wide distribution that is difficult to capture within a single remote sensing image, while its frequent interspersion with other vegetation results in significant intermixing. The characteristics of mixed <italic>tamarix chinensis</i> vary substantially across remote sensing images from different scenarios, and spectral confusion further complicates the process. These factors hinder the extraction and alignment of mixed <italic>tamarix chinensis</i> features during classification, resulting in low cross-scene classification accuracy. To address these challenges, this study proposes a deep learning-based cross-domain classification model, ER-GMMD, which leverages features extracted by deep residual networks for different mixed-growth patterns of <italic>tamarix chinensis,</i> and integrates dual feature alignment to address the cross-scene classification challenges of mixed-species <italic>tamarix chinensis</i>. Utilizing GF remote sensing images covering the <italic>tamarix chinensis</i> research area in the Yellow River Delta, along with field survey data, the model achieves precise classification of different mixed <italic>tamarix chinensis</i> types. Key results include: 1) The proposed model, trained with only 5% of the source domain samples, achieves an overall classification accuracy of 96.52% on the target domain samples, which is a 17.61% improvement compared with the traditional network U-Net without domain adaptation. 2) Compared with domain adaptation algorithms DAN and S-DMM, the proposed ER-GMMD model demonstrates higher accuracy on the constructed dataset, indicating its potential for high-precision classification of mixed vegetation in coastal wetlands.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4305-4317"},"PeriodicalIF":4.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829769","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images","authors":"Wentao Hu;Shuanggen Jin;Yuanyuan Zhang","doi":"10.1109/JSTARS.2025.3526207","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3526207","url":null,"abstract":"Total phosphorus (TP) and total nitrogen (TN) are critical water quality indicators in the Yangtze River and remote sensing techniques can inverse these parameters. However, current models suffer from shortcomings such as lower accuracy due to the fewer spectral bands available from a single satellite. In this article, GF-1, Landsat-8, and Sentinel-2 data are jointly used to develop a genetic algorithm-random forest (GA-RF) water quality inversion model weighted by the entropy method. These models are validated and applied to derive long-term time series of TP and TN in the lower Yangtze River from 2018 to 2023. The results indicate that the three-satellite GA-RF joint model shows the best estimation performance from the in-situ measurements: TP with MAE 0.0108 and RMSE 0.0132, and TN with MAE 0.32 and RMSE 0.40. From 2018 to 2023, the water quality shows an improved trend with TP decreasing by 8.91% and TN decreasing by 11.34% . The annual average TP shows a decreasing trend with 0.0017 mg/L per year, while TN shows a decreasing trend with 0.0557 mg/L per year. In terms of seasonal distribution, the highest values of TP and TN are mostly distributed in summer, and the lowest values are mostly distributed in winter. Spatially, both TP and TN increase from west to east. Furthermore, the effects of hydrometeorological factors on water quality are discussed as well as water environmental factors such as pH and NH3-N.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4992-5004"},"PeriodicalIF":4.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Extraction From High-Resolution Multispectral and SAR Images Using a Boundary-Link Multimodal Fusion Network","authors":"Zhe Zhao;Boya Zhao;Yuanfeng Wu;Zutian He;Lianru Gao","doi":"10.1109/JSTARS.2025.3525709","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3525709","url":null,"abstract":"Automatically extracting buildings with high precision from remote sensing images is crucial for various applications. Due to their distinct imaging modalities and complementary characteristics, optical and synthetic aperture radar (SAR) images serve as primary data sources for this task. We propose a novel boundary-link multimodal fusion network for joint semantic segmentation to leverage the information in these images. An initial building extraction result is obtained from the multimodal fusion network, followed by refinement using building boundaries. The model achieves high-precision building delineation by leveraging building boundary and semantic information from optical and SAR images. It distinguishes buildings from the background in complex environments, such as dense urban areas or regions with mixed vegetation, particularly when small buildings lack distinct texture or color features. We conducted experiments using the MSAW dataset (RGB-NIR and SAR data) and DFC track2 datasets (RGB and SAR data). The results indicate that our model significantly enhances extraction accuracy and improves building boundary delineation. The intersection over union metric is 2.5% to 3.5% higher than that of other multimodal joint segmentation methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3864-3878"},"PeriodicalIF":4.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824925","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}