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

{"title":"Regional Sea Surface Skin Temperature Retrieval From HY-1C and HY-1D COCTS","authors":"Zhuomin Li;Rui Chen;Mingkun Liu;Lei Guan","doi":"10.1109/JSTARS.2025.3560691","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560691","url":null,"abstract":"Haiyang-1C (HY-1C) and Haiyang-1D (HY-1D) are Chinese satellites for marine observation. They operationally work to form the networking of morning and afternoon satellites, improving the capacity of coverage in the temporal and spatial dimensions. These satellites carry Chinese ocean color and temperature scanners (COCTS), containing two thermal bands with central wavelengths of 10.8 and 12.0 μm and enabling sea surface temperature (SST) observations. Herein, regional algorithms for SST retrieval are developed for the South China Sea (SCS) by applying radiative transfer modeling. The SCS has unique atmospheric conditions characterized by high temperature and humidity, particularly in the central and southern regions. Atmospheric profiles sufficient to represent the atmospheric conditions of the SCS are selected. The relationship between the top-of-the-atmosphere simulated brightness temperature and the reanalysis skin SST of the selected profiles is determined. The HY-1C and HY-1D COCTS SSTs in the SCS are retrieved utilizing the obtained algorithm. The HY-1C/COCTS SSTs are evaluated using the sea and land surface temperature radiometer SST, and the HY-1D/COCTS SSTs are evaluated using the Visible Infrared Imaging Radiometer Suite SST. For the HY-1C/1D COCTS SSTs, the biases are around 0, and the standard deviations are around 0.5°C. The differences between the HY-1C and HY-1D COCTS SSTs are analyzed to further validate the accuracy of the retrieval method. The difference is 0.38°C during the day and 0.03°C at night, which indicates a diurnal warming phenomenon in the SCS.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11502-11511"},"PeriodicalIF":4.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964705","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072950","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":"Efficient Attention Transformer Network With Self-Similarity Feature Enhancement for Hyperspectral Image Classification","authors":"Yuyang Wang;Zhenqiu Shu;Zhengtao Yu","doi":"10.1109/JSTARS.2025.3560384","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560384","url":null,"abstract":"Recently, transformer has gained widespread application in hyperspectral image classification (HSIC) tasks due to its powerful global modeling ability. However, the inherent high-dimensional property of hyperspectral images (HSIs) leads to a sharp increase in the number of parameters and expensive computational costs. Moreover, self-attention operations in transformer-based HSIC methods may introduce irrelevant spectral–spatial information, and thus may consequently impact the classification performance. To mitigate these issues, in this article, we introduce an efficient deep network, named efficient attention transformer network (EATN), for practice HSIC tasks. Specifically, we propose two self-similarity descriptors based on the original HSI patch to enhance spatial feature representations. The center self-similarity descriptor emphasizes pixels similar to the central pixel. In contrast, the neighborhood self-similarity descriptor explores the similarity relationship between each pixel and its neighboring pixels within the patch. Then, we embed these two self-similarity descriptors into the original patch for subsequent feature extraction and classification. Furthermore, we design two efficient feature extraction modules based on the preprocessed patches, called spectral interactive transformer module and spatial conv-attention module, to reduce the computational costs of the classification framework. Extensive experiments on four benchmark datasets show that our proposed EATN method outperforms other state-of-the-art HSI classification approaches.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11469-11486"},"PeriodicalIF":4.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072947","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 Blur-Score-Guided Region Selection Method for Airborne Aircraft Detection in Remote Sensing Images","authors":"Yujian Wang;Yi Hou;Yuting Xie;Ruofan Wang;Shilin Zhou","doi":"10.1109/JSTARS.2025.3560662","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560662","url":null,"abstract":"Airborne aircraft detection is of paramount importance for optimizing airspace management and enhancing flight safety and efficiency in both commercial and private sectors. High-speed airborne aircraft (over 800 km/h) often introduce motion blur and diminish the semantic correlation between the aircraft and its background. Conventional methods for stationary aircraft detection are inadequate for addressing these challenges. To overcome these issues, we propose BS-DETR, a novel transformer-based object detection model for airborne aircraft in remote sensing images. Our approach includes an improved tenengrad gradient algorithm to extract motion blur information and construct a Blur-Score map. We also introduce an adaptive feature fusion mechanism to integrate the Blur-Score map with multiscale features. In addition, an aircraft region selector (ARS) is employed to identify regions with a high probability of containing aircraft, thereby eliminating irrelevant background. We have established a comprehensive airborne aircraft dataset, including diverse aircraft models, cloud formations, and aircraft contrails. Experimental results on this dataset demonstrate that BS-DETR outperforms other state-of-the-art object detectors, highlighting the effectiveness of incorporating Blur-Score maps, and removing ineffective backgrounds.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11649-11660"},"PeriodicalIF":4.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072948","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":"Planetary Boundary Layer Height Estimation: Methodology and Case Study Using NAST-I FIREX-AQ Field Campaign Data","authors":"Hyun-Sung Jang;Daniel K. Zhou;Xu Liu;Wan Wu;Allen M. Larar;Anna M. Noe","doi":"10.1109/JSTARS.2025.3556546","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3556546","url":null,"abstract":"The ratio of potential temperature (T_p) and dewpoint temperature (T_d), which is derived from retrievals of infrared hyperspectral measurements, is adopted as a new parameter for better estimating planetary boundary layer height (PBLH). A case study, conducted with National Airborne Sounder Testbed-Interferometer (NAST-I) measurements obtained during the Fire Influence on Regional to Global Environments and Air Quality field campaign, is presented herein. We use NAST-I geophysical parameter retrievals from the Single Field-of-view Sounder Atmospheric Product algorithm, which ensures higher vertical resolution of temperature and moisture profiles as well as accurate surface temperature and emissivity, to estimate PBLH with a higher horizontal spatial resolution of 2.6 km. As a result of using the ratio of potential and dewpoint temperatures, instead of individual thermodynamic retrievals, a more robust parameter for estimating PBLH is obtained. A quality control process is developed to filter out abnormal outliers. Additionally, those outliers are modified using statistics from nominal distributions of the T_p/T_d ratio and PBLH. A high consistency between NAST-I thermodynamically-retrieved PBLH and that from the European Centre for Medium-Range Weather Forecasts Reanalysis-5, which uses both dynamic and thermodynamic information, successfully supports the validity and significance of our approach.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10002-10009"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850847","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":"Positioning and Mitigating Mining-Induced Phase Gradients for InSAR Phase Unwrapping","authors":"Xin Tian;Xia Wu;Hanwen Yu;Mi Jiang","doi":"10.1109/JSTARS.2025.3560139","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560139","url":null,"abstract":"Interferometric synthetic aperture radar (SAR) has been widely used for deformation monitoring in mining areas. However, the nonlinear high-phase gradients caused by subsidence funnels are a major source of error in accurate measurements, as they present challenges for phase unwrapping. In this article, we present a methodology to position and mitigate the mining-induced phase gradients. First, we utilize YOLOv10 to detect subsidence funnels that appear as small targets in SAR interferograms. Second, we model the nonlinear phase gradients by means of generalized Gaussian distribution, followed by minimizing the angular deviation between observed and modelled phase patterns in each detected interferometric patch. After defringing mining-induced phase gradients, we evaluate the impact of nonlinear high phase gradients on phase unwrapping, using synthetic data and Sentinel-1 dataset over Datong mining area, Shanxi Province. Compared to phase unwrapping without defringing, the proposed approach reduced the RMSE by 35.5% in the simulation. For the real data, the average number of unclosed pixels was reduced by 30.7%.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11661-11669"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10963682","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072949","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 Transfer Learning Approach for Landslide Semantic Segmentation Based on Visual Foundation Model","authors":"Changhong Hou;Junchuan Yu;Daqing Ge;Liu Yang;Laidian Xi;Yunxuan Pang;Yi Wen","doi":"10.1109/JSTARS.2025.3559884","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559884","url":null,"abstract":"Landslides are one of the most destructive natural disasters in the world, threatening human life and safety. With excellent performance as a foundation model for image segmentation, the segment anything model (SAM) has provided a novel paradigm for semantic segmentation research. However, the lack of remote sensing images in the SAM training data limits its ability to recognize landslides. In addition, despite the transfer learning approach can transfer SAM feature extraction capability to the landslide segmentation task, but it will consume a lot of computational resources and training time. In order to solve these challenges, this study proposes a TransLandSeg model that transfers the segmentation capability of SAM while learning landslide features at a low training cost. To limit model training parameters, the adaptive transfer learning (ATL) module is purposely designed, the image encoder is frozen during model training, only the ATL module and mask decoder are trained, and the knowledge learned from the ATL module is input into the original network. Moreover, to select the best ATL module, we also designed 9 kinds of ATL modules and analyzed the accuracy of the TransLandSeg model with different ATL modules. We selected the Bijie landslide dataset and the Landslide4Sense dataset for model training and testing. The experiment results show that the TransLandSeg model increases the mean intersection over union by 1.48% –13.01% compared to other state-of-the-art semantic segmentation models. In addition, TransLandSeg requires only 1.3% of SAM parameters to enable SAM's powerful capabilities to transfer to landslide segmentation.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11561-11572"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962290","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073119","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":"RSEFormer: A Residual Squeeze-Excitation-Based Transformer for Pixelwise Hyperspectral Image Classification","authors":"Yusen Liu;Hao Zhang;Fashuai Li;Fei Han;Yicheng Wang;Hao Pan;Boyu Liu;Guoliang Tang;Genghua Huang;Tingting He;Yuwei Chen","doi":"10.1109/JSTARS.2025.3559190","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3559190","url":null,"abstract":"Hyperspectral image (HSI) classification plays an essential role in remote sensing image processing. Deep learning methods, especially the transformer, has achieved great success in HSI classification. However, due to the limited existing labeled data of HSI, the relation between objects is irregular in such a small dataset. Merely using the long-range attention based on transformers for learning may lead to bias results. In addition, it is challenging for current attention-based methods to extract attention between high-dimensional spectra, which affects the performance of the classification model. To this end, we propose a network that combines local spectral attention and global spatial-spectral attention, the residual depthwise separable squeeze-and-extraction transformer for HSI classification. Our framework integrates 3-D depthwise separable convolution (DSC) squeeze-and–excitation module, residual block, and sharpened attention vision transformer (SA-ViT) to extract spatial-spectral features from HSI. Three-dimensional DSC squeeze-and–excitation extracts spatial-spectral features and learns the local spectral implicit attention. Residual connection is introduced to hamper gradient vanishment during the network training. For global modeling, SA-ViT employs diagonal masking to eliminate self-token bias and learnable temperature parameters to sharpen attention score. Experimental results demonstrate that our method outperforms other approaches on five HSI benchmark datasets, achieving state-of-the-art performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11308-11323"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10962545","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900505","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":"Cross Validation of FY3D MWRI Passive Microwave LST With MODIS LST Under Clear-Sky Conditions","authors":"Yuting Gong;Huifang Li;Xiuqing Hu;Huanfeng Shen","doi":"10.1109/JSTARS.2025.3560132","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560132","url":null,"abstract":"Passive microwave (PMW) land surface temperature (LST) is widely used in climatology, agriculture, and environmental science due to its large range, all-weather capabilities, and other unique advantages. FY3D microwave radiation imager (MWRI) offers some of the global rare PMW LST products, but the accuracy is not clear due to the lack of research on the validation of the FY3D MWRI LST products. Therefore, cross validation of FY3D MWRI LST with moderate-resolution imaging spectroradiometer LST was investigated in this study. Based on the quality control, temporal matching, and spatial matching, the spatial and temporal distribution were validated between the two LST products from 2019 to 2023. In the spatial cross validation, the overall correlation coefficient (<italic>R</i>) of the two daily LST products distributed between 0.68 and 0.84, and the deviation ranged from 3 to 8 K. The <italic>R</i> of the two LST monthly products was between 0.78 and 0.89, with the deviation mainly distributed between 3 and 6 K. This suggests that the monthly product is more stable and has a more reliable accuracy than the daily FY3D MWRI LST product. In the temporal cross validation, the seven dominant global land cover types were analyzed. It can be found that the main distribution of <italic>R</i> was in the range of 0.7–0.9, and the deviation was mainly 3–6 K. With the improvement of the FY3D MWRI LST official retrieval algorithm, the accuracy of the FY3D MWRI LST products has improved significantly, and the products deserve extensive attention.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10786-10802"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10963739","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896519","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 Cross-Fusion Network for Salient Object Detection in Optical Remote Sensing Images","authors":"Weining Zhai;Panpan Zheng;Liejun Wang","doi":"10.1109/JSTARS.2025.3560315","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560315","url":null,"abstract":"Salient object detection (SOD) is usually taken as a key procedure on the preprocessing of remote sensing images (RSIs), in which RSI-SOD techniques are employed to accurately locate the most attractive targets from RSIs. The existed RSI-SOD models, however, face a challenge on how to balance global context and local detailed information efficiently due to varying object scales and cluttered backgrounds in RSIs. Also to improve the portability of the network to facilitate the practical application of the model, we propose a efficient network, multieffective combined network (MECNet). MECNet combines multiscale networks with an edge detection auxiliary network, utilizing an attention mechanism for enhanced performance. Within MECNet, the multiview combination block employs an attention-based approach to capture rich contextual information across scales, aiding in the detection of various-sized objects. The post-aggregation reassignment block utilizes multiscale fusion and edge features generated by the edge detection network to enrich semantic and detailed information, effectively handling intricate details. The channel enhancement decoder module employs channel attention to amplify channel cues, enhancing the detail quality of the prediction maps. Evaluated against state-of-the-art methods, MECNet demonstrates superior performance making it a promising solution for practical RSI-SOD applications.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10909-10923"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913470","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":"Mapping Nationwide Subfield Division Dynamics in Saudi Arabia Using Temporal Patterns of Sentinel-2 NDVI and Machine Learning","authors":"Ting Li;Oliver Miguel López Valencia;Matthew F. McCabe","doi":"10.1109/JSTARS.2025.3560071","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560071","url":null,"abstract":"Object-based analysis is widely used for extracting information from satellite data using machine learning, offering reduced sensitivity to fine-scale variability, noise, and computational cost compared to pixel-based methods. However, segmentation algorithms for center-pivot fields often treat fields as single units, neglecting that a field can be subdivided into different sections caused by varied management practices, such as differing planting and harvesting dates, crop types, and rotations. This variability is particularly prevalent in hot, arid regions, such as Saudi Arabia, where precise water and crop management are crucial for sustaining agricultural productivity. However, such subfield division reduces the accuracy of object-based agroinformatics insights and the effectiveness of large-scale analyses. A machine learning-based approach combining Kmeans clustering and cosine similarity was developed to quantify subfield divisions using temporal features derived from Sentinel-2 normalized difference vegetation index (NDVI) time series. The performance of discrete wavelet transformation(DWT) and Savitzky–Golay filtering was compared for processing the NDVI time series. When evaluated against a reference dataset, the approach achieved a maximum accuracy of 93.38% with DWT level 1 decomposition using the “haar” wavelet function. These parameters were applied to map the nationwide center-pivot subfield division dynamics across Saudi Arabia from 2019 to 2023. Results revealed that approximately 20% of center-pivot fields exhibited subfield divisions, ranging from 5740 fields (2083 km<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>) in 2020 to 7342 fields (2770 km<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>) in 2023. Larger fields were more prone to subfield divisions, with a median acreage of 40 ha compared to 20 ha for undivided fields. Dominant management strategies included half-to-half and 5:3:2 divisions. This approach enhances object-based agroinformatics products and facilitates more accurate food security assessments.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11686-11702"},"PeriodicalIF":4.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10963748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073114","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}