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

{"title":"A Nonparametric Translational Motion Compensation Algorithm for ISAR Imaging by Using the Alternating Iteration and LBFGS Algorithm","authors":"Yuexin Gao;Min Xue;Hanwen Yu;Jianlai Chen","doi":"10.1109/JSTARS.2025.3560365","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560365","url":null,"abstract":"A translational motion compensation method for a target with nonparametric translation in dechirping system is proposed in this paper. We establish the nonparametric translational motion compensation scheme based on the optimization of an image's sharpness. Since the impacts of the range shifts and phase errors on an image's quality are different, we convert the optimization into an alternating iteration of estimating two vectors. These two kinds of iterations are searching for range shifts and phase errors for each range profile, respectively. The Limited-Memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is used to solve the optimization problems with high efficiency. According to the application of the proposed algorithm to the real data, it is valid and could be faster and be of higher performance in comparison with some existing methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11623-11633"},"PeriodicalIF":4.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964234","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937978","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":"Improved Algorithm to Estimate All-Sky Shortwave Net Radiation Based on Top-of-Atmosphere Albedo","authors":"Gaofeng Wang;Tianxing Wang;Wanchun Leng;Pei Yu;Xuewei Yan","doi":"10.1109/JSTARS.2025.3560834","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560834","url":null,"abstract":"Shortwave net radiation (SWNR) serves as the vital variable of radiative energy balance and plays a key parameter in global climate, hydrological, and land surface process models. Solar zenith angle (SZA), PWC, DEM, aerosol optical depth (AOD), TOA albedo, and aerosol type are the key factors for estimating SWNR, and it is necessary to fully consider them. In the study, TOA albedo is estimated using MODIS data. An improved scheme is proposed for estimating SWNR by establishing a relationship between TOA albedo and SWNR based on SZA, PWC, DEM, and AOD parameters under different atmospheric conditions. The improved model is assessed using MODTRAN simulation data, ground measurements, and comparative analysis with Wang-2024, Tang-2006, and CERES single scanner footprint (SSF) product. The results demonstrate that the superior theoretical precision of the improved scheme, based on MODTRAN simulation data, significantly outperforms the existing methods, achieving bias and RMSE of less than 1 and 21 W/m², respectively. For rural aerosol, ground-based verification further revealed that the improved algorithm and Wang-2024 deliver superior accuracy for all-sky (bias<4.6 W/m² and RMSE<82 W/m²). Notably, the improved algorithm performed the highest accuracy for urban aerosol type (bias = 1.8 W/m² and RMSE = 69.8 W/m²), effectively resolving the underestimation issue of Wang-2024 and overestimation by Tang-2006 and CERES SSF. In addition, the improved algorithm demonstrates enhanced performance across varying AOD ranges. When AOD exceeds 0.7, the improved algorithm resolves the significant overestimation (25–85 W/m²) of the existing algorithms and CERES SSF. For AOD values below 0.7, the improved algorithm maintains its superior accuracy. Furthermore, the improved algorithm enables more detailed and precise mapping of SWNR with higher spatial resolution. With advancements in theoretical accuracy and broader applicability, the improved algorithm is expected to serve a pivotal role in diverse application scenarios as remote sensing technologies continue to evolve.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11060-11077"},"PeriodicalIF":4.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964711","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143908343","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":"Physical-Abstract Bidirectional-Guided Learning for High-Resolution Radar Target Recognition","authors":"Yuying Zhu;Yinan Zhao;Zhaoting Liu;Meilin He","doi":"10.1109/JSTARS.2025.3560711","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560711","url":null,"abstract":"Target recognition based on high-resolution radar has garnered increasing attention, with target-aspect sensitivity being one of the primary challenges. For that, this article proposes a physical-abstract bidirectional-guided learning network that leverages scattering center based physical characteristics to guide deep models training, thereby enhancing the robustness and interpretability of deep features. The core innovation lies in modeling this process as a bidirectional integration, enabling simultaneous parameter estimations of scattering center based physical models and mapping abstract representation to local scattering structures of targets. Furthermore, to improve adaptability and reduce computational complexity, several simple yet effective training strategies are introduced within the proposed framework. First, an adaptive method for determining the number of scattering centers and neural network architecture is presented. Second, a soft-threshold based target region extraction algorithm is developed, significantly reducing the parameter search space. The performance of the proposed algorithm is validated using one-dimensional (1-D) carrier-free ultra-wideband radar echoes and synthetic aperture radar (SAR) imagery. Experimental results show that the proposed method is capable of handling challenging conditions where there are significant differences in target aspect between the training and testing datasets. Moreover, integrating the bidirectional-guided learning strategy with a lightweight network yields comparable recognition performance with lower computation complexity, requiring only 0.64 million parameters and 0.018 GFLOPs per layer for 2-D SAR images.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"11014-11030"},"PeriodicalIF":4.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964768","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913522","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 County-Level Rice Planting Areas by Joint Use of High-Resolution Optical and Time Series SAR Imagery","authors":"Jia Xu;Haojie Wang;Lin Qiu;Hui Wang;Yang Mu","doi":"10.1109/JSTARS.2025.3560992","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560992","url":null,"abstract":"Timely and accurate mapping of rice spatial distribution is needed for ensuring food security, managing water usage, and optimizing agricultural production. Frequent cloudy and rainy weather during the rice growing season presents challenges in constructing comprehensive time-series features from optical images. In addition, the fragmentation and sparsity of farmland parcels within the county lead to low extraction accuracy. To address the above challenges, this study proposed an automated rice mapping framework for county-level rice mapping in cloudy and rainy regions by integrating the strengths of high-resolution optical and time-series Synthetic Aperture Radar (SAR) imagery. First, the HRTSNet model was developed to extract farmland parcels from GF-6 high resolution imagery. Subsequently, the long short-term memory (LSTM)-based temporal classification model was utilized to acquire rice cultivation information at parcel scale using time-series Sentinel-1 SAR data. The proposed method was validated at two counties in China. The results showed that the HRTSNet model achieved the highest mIoU and delineated the closest boundary maps to ground truth in extracting farmland parcels from high-resolution optical imagery. And the proposed method effectively integrated limited GF-6 imagery with time-series Sentinel-1 data and performed better than traditional machine learning algorithms like Random Forest and pixel-based methods, achieving an overall accuracy of over 88% and a Kappa coefficient of over 86% for the Dangtu and Rudong counties. In addition, the classification accuracy was effectively improved by incorporating the DPSVIm index. The results provide a potential solution for mapping county-level rice planting areas with limited optical imagery.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10547-10561"},"PeriodicalIF":4.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964810","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900504","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":"Improving Chinese Fir Plantations DBH Inversion Accuracy Using Ensemble Learning Models Base on UAV-LiDAR","authors":"Jiuen Xu;Yinyin Zhao;Xuejian Li;Lujin Lv;Jiacong Yu;Meixuan Song;Lei Huang;Fangjie Mao;Huaqiang Du","doi":"10.1109/JSTARS.2025.3560704","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3560704","url":null,"abstract":"Diameter at breast height (DBH) is a fundamental measurement indicator in forest resource surveys. This study explores the use of uncrewed aerial vehicle light detection and ranging (UAV-LiDAR) for individual tree DBH inversion in Chinese fir (<italic>Cunninghamia lanceolata</i>) plantations with complex terrain and rich understory vegetation, and compares the results with those from Backpack-LiDAR. First, individual tree segmentation was performed on the UAV-LiDAR point cloud data from the study area, and individual tree point cloud feature variables were extracted by applying different height thresholds. Then, three types of models—statistical model multiple linear regression (MLR), traditional machine learning models including K-nearest neighbor regression and support vector regression, and ensemble learning models including random forest, extreme gradient boosting, and categorical boosting (CatBoost)—were employed for DBH inversion. The results show that: 1) Using data above a 5-m height threshold effectively reduces interference from understory vegetation; 2) Key feature variables, such as canopy volume (V), tree height (Hmax), the interquartile range of cumulative height percentiles (AIHiq), and canopy area (S), significantly affect DBH inversion, with V contributing 25% to the feature importance; 3) Ensemble learning models, particularly CatBoost, outperform the other models, achieving an <italic>R</i>² of 0.81% —14.1% higher than MLR; 4) DBH inversion closely matches field observed data, and UAV-LiDAR performs better than Backpack-LiDAR in complex forest environments. This study provides an efficient and cost-effective approach to forest resource monitoring.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10846-10863"},"PeriodicalIF":4.7,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10964709","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913405","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":"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}