Journal of the Indian Society of Remote Sensing最新文献

{"title":"Factors Driving Intensification of Pre-Monsoon Tropical Cyclones Over the Bay of Bengal: A Comparative Study of Cyclones Fani and Yaas","authors":"Pankaj Lal Sahu, Sandeep Pattnaik, Prasenjit Rath","doi":"10.1007/s12524-024-01930-1","DOIUrl":"https://doi.org/10.1007/s12524-024-01930-1","url":null,"abstract":"<p>This research paper investigates the factors responsible for tropical cyclones’ intensification process, focusing on pre-monsoon cyclones Fani and Yaas. Using the WRF model, the study examines the dynamics and thermodynamics of the two cyclones in three phases: pre-intensification, intensification, and post-intensification. The results are compared with available satellite data sets and it was found that Fani had higher wind speeds and a more structured system with high specific humidity concentrations closer to the storm center than Yaas. Before the RI of Fani, there was a sudden increase in moisture convergence rate, hydrometeors, and diabatic heating, facilitating the formation of cloud ice at the upper level of the troposphere and the release of higher latent heat energy. In contrast, the lack of moisture convergence in Yaas resulted in a weaker updraft and reduced latent heat release. Further analysis suggests that the heating in Fani was more intense and widespread in the inner eyewall region than the core, compared to Yaas. In general, it is also noted that an enhanced bulk kinetic energy (BKE) is a clear indicator for intensification and during the intensification phase, sustained BKE production and moderate dissipation are evident. The latent heating budget parameters indicate a continuous accumulation of moisture with nearly constant moisture consumption during the intensification process. These features are more distinct for Fani compared to the Yaas. The study concludes that during RI, dynamical processes play a dominant role compared to thermodynamical processes.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"29 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of Digital Elevation Models on Spatial Characterisation of Landslides in the Kalka-Shimla Region of the Indian Himalayas","authors":"Ankur Sharma, Har Amrit Singh Sandhu","doi":"10.1007/s12524-024-01938-7","DOIUrl":"https://doi.org/10.1007/s12524-024-01938-7","url":null,"abstract":"<p>Landslides are complex geohazards responsible for damage to life, the natural environment, and essential infrastructures like buildings, roads, and transmission lines in mountainous regions. The modeling of topographic input parameters for landslide-related investigations is often based on Digital Elevation Models (DEMs), which serve as a crucial geospatial data source. The present study attempts to analyze the effects of DEMs, obtained from different sources and varying in spatial resolution, on terrain feature estimation and spatial characterization of landslide-affected areas in the Indian Himalayas. Carto-DEM version 3R1 and ALOS PALSAR DEM are used to generate two geodatabases of DEM-derived landslide causative factors, each including digital maps of Elevation, Slope, Aspect, Curvature, Terrain Ruggedness Index, and Distance to Drainage. The generated geodatabases are utilized for conducting a spatial frequency distribution analysis to characterize the selected area into spatial bins with similar topographic characteristics. A comparative study of this analysis reveals that both the DEMs exhibited comparable topographic characteristics on a general level. However, considerable variations are observed when both the geodatabases are scrutinized closely. The results of this study highlight that the quality of the DEM used may affect its usability in a specific investigation and hope to add to the scientific discourse on the effects of DEM on landslide-related studies.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"38 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141577860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Morphology and Soil Erosion Risk in Agrarian Watershed of Jharkhand India Using RUSLE, GIS and MCDA-AHP","authors":"Soumya Pandey, Neeta Kumari","doi":"10.1007/s12524-024-01936-9","DOIUrl":"https://doi.org/10.1007/s12524-024-01936-9","url":null,"abstract":"<p>Water induced surface erosion is one of the major factors for causing land degradation leading to massive food insecurity. Therefore, the estimation of water induced erosion risk becomes important mainly in an agrarian watershed. In this 2021 study, water erosion is holistically assessed using erosion models, while the role of topography was analysed through hydro-geomorphometric parameters. Qualitative erosion analysis is performed using the multi-criteria decision-making Analytical Hierarchical Process (MCDM-AHP) tool, while sediment yield is calculated using the RUSLE model. As the empirical formulas are data driven lack of data for smaller region does not provide quite accurate results hence in this experiment a more holistic approach is taken in addressing the erosion through both qualitative and quantitative approach. The morphometric study using a Geographical information system (GIS) showed that the Jumar watershed has rolling and slightly undulating terrain, with mild slope. The hydrological parameters indicated low infiltration capacity of soil and high surface runoff potential indicated that the watershed is at increasing risk (from low to moderate) of gully formation in the lowlands. The analysis of land use land cover (LULC) revealed a significant rise in urbanization, and barren land. Limited vegetation cover was observed during the summer and winter seasons. The RUSLE model showed about 26% of the total geographic area faced erosion between 100–2500 t/ha/year in the watershed. The MCDM-AHP model showed 27.25% of the total geographic area was under moderate to high susceptibility erosion. Both the models showed aggregable results in comparison to each other. It is suggested to plant cover crops during the fallow period and use of biopolymer mulches for land covering. Use of agricultural waste biochar as organic fertilizer is suggested to control erosion and nutrient pollution. This will also improve crop productivity.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"9 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An End-to-End Deep Learning Framework for Cyclone Intensity Estimation in North Indian Ocean Region Using Satellite Imagery","authors":"Manish Kumar Mawatwal, Saurabh Das","doi":"10.1007/s12524-024-01929-8","DOIUrl":"https://doi.org/10.1007/s12524-024-01929-8","url":null,"abstract":"<p>Prediction of Tropical cyclones (TCs), particularly intensity prediction, has always been challenging for climate researchers due to the complicated physical mechanisms in TC dynamics and the way it interacts with upper-ocean and atmospheric circulation. Furthermore, the available data set over the North Indian Ocean (NIO) is also very limited for Machine Learning (ML) model development. Here, we demonstrated a simple yet robust hybrid architecture leveraging a Convolutional Neural Network for automated prediction of the intensity of the cyclone based on IR satellite imagery of 2000–2022. The model comprises a binary classifier, a multiclass classifier, a YOLOv3 based cyclone detector and a regression module. The paper also highlights the discrepancy between the results of independent testing wherein training is done on 2000 to 2019 dataset and tested on 2020 to 2022 dataset, as well as the outcomes of a stratified train-test split performed over the entire dataset using a 70:15:15 ratio for training, validation and testing, respectively. The model is tuned for the NIO region with a binary classification accuracy score of 98.4% (± 0.003), multiclass classification accuracy of 63.83% (± 1.3) and RMSE of 16.2 (± 0.9) knots on stratified split. The results highlight the careful interpretation of the DL model’s performance when applied to time series problems. Additionally, it discusses the limitations stemming from the dataset's small size and the challenges posed by the 5 kt resolution of the best track intensity estimation from the Indian Meteorological Department (IMD). The internal representations learned by the model through feature maps analysis were studied, shedding light on the model’s decision-making process. The study underscores the need for further data accumulation and highlights avenues for enhancing model performance in the future.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"34 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Capability of DOVE Satellite Temporal Data for Mapping Harvest Dates of Sugarcane Crop Types Using Fuzzy Model","authors":"Shruti Pancholi, Anil Kumar","doi":"10.1007/s12524-024-01927-w","DOIUrl":"https://doi.org/10.1007/s12524-024-01927-w","url":null,"abstract":"<p>The information generated about crop harvesting can aid several purposes, including the maximization of crop yield, minimizing crop losses, assessing quality deterioration and crop health, and studying phenology. This study aims to detect the harvesting cycle of Sugarcane-plant and ratoon and analyze the underlying trends. The agriculture domain makes use of remote sensing data extensively for applications like crop yield forecast, crop type mapping, monitoring crop patterns, etc. Sugarcane is cultivated in abundance in the Muzzafarnagar district of Uttar Pradesh, India. The two variants of sugarcane (ratoon and plant) are commonly grown in this region along with other crops like wheat, paddy, and oil seeds (sesame). To monitor the harvesting of the sugarcane crop fields, the phenology of the crop type (from germination to maturity stage) was considered as base temporal data from the DOVE sensor. The temporal Planetscope DOVE sensor base data with particular harvesting dates were used to map harvested fields of sugarcane ratoon and plants on a particular date. Modified soil adjusted vegetation index 2 (MSAVI2) and its variant class-based sensor independent modified soil adjusted vegetation index 2 (CBSI-MSAVI2) were tested to reduce spectral dimensionality and map the harvested fields on approximately a weekly basis. The harvested sugarcane ratoon and plant fields were successfully mapped using the innovative machine-learning approach with a Mean Membership Difference (MMD) value of about 0.01 and 0.02 respectively.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"8 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the Seasonal and Spatial Variations of Cirrus Parameters, Radiative Characteristics and Precipitation over the Indian Subcontinent","authors":"J. S. Priya, V. Krishnakumar, Sona Baiju, R. G. Sreelekshmi, Afna Shoufeer","doi":"10.1007/s12524-024-01926-x","DOIUrl":"https://doi.org/10.1007/s12524-024-01926-x","url":null,"abstract":"<p>This study investigates the spatiotemporal oscillations of cirrus characteristics over the Indian subcontinent from 2013 to 2021 using Moderate Resolution Imaging Spectroradiometer observations. Analyzing Cirrus Fraction (CiF), Cirrus Reflectance (CiR), and radiative characteristics using the Clouds and the Earth’s Radiant Energy System data, the distinct spatial and seasonal fluctuations with respect to the regional precipitation characteristics is unveiled. Radiative characteristics demonstrate significant seasonal influences on Net Short-Wave Flux, Net Long Wave Flux, and Net Total Flux (NETF). Through linear regression analysis, a strong positive correlation is found between CiF, CiR and precipitation, indicating a robust linear relationship. Seasonal variations in cloud parameters and radiative characteristics are examined, revealing heightened Cloud Optical Thickness and Cloud Effective Radius during the South West monsoon season compared to other seasons. CiR and NETF are notably elevated during the Monsoon. These findings underscore the significant impact of the rain on cloud properties and energy flux dynamics over the Indian subcontinent.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"20 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment and Prioritization of Sub-Watersheds Vulnerable to Soil Erosion in an Ungauged River Basin Using MOORA, COPRAS, MARCOS and MABAC Methods","authors":"Vinoth Kumar Sampath, Nisha Radhakrishnan","doi":"10.1007/s12524-024-01942-x","DOIUrl":"https://doi.org/10.1007/s12524-024-01942-x","url":null,"abstract":"<p>Identifying soil erosion-prone zones in an ungauged river basin is crucial for devising and implementing timely soil protection measures to mitigate soil degradation and protect soil quality. Soil erosion damages the fragile ecosystem, decreases soil fertility, and reduces reservoir water storage, thereby impacting food production. The prime motive of the current research work is to assess and categorize on the basis of priority the sub-watersheds (SWs) susceptible to substantial soil erosion in the Ponnaniyar River basin (an ungauged river basin) based on the morphometric parameters that impact soil erosion. To achieve this research objective, four multi-criteria decision-making (MCDM) approaches based on the outranking approach and synthesis method are adopted to facilitate the decision-making process by considering an integrated and balanced assessment of multiple complex parameters for devising effective soil conservation measures to minimize soil erosion. Cartosat-1 digital elevation model (DEM) is employed to extract eighteen morphometric parameters under linear, shape, areal, relief and hypsometric aspects. The priority of SWs obtained by different MCDM techniques is evaluated using percentage of variation and intensity of variation. The outcomes show that the MABAC method is effective in prioritizing SWs with the least percentage of variation (59.61%) and intensity of variation (4.397). It is also found to be the best method for integration with the RSS method for determining SW priority with a root sum of squares value of 43. SW1 is identified to be highly vulnerable to soil erosion with a grade average value of 1.00 followed by SW2 (3.00), SW3 (3.25) and SW13 (5.00), requiring immediate implementation of watershed planning and management measures to control the extent of soil erosion and safeguard soil resources.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"14 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141550961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on Tobacco Plant Cross-Level Recognition in Complex Habitats in Karst Mountainous Areas Based on the U-Net Model","authors":"Qianxia Li, Lihui Yan, Zhongfa Zhou, Denghong Huang, Dongna Xiao, Youyan Huang","doi":"10.1007/s12524-024-01932-z","DOIUrl":"https://doi.org/10.1007/s12524-024-01932-z","url":null,"abstract":"<p>The extraction of crop information is one of the important research directions for precision agriculture remote sensing. Crop extraction is of great significance in crop refinement management, precision fertilization, growth monitoring and yield precision estimation. The karst mountainous areas in southern China are characterized by undulating terrain, broken cultivated land, scattered spatial distribution of tobacco planting plots, uneven growth of plants, and mixed planting of crops. As the flight height of UAVs increases, the area of tobacco planting plots decreases, and the texture features become increasingly blurred, which increases the difficulty of segmentation and affects the recognition accuracy. We conducted this study to explore whether the high-resolution sample datasets and the trained U-Net model are suitable for cross-level recognition. In this study, DJI Mavic 2 Pro was used to collect UAV RGB images with flight heights of 50 m, 60 m, 70 m and 90 m in complex habitats for extracting tobacco plants from the U-Net model. The results are as follows: (1) The precision of tobacco plant segmentation at different altitudes is 50 m &gt; 60 m &gt; 70 m &gt; 90 m, and Kappa coefficient is 0.92, 0.89, 0.86 and 0.34; the pressure is 0.96, 0.94, 0.93 and 0.22; the recall is 0.91, 0.90, 0.86 and 0.24; and the IoU is 0.88, 0.85, 0.8 and 0.23, respectively; and the precision of complex background segmentation is: a small number of weeds &gt; a large number of weeds, and the plot is flat &gt; the plot is broken. (2) With increasing flight height, the precision of tobacco segmentation of the U-Net model gradually decreases. Compared with 50 m, the precision of the 60 m segmentation results is reduced by 0.03, 0.02, 0.01 and 0.03, and that of 70 m is reduced by 0.06, 0.03, 0.05 and 0.08. The precision of the 90 m segmentation results is reduced by 0.58, 0.74, 0.67 and 0.65. The flight heights of 50 m, 60 m and 70 m have good experimental results, but the precision of 90 m segmentation is poor. The precision is mainly affected by the two factors of floor height and light. This study verified the feasibility and reliability of the high-precision extraction of tobacco plants at different altitudes by U-Net in complex habitats and has a certain reference value for research on the methodology and technical system of the deep learning recognition of crops in complex habitats in karst mountains.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"31 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Remote Sensing and Spatial Fuzzy Multi-criteria Decision Analysis to Identify Potential Dust Sources in Lake Urmia Basin, Northwest Iran","authors":"Saeid Hoseinzadeh Khachak, Omid Rafieyan, Khalil Valizadeh Kamran, Mohammadreza Dalalian, Gholam Hasan Mohammadi, Yusuf Alizade Govarchin Ghale","doi":"10.1007/s12524-024-01890-6","DOIUrl":"https://doi.org/10.1007/s12524-024-01890-6","url":null,"abstract":"<p>Air pollution as a result of desertification and dust transportation is one of the critical environmental challenges in the arid and semi-arid regions. Urmia Lake, the largest inland lake of Iran has lost most of its water over the past 2 decades. The lake bed is known as one of the aerosol pollution sources in the northwestern Iran. Although recent studies contributed to investigate the impacts of the drying up of Urmia Lake on the local and regional air quality, there is still a need to identify spatiotemporal aerosol pollution and dust generation sources in the study area. In this study, remote sensing techniques, fuzzy logic and Principal Component Analysis (PCA) were used to identify dust hot spots in the south and east parts of the Lake, where recent studies have highlighted the dramatic extent of salinization and desertification. Based on the results of this study, the lake's contribution to the local aerosol pollution declines with increasing distance from it. The results indicated that the potential of dust forming on the east side of the lake has increased, presenting a variety of challenges for inhabitants, including health and biological hazards. The fuzzy results have a high correlation with Electrical Conductivity (EC) (0.69), Aerosol Optical Depth (AOD) (0.46), and Leaf Area Index (0.45), respectively, while wind speed (0.22) and slope (0.24) have the lower correlation. The results of PCA indicate that AOD, Digital Elevation Model, and EC have the highest percentage in identifying dust generation sources among the effective parameters in determining dust production sources.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"174 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Semi-Supervised Model for Fine-Grained Identification of Oil Emulsions on the Sea Surface Using Hyperspectral Imaging","authors":"Ming Xie, Tao Gou, Shuang Dong, Ying Li","doi":"10.1007/s12524-024-01935-w","DOIUrl":"https://doi.org/10.1007/s12524-024-01935-w","url":null,"abstract":"<p>After oil spills occur in the ocean, oil pollutants usually appear in the form of oil emulsions under the influence of hydrodynamics. Hyperspectral remote sensing technology, which provides abundant spectral information of ground objects, has the potential of fine-grained classification on the types of oil emulsions. Aiming at the practical applications of oil emulsion extraction in hyperspectral images (HSIs), this study proposes a semi-supervised model for oil emulsion identification by integrating an image segmentation algorithm with a deep-learning-based classification model. In the proposed approach, the training data were filtered from HSI using an image segmentation algorithm, based on which a 1-dimensional convolutional neural network (1D-CNN) was trained to identify oil emulsions in the HSI. The model was tested on the HSIs of Deepwater Horizon oil spills obtained by AVIRIS. The overall accuracy and standard performance measurements of the proposed model are higher than 94% on the extracted dataset. The results indicated that the proposed model achieved similar detection results on sea water as the supervised model, and even higher accuracies on oil emulsion type identification. As a semi-supervised model, it also avoids the lengthy and time-consuming data labelling and has the potential for operational oil emulsions extraction and quantification.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"2 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141516539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}