基于文献计量的遥感学科发展趋势分析
Bibliometrics Spatial-temporal Evolution Analysis of the Development of Remote Sensing
- 2023年 页码:1-20
DOI: 10.11834/jrs.20232659
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黄铭瑞,简洪登,徐琛,郝俊生,闫珺,刘良云,范湘涛,郭华东.XXXX.基于文献计量的遥感学科发展趋势分析.遥感学报,XX(XX): 1-20
HUANG Mingrui,JIAN Hongdeng,XU Chen,HAO Junsheng,YAN Jun,LIU Liangyun,FAN Xiangtao,GUO Huadong. XXXX. Bibliometrics Spatial-temporal Evolution Analysis of the Development of Remote Sensing. National Remote Sensing Bulletin, XX(XX):1-20
遥感已成为支撑国家资源能源调查、粮食安全监测、生态环境保护、自然灾害评估、国防安全等的重要信息源。利用文献计量学方法定量分析遥感相关文献所承载知识的时空分布、数量关系和变化规律,梳理并提取遥感学科发展趋势及研究热点的时空演化特征,可为遥感学科的建设与发展提供重要科学支撑。对1962-2021年间发表并被SCI数据库收录的遥感相关文章进行文献计量研究,对比分析了国内外典型遥感卫星的文献发表及应用情况,系统剖析了1962-2021年间美国、欧洲、中国遥感研究热点及其变化过程,总结了美国、欧洲、中国学者在合成孔径雷达(SAR)、高光谱(Hyperspectral)及激光雷达(LiDAR)三大遥感前沿技术的研究特点。结果表明:(1)遥感学科SCI文章数自1998年起呈现快速增长、加速增长的趋势。1962-2012年全球累积发表遥感相关SCI文章69,666篇,2013-2021年全球累积发文100,131篇。2014年,中国(第一作者)年度发表遥感相关SCI文章2,048篇,首次超过美国(第一作者)年度1,454篇跃居全球第一,此后一直领先。2021年,中国(第一作者)年度发表遥感相关SCI文章8,063篇,占全球该年度遥感相关SCI文章19,121篇的42.17%;(2)在遥感技术方面,卫星遥感平台从单一卫星向专业小卫星、卫星星座方向发展;卫星传感器从全色、单波段起步,逐步向SAR、高光谱、LiDAR等前沿技术快速发展;(3)在遥感数据应用方面,Landsat、MODIS、Sentinel等国外数据自出现以来就一直被全球用户广泛使用,尤其是中国学者的遥感应用研究高度依赖这些国外卫星数据,近年来国产卫星的应用呈快速发展趋势;(4)当前中外遥感科学研究热点差异显著:美国遥感学者依托Landsat、MODIS、激光雷达等先进卫星和载荷技术,遥感已广泛深入各应用领域;欧洲遥感学者十分重视Sentinel系列卫星的科学研究和应用;中国遥感研究在所有领域和卫星应用方面都表现了突出的数量优势,且中国遥感学者更关注SAR、高光谱、LiDAR以及深度学习、神经网络、特征提取等前沿技术与算法研究。
Remote sensing (RS) has become an essential information source for national resource and energy surveys, food security monitoring, ecological environmental protection, natural disaster assessment, national defense security, etc. Bibliometric is a helpful method to analyze the development dynamics, hot spots, and evolution in the RS discipline. It provides a powerful approach to sort out and visualize the progress of RS development.This paper conducts a bibliometric analysis of RS-related Science Citation Index (SCI) papers published from 1962 to 2021; systematically finds the research hotspots and changes of RS in the United States, Europe, and China from 1962 to 2021; compares and analyzes the application of typical RS satellites globally and in China; and summarizes the research characteristics of US, European and Chinese scholars in the three frontier technologies (i.e., Synthetic Aperture Radar, Hyperspectral and LiDAR).The results show that (1) The number of SCI papers and authors in RS has shown a trend of rapid growth and accelerated growth since 1998, from 69,666 published in 2012 to 169,797 in 2021; and China has surpassed the US in its annual publication to become the first since 2014, and has been far ahead since then, with 8,063 RS SCI papers published by 2021, accounting for 42.17% of the 19,121 global publications; (2) In terms of RS technology, RS started from multispectral imaging, and developed rapidly to the frontier technologies of Synthetic Aperture Radar, Hyperspectral, LiDAR, Unmanned Aerial Vehicle(UAV), High-resolution image, and deep learning. Furthermore, RS gradually played an increasingly important role in many application fields; (3) In terms of RS data application, Landsat, MODIS, Sentinel, and other foreign data have been widely used by global users; Chinese scholars highly rely on these foreign satellite data to conduct RS research. In comparison, the application of domestic satellites is relatively rare, and the international influence of domestic satellites is very weak, which is very mismatched with China's status of its RS. (4) There are significant differences between the hot spots of RS research between China and other countries. Relying on advanced satellite and payload technologies (e.g., Landsat and MODIS), the US developed science-driven and demand-driven RS research, which has been widely used in various application fields. European RS scholars attached great importance to the research and application of Sentinel satellites, which have surpassed Landsat in the number of SCI papers they published. Chinese RS has shown outstanding quantitative advantages in all research fields and applications. Meanwhile, Chinese RS scholars pay more attention to Synthetic Aperture Radar, Hyperspectral, LiDAR, deep learning, neural networks, feature extraction, and other cutting-edge technologies and algorithms.
遥感文献计量学科发展趋势研究热点卫星数据应用领域
remote sensingbibliometricsdiscipline development trendsresearch hotspotsatellite dataapplication fields
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