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Bibliometric Analysis of Global Remote Sensing Research during 2010–2015

Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
The National Science Library, Chinese Academy of Sciences, Beijing 100090, China
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(11), 332;
Received: 30 August 2017 / Revised: 18 October 2017 / Accepted: 26 October 2017 / Published: 1 November 2017
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Bibliometric analysis based on the Science Citation Index Expanded published by Thomson Scientific was carried out to identify the research status and future trends of remote sensing (RS) during 2010–2015. The analysis revealed the institutional, national, spatio-temporal, and categorical patterns in remote sensing research both from the WP (whole publications) viewpoint and the HCP (highly-cited publications) viewpoint. Statistical analysis results showed that remote sensing research almost doubled during 2010–2015. Environmental sciences comprised the most attractive subject category among remote sensing research. The International Journal of Remote Sensing was the most productive journal, and Remote Sensing of Environment published the most HCP among the 31 distributed journals. The productive ranking of countries was led by the U.S. both from the WP viewpoint and the HCP viewpoint, and CAS (Chinese Academy of Sciences) was the most productive institute both from the WP viewpoint and the HCP viewpoint with lower CPP (average number of citations per paper). Keyword analysis illustrated that model and algorithm research were the key points in RS during 2010–2015. RS data including Moderate-Resolution Imaging Spectroradiometer (MODIS), Landsat, synthetic aperture radar (SAR), and LiDAR (light detection and ranging) were the most frequently adopted, but the data usage of UAVs (unmanned aerial vehicles) and small satellites will be promoted in the future. With the development of data acquisition abilities, big data issues will become the challenges and hotspots of RS research, and new algorithms will continue to emerge. View Full-Text
Keywords: remote sensing; bibliometric analysis; WP viewpoint; HCP viewpoint; CPP remote sensing; bibliometric analysis; WP viewpoint; HCP viewpoint; CPP

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Zhang, H.; Huang, M.; Qing, X.; Li, G.; Tian, C. Bibliometric Analysis of Global Remote Sensing Research during 2010–2015. ISPRS Int. J. Geo-Inf. 2017, 6, 332.

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