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.

Data play an important role in disaster mitigation applications, and the integrated employment of multidisciplinary data promotes the development of disaster science. Therefore it is very useful to identify the multidisciplinary data usage in the research of disaster events. In order to discover the correlation between multidisciplinary data and disaster research, three earthquake events, the Tangshan earthquake, the Wenchuan earthquake, and the Haidi earthquake were selected as typical study cases for this paper. A knowledge model for literature data mining was applied to analyze the correlation between earthquake events and multidisciplinary data types. The results indicate that high-cited papers show different data usage trends when compared with whole-set papers and also that data usage for the three earthquake events varies. According to analysis results, the factors that influence multidisciplinary data usage include the characteristics of spatial and temporal elements as well as differing interests of the data users.