Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021
As one of the earliest remote sensing indices, the Normalized Difference Vegetation Index (NDVI) has been employed extensively for vegetation research. However, despite an abundance of NDVI review articles, these studies are predominantly limited to either one subject area or one area, with systemat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/16/3967 |
| _version_ | 1797431753261973504 |
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| author | Yang Xu Yaping Yang Xiaona Chen Yangxiaoyue Liu |
| author_facet | Yang Xu Yaping Yang Xiaona Chen Yangxiaoyue Liu |
| author_sort | Yang Xu |
| collection | DOAJ |
| description | As one of the earliest remote sensing indices, the Normalized Difference Vegetation Index (NDVI) has been employed extensively for vegetation research. However, despite an abundance of NDVI review articles, these studies are predominantly limited to either one subject area or one area, with systematic NDVI reviews being relatively rare. Bibliometrics is a useful method of analyzing scientific literature that has been widely used in many disciplines; however, it has not yet been applied to comprehensively analyze NDVI research. Therefore, we used bibliometrics and scientific mapping methods to analyze citation data retrieved from the Web of Science during 1985–2021 with NDVI as the topic. According to the analysis results, the amount of NDVI research increased exponentially during the study period, and the related research fields became increasingly varied. Moreover, a greater number of satellite and aerial remote sensing platforms resulted in more diverse NDVI data sources. In future, machine learning methods and cloud computing platforms led by Google Earth Engine will substantially improve the accuracy and production efficiency of NDVI data products for more effective global research. |
| first_indexed | 2024-03-09T09:49:52Z |
| format | Article |
| id | doaj.art-48e891e7747c42f199b411d48fe82ee1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T09:49:52Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-48e891e7747c42f199b411d48fe82ee12023-12-02T00:15:06ZengMDPI AGRemote Sensing2072-42922022-08-011416396710.3390/rs14163967Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021Yang Xu0Yaping Yang1Xiaona Chen2Yangxiaoyue Liu3College of Geography and Environmental Science, Henan University, Kaifeng 475004, ChinaNational Earth System Science Data Center, National Science & Technology Infrastructure of China, Beijing 100101, ChinaNational Earth System Science Data Center, National Science & Technology Infrastructure of China, Beijing 100101, ChinaNational Earth System Science Data Center, National Science & Technology Infrastructure of China, Beijing 100101, ChinaAs one of the earliest remote sensing indices, the Normalized Difference Vegetation Index (NDVI) has been employed extensively for vegetation research. However, despite an abundance of NDVI review articles, these studies are predominantly limited to either one subject area or one area, with systematic NDVI reviews being relatively rare. Bibliometrics is a useful method of analyzing scientific literature that has been widely used in many disciplines; however, it has not yet been applied to comprehensively analyze NDVI research. Therefore, we used bibliometrics and scientific mapping methods to analyze citation data retrieved from the Web of Science during 1985–2021 with NDVI as the topic. According to the analysis results, the amount of NDVI research increased exponentially during the study period, and the related research fields became increasingly varied. Moreover, a greater number of satellite and aerial remote sensing platforms resulted in more diverse NDVI data sources. In future, machine learning methods and cloud computing platforms led by Google Earth Engine will substantially improve the accuracy and production efficiency of NDVI data products for more effective global research.https://www.mdpi.com/2072-4292/14/16/3967bibliometrixNDVIremote sensingnetwork analysisvisualizationWeb of Science |
| spellingShingle | Yang Xu Yaping Yang Xiaona Chen Yangxiaoyue Liu Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021 Remote Sensing bibliometrix NDVI remote sensing network analysis visualization Web of Science |
| title | Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021 |
| title_full | Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021 |
| title_fullStr | Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021 |
| title_full_unstemmed | Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021 |
| title_short | Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021 |
| title_sort | bibliometric analysis of global ndvi research trends from 1985 to 2021 |
| topic | bibliometrix NDVI remote sensing network analysis visualization Web of Science |
| url | https://www.mdpi.com/2072-4292/14/16/3967 |
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