Bibliometric Analysis of Global NDVI Research Trends from 1985 to 2021

Abstract

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.

Selected Agronomic Traits and Drone Application in Corn Yield Predictio

“…Observation of 70 days after planting (HST) showed the peak point of the NDVI value. Generally, NDVI is one of the commonly used index vegetation formulations in evaluating a plantation based on image analysis through drones, satellites, and other digital sensors (Aryal et al, 2022;Xu et al, 2022). This index was closely related to the greenness of the leaves in a planting plot (García-Martínez et al, 2020;Panday et al, 2020), so the NDVI curve will decrease when the generative phase is quadratic.…”

Global trends in research on cervicogenic headache: a bibliometric analysis

“…In their publication, Aria and Cuccurullo et al (17) detailed a bibliometric analysis utilizing a five-step methodology: study design, data collection, analysis, visualization, and interpretation (18)(19)(20). Figure 1 illustrates a schematic representation of this approach.…”

Bibliometric Analysis of Spatial Accessibility from 1999–2022

“…Spatial accessibility research witnessed fewer instances of collaboration among other countries, with fewer than 50 connections each. Countries that surpassed the 50-collaboration mark were recognized as central cooperation countries [44]. The USA for instance, primarily collaborated with China, the United Kingdom, Canada, and Germany, while China predominantly worked with the USA, the United Kingdom, Australia, and the Netherlands.…”

“…China's contribution to spatial accessibility scientific production has witnessed a steady increase over the years, reaching 42.8% in 2022 (Figure 4b). Apart from the sheer number of scientific publications, the collaborative network among countries provides insight into a nation's research prowess [44]. Figure 5 presents the global collaborations, highlighting that the USA (85) exhibited the highest number of connections with other countries, closely followed by the United Kingdom (84), Germany (69), Netherlands (66), France (63), Australia (60), Italy (54), China (53), Spain (52) and Switzerland (50).…”