Twenty-Five Years of Satellite-Based Particulate Matter Estimation: A Bibliometric Analysis

Abstract

Over the past two decades, the integration of remote sensing technologies for estimating particulate matter (PM) has increased rapidly, reflecting the global urgency of monitoring air pollution at large spatial scales. This study presents a bibliometric analysis of 4,155 journal articles published between 2000 and 2024, retrieved from the Scopus database. Only peer-reviewed English-language journal articles were included, while conference proceedings, reviews, and book chapters were excluded. By leveraging the Bibliometrix package in R, this analysis maps scientific trends in remote sensing applications for estimating particulate matter (PM_2.5 and PM₁₀). Results show that annual publications grew from fewer than 50 per year in the early 2000s to more than 500 by 2024, with China (n = 10,023), the United States (n = 6,501), and India (n = 1,325) emerging as leading contributors. Keyword co-occurrence and thematic clustering highlight the growing use of machine learning, data fusion, and health-linked applications. International collaboration networks reveal strong linkages among China, the USA, and Europe, while regions such as Africa and South America remain underrepresented. These findings suggest that future efforts should focus on targeted investments in the underrepresented areas and on integrating remote sensing, machine learning, and health data into air quality indices to enhance environmental monitoring and policy relevance.