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Home > Help Publication Information Title Japanese:   English: Evaluating different satellite-based Aerosol Optical Depth (AOD) in predicting inland daytime PM2.5 using machine learning-based regression approach  Author Japanese: RAMOS ROSEANNE, Mark Joseph Calubad, Varquez Alvin Christopher Galang.   English: Roseanne Ramos, Mark Joseph Calubad, Alvin Christopher Varquez.   Language English  Journal/Book name Japanese:   English: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  Volume, Number, Page Volume XLIX-B2-2026        pp. 1439-1435 Published date July 23, 2026  Publisher Japanese:   English: Copernicus Publications  Conference name Japanese:   English: XXV ISPRS Congress 2026  Conference site Japanese:   English: Toronto, Ontario  Official URL https://isprs-archives.copernicus.org/articles/XLIX-B2-2026/1439/2026/   DOI https://doi.org/10.5194/isprs-archives-XLIX-B2-2026-1439-2026 Abstract Aerosols play a critical role in the development of the boundary layer and build-up of air pollution in urban environments. Their presence in the atmosphere is quantified by Aerosol Optical Depth (AOD). Satellite sensors observe and retrieve AOD at varied spatial and temporal resolutions. In air quality monitoring, satellite-based AOD products are typically utilized to predict ground concentrations of fine particulate matter (PM2.5) through various modelling approaches. This study evaluates AOD products observed by Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI) of Himawari-8 in predicting inland daytime PM2.5 concentrations for test sites in Japan and South Korea. Prediction models were constructed using eXtreme Gradient Boosting (XGBoost) regression with input variables from observation datasets matched on ground PM2.5 station locations. In addition to AOD, twelve (12) predictor variables representing topographic and meteorological parameters were considered. Prediction results were evaluated using Kruskal-Wallis test and effect size analysis to compare the absolute error distributions across AOD products. Statistical results indicate that while models utilizing MODIS AOD generalize better to new data, the overall difference in prediction accuracy is statistically negligible. These findings suggest that no single AOD product is significantly superior in predicting ground-level PM2.5 concentrations, highlighting the potential of integrating AOD values from various sources. This work is essential for improving the accuracy of PM2.5 estimates and for supporting more effective mapping of urban air pollution.

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