Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model

• Published in: Remote Sensing
• Main Authors: Wonei Choi, Hyeongwoo Kang, Dongho Shin, Hanlim Lee
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2021
• Subjects: aerosol classification; aerosol remote sensing; space-borne remote sensing; aerosol type; machine learning; seasonal aerosol variation; Science; Q; Aerosol Robotic Network
• Online Access: https://doi.org/10.3390/rs13132464; https://doaj.org/article/234c2f9a34a740debdd93fc0315004b6

Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 in an investigation of the contributions of aerosol types, with or without Aerosol Robotic Network (AERONET) observations. In this study, we used the recently developed RF aerosol classification model to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing aerosols, and non-absorbing aerosols. Aerosol optical and microphysical properties for each aerosol type detected by the RF model were found to be reasonably consistent with those for typical aerosol types. In Asian capital cities, pollution-sourced aerosols, especially non-absorbing aerosols, were found to predominate, although Asian cities also tend to be seasonally affected by natural dust aerosols, particularly in East Asia (March–May) and South Asia (March–August). No specific seasonal effects on aerosol type were detected in Southeast Asia, where there was a predominance of non-absorbing aerosols. The aerosol types detected by the RF model were compared with those identified by other aerosol classification models. This study indicates that the satellite-based RF model may be used as an alternative in the absence of AERONET sites or observations.