Improving Spatial Coverage of Satellite Aerosol Classification Using a Random Forest Model

• Published in: Remote Sensing
• Main Authors: Wonei Choi, Hanlim Lee, Daewon Kim, Serin Kim
• Format: Text
• Language: English
• Published: Multidisciplinary Digital Publishing Institute 2021
• Subjects: aerosol classification; aerosol remote sensing; space-borne remote sensing; aerosol type; machine learning; TROPOMI; MODIS; AERONET; AOD; Aerosol Robotic Network
• Online Access: https://doi.org/10.3390/rs13071268

The spatial coverage of satellite aerosol classification was improved using a random forest (RF) model trained with observational data including target (aerosol type) and input (satellite measurement) variables. The AErosol RObotic NETwork (AERONET) aerosol-type dataset was used for the target variables. Satellite input variables with many missing data or low mean-decrease accuracy were excluded from the final input variable set, and good performance in aerosol-type classification was achieved. The performance of the RF-based model was evaluated on the basis of the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical SSA wavelength dependence for individual aerosol types was consistent with that obtained for aerosol types by the RF-based model. The spatial coverage of the RF-based model was also compared with that of previously developed models in a global-scale case study. The study demonstrates that the RF-based model allows satellite aerosol classification with improved spatial coverage, with a performance similar to that of previously developed models.