A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation

Abstract

A new method was developed for classifying aerosol types involving a machine-learning approach to the use of satellite data. An Aerosol Robotic NETwork (AERONET)-based aerosol-type dataset was used as a target variable in a random forest (RF) model. The contributions of satellite input variables to the RF-based model were quantified to determine an optimal set of input variables. The new method, based on inputs of satellite variables, allows the classification of seven aerosol types: pure dust, dust-dominant mixed, pollution-dominant mixed aerosols, and pollution aerosols (strongly, moderately, weakly, and non-absorbing). The performance of the model was statistically evaluated using AERONET data excluded from the model training dataset. Model accuracy for classifying the seven aerosol types was 59%, improving to 72% for four types (pure dust, dust-dominant mixed, strongly absorbing, and non-absorbing). The performance of the model was evaluated against an earlier aerosol classification method based on the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical wavelength dependences of SSA for individual aerosol types are consistent with those obtained for aerosol types by the new method. This study demonstrates that an RF-based model is capable of satellite aerosol classification with sensitivity to the contribution of non-spherical particles.

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

“…The satellite input variable dataset was collected from TROPOMI data and aerosol optical properties from MODIS data. Satellite input variables of previous studies were utilized by Choi et al [25], including AOD, AE, aerosol index, and trace-gas densities (CO and tropospheric NO 2 column densities). Aerosol formation is reported to depend on radiation exposure, particularly its effect on smog production [28].…”

“…However, Mao et al [16] investigated the performance of satellite aerosol classification models using measurement data from the ground-based AErosol RObotic NETwork (AERONET) [24], with the agreement between satellite-and ground-based results ranging from 36% to 91%. Choi et al [25] recently proposed a new satellite classification method for identifying aerosol types based on a 'random forest' (RF) model, which is a machine-learning technique. The RF-based aerosol classification model was trained using a set of observational data including target (AERONET aerosol type) and input (satellite measurement) variables to identify aerosol types without input from AERONET observations.…”

“…The RF-based aerosol classification model was trained using a set of observational data including target (AERONET aerosol type) and input (satellite measurement) variables to identify aerosol types without input from AERONET observations. Choi et al [25] introduced 11 satellite input variables to account for the combined effects of trace-gas data and aerosol optical properties from TROPOspheric Monitoring Instrument (TROPOMI) and Moderate Resolution Imaging Spectroradiometer (MODIS). The importance of satellite input variables was also investigated using the RF-based model [25] with satellite variable inputs to allow the classification of seven aerosol types.…”

“…Choi et al [25] introduced 11 satellite input variables to account for the combined effects of trace-gas data and aerosol optical properties from TROPOspheric Monitoring Instrument (TROPOMI) and Moderate Resolution Imaging Spectroradiometer (MODIS). The importance of satellite input variables was also investigated using the RF-based model [25] with satellite variable inputs to allow the classification of seven aerosol types. Model accuracy for the classification of the seven aerosol types was 59%, improving up to 73% when the seven classes were merged into four aerosol types.…”

“…The present study followed the work of Choi et al [25], with the RF-based model being further developed to investigate the feasibility of satellite aerosol classification. The use of several different satellite products may lead to insufficient spatial coverage in aerosol classification due to data being missing from each product.…”