Paper Info
Title
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.
Year
DOI
Venue
2021
10.3390/rs13040609
REMOTE SENSING
Keywords
DocType
Volume
aerosol classification, aerosol remote sensing, space-borne remote sensing, aerosol type, machine learning, TROPOMI, MODIS, AERONET
Journal
13
Issue
Citations 
PageRank 
4
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Wonei Choi112.81
Hanlim Lee2136.22
Jeonghyeon Park300.34