A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation
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...
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ftmdpi:oai:mdpi.com:/2072-4292/13/4/609/ 2023-08-20T03:59:11+02:00 A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation Wonei Choi Hanlim Lee Jeonghyeon Park agris 2021-02-08 application/pdf https://doi.org/10.3390/rs13040609 EN eng Multidisciplinary Digital Publishing Institute Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs13040609 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 4; Pages: 609 aerosol classification aerosol remote sensing space-borne remote sensing aerosol type machine learning TROPOMI MODIS AERONET Text 2021 ftmdpi https://doi.org/10.3390/rs13040609 2023-08-01T01:02:14Z 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. Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 13 4 609 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
aerosol classification aerosol remote sensing space-borne remote sensing aerosol type machine learning TROPOMI MODIS AERONET |
spellingShingle |
aerosol classification aerosol remote sensing space-borne remote sensing aerosol type machine learning TROPOMI MODIS AERONET Wonei Choi Hanlim Lee Jeonghyeon Park A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation |
topic_facet |
aerosol classification aerosol remote sensing space-borne remote sensing aerosol type machine learning TROPOMI MODIS AERONET |
description |
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. |
format |
Text |
author |
Wonei Choi Hanlim Lee Jeonghyeon Park |
author_facet |
Wonei Choi Hanlim Lee Jeonghyeon Park |
author_sort |
Wonei Choi |
title |
A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation |
title_short |
A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation |
title_full |
A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation |
title_fullStr |
A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation |
title_full_unstemmed |
A First Approach to Aerosol Classification Using Space-Borne Measurement Data: Machine Learning-Based Algorithm and Evaluation |
title_sort |
first approach to aerosol classification using space-borne measurement data: machine learning-based algorithm and evaluation |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13040609 |
op_coverage |
agris |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing; Volume 13; Issue 4; Pages: 609 |
op_relation |
Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs13040609 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13040609 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
4 |
container_start_page |
609 |
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1774718515894288384 |