Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning
Quantitative measurements of aerosol absorptive properties, e.g., the absorbing aerosol optical depth (AAOD) and the single scattering albedo (SSA), are important to reduce uncertainties of aerosol climate radiative forcing assessments. Currently, global retrievals of AAOD and SSA are mainly provide...
Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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Online Access: | http://resolver.tudelft.nl/uuid:765ed2e9-6835-4360-a1d7-cc9756021e1e https://doi.org/10.1109/JSTARS.2021.3108669 |
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fttudelft:oai:tudelft.nl:uuid:765ed2e9-6835-4360-a1d7-cc9756021e1e 2024-02-11T09:54:45+01:00 Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning Sun, J. (author) Veefkind, j. Pepijn (author) Van Velthoven, Peter (author) Levelt, Pieternel Felicitas (author) 2021 http://resolver.tudelft.nl/uuid:765ed2e9-6835-4360-a1d7-cc9756021e1e https://doi.org/10.1109/JSTARS.2021.3108669 en eng http://www.scopus.com/inward/record.url?scp=85117118359&partnerID=8YFLogxK IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing--1939-1404--af1f29ba-548e-4319-804e-dae0f7099597 http://resolver.tudelft.nl/uuid:765ed2e9-6835-4360-a1d7-cc9756021e1e https://doi.org/10.1109/JSTARS.2021.3108669 © 2021 J. Sun, j. Pepijn Veefkind, Peter Van Velthoven, Pieternel Felicitas Levelt Absorbing aerosol optical depth (AAOD) deep neural network (DDN) machine learning ozone monitoring instrument (OMI) single scattering albedo (SSA) ultra-violet aerosol index (UVAI) journal article 2021 fttudelft https://doi.org/10.1109/JSTARS.2021.3108669 2024-01-24T23:32:20Z Quantitative measurements of aerosol absorptive properties, e.g., the absorbing aerosol optical depth (AAOD) and the single scattering albedo (SSA), are important to reduce uncertainties of aerosol climate radiative forcing assessments. Currently, global retrievals of AAOD and SSA are mainly provided by the ground-based aerosol robotic network (AERONET), whereas it is still challenging to retrieve them from space. However, we found the AERONET AAOD has a relatively strong correlation with the satellite retrieved ultra-violet aerosol index (UVAI). Based on this, a numerical relation is built by a deep neural network (DNN) to predict global AAOD and SSA over land from the long-term UVAI record (2006-2019) provided by the ozone monitoring instrument (OMI) onboard Aura. The DNN predicted aerosol absorption is satisfying for samples with AOD at 550 nm larger than 0.1, and the DNN model performance is better for smaller absorbing aerosols (e.g., smoke) than larger ones (e.g., mineral dust). The comparison of the DNN predictions with AERONET shows a high correlation coefficient of 0.90 and a root mean square of 0.005 for the AAOD, and over 80% of samples are within the expected uncertainty of AERONET SSA (pm0.03). Atmospheric Remote Sensing Article in Journal/Newspaper Aerosol Robotic Network Delft University of Technology: Institutional Repository IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 9692 9710 |
institution |
Open Polar |
collection |
Delft University of Technology: Institutional Repository |
op_collection_id |
fttudelft |
language |
English |
topic |
Absorbing aerosol optical depth (AAOD) deep neural network (DDN) machine learning ozone monitoring instrument (OMI) single scattering albedo (SSA) ultra-violet aerosol index (UVAI) |
spellingShingle |
Absorbing aerosol optical depth (AAOD) deep neural network (DDN) machine learning ozone monitoring instrument (OMI) single scattering albedo (SSA) ultra-violet aerosol index (UVAI) Sun, J. (author) Veefkind, j. Pepijn (author) Van Velthoven, Peter (author) Levelt, Pieternel Felicitas (author) Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning |
topic_facet |
Absorbing aerosol optical depth (AAOD) deep neural network (DDN) machine learning ozone monitoring instrument (OMI) single scattering albedo (SSA) ultra-violet aerosol index (UVAI) |
description |
Quantitative measurements of aerosol absorptive properties, e.g., the absorbing aerosol optical depth (AAOD) and the single scattering albedo (SSA), are important to reduce uncertainties of aerosol climate radiative forcing assessments. Currently, global retrievals of AAOD and SSA are mainly provided by the ground-based aerosol robotic network (AERONET), whereas it is still challenging to retrieve them from space. However, we found the AERONET AAOD has a relatively strong correlation with the satellite retrieved ultra-violet aerosol index (UVAI). Based on this, a numerical relation is built by a deep neural network (DNN) to predict global AAOD and SSA over land from the long-term UVAI record (2006-2019) provided by the ozone monitoring instrument (OMI) onboard Aura. The DNN predicted aerosol absorption is satisfying for samples with AOD at 550 nm larger than 0.1, and the DNN model performance is better for smaller absorbing aerosols (e.g., smoke) than larger ones (e.g., mineral dust). The comparison of the DNN predictions with AERONET shows a high correlation coefficient of 0.90 and a root mean square of 0.005 for the AAOD, and over 80% of samples are within the expected uncertainty of AERONET SSA (pm0.03). Atmospheric Remote Sensing |
format |
Article in Journal/Newspaper |
author |
Sun, J. (author) Veefkind, j. Pepijn (author) Van Velthoven, Peter (author) Levelt, Pieternel Felicitas (author) |
author_facet |
Sun, J. (author) Veefkind, j. Pepijn (author) Van Velthoven, Peter (author) Levelt, Pieternel Felicitas (author) |
author_sort |
Sun, J. (author) |
title |
Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning |
title_short |
Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning |
title_full |
Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning |
title_fullStr |
Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning |
title_full_unstemmed |
Aerosol Absorption over Land Derived from the Ultra-Violet Aerosol Index by Deep Learning |
title_sort |
aerosol absorption over land derived from the ultra-violet aerosol index by deep learning |
publishDate |
2021 |
url |
http://resolver.tudelft.nl/uuid:765ed2e9-6835-4360-a1d7-cc9756021e1e https://doi.org/10.1109/JSTARS.2021.3108669 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_relation |
http://www.scopus.com/inward/record.url?scp=85117118359&partnerID=8YFLogxK IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing--1939-1404--af1f29ba-548e-4319-804e-dae0f7099597 http://resolver.tudelft.nl/uuid:765ed2e9-6835-4360-a1d7-cc9756021e1e https://doi.org/10.1109/JSTARS.2021.3108669 |
op_rights |
© 2021 J. Sun, j. Pepijn Veefkind, Peter Van Velthoven, Pieternel Felicitas Levelt |
op_doi |
https://doi.org/10.1109/JSTARS.2021.3108669 |
container_title |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
container_volume |
14 |
container_start_page |
9692 |
op_container_end_page |
9710 |
_version_ |
1790601654665478144 |