Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia

The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be in orbit in 2019 onboard the GEO-KOMPSAT 2B satellite and will continuously monitor air quality over Asia. The GEMS will make measurements in the UV spectrum (300–500 nm) with 0.6 nm resolution. In this study, an algorit...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Mijin Kim, Jhoon Kim, Omar Torres, Changwoo Ahn, Woogyung Kim, Ukkyo Jeong, Sujung Go, Xiong Liu, Kyung Jung Moon, Deok-Rae Kim
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2018
Subjects:
Q
Haf
Online Access:https://doi.org/10.3390/rs10020162
https://doaj.org/article/2cc7f74ba1f54167857dc2e08d3fa5bb
id ftdoajarticles:oai:doaj.org/article:2cc7f74ba1f54167857dc2e08d3fa5bb
record_format openpolar
spelling ftdoajarticles:oai:doaj.org/article:2cc7f74ba1f54167857dc2e08d3fa5bb 2023-05-15T13:07:00+02:00 Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia Mijin Kim Jhoon Kim Omar Torres Changwoo Ahn Woogyung Kim Ukkyo Jeong Sujung Go Xiong Liu Kyung Jung Moon Deok-Rae Kim 2018-01-01T00:00:00Z https://doi.org/10.3390/rs10020162 https://doaj.org/article/2cc7f74ba1f54167857dc2e08d3fa5bb EN eng MDPI AG http://www.mdpi.com/2072-4292/10/2/162 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs10020162 https://doaj.org/article/2cc7f74ba1f54167857dc2e08d3fa5bb Remote Sensing, Vol 10, Iss 2, p 162 (2018) remote sensing aerosol optical depth single scattering albedo aerosol layer height optimal estimation Science Q article 2018 ftdoajarticles https://doi.org/10.3390/rs10020162 2022-12-31T16:07:13Z The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be in orbit in 2019 onboard the GEO-KOMPSAT 2B satellite and will continuously monitor air quality over Asia. The GEMS will make measurements in the UV spectrum (300–500 nm) with 0.6 nm resolution. In this study, an algorithm is developed to retrieve aerosol optical properties from UV-visible measurements for the future satellite instrument and is tested using 3 years of existing OMI L1B data. This algorithm provides aerosol optical depth (AOD), single scattering albedo (SSA) and aerosol layer height (ALH) using an optimized estimation method. The retrieved AOD shows good correlation with Aerosol Robotic Network (AERONET) AOD with correlation coefficients of 0.83, 0.73 and 0.80 for heavy-absorbing fine (HAF) particles, dust and non-absorbing (NA) particles, respectively. However, regression tests indicate underestimation and overestimation of HAF and NA AOD, respectively. In comparison with AOD from the OMI/Aura Near-UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13 km × 24 km V003 (OMAERUV) algorithm, the retrieved AOD has a correlation coefficient of 0.86 and linear regression equation, AODGEMS = 1.18AODOMAERUV + 0.09. An uncertainty test based on a reference method, which estimates retrieval error by applying the algorithm to simulated radiance data, revealed that assumptions in the spectral dependency of aerosol absorptivity in the UV cause significant errors in aerosol property retrieval, particularly the SSA retrieval. Consequently, retrieved SSAs did not show good correlation with AERONET values. The ALH results were qualitatively compared with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products and were found to be well correlated for highly absorbing aerosols. The difference between the attenuated-backscatter-weighted height from CALIOP and retrieved ALH were mostly closed to zero when the retrieved AOD is higher than 0.8 and SSA is lower than 0.93. Although retrieval accuracy was not ... Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Haf ENVELOPE(-19.699,-19.699,64.145,64.145) Remote Sensing 10 2 162
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic remote sensing
aerosol optical depth
single scattering albedo
aerosol layer height
optimal estimation
Science
Q
spellingShingle remote sensing
aerosol optical depth
single scattering albedo
aerosol layer height
optimal estimation
Science
Q
Mijin Kim
Jhoon Kim
Omar Torres
Changwoo Ahn
Woogyung Kim
Ukkyo Jeong
Sujung Go
Xiong Liu
Kyung Jung Moon
Deok-Rae Kim
Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia
topic_facet remote sensing
aerosol optical depth
single scattering albedo
aerosol layer height
optimal estimation
Science
Q
description The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be in orbit in 2019 onboard the GEO-KOMPSAT 2B satellite and will continuously monitor air quality over Asia. The GEMS will make measurements in the UV spectrum (300–500 nm) with 0.6 nm resolution. In this study, an algorithm is developed to retrieve aerosol optical properties from UV-visible measurements for the future satellite instrument and is tested using 3 years of existing OMI L1B data. This algorithm provides aerosol optical depth (AOD), single scattering albedo (SSA) and aerosol layer height (ALH) using an optimized estimation method. The retrieved AOD shows good correlation with Aerosol Robotic Network (AERONET) AOD with correlation coefficients of 0.83, 0.73 and 0.80 for heavy-absorbing fine (HAF) particles, dust and non-absorbing (NA) particles, respectively. However, regression tests indicate underestimation and overestimation of HAF and NA AOD, respectively. In comparison with AOD from the OMI/Aura Near-UV Aerosol Optical Depth and Single Scattering Albedo 1-orbit L2 Swath 13 km × 24 km V003 (OMAERUV) algorithm, the retrieved AOD has a correlation coefficient of 0.86 and linear regression equation, AODGEMS = 1.18AODOMAERUV + 0.09. An uncertainty test based on a reference method, which estimates retrieval error by applying the algorithm to simulated radiance data, revealed that assumptions in the spectral dependency of aerosol absorptivity in the UV cause significant errors in aerosol property retrieval, particularly the SSA retrieval. Consequently, retrieved SSAs did not show good correlation with AERONET values. The ALH results were qualitatively compared with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products and were found to be well correlated for highly absorbing aerosols. The difference between the attenuated-backscatter-weighted height from CALIOP and retrieved ALH were mostly closed to zero when the retrieved AOD is higher than 0.8 and SSA is lower than 0.93. Although retrieval accuracy was not ...
format Article in Journal/Newspaper
author Mijin Kim
Jhoon Kim
Omar Torres
Changwoo Ahn
Woogyung Kim
Ukkyo Jeong
Sujung Go
Xiong Liu
Kyung Jung Moon
Deok-Rae Kim
author_facet Mijin Kim
Jhoon Kim
Omar Torres
Changwoo Ahn
Woogyung Kim
Ukkyo Jeong
Sujung Go
Xiong Liu
Kyung Jung Moon
Deok-Rae Kim
author_sort Mijin Kim
title Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia
title_short Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia
title_full Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia
title_fullStr Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia
title_full_unstemmed Optimal Estimation-Based Algorithm to Retrieve Aerosol Optical Properties for GEMS Measurements over Asia
title_sort optimal estimation-based algorithm to retrieve aerosol optical properties for gems measurements over asia
publisher MDPI AG
publishDate 2018
url https://doi.org/10.3390/rs10020162
https://doaj.org/article/2cc7f74ba1f54167857dc2e08d3fa5bb
long_lat ENVELOPE(-19.699,-19.699,64.145,64.145)
geographic Haf
geographic_facet Haf
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing, Vol 10, Iss 2, p 162 (2018)
op_relation http://www.mdpi.com/2072-4292/10/2/162
https://doaj.org/toc/2072-4292
2072-4292
doi:10.3390/rs10020162
https://doaj.org/article/2cc7f74ba1f54167857dc2e08d3fa5bb
op_doi https://doi.org/10.3390/rs10020162
container_title Remote Sensing
container_volume 10
container_issue 2
container_start_page 162
_version_ 1766030397916839936