Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution

Determining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution from 2012 to 2019 as a...

Full description

Bibliographic Details
Main Authors: Qi-Xiang Chen, Chun-Lin Huang, Shi-Kui Dong, Kai-Feng Lin
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/16/7/1210
_version_ 1827286553173426176
author Qi-Xiang Chen
Chun-Lin Huang
Shi-Kui Dong
Kai-Feng Lin
author_facet Qi-Xiang Chen
Chun-Lin Huang
Shi-Kui Dong
Kai-Feng Lin
author_sort Qi-Xiang Chen
collection DOAJ
description Determining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution from 2012 to 2019 as a major testbed. A series of multiple lognormal distributions with 1 to 5 peaks are firstly applied to fit the AOD histogram at each grid point, and the distribution with the highest correlation coefficient (R) gives preliminary estimations of BAOD, which is defined as either the intersection point of the first two normal distribution curves when having multiple peaks, or the midpoint between the peak AOD and the first AOD with non-zero probability when the mono peak is the best fit. Then, the lowest 1st to 100th percentile AOD distributions are compared with the preliminary BAOD distribution on a global scale. The final BAOD is obtained from the best cutoff percentile AOD distributions with the lowest bias compared with preliminary BAOD. Results show that the lowest 30th percentile AOD is the best estimation of BAOD for different AOD datasets and different seasons. Analysis of aerosol chemical information from MERRA-2 further supports this selection. Based on the BAOD, we updated the VIIRS aerosol type classification scheme, and the results show that the updated scheme is able to achieve reliable detection of aerosol type change in low aerosol loading conditions.
first_indexed 2024-04-24T10:35:23Z
format Article
id doaj.art-30feb597810941e5bef18e84123281bd
institution Directory Open Access Journal
issn 2072-4292
language English
last_indexed 2024-04-24T10:35:23Z
publishDate 2024-03-01
publisher MDPI AG
record_format Article
series Remote Sensing
spelling doaj.art-30feb597810941e5bef18e84123281bd2024-04-12T13:25:38ZengMDPI AGRemote Sensing2072-42922024-03-01167121010.3390/rs16071210Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal DistributionQi-Xiang Chen0Chun-Lin Huang1Shi-Kui Dong2Kai-Feng Lin3School of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Energy Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin 150001, ChinaDetermining background aerosol optical depth threshold value (BAOD) is critical to aerosol type identification and air pollution control. This study presents a statistical method to select the best BAOD threshold value using the VIIRS DB AOD products at 1 × 1 degree resolution from 2012 to 2019 as a major testbed. A series of multiple lognormal distributions with 1 to 5 peaks are firstly applied to fit the AOD histogram at each grid point, and the distribution with the highest correlation coefficient (R) gives preliminary estimations of BAOD, which is defined as either the intersection point of the first two normal distribution curves when having multiple peaks, or the midpoint between the peak AOD and the first AOD with non-zero probability when the mono peak is the best fit. Then, the lowest 1st to 100th percentile AOD distributions are compared with the preliminary BAOD distribution on a global scale. The final BAOD is obtained from the best cutoff percentile AOD distributions with the lowest bias compared with preliminary BAOD. Results show that the lowest 30th percentile AOD is the best estimation of BAOD for different AOD datasets and different seasons. Analysis of aerosol chemical information from MERRA-2 further supports this selection. Based on the BAOD, we updated the VIIRS aerosol type classification scheme, and the results show that the updated scheme is able to achieve reliable detection of aerosol type change in low aerosol loading conditions.https://www.mdpi.com/2072-4292/16/7/1210aerosol typebackground aerosolthreshold valueAODVIIRSMERRA-2
spellingShingle Qi-Xiang Chen
Chun-Lin Huang
Shi-Kui Dong
Kai-Feng Lin
Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution
Remote Sensing
aerosol type
background aerosol
threshold value
AOD
VIIRS
MERRA-2
title Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution
title_full Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution
title_fullStr Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution
title_full_unstemmed Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution
title_short Satellite-Based Background Aerosol Optical Depth Determination via Global Statistical Analysis of Multiple Lognormal Distribution
title_sort satellite based background aerosol optical depth determination via global statistical analysis of multiple lognormal distribution
topic aerosol type
background aerosol
threshold value
AOD
VIIRS
MERRA-2
url https://www.mdpi.com/2072-4292/16/7/1210
work_keys_str_mv AT qixiangchen satellitebasedbackgroundaerosolopticaldepthdeterminationviaglobalstatisticalanalysisofmultiplelognormaldistribution
AT chunlinhuang satellitebasedbackgroundaerosolopticaldepthdeterminationviaglobalstatisticalanalysisofmultiplelognormaldistribution
AT shikuidong satellitebasedbackgroundaerosolopticaldepthdeterminationviaglobalstatisticalanalysisofmultiplelognormaldistribution
AT kaifenglin satellitebasedbackgroundaerosolopticaldepthdeterminationviaglobalstatisticalanalysisofmultiplelognormaldistribution