Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land

This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AER...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Wenhao Zhang, Hui Xu, Lili Zhang
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2019
Subjects:
AHI
Q
Online Access:https://doi.org/10.3390/rs11091108
https://doaj.org/article/fe4d0e4035664d89aaad27a80d5f7c4e
id ftdoajarticles:oai:doaj.org/article:fe4d0e4035664d89aaad27a80d5f7c4e
record_format openpolar
spelling ftdoajarticles:oai:doaj.org/article:fe4d0e4035664d89aaad27a80d5f7c4e 2023-05-15T13:06:31+02:00 Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land Wenhao Zhang Hui Xu Lili Zhang 2019-05-01T00:00:00Z https://doi.org/10.3390/rs11091108 https://doaj.org/article/fe4d0e4035664d89aaad27a80d5f7c4e EN eng MDPI AG https://www.mdpi.com/2072-4292/11/9/1108 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11091108 https://doaj.org/article/fe4d0e4035664d89aaad27a80d5f7c4e Remote Sensing, Vol 11, Iss 9, p 1108 (2019) aerosol optical depth Himawari-8 AHI AERONE assessment Science Q article 2019 ftdoajarticles https://doi.org/10.3390/rs11091108 2022-12-31T16:05:48Z This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AERONET) level 2.0 direct sun AOD measurements over the last three years (May 2016−December 2018) at 58 selected AERONET sites. A comprehensive comparison between AHI and AERONET AOD was carried out, which yielded a correlation coefficient (R) of 0.82, a slope of 0.69, and a root mean square error (RMSE) of 0.16. The results indicate a good agreement between AHI and AERONET AOD, while revealing that the AHI aerosol retrieval algorithm tends to underestimate the atmospheric aerosol load. In addition, the expected uncertainty of AHI Level 3 AOD (Version 3.0) is ± (0.1 + 0.3 × AOD). Furthermore, the performance of the AHI aerosol retrieval algorithm exhibits regional variation. The best performance is reported over East Asia (R 0.86), followed by Southeast Asia (R 0.79) and Australia (R 0.35). The monthly and seasonal comparisons between AHI and AERONET show that the best performance is found in summer (R 0.93), followed by autumn (R 0.84), winter (R 0.82), and spring (R 0.76). The worst performance was observed in March (R 0.75), while the best performance appeared in June (R 0.94). The variation in the annual mean AHI AOD on the scale of hours demonstrates that AHI can perform continuous (no less than ten hours) aerosol monitoring. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 11 9 1108
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic aerosol optical depth
Himawari-8
AHI
AERONE
assessment
Science
Q
spellingShingle aerosol optical depth
Himawari-8
AHI
AERONE
assessment
Science
Q
Wenhao Zhang
Hui Xu
Lili Zhang
Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land
topic_facet aerosol optical depth
Himawari-8
AHI
AERONE
assessment
Science
Q
description This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AERONET) level 2.0 direct sun AOD measurements over the last three years (May 2016−December 2018) at 58 selected AERONET sites. A comprehensive comparison between AHI and AERONET AOD was carried out, which yielded a correlation coefficient (R) of 0.82, a slope of 0.69, and a root mean square error (RMSE) of 0.16. The results indicate a good agreement between AHI and AERONET AOD, while revealing that the AHI aerosol retrieval algorithm tends to underestimate the atmospheric aerosol load. In addition, the expected uncertainty of AHI Level 3 AOD (Version 3.0) is ± (0.1 + 0.3 × AOD). Furthermore, the performance of the AHI aerosol retrieval algorithm exhibits regional variation. The best performance is reported over East Asia (R 0.86), followed by Southeast Asia (R 0.79) and Australia (R 0.35). The monthly and seasonal comparisons between AHI and AERONET show that the best performance is found in summer (R 0.93), followed by autumn (R 0.84), winter (R 0.82), and spring (R 0.76). The worst performance was observed in March (R 0.75), while the best performance appeared in June (R 0.94). The variation in the annual mean AHI AOD on the scale of hours demonstrates that AHI can perform continuous (no less than ten hours) aerosol monitoring.
format Article in Journal/Newspaper
author Wenhao Zhang
Hui Xu
Lili Zhang
author_facet Wenhao Zhang
Hui Xu
Lili Zhang
author_sort Wenhao Zhang
title Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land
title_short Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land
title_full Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land
title_fullStr Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land
title_full_unstemmed Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land
title_sort assessment of himawari-8 ahi aerosol optical depth over land
publisher MDPI AG
publishDate 2019
url https://doi.org/10.3390/rs11091108
https://doaj.org/article/fe4d0e4035664d89aaad27a80d5f7c4e
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing, Vol 11, Iss 9, p 1108 (2019)
op_relation https://www.mdpi.com/2072-4292/11/9/1108
https://doaj.org/toc/2072-4292
2072-4292
doi:10.3390/rs11091108
https://doaj.org/article/fe4d0e4035664d89aaad27a80d5f7c4e
op_doi https://doi.org/10.3390/rs11091108
container_title Remote Sensing
container_volume 11
container_issue 9
container_start_page 1108
_version_ 1766008904152514560