Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low tempor...

Full description

Bibliographic Details
Published in:Remote Sensing
Main Authors: Lijuan Chen, Ying Fei, Ren Wang, Peng Fang, Jiamei Han, Yong Zha
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2021
Subjects:
Online Access:https://doi.org/10.3390/rs13122376
id ftmdpi:oai:mdpi.com:/2072-4292/13/12/2376/
record_format openpolar
spelling ftmdpi:oai:mdpi.com:/2072-4292/13/12/2376/ 2023-08-20T03:59:12+02:00 Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data Lijuan Chen Ying Fei Ren Wang Peng Fang Jiamei Han Yong Zha agris 2021-06-18 application/pdf https://doi.org/10.3390/rs13122376 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs13122376 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 12; Pages: 2376 aerosol optical depth high resolution spectral conversion GOCI MODIS Text 2021 ftmdpi https://doi.org/10.3390/rs13122376 2023-08-01T01:58:44Z High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low temporal resolution deficiency of polar orbiting satellite. In this study, we proposed an algorithm for retrieving high temporal resolution AOD using GOCI data and then applied the algorithm in the Yangtze River Delta, a typical region suffering severe air pollution issues. Based on Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by MODIS V5.2 algorithm and MODIS Bidirectional Reflectance Distribution Function (BRDF) data, after spectral conversion between MODIS and GOCI, the GOCI surface reflectance at different solar angles were obtained and used to retrieve AOD. Five indicators including correlation coefficient (R), significant level of the correlation (p value), mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) were employed to analyze the errors between the Aerosol Robotic Network (AERONET) observed AOD and the GOCI retrieved AOD. The results showed that the GOCI AOD retrieved by the continental aerosol look-up table was consistent with the AERONET AOD (R > 0.7, p ≤ 0.05). The highest R value of Taihu Station and Xuzhou CUMT Station are both 0.84 (8:30 a.m.); the minimum RMSE at Taihu and Xuzhou-CUMT stations were 0.2077 (11:30 a.m.) and 0.1937 (10:30 a.m.), respectively. Moreover, the results suggested that the greater the solar angle of the GOCI sensor, the higher the AOD retrieval accuracy, while the retrieved AOD at noon exhibited the largest error as assessed by MAE and MRE. We concluded that the inaccurate estimation of surface reflectance was the root cause of the retrieval errors. This study has implications in providing a deep understanding of the effects of solar angle changes on retrieving AOD using GOCI. Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 13 12 2376
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic aerosol optical depth
high resolution
spectral conversion
GOCI
MODIS
spellingShingle aerosol optical depth
high resolution
spectral conversion
GOCI
MODIS
Lijuan Chen
Ying Fei
Ren Wang
Peng Fang
Jiamei Han
Yong Zha
Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data
topic_facet aerosol optical depth
high resolution
spectral conversion
GOCI
MODIS
description High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low temporal resolution deficiency of polar orbiting satellite. In this study, we proposed an algorithm for retrieving high temporal resolution AOD using GOCI data and then applied the algorithm in the Yangtze River Delta, a typical region suffering severe air pollution issues. Based on Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by MODIS V5.2 algorithm and MODIS Bidirectional Reflectance Distribution Function (BRDF) data, after spectral conversion between MODIS and GOCI, the GOCI surface reflectance at different solar angles were obtained and used to retrieve AOD. Five indicators including correlation coefficient (R), significant level of the correlation (p value), mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) were employed to analyze the errors between the Aerosol Robotic Network (AERONET) observed AOD and the GOCI retrieved AOD. The results showed that the GOCI AOD retrieved by the continental aerosol look-up table was consistent with the AERONET AOD (R > 0.7, p ≤ 0.05). The highest R value of Taihu Station and Xuzhou CUMT Station are both 0.84 (8:30 a.m.); the minimum RMSE at Taihu and Xuzhou-CUMT stations were 0.2077 (11:30 a.m.) and 0.1937 (10:30 a.m.), respectively. Moreover, the results suggested that the greater the solar angle of the GOCI sensor, the higher the AOD retrieval accuracy, while the retrieved AOD at noon exhibited the largest error as assessed by MAE and MRE. We concluded that the inaccurate estimation of surface reflectance was the root cause of the retrieval errors. This study has implications in providing a deep understanding of the effects of solar angle changes on retrieving AOD using GOCI.
format Text
author Lijuan Chen
Ying Fei
Ren Wang
Peng Fang
Jiamei Han
Yong Zha
author_facet Lijuan Chen
Ying Fei
Ren Wang
Peng Fang
Jiamei Han
Yong Zha
author_sort Lijuan Chen
title Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data
title_short Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data
title_full Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data
title_fullStr Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data
title_full_unstemmed Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data
title_sort retrieval of high temporal resolution aerosol optical depth using the goci remote sensing data
publisher Multidisciplinary Digital Publishing Institute
publishDate 2021
url https://doi.org/10.3390/rs13122376
op_coverage agris
genre Aerosol Robotic Network
genre_facet Aerosol Robotic Network
op_source Remote Sensing; Volume 13; Issue 12; Pages: 2376
op_relation https://dx.doi.org/10.3390/rs13122376
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs13122376
container_title Remote Sensing
container_volume 13
container_issue 12
container_start_page 2376
_version_ 1774721447033307136