Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations
This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various space...
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ftmdpi:oai:mdpi.com:/2072-4292/13/6/1096/ 2023-08-20T03:59:12+02:00 Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations Soi Ahn Sung-Rae Chung Hyun-Jong Oh Chu-Yong Chung agris 2021-03-13 application/pdf https://doi.org/10.3390/rs13061096 EN eng Multidisciplinary Digital Publishing Institute Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs13061096 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 6; Pages: 1096 composite aerosol optical depth (AOD) cumulative distribution function (CDF) Northeast Asia AERONET data fusion retrieval algorithm Text 2021 ftmdpi https://doi.org/10.3390/rs13061096 2023-08-01T01:16:32Z This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records. Text Aerosol Robotic Network MDPI Open Access Publishing Remote Sensing 13 6 1096 |
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Open Polar |
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MDPI Open Access Publishing |
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language |
English |
topic |
composite aerosol optical depth (AOD) cumulative distribution function (CDF) Northeast Asia AERONET data fusion retrieval algorithm |
spellingShingle |
composite aerosol optical depth (AOD) cumulative distribution function (CDF) Northeast Asia AERONET data fusion retrieval algorithm Soi Ahn Sung-Rae Chung Hyun-Jong Oh Chu-Yong Chung Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations |
topic_facet |
composite aerosol optical depth (AOD) cumulative distribution function (CDF) Northeast Asia AERONET data fusion retrieval algorithm |
description |
This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records. |
format |
Text |
author |
Soi Ahn Sung-Rae Chung Hyun-Jong Oh Chu-Yong Chung |
author_facet |
Soi Ahn Sung-Rae Chung Hyun-Jong Oh Chu-Yong Chung |
author_sort |
Soi Ahn |
title |
Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations |
title_short |
Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations |
title_full |
Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations |
title_fullStr |
Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations |
title_full_unstemmed |
Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations |
title_sort |
composite aerosol optical depth mapping over northeast asia from geo-leo satellite observations |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13061096 |
op_coverage |
agris |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing; Volume 13; Issue 6; Pages: 1096 |
op_relation |
Atmospheric Remote Sensing https://dx.doi.org/10.3390/rs13061096 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13061096 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
6 |
container_start_page |
1096 |
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1774720118941548544 |