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Open AccessArticle

Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations

1
National Meteorological Satellite Center (NMSC), Korea Meteorological Administration (KMA), Jincheon-gun 27803, Korea
2
National Institute of Meteorological Sciences (NIMS), Korea Meteorological Administration (KMA), Seogwipo-si 63568, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Wei Gong
Remote Sens. 2021, 13(6), 1096; https://doi.org/10.3390/rs13061096
Received: 15 February 2021 / Revised: 7 March 2021 / Accepted: 9 March 2021 / Published: 13 March 2021
(This article belongs to the Special Issue Active and Passive Remote Sensing of Aerosols and Clouds)
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. View Full-Text
Keywords: composite aerosol optical depth (AOD); cumulative distribution function (CDF); Northeast Asia; AERONET; data fusion; retrieval algorithm composite aerosol optical depth (AOD); cumulative distribution function (CDF); Northeast Asia; AERONET; data fusion; retrieval algorithm
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MDPI and ACS Style

Ahn, S.; Chung, S.-R.; Oh, H.-J.; Chung, C.-Y. Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations. Remote Sens. 2021, 13, 1096. https://doi.org/10.3390/rs13061096

AMA Style

Ahn S, Chung S-R, Oh H-J, Chung C-Y. Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations. Remote Sensing. 2021; 13(6):1096. https://doi.org/10.3390/rs13061096

Chicago/Turabian Style

Ahn, Soi; Chung, Sung-Rae; Oh, Hyun-Jong; Chung, Chu-Yong. 2021. "Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations" Remote Sens. 13, no. 6: 1096. https://doi.org/10.3390/rs13061096

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