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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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/6/1096 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T13:16:42Z |
| format | Article |
| id | doaj.art-a930376483fe4a57a3495439bb68dc36 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T13:16:42Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a930376483fe4a57a3495439bb68dc362023-11-21T10:22:27ZengMDPI AGRemote Sensing2072-42922021-03-01136109610.3390/rs13061096Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite ObservationsSoi Ahn0Sung-Rae Chung1Hyun-Jong Oh2Chu-Yong Chung3National Meteorological Satellite Center (NMSC), Korea Meteorological Administration (KMA), Jincheon-gun 27803, KoreaNational Meteorological Satellite Center (NMSC), Korea Meteorological Administration (KMA), Jincheon-gun 27803, KoreaNational Meteorological Satellite Center (NMSC), Korea Meteorological Administration (KMA), Jincheon-gun 27803, KoreaNational Institute of Meteorological Sciences (NIMS), Korea Meteorological Administration (KMA), Seogwipo-si 63568, KoreaThis 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.https://www.mdpi.com/2072-4292/13/6/1096composite aerosol optical depth (AOD)cumulative distribution function (CDF)Northeast AsiaAERONETdata fusionretrieval algorithm |
| spellingShingle | Soi Ahn Sung-Rae Chung Hyun-Jong Oh Chu-Yong Chung Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations Remote Sensing composite aerosol optical depth (AOD) cumulative distribution function (CDF) Northeast Asia AERONET data fusion retrieval algorithm |
| title | 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_short | 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 |
| topic | composite aerosol optical depth (AOD) cumulative distribution function (CDF) Northeast Asia AERONET data fusion retrieval algorithm |
| url | https://www.mdpi.com/2072-4292/13/6/1096 |
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