A Spatio-Temporal Weighted Filling Method for Missing AOD Values
Aerosol Optical Depth (AOD) is a key parameter in defining the characteristics of atmospheric aerosols, evaluating atmospheric pollution, and studying aerosol radiative climate effects. However, a large amount of the AOD data obtained by satellite remote sensing are missing due to cloud cover and ot...
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ftmdpi:oai:mdpi.com:/2073-4433/13/7/1080/ 2023-08-20T03:59:12+02:00 A Spatio-Temporal Weighted Filling Method for Missing AOD Values Rongfeng Gao Xiaoping Rui Jiakui Tang agris 2022-07-08 application/pdf https://doi.org/10.3390/atmos13071080 EN eng Multidisciplinary Digital Publishing Institute Aerosols https://dx.doi.org/10.3390/atmos13071080 https://creativecommons.org/licenses/by/4.0/ Atmosphere; Volume 13; Issue 7; Pages: 1080 Beijing–Tianjin–Hebei MAIAC AOD spatio-temporal weighted AOD fill Text 2022 ftmdpi https://doi.org/10.3390/atmos13071080 2023-08-01T05:38:58Z Aerosol Optical Depth (AOD) is a key parameter in defining the characteristics of atmospheric aerosols, evaluating atmospheric pollution, and studying aerosol radiative climate effects. However, a large amount of the AOD data obtained by satellite remote sensing are missing due to cloud cover and other factors. To obtain AOD data with continuous distribution in space, this study considers the spatial and temporal correlation of AOD and proposes a spatio-temporal weighted filling method based on a sliding window to supply the missing AOD data blocks. The method uses the semivariogram and autocorrelation function to judge the spatial and temporal correlation of AOD and uses the AOD spatial autocorrelation threshold as the sliding window size, and then it builds a spatio-temporal weighted model for each window to fill in the missing values. We selected the area with full values for simulation. The results show that the accuracy of this method has been significantly improved compared with the mean filling method. The R2 reaches 0.751, the RMSE is 0.021, and the filling effect is smoother. Finally, this method was used to fill in the missing values of the MultiAngle Implementation of Atmospheric Correction (MAIAC) AOD in the Beijing–Tianjin–Hebei region in 2019, and AErosol RObotic NETwork (AERONET) AOD was used as the true value for testing. The results show that the filled AOD has a high correlation with AERONET AOD, the R2 is 0.785, and the RMSE is 0.120. A summary of the AOD values of the 13 cities in the Beijing–Tianjin–Hebei region shows that the values in the first and third quarters are higher than those in the second and fourth quarters, with the highest AOD value in March and the second highest in August; among the 13 cities, the AOD values in Chengde and Zhangjiakou are lower than those in the other cities. Text Aerosol Robotic Network MDPI Open Access Publishing Atmosphere 13 7 1080 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
Beijing–Tianjin–Hebei MAIAC AOD spatio-temporal weighted AOD fill |
spellingShingle |
Beijing–Tianjin–Hebei MAIAC AOD spatio-temporal weighted AOD fill Rongfeng Gao Xiaoping Rui Jiakui Tang A Spatio-Temporal Weighted Filling Method for Missing AOD Values |
topic_facet |
Beijing–Tianjin–Hebei MAIAC AOD spatio-temporal weighted AOD fill |
description |
Aerosol Optical Depth (AOD) is a key parameter in defining the characteristics of atmospheric aerosols, evaluating atmospheric pollution, and studying aerosol radiative climate effects. However, a large amount of the AOD data obtained by satellite remote sensing are missing due to cloud cover and other factors. To obtain AOD data with continuous distribution in space, this study considers the spatial and temporal correlation of AOD and proposes a spatio-temporal weighted filling method based on a sliding window to supply the missing AOD data blocks. The method uses the semivariogram and autocorrelation function to judge the spatial and temporal correlation of AOD and uses the AOD spatial autocorrelation threshold as the sliding window size, and then it builds a spatio-temporal weighted model for each window to fill in the missing values. We selected the area with full values for simulation. The results show that the accuracy of this method has been significantly improved compared with the mean filling method. The R2 reaches 0.751, the RMSE is 0.021, and the filling effect is smoother. Finally, this method was used to fill in the missing values of the MultiAngle Implementation of Atmospheric Correction (MAIAC) AOD in the Beijing–Tianjin–Hebei region in 2019, and AErosol RObotic NETwork (AERONET) AOD was used as the true value for testing. The results show that the filled AOD has a high correlation with AERONET AOD, the R2 is 0.785, and the RMSE is 0.120. A summary of the AOD values of the 13 cities in the Beijing–Tianjin–Hebei region shows that the values in the first and third quarters are higher than those in the second and fourth quarters, with the highest AOD value in March and the second highest in August; among the 13 cities, the AOD values in Chengde and Zhangjiakou are lower than those in the other cities. |
format |
Text |
author |
Rongfeng Gao Xiaoping Rui Jiakui Tang |
author_facet |
Rongfeng Gao Xiaoping Rui Jiakui Tang |
author_sort |
Rongfeng Gao |
title |
A Spatio-Temporal Weighted Filling Method for Missing AOD Values |
title_short |
A Spatio-Temporal Weighted Filling Method for Missing AOD Values |
title_full |
A Spatio-Temporal Weighted Filling Method for Missing AOD Values |
title_fullStr |
A Spatio-Temporal Weighted Filling Method for Missing AOD Values |
title_full_unstemmed |
A Spatio-Temporal Weighted Filling Method for Missing AOD Values |
title_sort |
spatio-temporal weighted filling method for missing aod values |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/atmos13071080 |
op_coverage |
agris |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Atmosphere; Volume 13; Issue 7; Pages: 1080 |
op_relation |
Aerosols https://dx.doi.org/10.3390/atmos13071080 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/atmos13071080 |
container_title |
Atmosphere |
container_volume |
13 |
container_issue |
7 |
container_start_page |
1080 |
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1774720836537679872 |