Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land
Land surface reflectance (LSR) and aerosol types are the two main factors that affect aerosol inversions over land. According to LSR determination methods, Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products are produced using the Deep Blue (DB) and Dark Target (DT) algorithms. Fi...
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ftmdpi:oai:mdpi.com:/2073-4433/8/8/155/ 2023-08-20T03:59:12+02:00 Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land Qingxin Wang Lin Sun Jing Wei Yikun Yang Ruibo Li Qinhuo Liu Liangfu Chen agris 2017-08-21 application/pdf https://doi.org/10.3390/atmos8080155 EN eng Multidisciplinary Digital Publishing Institute Aerosols https://dx.doi.org/10.3390/atmos8080155 https://creativecommons.org/licenses/by/4.0/ Atmosphere; Volume 8; Issue 8; Pages: 155 AERONET aerosol optical depth (AOD) MODIS DT algorithm DB algorithm land surface reflectance (LSR) Text 2017 ftmdpi https://doi.org/10.3390/atmos8080155 2023-07-31T21:12:13Z Land surface reflectance (LSR) and aerosol types are the two main factors that affect aerosol inversions over land. According to LSR determination methods, Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products are produced using the Deep Blue (DB) and Dark Target (DT) algorithms. Five aerosol types that are determined from Aerosol Robotic Network (AERONET) ground measurements are used to describe the global distribution of aerosol types in each algorithm. To assess the influence of LSR and the method used to determine aerosol type from aerosol retrievals, 10-km global aerosol products that cover 2013 are selected for validation using Level 2.0 aerosol observations from 175 AERONET sites. The variations in the retrieval accuracy of the DB and DT algorithms for different LSR values are analyzed by combining them with a global 10-km LSR database. Meanwhile, the adaptability of the MODIS products over areas covered with different aerosols is also explored. The results are as follows. (1) Compared with DT retrievals, the DB algorithm yields lower root mean squared error (RMSE) and mean absolut error (MAE) values, and a greater number of appropriate sample points fall within the expected error (EE). The DB algorithm shows higher overall reliability; (2) The aerosol retrieval accuracy of the DB and DT algorithms decline irregularly as the surface reflectance increases; the DB algorithm displays relatively high accuracy; (3) Both algorithms have a high retrieval accuracy over areas covered by weak absorbing aerosols, whereas dust aerosols and continental aerosols produce a low retrieval accuracy. The DB algorithm shows good retrieval results for most aerosols, but a lower accuracy for strong absorbing aerosols. Text Aerosol Robotic Network MDPI Open Access Publishing Atmosphere 8 8 155 |
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Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
AERONET aerosol optical depth (AOD) MODIS DT algorithm DB algorithm land surface reflectance (LSR) |
spellingShingle |
AERONET aerosol optical depth (AOD) MODIS DT algorithm DB algorithm land surface reflectance (LSR) Qingxin Wang Lin Sun Jing Wei Yikun Yang Ruibo Li Qinhuo Liu Liangfu Chen Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land |
topic_facet |
AERONET aerosol optical depth (AOD) MODIS DT algorithm DB algorithm land surface reflectance (LSR) |
description |
Land surface reflectance (LSR) and aerosol types are the two main factors that affect aerosol inversions over land. According to LSR determination methods, Moderate resolution Imaging Spectroradiometer (MODIS) aerosol products are produced using the Deep Blue (DB) and Dark Target (DT) algorithms. Five aerosol types that are determined from Aerosol Robotic Network (AERONET) ground measurements are used to describe the global distribution of aerosol types in each algorithm. To assess the influence of LSR and the method used to determine aerosol type from aerosol retrievals, 10-km global aerosol products that cover 2013 are selected for validation using Level 2.0 aerosol observations from 175 AERONET sites. The variations in the retrieval accuracy of the DB and DT algorithms for different LSR values are analyzed by combining them with a global 10-km LSR database. Meanwhile, the adaptability of the MODIS products over areas covered with different aerosols is also explored. The results are as follows. (1) Compared with DT retrievals, the DB algorithm yields lower root mean squared error (RMSE) and mean absolut error (MAE) values, and a greater number of appropriate sample points fall within the expected error (EE). The DB algorithm shows higher overall reliability; (2) The aerosol retrieval accuracy of the DB and DT algorithms decline irregularly as the surface reflectance increases; the DB algorithm displays relatively high accuracy; (3) Both algorithms have a high retrieval accuracy over areas covered by weak absorbing aerosols, whereas dust aerosols and continental aerosols produce a low retrieval accuracy. The DB algorithm shows good retrieval results for most aerosols, but a lower accuracy for strong absorbing aerosols. |
format |
Text |
author |
Qingxin Wang Lin Sun Jing Wei Yikun Yang Ruibo Li Qinhuo Liu Liangfu Chen |
author_facet |
Qingxin Wang Lin Sun Jing Wei Yikun Yang Ruibo Li Qinhuo Liu Liangfu Chen |
author_sort |
Qingxin Wang |
title |
Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land |
title_short |
Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land |
title_full |
Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land |
title_fullStr |
Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land |
title_full_unstemmed |
Validation and Accuracy Analysis of Global MODIS Aerosol Products over Land |
title_sort |
validation and accuracy analysis of global modis aerosol products over land |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2017 |
url |
https://doi.org/10.3390/atmos8080155 |
op_coverage |
agris |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Atmosphere; Volume 8; Issue 8; Pages: 155 |
op_relation |
Aerosols https://dx.doi.org/10.3390/atmos8080155 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/atmos8080155 |
container_title |
Atmosphere |
container_volume |
8 |
container_issue |
8 |
container_start_page |
155 |
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1774720701453828096 |