Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas
The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Te...
Published in: | Remote Sensing |
---|---|
Main Authors: | , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
MDPI AG
2022
|
Subjects: | |
Online Access: | https://doi.org/10.3390/rs14020373 https://doaj.org/article/f4fad6ca41ee427886c979ed75422b22 |
id |
ftdoajarticles:oai:doaj.org/article:f4fad6ca41ee427886c979ed75422b22 |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:f4fad6ca41ee427886c979ed75422b22 2023-05-15T13:06:51+02:00 Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas Muhammad Bilal Alaa Mhawish Md. Arfan Ali Janet E. Nichol Gerrit de Leeuw Khaled Mohamed Khedher Usman Mazhar Zhongfeng Qiu Max P. Bleiweiss Majid Nazeer 2022-01-01T00:00:00Z https://doi.org/10.3390/rs14020373 https://doaj.org/article/f4fad6ca41ee427886c979ed75422b22 EN eng MDPI AG https://www.mdpi.com/2072-4292/14/2/373 https://doaj.org/toc/2072-4292 doi:10.3390/rs14020373 2072-4292 https://doaj.org/article/f4fad6ca41ee427886c979ed75422b22 Remote Sensing, Vol 14, Iss 373, p 373 (2022) AOD SARA SREM AERONET MODIS VIIRS Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14020373 2022-12-30T20:17:13Z The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and ... Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 14 2 373 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
AOD SARA SREM AERONET MODIS VIIRS Science Q |
spellingShingle |
AOD SARA SREM AERONET MODIS VIIRS Science Q Muhammad Bilal Alaa Mhawish Md. Arfan Ali Janet E. Nichol Gerrit de Leeuw Khaled Mohamed Khedher Usman Mazhar Zhongfeng Qiu Max P. Bleiweiss Majid Nazeer Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas |
topic_facet |
AOD SARA SREM AERONET MODIS VIIRS Science Q |
description |
The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and ... |
format |
Article in Journal/Newspaper |
author |
Muhammad Bilal Alaa Mhawish Md. Arfan Ali Janet E. Nichol Gerrit de Leeuw Khaled Mohamed Khedher Usman Mazhar Zhongfeng Qiu Max P. Bleiweiss Majid Nazeer |
author_facet |
Muhammad Bilal Alaa Mhawish Md. Arfan Ali Janet E. Nichol Gerrit de Leeuw Khaled Mohamed Khedher Usman Mazhar Zhongfeng Qiu Max P. Bleiweiss Majid Nazeer |
author_sort |
Muhammad Bilal |
title |
Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas |
title_short |
Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas |
title_full |
Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas |
title_fullStr |
Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas |
title_full_unstemmed |
Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas |
title_sort |
integration of surface reflectance and aerosol retrieval algorithms for multi-resolution aerosol optical depth retrievals over urban areas |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14020373 https://doaj.org/article/f4fad6ca41ee427886c979ed75422b22 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing, Vol 14, Iss 373, p 373 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/2/373 https://doaj.org/toc/2072-4292 doi:10.3390/rs14020373 2072-4292 https://doaj.org/article/f4fad6ca41ee427886c979ed75422b22 |
op_doi |
https://doi.org/10.3390/rs14020373 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
2 |
container_start_page |
373 |
_version_ |
1766023743053758464 |