A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
High resolution aerosol optical depth retrieval over urban areas from Landsat-8 OLI images
https://orcid.org/0000-0001-8945-3302
Abstract The satellite-retrieved aerosol optical depth (AOD) provides unique estimation of aerosol loading across a continuous space. However, current AOD products with a relatively coarse resolution (≥1 km) can hardly capture the details in urban areas with large spatial AOD gradients. To address this issue, here we developed a novel retrieval algorithm for retrieving AOD with extra fine spatial resolution (30 m) from Landsat-8 satellite OLI images. In the algorithm, the three land surface reflectance (LSR) estimation schemes and four aerosol types are adopted to improve the retrieval accuracy. The algorithm is applied on Beijing and Wuhan city in China during 2014–2019. Results suggest that the retrieved AOD with the new algorithm exhibits good agreement with the ground-based measured AOD (R 2 = 0.920), and 81.63% of the AODs fall within the expected error line with a root-mean-square error of 0.112. Moreover, the high-resolution AOD products also successfully identified polluted sources and discrepancy of aerosol loading over different land cover types in two megacities in China, implying its great potential on detecting fine aerosol emission sources over the complex urban area. Such improvement of the new algorithm for retrieving 30 m AOD developed in this study demonstrates its substantial potentials in supporting further studies of air pollution management and human exposure at extra-fine spatial scale.
Graphical abstract Display Omitted
Highlights Urban AOD at 30 m spatial resolution are retrieved from Landsat-8 OLI images. Surface anisotropy and aerosol type variation are represented in retrieval. Detailed AOD gradient in urban area could be represented. Aerosol emission sources are identified with high resolution AOD dataset.
High resolution aerosol optical depth retrieval over urban areas from Landsat-8 OLI images
https://orcid.org/0000-0001-8945-3302
Abstract The satellite-retrieved aerosol optical depth (AOD) provides unique estimation of aerosol loading across a continuous space. However, current AOD products with a relatively coarse resolution (≥1 km) can hardly capture the details in urban areas with large spatial AOD gradients. To address this issue, here we developed a novel retrieval algorithm for retrieving AOD with extra fine spatial resolution (30 m) from Landsat-8 satellite OLI images. In the algorithm, the three land surface reflectance (LSR) estimation schemes and four aerosol types are adopted to improve the retrieval accuracy. The algorithm is applied on Beijing and Wuhan city in China during 2014–2019. Results suggest that the retrieved AOD with the new algorithm exhibits good agreement with the ground-based measured AOD (R 2 = 0.920), and 81.63% of the AODs fall within the expected error line with a root-mean-square error of 0.112. Moreover, the high-resolution AOD products also successfully identified polluted sources and discrepancy of aerosol loading over different land cover types in two megacities in China, implying its great potential on detecting fine aerosol emission sources over the complex urban area. Such improvement of the new algorithm for retrieving 30 m AOD developed in this study demonstrates its substantial potentials in supporting further studies of air pollution management and human exposure at extra-fine spatial scale.
Graphical abstract Display Omitted
Highlights Urban AOD at 30 m spatial resolution are retrieved from Landsat-8 OLI images. Surface anisotropy and aerosol type variation are represented in retrieval. Detailed AOD gradient in urban area could be represented. Aerosol emission sources are identified with high resolution AOD dataset.
High resolution aerosol optical depth retrieval over urban areas from Landsat-8 OLI images
https://orcid.org/0000-0001-8945-3302
Abstract The satellite-retrieved aerosol optical depth (AOD) provides unique estimation of aerosol loading across a continuous space. However, current AOD products with a relatively coarse resolution (≥1 km) can hardly capture the details in urban areas with large spatial AOD gradients. To address this issue, here we developed a novel retrieval algorithm for retrieving AOD with extra fine spatial resolution (30 m) from Landsat-8 satellite OLI images. In the algorithm, the three land surface reflectance (LSR) estimation schemes and four aerosol types are adopted to improve the retrieval accuracy. The algorithm is applied on Beijing and Wuhan city in China during 2014–2019. Results suggest that the retrieved AOD with the new algorithm exhibits good agreement with the ground-based measured AOD (R 2 = 0.920), and 81.63% of the AODs fall within the expected error line with a root-mean-square error of 0.112. Moreover, the high-resolution AOD products also successfully identified polluted sources and discrepancy of aerosol loading over different land cover types in two megacities in China, implying its great potential on detecting fine aerosol emission sources over the complex urban area. Such improvement of the new algorithm for retrieving 30 m AOD developed in this study demonstrates its substantial potentials in supporting further studies of air pollution management and human exposure at extra-fine spatial scale.
Graphical abstract Display Omitted
Highlights Urban AOD at 30 m spatial resolution are retrieved from Landsat-8 OLI images. Surface anisotropy and aerosol type variation are represented in retrieval. Detailed AOD gradient in urban area could be represented. Aerosol emission sources are identified with high resolution AOD dataset.
High resolution aerosol optical depth retrieval over urban areas from Landsat-8 OLI images
Atmospheric Environment ; 261
2021-06-28
Article (Journal)
Electronic Resource
English
Retrieval and Validation of Atmospheric Aerosol Optical Depth From AVHRR Over China
| Online Contents | 2016
Retrieval and Validation of Atmospheric Aerosol Optical Depth From AVHRR Over China
| Online Contents | 2016
Retrieval of aerosol optical depth and surface reflectance over land from NOAA AVHRR data
| Online Contents | 2013