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• W2022677975 abstract "Human activity is one of the most important aerosol sources. Because the underlaying surface feature records most human activities, it is important to recognize the correlation between aerosol distribution and the underlaying surface. In this research, the dark object algorithm and a second-generation operational algorithm of Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol retrieval are used to estimate aerosol optical depth from Enhanced Thematic Mapper Plus (ETM+) images acquired by the Landsat 7 satellite system in urban regions, and the correlations between aerosol distribution and urban underlaying surface features (including landform, land cover and urbanization level) is analysed. Results show that (1) it is feasible to apply a second-generation algorithm to retrieve aerosol optical depth with ETM+ images. When a validation is performed with the ground observation meteorological range converted into aerosol optical depth with the correlation model acquired by a Moderate-Resolution Atmospheric Transmission (MODTRAN) simulation, the retrieval error is about 0.0094. For higher spatial resolution of an ETM+ image, it is better to study the aerosol distribution features in the urban regions. Additionally, (2) there are obvious variations in spatial distribution of aerosol over the different features of the underlaying surface. For the landform, aerosol optical depth is mountain < hill < plain; for the land cover, aerosol optical depth is dense vegetation < sparse vegetation < water < bare soil < residential area; for the different urbanization-level regions, there is bigger and bigger aerosol optical depth with increasing of the urbanization level. On the whole, as human activities increase, so too does the aerosol optical depth." @default.
• W2022677975 created "2016-06-24" @default.
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• W2022677975 date "2011-11-08" @default.
• W2022677975 modified "2023-09-27" @default.
• W2022677975 title "Aerosol retrieval with satellite image and correlation analyses between aerosol distribution and urban underlaying surface" @default.
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• W2022677975 doi "https://doi.org/10.1080/01431161.2010.547883" @default.
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