Abstract
Aerosol optical depth (AOD) is a critical variable in estimating aerosol concentration in the atmosphere, evaluating severity of atmospheric pollution, and studying their impact on climate. With the assistance of the 6S radiative transfer model, we simulated apparent reflectancein relation to AOD in each Moderate Resolution Imaging Spectroradiometer (MODIS) waveband in this study. The closeness of the relationship was used to identify the most and least sensitive MODIS wavebands. These two bands were then used to construct three aerosol indices (difference, ratio, and normalized difference) for estimating AOD quickly and effectively. The three indices were correlated, respectively, with in situ measured AOD at the Aerosol Robotic NETwork (AERONET) Lake Taihu, Beijing, and Xianghe stations. It is found that apparent reflectance of the blue waveband (band 3) is the most sensitive to AOD while the mid-infrared wavelength (band 7) is the least sensitive. The difference aerosol index is the most accurate in indicating aerosol-induced atmospheric pollution with a correlation coefficient of 0.585, 0.860, 0.685, and 0.333 at the Lake Taihu station, 0.721, 0.839, 0.795, and 0.629 at the Beijing station, and 0.778, 0.782, 0.837, and 0.643 at the Xianghe station in spring, summer, autumn and winter, respectively. It is concluded that the newly proposed difference aerosol index can be used effectively to study the level of aerosol-induced air pollution from MODIS satellite imagery with relative ease.
Highlights
Aerosols refer to solid or liquid particulates suspended in the atmosphere
The influence of Aerosol optical depth (AOD) on apparent reflectance is suppressed at a longer wavelength because the larger dimension of wavelength than aerosol diameter undermines backscattering
With the assistance of the 6S radiative transfer model, the response of apparent reflectance in each of Moderate Resolution Imaging Spectroradiometer (MODIS) wavebands in relation to AOD was simulated
Summary
Aerosols refer to solid or liquid particulates suspended in the atmosphere. As they strongly absorb and scatter solar radiation over the ultraviolet, visible light, and infrared spectrum, they exert a significant influence on global climate and weather processes [1,2]. These methods are common in that in situ measured data were statistically analyzed to establish the relationship between apparent reflectance and the physical and chemical properties of aerosols. Such empirical statistical relationship faces limitations as it is subject to the influence of the underlying surface, atmospheric conditions, as well as the observation conditions. A comparison among the seven responses reveals the most and least sensitive MODIS wavebands These two bands were used to construct three aerosol indices that can be derived from remotely sensed imagery, for the effective detection of atmospheric aerosol pollution with relative ease. The relationship between in situ measured AOD at the Lake Taihu, Beijing, and Xianghe stations in the Aerosol Robotic NETwork (AERONET) over one year with the proposed aerosol indices was analyzed to validate the applicability of the proposed aerosol indices in estimating atmospheric pollution caused by aerosols
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