Retrieval of Aerosol Optical Depth over Arid Areas from MODIS Data
AbstractModerate Resolution Imaging Spectroradiometer (MODIS) data have been widely applied for the remote sensing of aerosol optical depth (AOD) because the MODIS sensor features a short revisit period and a moderate spatial resolution. The Dense Dark Vegetation (DDV) method is the most popular retrieval method. However, the DDV method can only be used to retrieve the AOD with high precision when the surface reflectance in the visible spectrum is low, such as over dense vegetation or water. To obtain precise AOD values in areas with higher reflectance, such as arid areas, Land Surface Reflectance (LSR) must be estimated accurately. This paper proposes a method of estimating LSR for AOD retrieval over arid areas from long-term series of MODIS images. According to the atmospheric parameters (AOD and water vapor), the clearest image without clouds was selected from the long-term series of continuous MODIS images. Atmospheric correction was conducted based on similar ground-measured atmospheric parameters and was used to estimate the LSR and retrieve the AOD at adjacent times. To validate this method, aerosol inversion experiments were performed in northern Xinjiang, in which the inverted AOD was compared to ground-measured AOD and MODIS aerosol products (MOD04). The AOD retrieved using the new algorithm was highly consistent with the AOD derived from ground-based measurements, with a correlation coefficient of 0.84. Additionally, 82.22% of the points fell within the expected error defined by NASA. The precision of the retrieved AOD data was better than that of MOD04 AOD products over arid areas. View Full-Text
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Tian, X.-P.; Sun, L. Retrieval of Aerosol Optical Depth over Arid Areas from MODIS Data. Atmosphere 2016, 7, 134.
Tian X-P, Sun L. Retrieval of Aerosol Optical Depth over Arid Areas from MODIS Data. Atmosphere. 2016; 7(10):134.Chicago/Turabian Style
Tian, Xin-peng; Sun, Lin. 2016. "Retrieval of Aerosol Optical Depth over Arid Areas from MODIS Data." Atmosphere 7, no. 10: 134.
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