Abstract
This study is to evaluate the sensitivity of Aerosol Optical Depth (AOD τ) to aerosol vertical profile and type, using the Moderate Resolution Imaging Spectroradiometer (MODIS) collection 6 algorithm over land. Four experiments were performed, using different aerosol properties including 3 possible non-dust aerosol models and 14 vertical distributions. The algorithm intrinsic uncertainty was investigated as well as the interplay effect of aerosol vertical profile and type on the retrieval. The results show that the AOD retrieval is highly sensitive to aerosol vertical profile and type. With 4 aerosol vertical distributions, the algorithm with a fixed vertical distribution gives about 5% error in the AOD retrieval with aerosol loading τ ≤ 0 . 5 . With pure aerosols (smoke and dust), the retrieval of AOD shows errors ranging from 2% to 30% for a series of vertical distributions. Errors in aerosol type assumption in the algorithm can lead to errors of up to 8% in the AOD retrieval. The interplay effect can give the AOD retrieval errors by over 6%. In addition, intrinsic algorithm errors were found, with a value of >3% when τ> 3.0. This is due to the incorrect estimation of the surface reflectance. The results suggest that the MODIS algorithm can be improved by considering a realistic aerosol model and its vertical profile, and even further improved by reducing the algorithm intrinsic errors.
Highlights
Aerosol Optical Depth (AOD) retrieved from satellite data is of importance for a number of studies including climate change, air quality, and human health due to the advantage of large spatial and temporal coverage
Errors in aerosol type assumption in the algorithm can lead to errors of up to 8% in the AOD retrieval
The results suggest that the Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm can be improved by considering a realistic aerosol model and its vertical profile, and even further improved by reducing the algorithm intrinsic errors
Summary
Aerosol Optical Depth (AOD) retrieved from satellite data is of importance for a number of studies including climate change, air quality, and human health due to the advantage of large spatial and temporal coverage. Many AOD products from different algorithms/instruments have been developed and extensively validated with the data collected by the Aerosol Robotic Network (AERONET). The current accuracy of the MODIS AOD over land is still low. To consider the mixture of fine and coarse aerosols, the algorithm assumes that the fine and coarse aerosols independently contribute to the TOA reflectance. Where the superscript f and c indicate fine and coarse mode, respectively, η is the fine ratio proposed in [1]. This method for the calculation of the total reflectance at TOA is called Standard Linear
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