A Simplified high resolution MODIS Aerosol Retrieval Algorithm (SARA) for use over mixed surfaces

Aerosol Optical Depth (AOD) is a measure of the columnar atmospheric aerosol content. Satellite remote sensing has been used to retrieve AOD over land and ocean at spatial resolutions of several to several tens of km. In most cases, a Radiative Transfer Model (RTM) is used to construct a look-up table (LUT) to act as a map between measurements and physical quantities. In the current study, MODerate resolution Imaging Spectroradiometer (MODIS) measurements were used to develop a Simplified Aerosol Retrieval Algorithm (SARA) for use over Hong Kong at high (500 m) spatial resolution, without using a LUT. Instead, RTM calculations were applied directly to the MODIS data, with the aerosol properties derived from a local urban Aerosol Robotic Network (AERONET) station at the Hong Kong Polytechnic University, and surface reflectance from the MOD09GA level-2 daily surface reflectance product. For validation, the SARA AOD at 500 m, along with the operational MODIS aerosol product (MOD04 C005) at 10 km spatial resolution, was compared with data from a ground-based Microtops II sun photometer at the Hong Kong International Airport, a Sky-radiometer at the City University of Hong Kong, and an AERONET station at Hong Kong Hok Tsui. The 500 m AOD retrieved from the SARA showed a high consistency with ground-based AOD measurements, with average correlation coefficient similar to 0.964, root mean square error (RMSE) similar to 0.077, and mean absolute error (MAE) similar to 0.065. The SARA AOD showed a good agreement with MOD04 C005 AOD over both urban and rural areas of Hong Kong, with average correlation coefficient similar to 0.917, RMSE similar to 0.087, and MAE similar to 0.072. The results demonstrate that our simplified AOD algorithm is better able to represent aerosol conditions over Hong Kong than the MOD04 C005 standard product as well as other higher resolution algorithms which have been tested over Hong Kong. (C) 2013 Elsevier Inc. All rights reserved.