Publications

Bilal, Muhammad; Nichol, Janet E.; Chan, Pak W. (2014). Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms. REMOTE SENSING OF ENVIRONMENT, 153, 50-60.

Abstract
An effective Simplified Aerosol Retrieval Algorithm (SARA) based on real viewing geometry and encompassing a wide range of aerosol conditions and types (omega(0) = 030-1.0) was tested over Beijing, a city under the influence of both anthropogenic emissions and dust storms. The SARA AOD was retrieved at seven resolutions (500 m, 1 km, 2 km, 4 km, 6 km, 8 km, and 10 km) using MODIS level 1 and 2 data products (MOD02, MOD03, and MOD09) for the years 2012 and 2013. For comparison purposes, the MODIS dark-target (DT) AOD observations at 10 km resolution were obtained for the same time period. Both algorithms, SARA and MODIS DT were validated using Beijing_RADI and Beijing_CAMS AERONET AOD measurements and accuracy was evaluated using the Confidence Envelope of Expected Error (CEEE). The SARA AOD achieved very high correlation (0.972-0.994) with low Root Mean Square Error (RMSE similar to 0.067-0.133) and very high accuracy (87-95%). In comparison, the MODIS DT algorithm overestimated AOD for both low and high AOD observations with large RMSE (0.115-0342) and very low accuracy (20-52%). The robustness and accuracy of SARA and the MODIS DT algorithms for low (ADD < 0.40) and high (AOD > 0.40) aerosol loadings was evaluated using the Fraction of Expected Error (FEE). The SARA algorithm achieved 46-60% higher average accuracy than the MODE DT algorithm indicating SARA's greater accuracy and reliability in AOD retrieval over Beijing under low and high aerosol loadings, including severe dust storms. The SARA algorithm is unique among satellite based AOD retrievals, in its ability to depict extreme dust storms (AOD similar to 5.0) at fine spatial resolution (500 m) over urban and suburban areas such as Beijing, XiangHe and the Bohai Sea, as is demonstrated by the dust storm of 17th April 2006. (C) 2014 Elsevier Inc. All rights reserved.

DOI:
10.1016/j.rse.2014.07.015

ISSN:
0034-4257