Publications

Wong, MS, Lee, KH, Nichol, JE, Li, ZQ (2010). "Retrieval of Aerosol Optical Thickness Using MODIS 500 x 500 m(2), a Study in Hong Kong and the Pearl River Delta Region". IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 48(8), 3318-3327.

Abstract
Aerosol detection and monitoring by satellite observations has been substantially developed over the past decades. While several state-of-the-art aerosol retrieval techniques provide aerosol properties at global scale, high spatial detail that is suitable for urbanized regions is unavailable because most of the satellite-based products are at coarse resolution. A refined aerosol retrieval algorithm using the MODerate Resolution Imaging Spectroradiometer (MODIS) to retrieve aerosol properties at 500-m resolution over land is described here. The rationale of our technique is to first estimate the aerosol reflectances by decomposing the top-of-atmosphere reflectance from surface reflectance and Rayleigh path reflectance. For the determination of surface reflectances, a modified minimum reflectance technique (MRT) is used, and MRT images are computed for different seasons. A good agreement is obtained between the surface reflectances of MRT images and MODIS land surface reflectance products (MOD09), with a correlation of 0.9. For conversion of aerosol reflectance to aerosol optical thickness (AOT), comprehensive lookup tables are constructed which consider aerosol properties and sun-viewing geometry in the radiative transfer calculations. The resulting 500-m AOT images are highly correlated (r = 0.937) with AErosol RObotic NETwork sunphotometer observations in Hong Kong for most of the year corresponding to the long dry season. This study demonstrates a method for aerosol retrieval at fine resolution over urbanized regions, which can assist the study of aerosol spatial distribution. In addition, the MODIS 500-m AOT images can also be used to pinpoint source areas of cross-boundary aerosols from the Pearl River Delta region.

DOI:
10.1109/TGRS.2010.2045124

ISSN:
0196-2892