An improved aerosol optical depth map based on machine-learning and modis data: Development and application in South America

Abstract

In zones where aerosol properties have been poorly characterized, satellite-based (MODIS) and ground-based (AERONET) aerosol optical depth (AOD) values typically differ. In this work, we use machine-learning based methods (artificial neural networks and support vector machines) to obtain corrected AOD values taken from MODIS in regions that are positioned far from AERONET stations. The method has been validated using several approaches. The area suitable for improvement covers 62% of the South American continent, and the degree of improvement compared to MODIS values, expressed in terms of the fraction of data within the MODIS error, was found to be 38% and 86% for the Terra and Aqua satellites, respectively. The results show absolute monthly average differences between the MODIS and the proposed method of up to ± 0.6 AOD units. The MODIS AOD distribution for the analyzed period shows a mode of –0.04, while that for the method presented here is 0.08.