A method to improve the MODIS-AERONET AOD correlation based on machine learning techniques: Application to South America

BETHANIA LANZACO; LUIS E. OLCESE; GUSTAVO G. PALANCAR; BEATRIZ M. TOSELLI

AEROSOL AND AIR QUALITY RESEARCH

TAIWAN ASSOC AEROSOL RES-TAAR

Lugar: Taiwan; Año: 2016 vol. 16 p. 1509 - 1509

1680-8584

e present a method to correct aerosol optical depth (AOD) values taken from Collection 6 MODIS observations, which resulted in values closer to those recorded by the ground-based network AERONET. The method is based on machine learning techniques (Artificial Neural Networks and Support Vector Regression), and uses MODIS AOD values and meteorological parameters as inputs.The method showed improved results, compared with the direct MODIS AOD, when applied to nine stations in South America. The percentage of improvement, measured in terms of R2, ranged from 2% (Alta Floresta) to 79% (Buenos Aires). This improvement was also quantified considering the percentage of data within the MODIS expected error, being 91% for this method and 57% for direct correlation.The method corrected not only the systematic bias in temporal data series but also the outliers. To highlight this ability, the results for each AERONET station were individually analyzed.Considering the results as a whole, this metho