Improving aerosol retrieval accuracy by integrating AERONET, MISR and MODIS data

Retrieval of aerosol optical thickness (AOT) by ground- and satellite-based remote sensing provides different accuracy, coverage, and resolution. An important challenge is how to best utilize information from multiple instruments to further improve the quality of retrievals. In this study, we explored whether the accuracy of AOT retrievals could be improved by fusion of ground- and satellite-based data using neural network techniques. MISR and MODIS satellite data were obtained for several 16-day periods during 2002 and 2003 covering the continental USA. These data are joined spatially and temporally with AOT measurements from 34 AERONET ground-based stations over the continental USA. The R² accuracies of MODIS and MISR retrievals were estimated at 0.57 and 0.66, when AERONET AOT is used as the ground truth. When radiance and geometric attributes are used together with MISR and MODIS AOT as attributes for prediction of AERONET AOT, the R² accuracy was increased up to 10%.