A robust calibration approach for PM 10 prediction from MODIS aerosol optical depth
Investigating the human health effects of atmospheric particulate matter (PM) using satellite data are gaining more attention due to their wide spatial coverage and temporal advantages. Such epidemiological studies are, however, susceptible to bias errors and resulted in poor predictive output in some locations. Current methods calibrate aerosol optical depth (AOD) retrieved from MODIS to further predict PM. The recent satellite-based AOD calibration uses a mixed effects model to predict location-specific PM on a daily basis. The shortcomings of this daily AOD calibration are for areas of high probability of persistent cloud cover throughout the year such as in the humid tropical region along the equatorial belt. Contaminated pixels due to clouds causes radiometric errors in the MODIS AOD, thus causes poor predictive power on air quality. In contrary, a periodic assessment is more practical and robust especially in minimizing these cloud-related contaminations. In this paper, a simple yet robust calibration approach based on monthly AOD period is presented. We adopted the statistical fitting method with the adjustment technique to improve the predictive power of MODIS AOD. The adjustment was made based on the long-term observation (2001–2006) of PM 10-AOD residual error characteristic. Furthermore, we also incorporated the ground PM measurement into the model as a weighting to reduce the bias of the MODIS-derived AOD value. Results indicated that this robust approach with monthly AOD calibration reported an improved average accuracy of PM 10 retrieval from MODIS data by 50% compared to widely used calibration methods based on linear regression models, in addition to enabling further spatial patterns of periodic PM exposure to be undertaken.