Merging MODIS and Ground-Based Fine Mode Fraction of Aerosols Based on the Geostatistical Data Fusion Method

With the rapid development of the economy and society, fine particulate matter (PM2.5) has not only caused severe environmental problems, but also posed a threat to public health. In order to improve the estimated accuracy of PM2.5, the input data fine mode fraction (FMF), a key parameter to the PM2.5 remote sensing method (PMRS), should be improved due to its significant errors. In this study, we merge the observations of the fine mode fraction (FMF) from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Aerosol Robotic Network (AERONET) and the Sun-sky radiometer Observation Network (SONET) using the universal kriging (UK) method to obtain accurate FMF distribution over eastern China. PM2.5 mass concentration is estimated by the fusion and MODIS FMF distributions using the PMRS model. The results show that the parameters in the variogram are relatively stable except for significant differences in correlation lengths in summer. The FMF in the Winter of 2015 shows that the mean error decreases from 0.38 to 0.13 compared with that from MODIS using leave-one-out cross-validation, with the maximum error decreasing from 0.75 to 0.34, indicating that the UK method can provide better estimates of FMF. We also find that PM2.5 estimated from FMF fusion results is closer to the in situ PM2.5 from the Ministry of Environmental Protection (MEP) (87.2 vs. 88.9 μg/m3).