A Regionally Robust High-Spatial-Resolution Aerosol Retrieval Algorithm for MODIS Images Over Eastern China

Abstract

Moderate resolution imaging spectroradiometer (MODIS) has been widely used in related aerosol studies because of its long data records. However, operational aerosol optical depth (AOD) products at coarse spatial resolutions limit their applications on small and medium scales. Thus, high-spatial-resolution AOD products are needed. In this paper, a regionally robust high-resolution aerosol retrieval algorithm is developed for MODIS images over Eastern China which has complex surfaces and severe air pollution. Several major challenges in aerosol retrieval are resolved including: 1) surface reflectance by correcting for the effects of surface bidirectional reflectance distribution function using the RossThick-LiSparse model; 2) aerosol models assumed by time-series data analysis with historical aerosol optical properties measurements from the Aerosol Robotic Network (AERONET) sites; and 3) cloud screening using the proposed universal dynamic threshold cloud detection algorithm. Moreover, gas (i.e., ozone and water vapor) absorption is also corrected. Finally, our AOD retrievals are compared with the newest AERONET Version 3 Level 2.0 AOD ground-based measurements, latest MODIS Collection 6.1 AOD products at 3- and 10-km resolutions, and multiangle implementation of the atmospheric correction (MAIAC) AOD product at a 1-km resolution. The results suggest that our algorithm performs well over dark vegetated and bright urban surfaces and that 78.56% of the retrievals meet the acceptable expected error of ±(0.05% + 20%) with a mean absolute error and a root-mean-square error of 0.074 and 0.125, respectively. Comparison results indicate that the newly generated 1-km AOD data set is much better than the routine MOD04 3- and 10-km dark target data sets, and slightly better than the 10-km deep blue (with lower resolution) and 1-km MAIAC (with narrower space coverage) AOD products. This attests to the robustness of our algorithm that generates an AOD product with a more continuous coverage and finer resolution over complex surfaces.