China Collection 2.0: The aerosol optical depth dataset from the synergetic retrieval of aerosol properties algorithm

A wide range of data products have been published since the operation of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's TERRA and AQUA satellites. Based on DarkTarget and DeepBlue method, NASA has published Aerosol Optical Depth (AOD) products Collection 5.0 and Collection 5.1 at 10 km spatial resolution. The Collection 6.0 will be published soon with spatial resolution of 3 km. Although validated globally, regional and systematic errors are still found in the MODIS-retrieved ADD products. This is especially remarkable for bright heterogeneous land surface, such as mainland China. In order to solve the aerosol retrieval problem over heterogeneous bright land surface, the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) has been developed based on the synergetic use of the MODIS data of TERRA and AQUA satellites. Using the SRAP algorithm, we produced AOD dataset-China Collection 2.0, dated from August 2002 to August 2012, and compared the AOD results with AErosol Robotic NETwork (AERONET) and Chinese Meteorological Administration Aerosol Remote Sensing Network (CARSNET) measurements. We find that 62% of China Collection 2.0 AOD values are within an expected error (EE) range of +/-(0.05 + 20%) and that 56% are within an EE range of +/-(0.05 + 15%) when compared with AERONET-observed values. For CARSNET validation, we find that 60% of China Collection 2.0 AOD values are within an expected error (EE) range of +/-(0.05 + 20%) and that 53% are within an EE range of +/-(0.05 + 15%). In addition, we also compare the AOD results with MODIS aerosol products, the cross validation shows that the two AOD have good consistency. Monthly averaged AOD results show that AOD is generally high in China's eastern coastal region from March to August, and AOD is not more than 0.5 in other months. Season averaged results show that the higher values of AOD are mostly distributed in eastern and southern China. (C) (2)014 Elsevier Ltd. All rights reserved.