Aerosol Optical Depth Datasets over China from Satellite Data : China Collection 2.1
Xue, Yong; Xu, Hui; Guang, Jie

Atmospheric aerosols cause scattering and absorption of incoming solar radiation. Additional anthropogenic aerosols released into the atmosphere thus exert a direct radiative forcing on the climate system (Bellouin et al., 2005). On account of the large spatial and temporal variability of these aerosols, remote sensing from satellites delivers the most reliable information about global aerosol distributions. The measurable quantity from space is the aerosol optical depth (AOD), which is derived from the solar radiation reflected to space (Kaufman et al., 1997).

Currently, reliable retrievals of AOD over land were made using Moderate Resolution Imaging Spectroradiometer (MODIS) (Kaufman et al., 1997), Multiangle Imaging Spectroradiometer (MISR) (Martonchik et al., 1998), MEdium Resolution Imaging Spectrometer (MERIS), as well as Sea-viewing Wide Field-of-view Sensor (SeaWiFS) (Von Hoyningen-Huene et al., 2003; Von Hoyningen-Huene et al., 2006). A range of algorithms has been designed because the satellite sensors have different temporal, spatial, polarization, angular and spectral information content characteristics. However, different satellite instruments and algorithms do not always give consistent values of aerosol properties.

The aerosol model and surface reflectance are two critical factors associated with slope and intercept of the regression line in the scatter plots, greatly impacting the quality of AOD retrieval (Chu et al., 2002; Li et al., 2007). The temporal and spatial match of AODs from satellite retrieval and ground measurements and sensor calibration uncertainties could also impact the quality of AOD retrieval for different algorithms (Xie et al., 2011).
In this work, we used data from multiple satellites (including MODIS, MISR, MERIS and SeaWiFS) and multiple algorithms (including the synergetic retrieval of aerosol properties (SRAP-MODIS; Xue and Cracknell, 1995; Tang et al., 2005), Deep Blue (DB; Hsu et al., 2004) and Dark Target (DT; Remer et al., 2006) for MODIS-retrieved AOD) for the production of the merged AOD dataset using minimum variance estimate algorithm. The merged AOD datasets were generated over China for 2008. To evaluate the merged AOD data sets, inter-comparisons were made using the AOD values from AErosol RObotic NETwork (AERONET) and China meteorological administration Aerosol Remote Sensing NETwork (CARSNET). The verification result suggests that, the merged AOD datasets had a good quality and more expanded spatial coverage.