Abstract
In this study, using Aerosol Robotic Network aerosol optical depth (AOD) products at three stations in the North China Plain (NCP)—a heavily polluted region in China—the AOD products from six satellite-borne radiometers: the Moderate Resolution Imagining Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR), Ozone Mapping Imaging (OMI), the Visible Infrared Imaging Radiometer (VIIRS), the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), and Polarization and Directionality of the Earth’s Reflectances (POLDER), were thoroughly validated, shedding new light on their advantages and disadvantages. The MODIS Deep Blue (DB) products provide more accurate retrievals than the MODIS Dark Target (DT) and other satellite products at the Beijing site (BJ,a megacity), with higher correlations with AERONET (R > 0.93), lower mean absolute bias (MB < 0.012), and higher percentages (>68%) falling within the expected error (EE). All MODIS DT and DB products perform better than the other satellite products at the Xianghe site (XH, a suburb). The MODIS/Aqua DT products at both 3-km and 10-km resolutions performed better than the other space-borne AOD products at the Xinglong site (XL, a rural area at the top of a mountain). MISR, VIIRS, and SeaWiFS tend to underestimate high AOD values and overestimate AOD values under very low AOD conditions in the NCP. Both OMI and POLDER significantly underestimate the AOD. In terms of data volume, MISR with the limited swath width of 380 km has less data volume than the other satellite sensors. MODIS products have the highest sampling rate, especially the MODIS DT and DB merged products, and can be used for various climate study and air-quality monitoring.
Highlights
Atmospheric aerosols play a critical role in the earth–climate system
1 shows the station validation periods and evaluation at theIn three sites, including the number were generally highest among the three sites except in July and when data samples were of satellite andthe matchups, the mean bias
R is represented by the polar angle, the centered root-mean-square difference (CRMSD) is denoted by the radial lines labeled by the cosine of the angle made with the abscissa, and the standard deviation is normalized by the standard deviation of Aerosol Robotic Network (AERONET) (NSD), as shown by the radius, and all are NETwork) matchups, the mean bias, the root mean square difference (RMSD), the linear correlation coefficient (R), the slope and the intercept of linear regression, and the percentages of satellite aerosol optical depth (AOD) falling within the expected error (EE), above the EE, and below the EE
Summary
Atmospheric aerosols play a critical role in the earth–climate system. Aerosols influence Earth’s energy budget by scattering and absorbing solar and terrestrial radiation or by modifying the cloud properties and precipitation patterns [1]. They affect the atmospheric chemistry process and air quality. Aerosol forcing has been identified as one of the greatest uncertainties in our understanding of the global climate system due to the high variability in concentration, size, composition, shape, and optical properties [2,3]. Global and local aerosol properties have been extensively observed using various satellite radiometers during the last decades. Satellite-borne sensors, such as the Advanced Very High
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