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Remote Sens. 2017, 9(5), 432;

Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain

LAGEO, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Department of Earth and Atmospheric Sciences, University of Nebraska–Lincoln, Lincoln, NE 68588, USA
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China
College of Earth Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
Department of Chemical and Biochemical Engineering, Center for Global and Regional Environmental Studies, and Informatics Initiative, The University of Iowa, Iowa City, IA 52241, USA
State Key Laboratory of Severe Weather (LASW) and Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences (CAMS), CMA, Beijing 100081, China
Climate and Radiation Laboratory, NASA GSFC, Greenbelt, MD 20771, USA
Space Science and Engineering Center, University of Wisconsin–Madison, Madison, WI 53706, USA
Qatar Environment & Energy Research Institute, Qatar Foundation, Ar-Rayyan, Qatar
Authors to whom correspondence should be addressed.
Academic Editors: Omar Torres, Yang Liu, Richard Müller and Prasad S. Thenkabail
Received: 13 February 2017 / Revised: 24 April 2017 / Accepted: 27 April 2017 / Published: 2 May 2017
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution)
Full-Text   |   PDF [1885 KB, uploaded 2 May 2017]   |  


The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical depth (AOD). The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. The “MODIS-like” VIIRS data (VIIRS_ML) are being produced experimentally at NASA, from a version of the “dark-target” algorithm that is applied to MODIS. In this study, the AOD and aerosol model types from these two VIIRS retrieval algorithms over the North China Plain (NCP) are evaluated using the ground-based CE318 Sunphotometer (CE318) measurements during 2 May 2012–31 March 2014 at three sites. These sites represent three different surface types: urban (Beijing), suburban (XiangHe) and rural (Xinglong). Firstly, we evaluate the retrieved spectral AOD. For the three sites, VIIRS_EDR AOD at 550 nm shows a positive mean bias (MB) of 0.04–0.06 and the correlation of 0.83–0.86, with the largest MB (0.10–0.15) observed in Beijing. In contrast, VIIRS_ML AOD at 550 nm has overall higher positive MB of 0.13–0.14 and a higher correlation (0.93–0.94) with CE318 AOD. Secondly, we evaluate the aerosol model types assumed by each algorithm, as well as the aerosol optical properties used in the AOD retrievals. The aerosol model used in VIIRS_EDR algorithm shows that dust and clean urban models were the dominant model types during the evaluation period. The overall accuracy rate of the aerosol model used in VIIRS_ML over NCP three sites (0.48) is higher than that of VIIRS_EDR (0.27). The differences in Single Scattering Albedo (SSA) at 670 nm between VIIRS_ML and CE318 are mostly less than 0.015, but high seasonal differences are found especially over the Xinglong site. The values of SSA from VIIRS_EDR are higher than that observed by CE318 over all sites and all assumed aerosol modes, with a positive bias of 0.02–0.04 for fine mode, 0.06–0.12 for coarse mode and 0.03–0.05 for bi-mode at 440 nm. The overestimation of SSA but positive AOD MB of VIIRS_EDR indicate that other factors (e.g., surface reflectance characterization or cloud contamination) are important sources of error in the VIIRS_EDR algorithm, and their effects on aerosol retrievals may override the effects from non-ideality in these aerosol models. View Full-Text
Keywords: aerosol optical depth; aerosol models; VIIRS; NCP region aerosol optical depth; aerosol models; VIIRS; NCP region

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhu, J.; Xia, X.; Wang, J.; Che, H.; Chen, H.; Zhang, J.; Xu, X.; Levy, R.C.; Oo, M.; Holz, R.; Ayoub, M. Evaluation of Aerosol Optical Depth and Aerosol Models from VIIRS Retrieval Algorithms over North China Plain. Remote Sens. 2017, 9, 432.

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