1State Key Laboratory of Remote Sensing Science, jointly sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing
2Faculty of Computing, London Metropolitan University, 166-220 Holloway Road, London N7 8DB, UK
3Department of Physics, University of Helsinki, Helsinki, Finland
4Finnish Meteorological Institute, Climate Change Unit, Helsinki, Finland
5Netherlands Organisation for Applied Scientific Research TNO, Utrecht, The Netherlands
6German Remote Sensing Data Center, German Aerospace Center, Oberpfaffenhofen, 82234 Wessling, Germany
7School of Geography, Beijing Normal University, Beijing, China
8Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, No.9 Dengzhuang South Road, Haidian District, Beijing 100094, China
9University of the Chinese Academy of Sciences, Beijing 100049, China
Received: 20 Sep 2011 – Published in Atmos. Chem. Phys. Discuss.: 03 Feb 2012
Abstract. A novel approach for the joint retrieval of aerosol optical depth (AOD) and aerosol type, using Meteosat Second Generation – Spinning Enhanced Visible and Infrared Imagers (MSG/SEVIRI) observations in two solar channels, is presented. The retrieval is based on a Time Series (TS) technique, which makes use of the two visible bands at 0.6 μm and 0.8 μm in three orderly scan times (15 min interval between two scans) to retrieve the AOD over land. Using the radiative transfer equation for plane-parallel atmosphere, two coupled differential equations for the upward and downward fluxes are derived. The boundary conditions for the upward and downward fluxes at the top and at the bottom of the atmosphere are used in these equations to provide an analytic solution for the AOD. To derive these fluxes, the aerosol single scattering albedo (SSA) and asymmetry factor are required to provide a solution. These are provided from a set of six pre-defined aerosol types with the SSA and asymmetry factor. We assume one aerosol type for a grid of 1°×1° and the surface reflectance changes little between two subsequent observations. A k-ratio approach is used in the inversion to find the best solution of atmospheric properties and surface reflectance. The k-ratio approach assumes that the surface reflectance is little influenced by aerosol scattering at 1.6 μm and therefore the ratio of surface reflectances in the solar band for two subsequent observations can be well-approximated by the ratio of the reflectances at 1.6 μm. A further assumption is that the surface reflectance varies only slightly over a period of 30 min. The algorithm makes use of numerical minimisation routines to obtain the optimal solution of atmospheric properties and surface reflectance by selection of the most suitable aerosol type from pre-defined sets.
Revised: 02 Aug 2012 – Accepted: 23 Sep 2012 – Published: 10 Oct 2012
A detailed analysis of the retrieval results shows that it is suitable for AOD retrieval over land from SEVIRI data. Six AErosol RObotic NETwork (AERONET) sites with different surface types are used for detailed analysis and 42 other AERONET sites are used for validation. From 445 collocations representing stable and homogeneous aerosol type, we find that >75% of the MSG-retrieved AOD at 0.6 and 0.8 μm values compare favourably with AERONET observed AOD values, within an error envelope of ± 0.05 ± 0.15 τ and a high correlation coefficient (R>0.86). The AOD datasets derived using the TS method with SEVIRI data is also compared with collocated AOD products derived from NASA TERRA and AQUA MODIS (The Moderate-resolution Imaging Spectroradiometer) data using the Dark Dense Vegetation (DDV) method and the Deep Blue algorithms. Using the TS method, the AOD could be retrieved for more pixels than with the NASA Deep Blue algorithm. This method is potentially also useful for surface reflectance retrieval using SEVIRI observations. The current paper focuses on AOD retrieval and analysis, and the analysis and validation of reflectance will be given in a following paper.
Mei, L., Xue, Y., de Leeuw, G., Holzer-Popp, T., Guang, J., Li, Y., Yang, L., Xu, H., Xu, X., Li, C., Wang, Y., Wu, C., Hou, T., He, X., Liu, J., Dong, J., and Chen, Z.: Retrieval of aerosol optical depth over land based on a time series technique using MSG/SEVIRI data, Atmos. Chem. Phys., 12, 9167-9185, doi:10.5194/acp-12-9167-2012, 2012.