Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66093
Title: An improved algorithm for retrieving the fine-mode fraction of aerosol optical thickness, Part 1 : Algorithm development
Authors: Yan, X
Li, Z
Shi, W
Luo, N
Wu, T
Zhao, W
Keywords: Aerosol fine-mode fraction
AOT
MODIS
PM2.5
Issue Date: 2017
Publisher: Elsevier
Source: Remote sensing of environment, 2017, v. 192, p. 87-97 How to cite?
Journal: Remote sensing of environment 
Abstract: The fine-mode fraction (FMF) can be a useful tool to separate natural aerosols from man-made aerosols and to assist in estimating surface concentrations of particulate matter with a diameter < 2.5 μm. A LookUp Table-based Spectral Deconvolution Algorithm (LUT-SDA) was developed here for satellite-based applications using data such as MODerate resolution Imaging Spectroradiometer (MODIS) measurements. This method was validated against ground-based FMF retrievals from the Aerosol Robotic Network (AERONET). The LUT-SDA was then applied to two MODIS-retrieved aerosol optical thickness (AOT) products for the period of December 2013 to July 2015: the MODIS Collection 6 (C6) Dark Target (DT) AOT product and the simplified high-resolution MODIS Aerosol Retrieval Algorithm (SARA) AOT product. In comparison with the MODIS C6 FMF product in three study areas (Beijing, Hong Kong, and Osaka), FMFs estimated by the LUT-SDA agreed more closely with those retrieved from the AERONET with a very low bias. Eighty percent of the FMF values fell within the expected error range of ± 0.4. The root mean square error (RMSE) was 0.168 with few anomalous values, whereas the RMSE for the MODIS FMF was 0.340 with more anomalous values. The LUT-SDA FMF estimated using SARA AOT data conveys more detailed information on urban pollution than that from MODIS C6 DT AOT data. As a demonstration, the seasonally-averaged spatial distribution of the FMF in Beijing was obtained from the LUT-SDA applied to SARA AOT data and compared with that of the AERONET-retrieved FMF. Their seasonal trends agreed well.
URI: http://hdl.handle.net/10397/66093
ISSN: 0034-4257
EISSN: 1879-0704
DOI: 10.1016/j.rse.2017.02.005
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