Mixing weight determination for retrieving optical properties of polluted dust with MODIS and AERONET data

Kuo En Chang, Ta Chih Hsiao, N. Christina Hsu, Neng Huei Lin, Sheng Hsiang Wang, Gin Rong Liu, Chian Yi Liu, Tang Huang Lin

研究成果: 雜誌貢獻期刊論文同行評審

2 引文 斯高帕斯(Scopus)

摘要

In this study, an approach in determining effective mixing weight of soot aggregates from dust-soot aerosols is proposed to improve the accuracy of retrieving properties of polluted dusts by means of satellite remote sensing. Based on a pre-computed database containing several variables (such as wavelength, refractive index, soot mixing weight, surface reflectivity, observation geometries and aerosol optical depth (AOD)), the fan-shaped look-up tables can be drawn out accordingly for determining the mixing weights, AOD and single scattering albedo (SSA) of polluted dusts simultaneously with auxiliary regional dust properties and surface reflectivity. To validate the performance of the approach in this study, 6 cases study of polluted dusts (dust-soot aerosols) in Lower Egypt and Israel were examined with the ground-based measurements through AErosol RObotic NETwork (AERONET). The results show that the mean absolute differences could be reduced from 32.95% to 6.56% in AOD and from 2.67% to 0.83% in SSA retrievals for MODIS aerosol products when referenced to AERONET measurements, demonstrating the soundness of the proposed approach under different levels of dust loading, mixing weight and surface reflectivity. Furthermore, the developed algorithm is capable of providing the spatial distribution of the mixing weights and removing the requirement to assume that the dust plume properties are uniform. The case study further shows the spatially variant dust-soot mixing weight would improve the retrieval accuracy in AODmixture and SSAmixture about 10.0% and 1.4% respectively.

原文???core.languages.en_GB???
文章編號085002
期刊Environmental Research Letters
11
發行號8
DOIs
出版狀態已出版 - 28 7月 2016

指紋

深入研究「Mixing weight determination for retrieving optical properties of polluted dust with MODIS and AERONET data」主題。共同形成了獨特的指紋。

引用此