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- W1591131749 abstract "Satellite sensors have provided new datasets for monitoring regional and urban air quality. Satellite sensors provide comprehensive geospatial information on air quality with both qualitative remotely sensed imagery and quantitative data, such as aerosol optical depth which is the basic unknown parameter for any atmospheric correction method in the pre-processing of satellite imagery. This article presents a new method for retrieving aerosol optical thickness directly from satellite remotely sensed imagery for short wavelength bands in which atmospheric scattering is the dominant contribution to the at-satellite recorded signal. The method is based on the determination of the aerosol optical thickness through the application of the contrast tool (maximum contrast value), the radiative transfer calculations and the ‘tracking’ of the suitable darkest pixel in the scene. The proposed method that needs no a-priori information has been applied to LANDSAT-5 TM, LANDSAT-7 ETM+, SPOT-5 and IKONOS data of two different geographical areas: West London and Cyprus. The retrieved aerosol optical thickness values show high correlations with in-situ visibility data acquired during the satellite overpass. Indeed, for the West London area a logarithmic regression was fitted for relating the determined aerosol optical thickness with the in-situ visibility values. A high correlation coefficient (r2= 0.82; p= 0.2) was found. Plots obtained from Tanre et al. (1979, 1990) and Forster (1984) were reproduced and estimates for these areas were generated with the proposed method so as to compare the results. The author's results show good agreement with Forster's aerosol optical thickness vs. visibility results and a small deviation from Tanre's model estimates." @default.
- W1591131749 created "2016-06-24" @default.
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- W1591131749 date "2008-09-30" @default.
- W1591131749 modified "2023-09-23" @default.
- W1591131749 title "Description of a New Method for Retrieving the Aerosol Optical Thickness from Satellite Remotely Sensed Imagery Using the Maximum Contrast Value and Darkest Pixel Approach" @default.
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- W1591131749 doi "https://doi.org/10.1111/j.1467-9671.2008.01121.x" @default.
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