PM10 retrieval over the water surface of Penang Straits from Landsat TM5 data

Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Saleh, N. Mohd.; Hashim, Syahril Amin

doi:10.1117/12.719064

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The use of satellite remote sensing to assess and map atmospheric pollution has received extensive attention from researchers who have developed a variety of techniques (e.g. Kaufman et al 1990; Finzi and Lechi 1991; Sifakis and Deschamps 1992; Wald et al 1999; Kanaroglou et al 2002; Pawan et al 2006; Hadjimitsis and Clayton 2006; Lim et al 2007; Hadjimitsis et al 2007a, b; Kumar et al 2008). Satellite remote sensing is certainly a valuable tool for assessing and mapping air pollution due to their major benefit of providing complete and synoptic views of large areas in one image on a systematic basis due to the good temporal resolution of various satellite sensors (Wald et al 1999, Hadjimitsis et al 2002).…”

Section: Introductionmentioning

confidence: 99%

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

Hadjimitsis

2008

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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 ( r 2 = 0.82; p = 0.2) was found. Plots obtained from Tanre et al. (1979) 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.

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Section: Introductionmentioning

confidence: 99%

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

Hadjimitsis

2008

Transactions in GIS

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“…The details of the algorithm have been described elsewhere. [3][4][5] In short, our algorithm for particle concentration is given by A = e 0 + e 1 R atm1 + e 2 R atm3 , where A is the particle concentration of PM10, R atmi is the atmospheric reflectance (i = 1 and 3 are the band numbers), and e j is the algorithm coefficient ( j = 0, 1, and 2 are empirically determined).…”

Section: Satellite Imagery Offers a Lower-cost And Less Time Consuminmentioning

confidence: 99%

Mapping atmospheric pollution using remote sensing

San¹

2007

SPIE Newsroom

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PM10 retrieval over the water surface of Penang Straits from Landsat TM5 data

Cited by 2 publications

References 9 publications

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

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

Mapping atmospheric pollution using remote sensing

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