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Proceedings Paper

Evaluation of the surface reflectance retrieval on the satellite data
Author(s): Chunyan Xu; Xuezhi Feng; Pengfeng Xiao; Peifa Wang
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Paper Abstract

Electromagnetic radiance acquired by sensors is distorted mainly by atmospheric absorbing and scattering. Atmospheric correction is required for quantitatively analysis of remote sensing information. Radiation transfer model based atmospheric correction usually needs some atmospheric parameters to be chosen and estimated reasonably in advance when atmospheric observation data is lacked. In our work, a radiometric calibration was applied on the satellite data using revised coefficients at first. Then several parameters were determined for the correction process, taking into account the earth's surface and atmospheric properties of the study area. Moreover, the atmospheric correction was implemented using 6S code and the surface reflectance was retrieved. Lastly, the influence of atmospheric correction on spectral response characteristics of different land covers was discussed in respects of the spectral response curve, NDVI and the classification process, respectively. The results showed that the reflectance of all land covers decreases evidently in three visible bands, but increases in the near-infrared and shortwave infrared bands after atmospheric correction. NDVI of land covers also increases obviously after atmospheric influence was removed, and NDVI derived from the surface reflectance is the highest comparing to that from the original digital number and the top of atmosphere reflectance. The accuracy of the supervised classification is improved greatly, which is up to 87.23%, after the atmospheric effect is corrected. Methods of the parameter determination can be used for reference in similar studies.

Paper Details

Date Published: 8 August 2007
PDF: 9 pages
Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67520Z (8 August 2007); doi: 10.1117/12.760500
Show Author Affiliations
Chunyan Xu, Nanjing Univ. (China)
Xuezhi Feng, Nanjing Univ. (China)
Pengfeng Xiao, Nanjing Univ. (China)
Peifa Wang, Nanjing Univ. (China)


Published in SPIE Proceedings Vol. 6752:
Geoinformatics 2007: Remotely Sensed Data and Information

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