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DOI: 10.14569/IJARAI.2015.041101
PDF

Vicarious Calibration Data Screening Method Based on Variance of Surface Reflectance and Atmospheric Optical Depth Together with Cross Calibration

Author 1: Kohei Arai

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 4 Issue 11, 2015.

  • Abstract and Keywords
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Abstract: Vicarious calibration data screening method based on the measured atmospheric optical depth and the variance of the measured surface reflectance at the test sites is proposed. Reliability of the various calibration data has to be improved. In order to improve the reliability of the vicarious calibration data, some screenings have to be made. Through experimental study, it is found that vicarious calibration data screening would be better to apply with the measured atmospheric optical depth and variance of the measured surface reflectance due to the facts that thick atmospheric optical depth means that the atmosphere contains serious pollution sometime and that large deviation of the surface reflectance from the average means that the solar irradiance has an influence due to cirrus type of clouds. As the results of the screening, the uncertainty of vicarious calibration data from the approximated radiometric calibration coefficient is remarkably improved. Also, it is found that cross calibration uncertainty is poorer than that of vicarious calibration.

Keywords: Vicarious calibration; Surface reflectance; Atmospheric Optical Depth; Sky-radiometer; Terra/ASTER; Satellite remote sensing

Kohei Arai, “Vicarious Calibration Data Screening Method Based on Variance of Surface Reflectance and Atmospheric Optical Depth Together with Cross Calibration” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 4(11), 2015. http://dx.doi.org/10.14569/IJARAI.2015.041101

@article{Arai2015,
title = {Vicarious Calibration Data Screening Method Based on Variance of Surface Reflectance and Atmospheric Optical Depth Together with Cross Calibration},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2015.041101},
url = {http://dx.doi.org/10.14569/IJARAI.2015.041101},
year = {2015},
publisher = {The Science and Information Organization},
volume = {4},
number = {11},
author = {Kohei Arai}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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