Atmospheric Correction of Landsat ETM+ Remote Sensing Data Using 6S Code and its Validation

Abstract:

Atmospheric correction is one of the most important pre-processing steps in quantitative remote sensing. To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, atmospheric correction is a necessary step. Furthermore, multi-temporal images after atmospheric correction can be compared to each other quantitatively. The Second simulation of satellite signal in the solar spectrum (6S) radiative code can process many types of satellite data and provide several standard atmosphere and aerosol models for atmospheric correction. This paper demonstrates atmospheric correction of Landsat ETM+ data using 6S code. Comparing images before and after atmospheric correction, the different of image before and after correction was not obvious using visual interpretation. Therefore, different ground object spectral curves after atmospheric correction are illustrated. They were similar with the standard ground object spectra. The correlation coefficient of ETM+ band 1 to band 4 and NDVI (Normalized Difference Vegetation Index) after atmospheric correction increases. The atmospheric correction removed the atmosphere effect, so the inherent relevant increased. 6S code was an effective tool for atmospheric correction of remote senisng data.

• Periodical: Applied Mechanics and Materials (Volumes 29-32)
• Pages: 2365-2368
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.29-32.2365
• Online since: August 2010
• Authors: Xiao Feng Yang, Xing Ping Wen
• Keywords: 6S, Atmospheric Correction, Landsat ETM+, Remote Sensing, Validation

References

[1] S. L. Liang, H. L. Fang, and M. Z. Chen: Atmospheric correction of landsat ETM+ land surface imagery - Part I: Methods, IEEE Transactions on Geoscience and Remote Sensing, vol. 39 (2001), pp.2490-2498.

DOI: 10.1109/36.964986

[2] X. Wen and X. Yang: Change detection from remote sensing imageries using spectral change vector analysis, in Asia-Pacific Conference on Information Processing (APCIP 2009), Shenzhen, China, (2009), pp.189-192.

DOI: 10.1109/apcip.2009.183

[3] X. Wen and X. Yang: A new change detection method for two remote sensing images based on spectral matching, in International Conference on Industrial Mechatronics and Automation (ICIMA 2009), Chengdu, China, (2009), pp.89-92.

DOI: 10.1109/icima.2009.5156567

[4] Y. J. Kaufman and D. Tanre: Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: From AVHRR to EOS-MODIS, Remote Sensing of Environment, vol. 55 (1996), pp.65-79.

DOI: 10.1016/0034-4257(95)00193-x

[5] X. Wen and X. Yang: Haze removal from the visible bands of CBERS remote sensing data, in 2009 International Conference on Industrial and Information Systems (IIS 2009), Haikou, China, (2009), pp.118-121.

DOI: 10.1109/iis.2009.58

[6] E. F. Vermote and A. Vermeulen, Atmospheric correction algorithm: spectral reflectances (MOD09), in MODIS Algorithm Technical Background Document: National Aeronautics and Space Administration, (1999).

[7] A. Ghulam, Q. Qin, L. Zhu, and P. Abdrahman: Satellite remote sensing of groundwater: quantitative modelling and uncertainty reduction using 6S atmospheric simulations, International Journal of Remote Sensing, vol. 25 (2004), pp.5509-5524.

DOI: 10.1080/01431160410001719821

[8] J. A. Pedelty, J. T. Morisette, and J. A. Smith: Comparison of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Earth Observing One (EO-1) Advanced Land Imager, Optical Engineering, vol. 43 (2004), pp.954-962.

DOI: 10.1117/1.1651556

[9] C. H. Song and C. E. Woodcock: Monitoring forest succession with multitemporal Landsat images: Factors of uncertainty, IEEE Transactions on Geoscience and Remote Sensing, vol. 41 (2003), pp.2557-2567.

DOI: 10.1109/tgrs.2003.818367

[10] W. J. Zhao, M. Tamura, and H. Takahashi: Atmospheric and spectral corrections for estimating surface albedo from satellite data using 6S code, Remote Sensing of Environment, vol. 76 (2001), pp.202-212.

DOI: 10.1016/s0034-4257(00)00204-2

[11] E. F. Vermote, D. Tanré, J. L. Deuzé, M. Herman, and J. J. Morcrette, Second Simulation of the Satellite Signal in the Solar Spectrum (6S), 6S User Guide Version 2, 1997, p.218.

[12] E. F. Vermote, D. Tanre, J. L. Deuze, M. Herman, and J. J. Morcrette: Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An overview, IEEE Transactions on Geoscience and Remote Sensing, vol. 35 (1997), pp.675-686.

DOI: 10.1109/36.581987