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An estimation of surface reflectance for Advanced Himawari Imager (AHI) data using 6SV

  • Seong, Noh-hun (Department of Spatial Information Engineering, Pukyong National University) ;
  • Lee, Chang Suk (Department of Spatial Information Engineering, Pukyong National University) ;
  • Choi, Sungwon (Department of Spatial Information Engineering, Pukyong National University) ;
  • Seo, Minji (Department of Spatial Information Engineering, Pukyong National University) ;
  • Lee, Kyeong-Sang (Department of Spatial Information Engineering, Pukyong National University) ;
  • Han, Kyung-Soo (Department of Spatial Information Engineering, Pukyong National University)
  • Received : 2016.02.25
  • Accepted : 2016.02.29
  • Published : 2016.02.28

Abstract

The surface reflectance is essential to retrieval various indicators related land properties such as vegetation index, albedo and etc. In this study, we estimated surface reflectance using Himawari-8 / Advanced Himawari Imager (AHI) channel data. In order to estimate surface reflectance from Top of Atmosphere (TOA) reflectance, the atmospheric correction is necessary because all of the TOA reflectance from optical sensor is affected by gas molecules and aerosol in the atmosphere. We used Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) Radiative Transfer Model (RTM) to correct atmospheric effect, and Look-Up Table (LUT) to shorten the calculation time. We verified through comparison Himawri-8 / AHI surface reflectance and Proba-V S1 products. As a result, bias and Root Mean Square Error (RMSE) are calculated about -0.02 and 0.05.

Keywords

References

  1. Berthelot, B., G. Dedieu, F. Cabot, and S. Adam, 1994. Estimation of surface reflectances and vegetation index using NOAA/AVHRR: methods and results at global scale. Proc. of 6th International Symposium Physical Measurements and Signatures in Remote Sensing. France. 17th-21st January, pp. 33-40.
  2. Hadjimitsis, D. G., C. R. I. Clayton, and V. S. Hope, 2004. An assessment of the effectiveness of atmospheric correction algorithms through the remote sensing of some reservoirs. International Journal of Remote Sensing, 25(18): 3651-3674. https://doi.org/10.1080/01431160310001647993
  3. Lee, C.S., J.M. Yeom, H.L. Lee, J.J. Kim, and K.S. Han, 2015. Sensitivity Analysis of 6S-Based Look-Up Table for Surface Reflectance Retrieval, Asia-Pacific Journal of Atmospheric Sciences, 51(1): 91-101. https://doi.org/10.1007/s13143-015-0062-9
  4. Liang, S., D. Wang, T. He, 2010. GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document for Surface Albedo, NOAA NESDIS CENTER for SATELLITE APPLICATIONS and REARCH.
  5. Liang, S., H. Fang, and M. Chen, 2001. Atmospheric correction of Landsat ETM+ land surface imagery. I. Methods. Geoscience and Remote Sensing, IEEE Transactions on, 39(11): 2490-2498. https://doi.org/10.1109/36.964986
  6. Nunes, A. S. L., A. R. S. Marcal, and R. A. Vaughan, 2008. Fast over-land atmospheric correction of visible and near-infrared satellite images, International Journal of Remote Sensing, 29(12): 3523-3531. https://doi.org/10.1080/01431160701592445
  7. Paltridge, G. W., and C. M. R. Platt, 1976. Radiative processes in meteorology and climatology. Elsevier Scientific.
  8. Ricchiazzi, P., S. Yang, C. Gautier, D. Sowle, 1998. SBDART: A Research and Teaching Software Tool for Plane-Parallel Radiative Transfer in the Earth's Atmosphere. Bulletin of the American Meteorological Society, 79(10): 2101-2114. https://doi.org/10.1175/1520-0477(1998)079<2101:SARATS>2.0.CO;2
  9. Saunders, R. W., and K. T. Kriebel, 1988. An improved method for detecting clear sky and cloudy radiances from AVHRR data. International Journal of Remote Sensing, 9(1): 123-150. https://doi.org/10.1080/01431168808954841
  10. Vermote, E. F., and A. Vermeulen, 1999. Atmospheric correction algorithm: spectral reflectances (MOD09), Algorithm theoretical basis document (ATBD) version 4. NASA contract NAS5-96062
  11. Vermote, E. F., D. Tanre, J. J. Deuze, M. Herman, and J. Morcrette, 1997. Second simulation of the satellite signal in the solar spectrum, 6S: An overview, IEEE Transactions on Geoscience and Remote Sensing, 35(3): 675-686 https://doi.org/10.1109/36.581987
  12. Vermote, E. F., D. Tanre, J. J. Deuze, M. Herman, J. Morcrette, and S. Y. Kotchenove, 2006. Second Simulation of a Satellite Signal in the Solar Spectrum - Vector (6SV), 6S user guide, version 3.