DOI QR코드

DOI QR Code

Near-Infrared Spectral Characteristics in Presence of Sun Glint Using CASI-1500 Data in Shallow Waters

  • Jeon, Joo-Young (Department of Convergence Study on the Ocean Science and Technology School, Ocean Science & Technology School (OST)) ;
  • Kim, Sun-Hwa (Korea Ocean Satellite Center, Korea Institute of Ocean Science & Technology) ;
  • Yang, Chan-Su (Department of Convergence Study on the Ocean Science and Technology School, Ocean Science & Technology School (OST))
  • Received : 2015.04.02
  • Accepted : 2015.07.31
  • Published : 2015.08.31

Abstract

Sun glint correction methods of hyperspectral data that have been developed so far have not considered the various situations and are often adequate for only certain conditions. Also there is an inaccurate assumption that the signal in NIR wavelength is zero. Therefore, this study attempts to analyze the NIR spectral properties of sun glint effect in coastal waters. For the analysis, CASI-1500 airborne hyperspectral data, bathymetry data and in-situ data obtained at coastal area near Sin-Cheon, Jeju Island, South Korea were used. The spectral characteristics of radiance and reflectance at the five NIR wavelengths (744 nm, 758 nm, 772 nm, 786 nm, and 801 nm) are analyzed by using various statistics, spatial and spectral variation of sun-glinted area under conditions of the bottom types of benthos, barren rocks and sand with similar water depth. Through the quantitative analysis, we found that the relation of water depth or bottom type with sun glint is relatively less which is a similar result with the previous studies. However the sun glint are distributed similarly with the patterns of the direction of wave propagation. It is confirmed that the areas with changed direction of wave propagation were not affected by the sun glint. The spatial and spectral variations of radiance and reflectance are mainly caused by the effect of sun glint and waves. The radiance or reflectance of more sun-glinted areas are increased approximately 1.5 times and the standard deviations are also increased three times compared to the less sun glinted areas. Through this study, the further studies of sun glint correction method in coastal water using the patterns of wave propagation and diffraction will be placed.

Keywords

References

  1. Cox, C. and W. Munk, 1954a. Measurement of the roughness of the sea surface from photographs of the suns glitter, Journal of the Optical Society of America, 44 (11): 838-850. https://doi.org/10.1364/JOSA.44.000838
  2. Cox, C. and W. Munk, 1954b. Statistics of the sea surface derived from sun glitter, Journal of Marine Research, 13(2): 198-227.
  3. Cox, C. and W. Munk, 1956. Slopes of the sea surface deduced from photographs of sun glitter, Scripps Institute of Oceanography Bulletin, 6(9): 401-488.
  4. Gao, B.C., M.J. Montes, C.O. Davis, and A.F.H. Goetz, 2009. Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean, Remote Sensing of Environment, 113: S17-S24. https://doi.org/10.1016/j.rse.2007.12.015
  5. Goodman, J.A., Z. Lee, and S.L. Ustin, 2008. Influence of Atmospheric and Sea-Surface Corrections on Retrieval of Bottom Depth and Reflectance Using a Semi-Analytical Model: A Case Study in Kaneohe Bay, Hawaii, Applied Optics, 47: F1-F11. https://doi.org/10.1364/AO.47.0000F1
  6. Hamilton M.K., C.O. Davis, W.J. Rhea, S.H. Pilorz, and K.L. Carder, 1993. Estimating chlorophyll content and bathymetry of Lake Tahoe using AVIRIS data, Remote Sensing Environment, 44: 217-230. https://doi.org/10.1016/0034-4257(93)90017-R
  7. Hedley, J., A. Harborne, and P. Mumby, 2005. Simple and Robust Removal of Sun Glint for Mapping Shallow-Water Benthos, International Journal of Remote Sensing, 26: 2107-2112. https://doi.org/10.1080/01431160500034086
  8. Heege, T. and J. Fischer, 2000. Sun glitter correction in remote sensing imaging spectrometry, Proc. of 2000 SPIE Ocean Optics XV Conference, Monaco, Oct. 16-20.
  9. Hochberg, E.J., M.J. Atkinson, and S. Andrefouet, 2003. Spectral reflectance of coral reef bottomtypes worldwide and implications for coral reef remote sensing, Remote Sensing Environment, 85: 159-173. https://doi.org/10.1016/S0034-4257(02)00201-8
  10. Jeon, J.Y., S.H. Kim, and C.S. Yang, 2014. Comparison of Sun Glint Correction Methods for CASI-1500 Data in Shallow Waters, Proc. of 12th Biennial Conference of Pan Ocean Remote Sensing Conference(PORSEC 2014), Bali, Indonesia, Nov. 4-7, 1: 1-7.
  11. Kay, S., J.D. Hedley, and L. Samantha, 2009. Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: a Review of Methods for Visible and Near-Infrared Wavelengths, Remote Sensing, 1: 697-730. https://doi.org/10.3390/rs1040697
  12. Kutser, T., A.G. Dekker, and W. Skirving, 2003. Modeling spectral discrimination of Great Barrier Reef benthic communities by remote sensing instruments, Limnology and Oceanography, 48(1): 497-510. https://doi.org/10.4319/lo.2003.48.1_part_2.0497
  13. Kutser, T., E. Vahtmae, and J.A. Praks, 2009. Sun Glint Correction Method for Hyperspectral Imagery Containing Areas with Non-Negligible Water Leaving NIR Signal, Remote Sensing Environment, 113: 2267-2274. https://doi.org/10.1016/j.rse.2009.06.016
  14. Lavender, S., M. Pinkerton, G. Moore, J. Aiken, and D. Blondeau-Patissier, 2005. Modication to the atmospheric correction of SeaWiFS ocean colour images over turbid waters, Continental Shelf Research, 25(4): 539-555. https://doi.org/10.1016/j.csr.2004.10.007
  15. Lee, Z., K. Carder, C. Mobley, R. Steward, and J. Patch, 1999. Hyperspectral Remote Sensing for Shallow Waters: 2. Deriving Bottom Depths and Water Properties by Optimization, Applied Optics, 38: 3831-3843. https://doi.org/10.1364/AO.38.003831
  16. Lyzenga, D., N. Malinas, and F. Tanis, 2006. Multispectral Bathymetry Using a Simple Physically Based Algorithm, IEEE Transactions on Geoscience Remote Sensing, 44: 2251-2259. https://doi.org/10.1109/TGRS.2006.872909
  17. Sandidge, J.C. and R.J. Holyer, 1998. Coastal bathymetry from hyperspectral observations of water radiance, Remote Sensing of Environment, 65(3): 341-352. https://doi.org/10.1016/S0034-4257(98)00043-1
  18. Siegel, D.A., M. Wang, S. Maritorena, and W. Robinson, 2000. Atmospheric correction of satellite ocean color imagery: The black pixel assumption, Applied Optics, 39(21): 3582-3591. https://doi.org/10.1364/AO.39.003582