Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Analysis of BRD Components Over Major Land Types of Korea

  • Kim, Sang-Il (Dept. of Geoinformatic Engineering, Pukyung National University) ;
  • Han, Kyung-Soo (Dept. of Geoinformatic Engineering, Pukyung National University) ;
  • Park, Soo-Jea (Dept. of Geoinformatic Engineering, Pukyung National University) ;
  • Pi, Kyoung-Jin (Dept. of Geoinformatic Engineering, Pukyung National University) ;
  • Kim, In-Hwan (Dept. of Geoinformatic Engineering, Pukyung National University) ;
  • Lee, Min-Ji (Dept. of Geoinformatic Engineering, Pukyung National University) ;
  • Lee, Sun-Gu (Satellite Operations & Application Division, Satellite Information Research Institute (SIRI)) ;
  • Chun, Young-Sik (Satellite Operations & Application Division, Satellite Information Research Institute (SIRI))
  • Received : 2010.11.18
  • Accepted : 2010.12.14
  • Published : 2010.12.30
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Abstract

The land surface reflectance is a key parameter influencing the climate near the surface. Therefore, it must be determined with sufficient accuracy for climate change research. In particular, the characteristics of the bidirectional reflectance distribution function (BRDF) when using earth observation system (EOS) are important for normalizing the reflected solar radiation from the earth's surface. Also, wide swath satellites like SPOT/VGT (VEGETATION) permit sufficient angular sampling, but high resolution satellites are impossible to obtain sufficient angular sampling over a pixel during short period because of their narrow swath scanning. This gives a difficulty to BRDF model based reflectance normalization of high resolution satellites. The principal objective of the study is to add BRDF modeling of high resolution satellites and to supply insufficient angular sampling through identifying BRDF components from SPOT/VGT. This study is performed as the preliminary data for apply to high-resolution satellite. The study provides surface parameters by eliminating BRD effect when calculated biophysical index of plant by BRDF model. We use semi-empirical BRDF model to identify the BRD components. This study uses SPOT/VGT satellite data acquired in the S1 (daily) data. Modeled reflectance values show a good agreement with measured reflectance values from SPOT satellite. This study analyzes BRD effect components by using the NDVI(Normalized Difference Vegetation Index) and the angle components such as solar zenith angle, satellite zenith angle and relative azimuth angle. Geometric scattering kernel mainly depends on the azimuth angle variation and volumetric scattering kernel is less dependent on the azimuth angle variation. Also, forest from land cover shows the wider distribution of value than cropland, overall tendency is similar. Forest shows relatively larger value of geometric term ($K_1{\cdot}f_1$) than cropland, When performed comparison between cropland and forest. Angle and NDVI value are closely related.

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References

  1. Chopping, M. J., 2000. Testing a LiSK BRDF model with in situ bidirectional reflectance factor measurements over semiarid grasslands. Remote Sensing of Environment, 74: 287- 312. https://doi.org/10.1016/S0034-4257(00)00122-X
  2. Csiszar, I., Gutman, G., Romanov, P., Leroy, M., and Hautecoeur, O., 2001. Using ADEOS/ POLDER data to reduce angular variability of NOAA/AVHRR reflectances. Remote Sensing of Environment, 76: 399-409. https://doi.org/10.1016/S0034-4257(01)00188-2
  3. Duchemin, B. and Maisongrande, P., 2002. Normalization of directional effects in 10-day global syntheses derived from VEGETATION/ SPOT: I. Investigation of concepts based on simulation. Remote Sensing of Environment., 81: 90-100. https://doi.org/10.1016/S0034-4257(01)00336-4
  4. Gao, W., 1993. A simple bidirectional-reflectance model applied to a tallgrass canopy. Remote Sensing of Environment., 45: 209-224. https://doi.org/10.1016/0034-4257(93)90043-W
  5. Holben, Brent N., 1986. Characteristics of maximumvalue composite images from temporal AVHRR data. International Journal of Remote Sensing., 7: 11, 1417-1434. https://doi.org/10.1080/01431168608948945
  6. J. A. Hartigan and M. a. Wong., 1979. Algorithm As 135: A K-means clustering algorithm, Applied Statistics, 28, 100-108. https://doi.org/10.2307/2346830
  7. Jiang, Z., A. R. Huete, J. Chen, Y. Chen, Li, G. Yan and X. Zhang., 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment, 101(3): 366-378. https://doi.org/10.1016/j.rse.2006.01.003
  8. Jongmin Yeom, kyungsoo Han, and Youngseup Kim., 2005. A Reflectance Normalization Via BRDF Model for the Korean Vegetation using MODIS 250m Data . Korean Journal of Remote Sensing, 21(6): 361-368. https://doi.org/10.7780/kjrs.2005.21.6.445
  9. Jong-Min Yeom and Kyung-Soo Han., 2009. An Efficiency Analysis for Data Synthesis of Sun- and Geo-Synchronous Satllites in kernel-driven BRDF Model. Asia-Pacific Journal of Atmospheric Sciences, 45(4): 499- 511.
  10. Justice, C. O., Markham, B. L., Townshend, J. R. G., Holber, B. N., and Tucker, C. J., 1985. Analysis of the phenology of global vegetation using meteorological data. International Journal of Remote Sensing., 6(8): 1271-1381. https://doi.org/10.1080/01431168508948281
  11. J. R. Jensen., 1995. Intoductory digital image processing: A remote sensing perspective. Englewood Cliffs, NJ, Prentice Hall, 316.
  12. Kimes, D. S., Newcombe, W. W., Tucker, C. J., Zonnefeld, I. W., van Vijingaarden, W., de Leeuw, J., and Epema, G. F., 1985. Directional reflectance factor distributions for cover types of Northern Africa. Remote Sensing of Environment, 18: 1-19. https://doi.org/10.1016/0034-4257(85)90034-3
  13. Roujean, J. L, M. Leroy, and P. Y. Deschamps., 1992. A bidirectional reflectance model of the earth's surface for the correction of remote sensing data. J. Geophys. Res., 97: 20,455- 20,468.
  14. Strahler, A. H. and Jupp, D. L. B., 1990. Modeling bidirectional reflectance of forests and woodlands using Boolean models and geometric optics. Remote Sensing of Environment., 34: 153-166. https://doi.org/10.1016/0034-4257(90)90065-T
  15. Tarpley, J. D., Schneider, S. R., and Money, R. L., 1984. Global vegetation indices from the NOAA-7 meteorological satellite. J. Climate Appl. Meteor., 23: 491-494. https://doi.org/10.1175/1520-0450(1984)023<0491:GVIFTN>2.0.CO;2
  16. Walthall, C. L., 1985. A study of reflectance anisotropy and canopy structure using a simple empirical model. Remote Sensing of Environment., 61: 118-128.
  17. Wanner, W., X. Li, and A. H. Strahler., 1995. On the derivation of kernels for kernel-driven models of bidirectional reflectance. J. Geophys. Res., 100(D10), 21077-21089. https://doi.org/10.1029/95JD02371