Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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A Reflectance Normalization Via BRDF Model for the Korean Vegetation using MODIS 250m Data

한반도 식생에 대한 MODIS 250m 자료의 BRDF 효과에 대한 반사도 정규화

  • Yeom, Jong-Min (Dept. of Environmental Atmospheric Science, Pukyung National University) ;
  • Han, Kyung-Soo (Dept. of Satellite Information Science. Pukyung National University) ;
  • Kim, Young-Seup (Dept. of Satellite Information Science. Pukyung National University)
  • 염종민 (부경대학교 환경대기과학과) ;
  • 한경수 (부경대학교 위성정보과학과) ;
  • 김영섭 (부경대학교 위성정보과학과)
  • Published : 2005.12.01
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Abstract

The land surface parameters should be determined with sufficient accuracy, because these play an important role in climate change near the ground. As the surface reflectance presents strong anisotropy, off-nadir viewing results a strong dependency of observations on the Sun - target - sensor geometry. They contribute to the random noise which is produced by surface angular effects. The principal objective of the study is to provide a database of accurate surface reflectance eliminated the angular effects from MODIS 250m reflective channel data over Korea. The MODIS (Moderate Resolution Imaging Spectroradiometer) sensor has provided visible and near infrared channel reflectance at 250m resolution on a daily basis. The successive analytic processing steps were firstly performed on a per-pixel basis to remove cloudy pixels. And for the geometric distortion, the correction process were performed by the nearest neighbor resampling using 2nd-order polynomial obtained from the geolocation information of MODIS Data set. In order to correct the surface anisotropy effects, this paper attempted the semiempirical kernel-driven Bi- directional Reflectance Distribution Function(BRDF) model. The algorithm yields an inversion of the kernel-driven model to the angular components, such as viewing zenith angle, solar zenith angle, viewing azimuth angle, solar azimuth angle from reflectance observed by satellite. First we consider sets of the model observations comprised with a 31-day period to perform the BRDF model. In the next step, Nadir view reflectance normalization is carried out through the modification of the angular components, separated by BRDF model for each spectral band and each pixel. Modeled reflectance values show a good agreement with measured reflectance values and their RMSE(Root Mean Square Error) was totally about 0.01(maximum=0.03). Finally, we provide a normalized surface reflectance database consisted of 36 images for 2001 over Korea.

지표변수는 지면 근처의 기후변화에 중요한 역할을 하기 때문에, 충분히 높은 정확성을 가진 값이 산출되어야 한다 하지만 지표 반사도는 강한 이방성(non-Lambertian) 특징을 가지고 있기 때문에, 위성 천저각으로부터 멀어질수록 태양-지점-위성과의 기하학적 영향을 더욱 강하게 받는 효과를 가져온다. 또한 지표 각 영향을 포함하고 있는 지표 반사도는 노이즈를 가지게 된다. 따라서 본 연구의 목적은 한반도 지역의 MODIS 반사도 자료(250m)를 이용하여 각 영향이 제거된 보다 정확한 반사도 값에 대한 데이터베이스를 제공하는 것이다. 본 연구에서는 매일 2회씩 제공하는 MODIS(Moderate Resolution Imaging Spoctroradiometer) 센서의 가시영역과 근적외영역의 반사도(250m)자료를 이용하였다. 먼저 구름화소를 제거하기 위해서 연속적인 물리과정을 통하여 각각의 구름 화소를 제거하였다. 그리고 지리보정은 MODIS 센서에서 제공하는 지리정보자료를 이용하여 2차 다항회귀식을 통한 최근접 내삽법을 사용하였다. 본 연구에서는 지표 이방성 효과를 보정하기 위해서 반 경험적 양방향성분포함수(BRDF) 모델을 사용하였다. 이 알고리즘은 위성으로부터 관측된 위성천정각, 태양천정각, 위성방위각, 태양방위각과 같은 각 성분을 이용하여 Kernel-deriven 모델의 역변환을 통하여 지표 반사도를 재생산한다. 먼저 우리는 BRDF 모델을 수행하기 위해 총 31일 모델 관측 실행기간을 고려하였다. 다음 단계로 각각의 화소 및 밴드에 대해서 BRDF 모델을 통하여 분리된 각 성분들을 변조함으로써 위성 직하점 반사도 정규화가 수행되었다. 모델을 이용하여 산출된 반사도 값은 실제 위성 반사도 값과 잘 일치하였고, RMSE(Root Mean Square Error)값은 전체적으로 약 0.01(최고값=0.03)이였다. 마지막으로, 우리는 한반도 지역에 대해서 2001년 동안 총 36개로 구성된 정규화 지표반사도 값의 데이터베이스를 구축하였다.

Keywords

References

  1. 서명석, 이동규, 1999. NOAA/AVHRR 주간 자료로부터 지면 자료 추출을 위한 구름 탐지 알고리즘의 개발. 한국원격탐사학회지, 15(3): 239-251
  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. Kimes, D. S., 1983. Dynamics of Directional Reflectance Factor Distributions for Vegetation Canopies. Applied Optics, 22(9): 1364-1372 https://doi.org/10.1364/AO.22.001364
  4. 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
  5. 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
  6. Gutman, G., 1994. Normalization of multi-annual global AVHRR reflectance data over land surface to common sun-target-sensor geometry. Advanced Space Research., 14: 121-124
  7. Han, K. S., Champeaux, J. L., and Roujean, J. L., 2004. A land cover classification product over France at 1 km resolution using SPOT4/VEGETATION data. Remote Sensing of Environment., 92: 52-66 https://doi.org/10.1016/j.rse.2004.05.005
  8. Jackson, R. D., Slater, P. N., and Pinter, P. J., 1983. Discrimination of growth and water stress in wheat by various vegetation indices through clear and turbid atmospheres. Remote Sensing of Environment., 15: 187-208
  9. 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
  10. Leroy, M., Deuze, J. L., Breon, F. M., Hautecoeur, O., Herman, M., Duriez, J. C.. Tanre, D., Bouffies, S., Chazetle, P., and Roujean, J. L., 1997. Retrieval of atmospheric properties and surface bidirectional reflectance over the land from POLDER/ ADEAOS. Journal of Geophysical Research., 102: 17023-17037 https://doi.org/10.1029/96JD02662
  11. Leroy, M. & Roujean, J. L., 1994. Sun and view angle correction on reflectance derived from NOAA/ AVHRR. IEEE Transactions on Geoscience and Remote Sensing., 32-3: 684-679 https://doi.org/10.1109/36.297985
  12. McClain, E. P., 1993. Evaluation of CLAVR Phase- I algorithm performance, final report, Rep. 40AANE-201-424, U.S. Dep. of Commerce/ NOAA/NEDIS, Washington, D. C
  13. Roujean, J. L., Leroy, M., and Dechamps, P. Y, 1992. A bidirectional reflectance model of the earth's surface for the correction of remote sensing data. Journal of Geophysical Research., 97: 20455-20468 https://doi.org/10.1029/92JD01411
  14. Saunders, R. W. and Kriebel, K. T., 1988. An improved method for detecting clear sky and cloudy radiance from AVHRR data, International Journal of Remote Sensing., 21,077-21,090 https://doi.org/10.1080/01431168808954841
  15. Shine, D., Pollard, J. K, and Muller, J. P., 1996. Cloud detection from thermal infrared images using a segmentation technique. International Journal of Remote Sensing., 17, 14,2845-2856 https://doi.org/10.1080/01431169608949110
  16. Simpson, J. J. and Gobat, J. I., 1996. Improved cloud detection for the daytime AVHRR scenes over land. Remote Sensing of Environment., 55: 123-150 https://doi.org/10.1016/0034-4257(95)00198-0
  17. Strahler AH and Jupp DLB., 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
  18. 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
  19. Tucker, C. J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment., 8: 127-150 https://doi.org/10.1016/0034-4257(79)90013-0
  20. Tucker, C. J., Newcomb, W. W., Los, S. O., and Prince, S. D., 1991. Mean and inter-year variation of growing-season normalized difference vegetation index for the Sahel 1981-1989. International Journal of Remote Sensing., 12: 1113-1115
  21. Walthall, C. L., 1985. A study of reflectance anisotropy and canopy structure using a simple empirical model. Remote Sensing of Environment., 61: 118-128 https://doi.org/10.1016/S0034-4257(96)00245-3
  22. Wu, A, Li, Z., and Cihlar, J., 1995. Effects of land cover type and greenness on advanced very high resolution radiometer bidirectional reflectances: analysis and removal. Journal of Geophysical Research., 100: 9179-9192 https://doi.org/10.1029/95JD00512