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Turbid water atmospheric correction for GOCI: Modification of MUMM algorithm

GOCI영상의 탁한 해역 대기보정: MUMM 알고리즘 개선

  • Lee, Boram (Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology) ;
  • Ahn, Jae Hyun (Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology) ;
  • Park, Young-Je (Korea Ocean Satellite Center, Korea Institute of Ocean Science and Technology) ;
  • Kim, Sang-Wan (Geoinformation Engineering, Sejong University)
  • 이보람 (해양위성센터, 한국해양과학기술원) ;
  • 안재현 (해양위성센터, 한국해양과학기술원) ;
  • 박영제 (해양위성센터, 한국해양과학기술원) ;
  • 김상완 (세종대학교 지구정보공학과)
  • Received : 2013.03.08
  • Accepted : 2013.04.22
  • Published : 2013.04.30

Abstract

The early Sea-viewing Wide Field-of-view Sensor(SeaWiFS) atmospheric correction algorithm which is the basis of the atmospheric correction algorithm for Geostationary Ocean Color Imager(GOCI) assumes that water-leaving radiances is negligible at near-infrared(NIR) wavelengths. For this reason, all of the satellite measured radiances at the NIR wavelengths are assigned to aerosol radiances. However that assumption would cause underestimation of water-leaving radiances if it were applied to turbid Case-2 waters. To overcome this problem, Management Unit of the North Sea Mathematical Models(MUMM) atmospheric correction algorithm has been developed for turbid waters. This MUMM algorithm introduces new parameter ${\alpha}$, representing the ratio of water-leaving reflectance at the NIR wavelengths. ${\alpha}$ is calculated by statistical method and is assumed to be constant throughout the study area. Using this algorithm, we can obtain comparatively accurate water-leaving radiances in the moderately turbid waters where the NIR water-leaving reflectance is less than approximately 0.01. However, this algorithm still underestimates the water-leaving radiances at the extremely turbid water since the ratio of water-leaving radiance at two NIR wavelengths, ${\alpha}$ is changed with concentration of suspended particles. In this study, we modified the MUMM algorithm to calculate appropriate value for ${\alpha}$ using an iterative technique. As a result, the accuracy of water-leaving reflectance has been significantly improved. Specifically, the results show that the Root Mean Square Error(RMSE) of the modified MUMM algorithm was 0.002 while that of the MUMM algorithm was 0.0048.

천리안 위성 해양탑재체(Geostationary Ocean Color Imager, GOCI) 대기보정의 근간이 되는 Sea-viewing Wide Field-of-view Sensor(SeaWiFS) 초기 대기보정 기법은 근적외선 파장대의 해수 반사도를 0으로 가정한다. 이러한 가정에 근거하여 근적외선 파장에서 탐지되는 모든 신호는 에어로졸 산란에 의한 반사도로 간주된다. 그러나 이러한 가정은 탁한 해역에서 해수 반사도를 과소 추정하는 문제점을 야기시킨다. 이를 해결하기 위하여 Management Unit of the North Sea Mathematical Models(MUMM) 대기보정 알고리즘이 개발되었다. 이 알고리즘은 근적외선 파장에서 탐지되는 해수 반사도 비율인 ${\alpha}$를 도입하였다. ${\alpha}$는 통계적 방법에 의하여 결정되며 영상 내의 모든 픽셀에 고정적인 값으로 사용된다. 이 알고리즘은 근적외선 해수 반사도가 0.01보다 작은 중간 탁도의 해역에서는 잘 맞는 반면 매우 탁한 해역에서는 ${\alpha}$가 탁도에 따라 변하기 때문에 오차율이 다시 증가한다. 본 연구에서는 매우 탁한 해역 해수 반사도의 정확도를 향상시키고자 ${\alpha}$를 고정하지 않고, 반복계산을 통해 탁도에 적합한 ${\alpha}$를 계산하도록 MUMM 알고리즘을 수정 보완하였다. 그 결과 MUMM 알고리즘의 모든 밴드의 평균 Root Mean Square Error(RMSE)는 0.0048인 반면 수정된 MUMM 알고리즘은 0.002로 개선된 결과를 얻었다.

Keywords

References

  1. Ahn, J.H., Y.J. Park, J.H. Ryu, B. Lee and I.S. Oh, 2012. Development of Atmospheric Correction Algorithm for Geostationary Ocean Color Imager(GOCI), Ocean Science Journal, 47(3): 247-259. https://doi.org/10.1007/s12601-012-0026-2
  2. Choi, J.K., Y.J. Park, J.H. Ahn, H.-S. Lim, J. Eom and J.-H. Ryu, 2012. GOCI, the world's first geostationary ocean color observation satellite, for the monitoring of temporal variability in coastal water turbidity, Journal of Geophysical Research, 117, C09004. https://doi.org/10.1029/2012JC008046
  3. Choi, Y.-K., and J.-N. Kwon, 1998. Seasonal variation of transparency in the southeastern Yellow Sea, Journal of the Korean Fisheries Society, 31(3): 323-329.
  4. Doron, M., S. Belanger, D. Doxaran and M. Babin, 2011. Spectral variations in the near-infrared ocean reflectance, Remote Sensing of Environment, 115(2011): 1617-1631. https://doi.org/10.1016/j.rse.2011.01.015
  5. Doxaran, D., J.M. Froidefond and P. Castaing, 2003. Remote-sensing reflectance of turbid sedimentdominated waters. Reduction of sediment type variations and changing illumination conditions effects by use of reflectance ratios, Applied Optics, 42: 2623-2634. https://doi.org/10.1364/AO.42.002623
  6. Doxaran, D., J.M. Froidefond, P. Castaing and M. Babin, 2009. Dynamics of the turbidity maximum zone in a macrotidal estuary(the Gironde, France): Observation from field and MODIS satellite data, Estuarine, Coastal and Shelf Science, 81: 321-332. https://doi.org/10.1016/j.ecss.2008.11.013
  7. Gordon, H.R., 1978. Removal of atmospheric effects from satellite imagery of the oceans, Applied Optics, 17: 1631-1636 https://doi.org/10.1364/AO.17.001631
  8. Gordon, H.R. and M. Wang, 1994. Retrieval of waterleaving radiance and aerosol optical thickness over the oceans with SeaWiFS: a preliminary algorithm, Applied Optics, 33(3): 443-452. https://doi.org/10.1364/AO.33.000443
  9. Hu, C., K.L. Carder and F.E. Muller-Karger, 2000. Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters: A Practical Method, Remote Sensing of Environment, 74: 195-206. https://doi.org/10.1016/S0034-4257(00)00080-8
  10. Ruddick, K.G., F. Ovidio and M. Rijkeboer, 2000. Atmospheric correction of SeaWiFS imagery for turbid coastal and inland waters, Applied Optics, 39(6): 897-912. https://doi.org/10.1364/AO.39.000897
  11. Ruddick, K.G., V.D. Cauwer and Y.J. Park, 2006. Seaborne measurements of near infrared waterleaving reflectance: The similarity spectrum for turbid waters, Limnology Oceanography, 51(2): 1167-1179. https://doi.org/10.4319/lo.2006.51.2.1167
  12. IOCCG, 2003. Remote Sensing of Ocean Colour in Coastal, and Other Optically-Complex, Waters; In: Reports of the International Ocean-Colour Coordinating Group, No. 3, (ed.) Sathyendranath, S, Dartmouth, 140pp.
  13. 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
  14. Wang, M. and H.R. Gordon, 1994. A simple, moderately accurate, atmospheric correction algorithm for SeaWiFS, Remote Sensing of Environment, 50: 231-239. https://doi.org/10.1016/0034-4257(94)90073-6
  15. Wang, M., W. Shi, 2007. The NIR-SWIR combined atmospheric correction approach for MODIS ocean color data processing, Optics Express, 15(24): 15722-15733. https://doi.org/10.1364/OE.15.015722

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