Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Landsat 8-based High Resolution Surface Broadband Albedo Retrieval

Landsat 8 위성 기반 고해상도 지표면 광대역 알베도 산출

  • Lee, Darae (Division of Earth Environmental System Science(Major of Spatial Information Engineering), Pukyong National University) ;
  • Seo, Minji (Division of Earth Environmental System Science(Major of Spatial Information Engineering), Pukyong National University) ;
  • Lee, Kyeong-sang (Division of Earth Environmental System Science(Major of Spatial Information Engineering), Pukyong National University) ;
  • Choi, Sungwon (Division of Earth Environmental System Science(Major of Spatial Information Engineering), Pukyong National University) ;
  • sung, Noh-hun (Division of Earth Environmental System Science(Major of Spatial Information Engineering), Pukyong National University) ;
  • Kim, Honghee (Division of Earth Environmental System Science(Major of Spatial Information Engineering), Pukyong National University) ;
  • Jin, Donghyun (Division of Earth Environmental System Science(Major of Spatial Information Engineering), Pukyong National University) ;
  • Kwon, Chaeyoung (Division of Earth Environmental System Science(Major of Spatial Information Engineering), Pukyong National University) ;
  • Huh, Morang (P.K SYSTEM Inc.) ;
  • Han, Kyung-Soo (Division of Earth Environmental System Science(Major of Spatial Information Engineering), Pukyong National University)
  • 이다래 (부경대학교 지구환경시스템과학부 공간정보시스템공학과) ;
  • 서민지 (부경대학교 지구환경시스템과학부 공간정보시스템공학과) ;
  • 이경상 (부경대학교 지구환경시스템과학부 공간정보시스템공학과) ;
  • 최성원 (부경대학교 지구환경시스템과학부 공간정보시스템공학과) ;
  • 성노훈 (부경대학교 지구환경시스템과학부 공간정보시스템공학과) ;
  • 김홍희 (부경대학교 지구환경시스템과학부 공간정보시스템공학과) ;
  • 진동현 (부경대학교 지구환경시스템과학부 공간정보시스템공학과) ;
  • 권채영 (부경대학교 지구환경시스템과학부 공간정보시스템공학과) ;
  • 허모랑 ((주)피케이시스템) ;
  • 한경수 (부경대학교 지구환경시스템과학부 공간정보시스템공학과)
  • Received : 2016.12.21
  • Accepted : 2016.12.28
  • Published : 2016.12.31
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Abstract

Albedo is one of the climate variables that modulate absorption of solar energy, and its retrieval is important process for climate change study. High spatial resolution and long-term consistent periods are important considerations in order to efficiently use the retrieved albedo data. This study retrieved surface broadband albedo based on Landsat 8 as high resolution which is consistent with Landsat 7. First of all, we analyzed consistency of Landsat 7 channel and Landsat 8 channel. As a result, correlation coefficient(R) on all channels is average 0.96. Based on this analysis, we used multiple linear regression model using Landsat 7 albedo, which is being used in many studies, and Landsat 8 reflectance channel data. The regression coefficients of each channel calculated by regression analysis were used to derive a formula for converting the Landsat 8 reflectance channel data to broadband albedo. After Landsat 8 albedo calculated using the derived formula is compared with Landsat 7 albedo data, we confirmed consistency of two satellite using Root Mean Square Error (RMSE), R-square ($R^2$) and bias. As a result, $R^2$ is 0.89 and RMSE is 0.003 between Landsat 7 albedo and Landsat 8 albedo.

알베도는 태양에너지의 흡수량을 결정하는 주요 기후 변수 중 하나로서, 이러한 알베도를 산출하는 것은 기후 변화 연구에 있어 중요한 과정이다. 이 때, 산출된 알베도 자료를 효율적으로 사용하기 위해서는 높은 공간해상도와 장기간의 일관성 있는 산출이 중요하게 고려된다. 따라서 본 연구에서는 Landsat 8을 기반으로 Landsat 7과의 일관성을 유지한 고해상도 지표면 광대역 알베도를 산출하였다. 먼저, Landsat 7과 Landsat 8의 채널 별 일관성을 분석한 결과, 상관계수(R)가 평균 0.96으로 높은 상관성을 보였다. 이러한 결과를 바탕으로 Landsat 7 알베도와 Landsat 8 반사도 채널 자료를 다중회귀분석에 적용하여 Landsat 8 광대역 알베도 전환 식을 도출하였다. 도출된 식을 통해 Landsat 8 지표면 광대역 알베도를 산출하고, Landsat 7 알베도 자료와 비교하여 검증하였다. 그 결과 R-square($R^2$)가 0.89, Root Mean Square Error (RMSE)가 0.003의 높은 정확도를 보였다.

Keywords

References

  1. Franch, B., E.F. Vermote and M. Claverie, 2014. Intercomparison of landsat albedo retrieval techniques and evaluation against in situ measurements across the us surfrad network. Remote Sensing of Environment, 152: 627-637. https://doi.org/10.1016/j.rse.2014.07.019
  2. Greuell, W. and J. Oerlemans, 2004. Narrowband-tobroadband albedo conversion for glacier ice and snow: equations based on modeling and ranges of validity of the equations. Remote sensing of environment, 89(1): 95-105. https://doi.org/10.1016/j.rse.2003.10.010
  3. Kato, S. and Y. Yamaguchi, 2005. Analysis of urban heat-island effect using ASTER and ETM+ Data: Separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux. Remote Sensing of Environment, 99(1): 44-54. https://doi.org/10.1016/j.rse.2005.04.026
  4. Li, P., L. Jiang, and Z. Feng, 2013. Cross-comparison of vegetation indices derived from landsat-7 enhanced thematic mapper plus (ETM+) and landsat-8 operational land imager (OLI) sensors. Remote Sensing, 6(1): 310-329. https://doi.org/10.3390/rs6010310
  5. Liang, S., C.J. Shuey, A.L. Russ, H. Fang, M. Chen, C.L. Walthall, C.S.T. Daughtry, and R. Hunt, 2003. Narrowband to broadband conversions of land surface albedo: II. Validation. Remote Sensing of Environment, 84(1): 25-41. https://doi.org/10.1016/S0034-4257(02)00068-8
  6. Liang, S., 2001. Narrowband to broadband conversions of land surface albedo I: Algorithms. Remote Sensing of Environment, 76(2): 213-238. https://doi.org/10.1016/S0034-4257(00)00205-4
  7. Liu, S., G. Hu, L. Lu, and D. Mao, 2007. Estimation of regional evapotranspiration by TM/ETM+ data over heterogeneous surfaces. Photogrammetric Engineering & Remote Sensing, 73(10): 1169-1178. https://doi.org/10.14358/PERS.73.10.1169
  8. Shuai, Y., J.G. Masek, F. Gao, and C.B. Schaaf, 2011. An algorithm for the retrieval of 30-m snowfree albedo from Landsat surface reflectance and MODIS BRDF. Remote Sensing of Environment, 115(9): 2204-2216. https://doi.org/10.1016/j.rse.2011.04.019
  9. Sobrino, J. A., R. Oltra-Carrio, J.C. Jimenez-Munoz, Y. Julien, G. Soria, B. Franch, and C. Mattar, 2012. Emissivity mapping over urban areas using a classification-based approach: Application to the Dual-use European Security IR Experiment (DESIREX). International Journal of Applied Earth Observation and Geoinformation, 18: 141-147. https://doi.org/10.1016/j.jag.2012.01.022
  10. Tasumi, M., R.G. Allen, and R. Trezza, 2008. Atsurface reflectance and albedo from satellite for operational calculation of land surface energy balance, Journal of Hydrologic Engineering, 13(2): 51-63. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:2(51)
  11. Zhao, W., M. Tamura, and H. Takahashi, 2001. Atmospheric and spectral corrections for estimating surface albedo from satellite data using 6S code. Remote sensing of Environment, 76(2): 202-212. https://doi.org/10.1016/S0034-4257(00)00204-2

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