Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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A Study on the Land Surface Emissivity (LSE) Distribution of Mid-wavelength Infrared (MWIR) over the Korean Peninsula

한반도 중파장적외선 지표 복사율 분포 연구

  • Sun, Jongsun (Department of Earth System Sciences, Yonsei University) ;
  • Park, Wook (Department of Earth System Sciences, Yonsei University) ;
  • Won, Joong-sun (Department of Earth System Sciences, Yonsei University)
  • 선종선 (연세대학교 지구시스템과학과) ;
  • 박욱 (연세대학교 지구시스템과학과) ;
  • 원중선 (연세대학교 지구시스템과학과)
  • Received : 2016.07.14
  • Accepted : 2016.09.13
  • Published : 2016.10.31
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Abstract

Surface emissivity and its background values according to each sensor are mandatorily necessary for Mid-Wavelength Infrared (MWIR) remote sensing to retrieve surface temperature and temporal variation. This study presents the methods and results of Land Surface Emissivity (LSE) of the MWIR according to land cover over the Korean Peninsula. The MWIR emissivity was estimated by applying the Temperature Independent Spectral Indices (TISI) method to the Visible Infrared Imaging Radiometer Suite (VIIRS) band 4 Day/Night images ($3.74{\mu}m$ in center wavelength). The obtained values were classified according to land-cover types, and the obtained emissivity was then compared with those calculated from a standard Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) spectral library. The annual means of MWIR emissivity of Deciduous Broadleaf Forest (0.958) and Mixed Forest (0.935) are higher than those of Croplands (0.925) and Natural Vegetation Mosaics (0.935) by about 2-3%. The annual mean of Urban area is the lowest (0.914) with an annual variation of about 2% which is by larger than those (1%) of other land-covers. The TISI and VIIRS based emissivity is slightly lower than the ASTER spectral library by about 2-3% supposedly due to various reasons such as lack of land cover homogeneity. The results will be used to understand the MWIR emissivity properties of the Korean Peninsula and to examine the seasonal and other environmental changes using MWIR images.

적외선을 이용한 지표 온도 추정 및 변화 탐지를 위해서는 해당 파장대역의 각 센서에 따른 지표 복사율 추정 및 배경값에 대한 확보가 필요하다. 이 연구는 위성 영상으로부터 한반도에서의 중파장적외선 복사율 산출방법에 대한 제안 및 토지피복 종류별 대표 복사율을 산출하여 한반도 배경값을 제시할 수 있다. 중파장적외선 복사율을 추정하기 위해 Visible Infrared Imaging Radiometer Suite(VIIRS)의 $3.74{\mu}m$ 파장 대역 밴드4 영상에 Temperature Independent Spectral Indices(TISI) 방법을 적용하여 복사율을 계산하였으며, 또한 이와 비교하기 위해 Advanced Spaceborne Thermal Emission Reflection Radiometer(ASTER) spectral library로부터 토지피복에 따른 복사율도 계산하였다. 그 결과 활엽수립(0.958) 및 혼합림(0.955) 지역의 연평균 복사율이 가장 높았으며 농지(0.925) 및 자연식생(0.935) 지역보다는 약 2-3% 높게 나타났다. 도심지역의 경우 0.914로 가장 낮으며 연간 변화율이 1%인 다른 지역과는 달리 약 2%로 그 편차가 크다. ASTER spectral library와 비교한 결과 위성영상에서 추정한 중파장적외선 복사율은 동일한 토지 피복에 비해 약 2-3% 낮게 나타나는데, 이는 실제 지표면이 불균질한 점 외 기타 다양한 원인에 의한 것으로 추정된다. 이러한 연구 결과는 중파장적외선 영상을 이용하여 지표온도 추정 및 토지피복도의 계절 및 외부환경 변화에 의한 한반도 중파장적외선 복사율의 변화 특성을 이해하는데 기초 자료로 활용될 것이다.

Keywords

References

  1. Avdelidis, N.P., and A. Moropoulou, 2003. Emissivity considerations in building thermography. Energy and Buildings, 35(7): 663-667. https://doi.org/10.1016/S0378-7788(02)00210-4
  2. Baldridge, A.M., S.J. Hook, C.I. Grove, and G. Rivera, 2009. The ASTER spectral library version 2.0. Remote Sensing of Environment, 113(4): 711-715. https://doi.org/10.1016/j.rse.2008.11.007
  3. Becker, F., and Z.-L. Li, 1990. Temperature-independent spectral indices in thermal infrared bands. Remote sensing of environment, 32(1): 17-33. https://doi.org/10.1016/0034-4257(90)90095-4
  4. Boyd, D.S., and F. Petitcolin, 2004. Remote sensing of the terrestrial environment using middle infrared radiation (3.0-3.5 ${\mu}m$), International Journal of Remote Sensing, 25(17): 3343-3368. https://doi.org/10.1080/01431160310001654356
  5. Cristobal, J., J.C. Jimenez-Munoz, J.A. Sobrino, M. Ninyerola, and X. Pons, 2009. Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. Journal of Geophysical Research, 114(D08): 103.
  6. Dash, P., F.-M. Gottsche, F.-S. Olesen, and H. Fischer, 2002. Land surface temperature and emissivity estimation from passive sensor data: Theory and practice-current trends. International Journal of Remote Sensing, 23(13): 2563-2594. https://doi.org/10.1080/01431160110115041
  7. Dash, P., F.-M. Gottsche, F.-S. Olesen, and H. Fischer, 2005. Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites and comparison with a vegetation fraction method. Remote sensing of environment, 96(1): 1-17. https://doi.org/10.1016/j.rse.2004.12.023
  8. Emami, H., A. Safari, and B. Mojaradi. 2016. Fusion Methods for Land Surface Emissivity and Temperature Retrieval of the Landsat Data Continuity Mission Data. IEEE Transactions on Geoscience and Remote Sensing, 54(7): 3842-3855. https://doi.org/10.1109/TGRS.2016.2529422
  9. Hulley, G.C., C.G., Hughes, and S.J. Hook, 2012. Quantifying uncertainties in land surface temperature and emissivity retrievals from ASTER and MODIS thermal infrared data. Journal of Geophysical Research: Atmospheres, 117(D23): 1-18.
  10. Nerry, F., F. Petitcolin, and M.P. Stoll, 1998. Bidirectional reflectivity in AVHRR channel 3: application to a region in northern Africa. Remote sensing of environment, 66(3): 298-316. https://doi.org/10.1016/S0034-4257(98)00066-2
  11. Park, W., 2015. Land Surface Temperature Retrieval from a Space-born Single-Channel Mid-wavelength Infrares (MWIR), Yonsei University, Seoul, Korea.
  12. Petitcolin, F., and E. Vermote, 2002. Land surface reflectance, emissivity and temperature from MODIS middle and thermal infrared data. Remote sensing of environment, 83(1): 112-134. https://doi.org/10.1016/S0034-4257(02)00094-9
  13. Prakash, S., H. Norouzi, M. Azarderakhsh, R. Blake, and K. Tesfagiorgis. 2016. Global Land Surface Emissivity Estimation From AMSR2 Observations. IEEE Geoscience and Remote Sensing Letters, 13(9): 1270-1274. https://doi.org/10.1109/LGRS.2016.2581140
  14. Prata, A.J., 1993. Land surface temperatures derived from the advanced very high resolution radiometer and the along-track scanning radiometer: 1. Theory. Journal of Geophysical Research, 98(D9): 16689-16702. https://doi.org/10.1029/93JD01206
  15. Schmugge, T., A. French, J.C. Ritchie, A. Rango, and H. Pelgrum, 2002. Temperature and emissivity separation from multispectral thermal infrared observations. Remote sensing of environment. 79(2): 189-198. https://doi.org/10.1016/S0034-4257(01)00272-3
  16. Shukla, J., and Y. Mintz, 1982. Influence of land-surface evapotranspiration on the earth's climate. Science, 215(4539), 1498-1501. https://doi.org/10.1126/science.215.4539.1498
  17. Snyder, W.C., Z. Wan, Y. Zhang, and Y.-Z. Feng, 1997. Thermal infrared ($3-14{\mu}m$) bidirectional reflectance measurements of sands and soils. Remote Sensing of Environment, 60(1): 101-109. https://doi.org/10.1016/S0034-4257(96)00166-6
  18. Snyder, W.C., Z. Wan, Y. Zhang, and Y.-Z. Feng, 1998. Classification-based emissivity for land surface temperature measurement from space. International Journal of Remote Sensing, 19(14): 2753-2774. https://doi.org/10.1080/014311698214497
  19. Sobrino, J.A., and N. Raissouni, 2000. Toward remote sensing methods for land cover dynamic monitoring: application to Morocco. International Journal of remote sensing, 21(2): 353-366. https://doi.org/10.1080/014311600210876
  20. Sobrino, J.A., J.C. Jimenez-Munoz, G. Soria, M. Romaguera, L. Guanter, J. Moreno, A. Plaza, and P. Martinez, 2008. Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Transactions on Geoscience and Remote Sensing, 46(2): 316-327. https://doi.org/10.1109/TGRS.2007.904834
  21. Van de Griend, A., M. Owe, 1993. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of remote sensing, 14(6): 1119-1131. https://doi.org/10.1080/01431169308904400
  22. Wang, H., Q. Xiao, H. Li, Y. Du, and Q. Liu. 2015. Investigating the impact of soil moisture on thermal infrared emissivity using ASTER data. IEEE Geoscience and Remote Sensing Letters, 12(2): 294-298. https://doi.org/10.1109/LGRS.2014.2336912
  23. Willmes, S., M. Nicolaus, and C. Haas. 2014. The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study. The Cryosphere, 8(3): 891-904. https://doi.org/10.5194/tc-8-891-2014