Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
권호 표지
DOI QR코드

DOI QR Code

Retrieval of Relative Surface Temperature from Single-channel Middle-infrared (MIR) Images

단일밴드 중적외선 영상으로부터 표면온도 추정을 위한 상대온도추정알고리즘의 연구

  • Wook, Park (Department of Earth System Sciences, Yonsei University) ;
  • Won, Joong-Sun (Department of Earth System Sciences, Yonsei University) ;
  • Jung, Hyung-Sup (Department of Geo-Informatics, University of Seoul)
  • 박욱 (연세대학교 지구시스템과학과) ;
  • 원중선 (연세대학교 지구시스템과학과) ;
  • 정형섭 (서울시립대학교 공간정보공학과)
  • Received : 2012.11.12
  • Accepted : 2012.12.19
  • Published : 2013.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, a novel method is proposed for retrieving relative surface temperature from single-channel middle infra-red (MIR, 3-5 ${\mu}m$) remotely sensed data. In order to retrieve absolute temperature from MIR data, it is necessary to accommodate at least atmospheric effects, surface emissivity and reflected solar radiance. Instead of retrieving kinematic temperature of each target, we propose an alternative to retrieve the relative temperature between two targets. The core idea is to minimize atmospheric effects by assuming that the differential at-sensor radiance between two targets experiences the same atmospheric effects. To reduce effective simplify atmospheric parameters, each atmospheric parameter was examined by MODTRAN and MIR emissivity derived from ASTER spectral libraries. Simulation results provided a required accuracy of 2 K for materials with a temperature of 300 K within 0.1 emissivity errors. The algorithm was tested using MODIS band 23 MIR day time images for validation. The accuracy of retrieved relative temperature was $0.485{\pm}1.552$ K. The results demonstrated that the proposed algorithm was able to produce relative temperature with a required accuracy from only single-channel radiance data. However, this method has limitations when applied to materials having very low temperatures using day time MIR images.

3-5 ${\mu}m$ 파장대의 중적외선 영상으로부터 정밀한 절대온도를 추정하기 위해서는 지표 복사율, 대기효과, 낮 영상의 경우 반사되는 태양빛에 대한 정보를 필요로 하며, 이는 온도 추정 시 오차를 발생시키는 주요 원인이 된다. 이 연구는 이를 해결하기 위해 상대적인 온도 차이를 추정하는 방법을 제안하고자 한다. 제안된 알고리즘의 기본 방향은 온도 추정을 위한 입력자료를 최소화 시키는 것이다. 이를 위해 인접한 지역에 위치한 두 대상물체가 받는 대기효과는 동일하다고 가정하였으며 MODTRAN 및 ASTER spectral library로부터 입력자료를 단순화 시키는 연구가 수행되었다. 시뮬레이션 연구 결과 제안된 상대온도추정알고리즘의 정밀도는 300 K의 온도에서 0.1의 지표 복사율 오차에 대해 2 K 이내의 비교적 높은 정밀도를 나타냈다. 그러나 낮 영상에서 저온인 경우에는 정밀도가 크게 감소하였다. 알고리즘의 검증을 위해 MODIS band 23 중적외선 낮 영상에 적용하였으며, 이를 MODIS LST 자료와 비교를 수행한 결과 $0.485{\pm}1.552$ K의 오차를 나타내었다. 이 결과로부터 제안된 알고리즘이 외부 입력자료를 필요로 하지 않고 단지 영상 만으로부터 비교적 높은 정밀도로 온도 추정이 가능함을 보였다. 그러나 제안된 알고리즘은 상대온도만을 알 수 있으며, 절대온도를 추정하기 위해서는 기준온도에 대한 정보가 필요하다는 한계점도 있다.

Keywords

References

  1. Baek, S.-G. and D.-H. Jang, 2012. Evaluating the land surface characterization of high-resolution middle-infrared data for day and night time, Journal of the Korean Association of Geographic Information Studies, 15(2): 113-125 (in Korean with English abstract). https://doi.org/10.11108/kagis.2012.15.2.113
  2. Becker, F. and Z.L. Li, 1990. Temperature independent spectral indices in thermal infrared bands, Remote Sensing of Environment, 32: 17-33. https://doi.org/10.1016/0034-4257(90)90095-4
  3. Boyd, D.S. and F. Petitcolin, 2004. Remote sensing of the terrestrial environment using middle infrared radiation (3.0-5.0 ${\mu}m$), International Journal of Remote Sensing, 25(17): 3343-3368. https://doi.org/10.1080/01431160310001654356
  4. Gillespie, A., S. Rokugawa, T. Matsunaga, J.S. Cothern, S. Hook, and A.B. Kahle, 1998, A temperature and emissivity separation algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images, IEEE Transactions on Geoscience and Remote Sensing, 36(4): 1113-1126. https://doi.org/10.1109/36.700995
  5. Jimenez-Munoz, J.C. and J.A. Sobrino, 2003. A generalized single-channel method for retrieving land surface temperature from remote sensing data, Journal of Geophysical Research, 108(D22): 4688. https://doi.org/10.1029/2003JD003480
  6. Kaufman, Y.J. and L.A. Remer, 1994. Detection of forests using MID-IR reflectance: An application for aerosol studies, IEEE Transactions on Geoscience and Remote Sensing, 32(3): 672-683. https://doi.org/10.1109/36.297984
  7. Mushkin, A., L.K. Balick, and A.R. Gillespie, 2005. Extending surface temperature and emissivity retrieval to the mid-infrared (3-5${\mu}m$) using the Multispectral Thermal Imager (MTI), Remote Sensing of Environment, 98: 141-151. https://doi.org/10.1016/j.rse.2005.06.003
  8. Park, W., Y.-K. Lee, J.-S. Won, S.-G. Lee, and J.-M. Kim, 2008. A basic study for the retrieval of surface temperature from single channel middle-infrared images, Korean Journal of Remote Sensing, 24(2):189-194 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2008.24.2.189
  9. Petitcolin, F. and E. Vermote, 2002. Land surface reflectance, emissivity and temperature from MODIS middle and thermal infrared data, Remote Sensing of Environment, 83: 112-134. https://doi.org/10.1016/S0034-4257(02)00094-9
  10. Qin, Z., A. Karnieli, and P. Berliner, 2001. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region, International Journal of Remote Sensing, 22(18): 3719-3746. https://doi.org/10.1080/01431160010006971
  11. Salisbury, J.W. and D.M. D'Aria, 1994. Emissivity of terrestrial materials in the 3-5${\mu}m$ atmospheric window, Remote Sensing of Environment, 47: 345-361. https://doi.org/10.1016/0034-4257(94)90102-3
  12. Sobrino, J.A., Z.L. Li, M.P. Stoll, and F. Becker, 1994. Improvements in the split-window technique for land surface temperature determination, IEEE Transactions on Geoscience and Remote Sensing, 32(2): 243-253. https://doi.org/10.1109/36.295038
  13. Sobrino, J.A., J.C. Jimenez-Munoz, and L. Paolini, 2004. Land surface temperature retrieval from LANDSAT TM 5, Remote Sensing of Environment, 90: 434-440. https://doi.org/10.1016/j.rse.2004.02.003
  14. Valor, E. and V. Caselles, 1996. Mapping land surface emissivity from NDVI: application to European, African and South American areas, Remote Sensing of Environment, 57: 167-184. https://doi.org/10.1016/0034-4257(96)00039-9
  15. Van de Griend, A.A. and M. Owe, 1993. On the relationship between thermal emissivity and the Normalized Defference Vegetation Index for natural surfaces, International Journal of Remote Sensing, 14: 1119-1131. https://doi.org/10.1080/01431169308904400
  16. Wan, Z. and J. Dozier, 1996. A generalized split-window algorithm for retrieving land-surface temperature from space, IEEE Transactions on Geoscience and Remote Sensing, 34(4): 892-905. https://doi.org/10.1109/36.508406
  17. Wan, Z. and Z.L. Li, 1997. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data, IEEE Transactions on Geoscience and Remote Sensing, 35(4): 980-995. https://doi.org/10.1109/36.602541
  18. Zhukov, B., E. Lorenz, D. Oertel, M. Wooster, and G. Roberts, 2006. Spaceborne detection and characterization of fires during the bi-spectral infrared detection (BIRD) experimental small satellite mission (2001-2004), Remote Sensing of Environment, 100: 29-51. https://doi.org/10.1016/j.rse.2005.09.019

Cited by

  1. An Efficient Method to Estimate Land Surface Temperature Difference (LSTD) Using Landsat Satellite Images vol.29, pp.2, 2013, https://doi.org/10.7780/kjrs.2013.29.2.4