Korean Journal of Remote Sensing (대한원격탐사학회지)

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

DOI QR Code

Development of Land Surface Temperature Retrieval Algorithm from the MTSAT-2 Data

  • Kim, Ji-Hyun (Department of Atmospheric Science, Kongju National University) ;
  • Suh, Myoung-Seok (Department of Atmospheric Science, Kongju National University)
  • Received : 2011.11.07
  • Accepted : 2011.12.11
  • Published : 2011.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

Land surface temperature (LST) is a one of the key variables of land surface which can be estimated from geostationary meteorological satellite. In this study, we have developed the three sets of LST retrieval algorithm from MTSAT-2 data through the radiative transfer simulations under various atmospheric profiles (TIGR data), satellite zenith angle, spectral emissivity, and surface lapse rate conditions using MODTRAN 4. The three LST algorithms are daytime, nighttime and total LST algorithms. The weighting method based on the solar zenith angle is developed for the consistent retrieval of LST at the early morning and evening time. The spectral emissivity of two thermal infrared channels is estimated by using vegetation coverage method with land cover map and 15-day normalized vegetation index data. In general, the three LST algorithms well estimated the LST without regard to the satellite zenith angle, water vapour amount, and surface lapse rate. However, the daytime LST algorithm shows a large bias especially for the warm LST (> 300 K) at day time conditions. The night LST algorithm shows a relatively large error for the LST (260 ~ 280K) at the night time conditions. The sensitivity analysis showed that the performance of weighting method is clearly improved regardless of the impacting conditions although the improvements of the weighted LST compared to the total LST are quite different according to the atmospheric and surface lapse rate conditions. The validation results of daytime (nighttime) LST with MODIS LST showed that the correlation coefficients, bias and RMSE are about 0.62~0.93 (0.44~0.83), -1.47~1.53 (-1.80~0.17), and 2.25~4.77 (2.15~4.27), respectively. However, the performance of daytime/nighttime LST algorithms is slightly degraded compared to that of the total LST algorithm.

Keywords

References

  1. Becker, F., and Z.L. Li, 1990. Toward a local split window method over land surface, International Journal of Remote Sensing, 3: 369-393.
  2. Becker, F. and Z.-L. Li, 1995. Surface temperature and emissivity at various scales: Definition, measurement and related problems, Remote Sensing Reviews, 12: 225-253. https://doi.org/10.1080/02757259509532286
  3. Han, K.-S., A.A. Viau, and F. Anctil, 2004. An analysis of GOES and NOAA derived land surface temperatures estimated over a boreal forest, International Journal of Remote Sensing, 25: 4761-4780. https://doi.org/10.1080/01431160410001680446
  4. Hong, K.O., M.S. Suh, and J.H. Kang, 2009a. Development of a Land Surface Temperature retrieval Algorithm from MTSAT-1R data, Asia-Pacific Journal of Atmospheric Sciences, 45(3): 412-421.
  5. Hong, K.O., M.S. Suh, and J.H. Kang, 2009b. Improvement of COMS Land Surface Temperature Retrieval Algorithm, Korean Journal of Remote Sensing, 25(6): 1-10. https://doi.org/10.7780/kjrs.2009.25.6.507
  6. Jiang, J-M., Z.-L. Li, and F. Nerry, 2006. Land surface emissivity retrieval from combined mid-infrared and thermal infrared data of MSG-SEVIRI, Remote Sensing of the Environment, 105: 326-340. https://doi.org/10.1016/j.rse.2006.07.015
  7. Kang, J.-H., M.-S. Suh, and C.-H., Kwak, 2010. Land cover classification over East Asian region using recent MODIS data (2006-2008). Atmosphere, 20: 415-426.
  8. Kerr, Y.H., J.P. Lagouarde, and J. Imbernon, 1992. Accurate land surface temperature retrieval from AVHRR data with use of an improved split window, Remote Sensing of the Environment, 41: 197-209. https://doi.org/10.1016/0034-4257(92)90078-X
  9. Peres L. and C.C. DaCamara, 2004. Land surface temperature and emissivity estimation based on the two-temperature method: sensitivity analysis using simulated MSG/SEVIRI data, Remote Sensing of the Environment, 91: 377-389. https://doi.org/10.1016/j.rse.2004.03.011
  10. Prata, A.J. and R.P. Cechet, 1999. An assessment of the accuracy of land surface temperature determination from the GMS-5 VISSR, Remote Sensing of the Environment, 67: 1-14. https://doi.org/10.1016/S0034-4257(98)00055-8
  11. Price, J.C., 1984. Land surface temperature measurements from the split-window channels of the NOAA 7 AVHRR, Journal of Geophysical Research, 89: 7231-7237. https://doi.org/10.1029/JD089iD05p07231
  12. Sobrino, J.A. and M. Romaguera, 2004. Land surface temperature retrieval from MSG1-SEVIRI data, Remote Sensing of the Environment, 92: 247-254. https://doi.org/10.1016/j.rse.2004.06.009
  13. Suh, M.-S., S.-H. Kim, and J.-H. Kang, 2008. A comparative study of algorithms for estimating land surface temperature from MODIS data, Korean Journal of Remote Sensing, 24: 65-78. https://doi.org/10.7780/kjrs.2008.24.1.65
  14. Ulivieri, C., M.M. Castronouvo, R. Francioni, and A. Cardillo, 1994. A split-window algorithm for estimating land surface temperature from Korean Journal of Remote Sensing, Vol.25, No.6, 2009-8 satellites, Advances in Space Research, 14: 59-65. https://doi.org/10.1016/0273-1177(94)90193-7
  15. Valor, E. and V. Caselles, 1996. Mapping land surface emissivity from NDVI: Application to European, African, and South American areas, Remote Sensing of the Environment, 57: 164-184.
  16. Voogt, J.A. and T.R. Oke, 2003. Thermal remote sensing of urban climates, Remote Sensing of Environment, 86:370-384. https://doi.org/10.1016/S0034-4257(03)00079-8
  17. Wan Z. and J. Dozier, 1996. A generalized split window algorithm for retrieval of land surface temperature from space, IEEE Transactions on Geoscience and Remote Sensing, 34: 892-905. https://doi.org/10.1109/36.508406