Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Improvement of COMS Land Surface Temperature Retrieval Algorithm

  • Hong, Ki-Ok (Department of Atmospheric Science, Kongju National University) ;
  • Suh, Myoung-Seok (Department of Atmospheric Science, Kongju National University) ;
  • Kang, Jeon-Ho (Department of Atmospheric Science, Kongju National University)
  • Published : 2009.12.30
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Abstract

Land surface temperature (LST) is a key environmental variable in a wide range of applications, such as weather, climate, hydrology, and ecology. However, LST is one of the most difficult surface variables to observe regularly due to the strong spatio-temporal variations. So, we have developed the LST retrieval algorithm from COMS (Communication, Ocean and Meteorological Satellite) data through the radiative transfer simulations under various atmospheric profiles (TIGR data), satellite zenith angle (SZA), spectral emissivity, and surface lapse rate conditions using MODTRAN 4. However, the LST retrieval algorithm has a tendency to overestimate and underestimate the LST for surface inversion and superadiabatic conditions, respectively. To minimize the overestimation and underestimation of LST, we also developed day/night LST algorithms separately based on the surface lapse rate (local time) and recalculated the final LST by using the weighted sum of day/night LST. The analysis results showed that the quality of weighted LST of day/night algorithms is greatly improved compared to that of LST estimated by original algorithm regardless of the surface lapse rate, spectral emissivity difference (${\Delta}{\varepsilon}$) SZA, and atmospheric conditions. In general, the improvements are greatest when the surface lapse rate and ${\Delta}{\varepsilon}$ are negatively large (strong inversion conditions and less vegetated surface).

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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 https://doi.org/10.1080/01431169008955028
  2. Becker, F., and Li, Z.-L., 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, 2009. Development of a Land Surface Temperatureretrieval Algorithm from MTSAT-1R data, Asia-Pacific Journal of Atmospheric Sciences, 45(3): 412-421
  5. Jiang, J-M., Li Z-L., 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
  6. 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
  7. KMA, 2008. Development of Meteorological Data Processing Systems of Communication, Ocean and Meteorological Satellite (V), 953pp
  8. Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., and Merchant, J. W., 2000. Development of a global land cover characteristics data base and IGBP DISCover from 1 km AVHRR data, International Journal of Remote Sensing, 21: 1303 - 1330 https://doi.org/10.1080/014311600210191
  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, 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 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 https://doi.org/10.1016/0034-4257(96)00039-9
  16. Wan Z., and J. Dozier, 1996. A generalized splitwindow 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