Document Type : علمی - پژوهشی


1 M.Sc. Student of Remote Sensing and GIS, Department of Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Associate Professor of Remote sensing and GIS Group, Department of Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Associate Professor of Water Sciences and Engineering Department, Faculty of Engineering and Technology, Imam Khomeini International University, Qazvin, Iran.


Calculating the canopy temperature and land surface temperatureusing satellite imagery is very attractive to estimate actual evapotranspiration (ET) by energy balance algorithm. In studies, to evaluate ET, the accuracy of the calculated thermal gradient between surface and air, as well as temperature difference between various land covers is very important. To calculate land surface temperature (LST) in Shahr-E-Kord plain, the study area, there were three principal challenges. First, the absence of enough studies about calculating LST using Landsat8 thermal bands, the second, lack of canopy temperature and land surface temperature observed data, and finally, the only available data for surface temperature was minimum daily surface temperature in the climatology and synoptic stations. In this study, in order to convert the surface brightness temperature to the LST, the split-window algorithm of NOAA-AVHRR was used. Also, the proposed SEBAL algorithm was applied to calculate the surface emissivity. Due to the lack of the reference weather stations, after calculating LST at the satellite overpass time in non-reference weather stations, the deviation error calculation method was used to calibrate satellite LST and to prepare daily LST layers. Results showed that all calculated correlation coefficients were more than 0.9. Also, all existing regression relations were significant at 95% and even 99% level of confidence. In different day-images, maximum difference of calculated deviation errors was less than 0.5 K and, the calculated RMSEs were between 1.9 to 2.2 K, acceptable comparing to similar studies.


  1. Allen, R.G., 1998, Crop Evapotranspiration (Guidelines for Computing Crop Water Requirements), FAO Irrigation and Drainage Paper No.56.
  2. Allen, R.G., Tasumi, M., Trezza, R., Waters, R. & Bastiaanssen, W., 2002, SEBAL (Surface Energy Balance Algorithms for Land), Advance Training and Users Manual–Idaho Implementation, version, 1, 97.
  3. Bolgrien, D.W., Granin, N.G. & Levin, L., 1995, Surface Temperature Dynamics of Lake Baikal Observed from AVHRR Images, Photogrammetric Engineering and Remote Sensing, 61: 211-216.
  4. Brivio, P.A., Zilioli, E. & Lechi, G.L., 2006, Principi e Metodi di Telerilevamento, CittàStudi.
  5. Choudhury, Bhaskar J. et al., 1994, Relations Between Evaporation Coefficients and Vegetation Indices Studied by Model Simulations, Remote sensing of environment, 50.1: 1-17.
  6. Cracknell, A.P., 1997, The Advanced Very High Resolution Radiometer (AVHRR), Taylor & Francis Ltd., London, U.K., 534 pp.
  7. Duffie Jone, A., & Beckman, William A., 1991, Solar Engineering of Thermal Process, John Wiley& Sons Inc.
  8. Huete, A.R., 1998, Introduction to Vegetation Indices, Retrieved from http://www. 98/huete1.htm 6 July 2003..
  9. Huete, Alfredo R.A., 1988, Soil-Adjusted Vegetation Index (SAVI), Remote sensing of environment 25.3: 295-309.
  10. Jensen, M.E., Burman, R.D. & Allen, R.G., 1990, Evapotranspiration and Irrigation Water Requirements, ASCE Manuals and Reports on Engineering, Practices NO 70. ASCE. New York.
  11. Jimenez-Munoz, J.C. & Sobrino, J., 2007, Feasibility of Retrieving Land-Surface Temperature from ASTER TIR Bands Using Two-Channel Algorithms: A Case Study of Agricultural Areas, Geoscience and Remote Sensing Letters, IEEE,4(1), 60-64.
  12. Liu, Y., Yamaguchi, Y. & Ke, C., 2007, Reducing the Discrepancy Between ASTER and MODIS Land Surface Temperature Products, Sensors, 7(12), 3043-3057.
  13. Miller, Woodruff & Millis, Eric, 1989, Estimating Evaporation from Utah’s Great Salt Lake Using Thermal Infrared Satellite Imagery 1, 541-550.
  14. Peterson, Christopher G. & Stevenson, R. Jan, 1989, Substratum Conditioning and Diatom Colonization in Different Current Regimes1, Journal of Phycology 25.4: 790-793.
  15. Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H., 1994, Modified Soil Adjusted Vegetation Index (MSAVI), Remote Sensing of Environment, 48:119-126.
  16. Qi, J., Kerr, Y., Chehbouni, A., 1994, External Factor Consideration in Vegetation Index Development, Proc. of Physical Measurements and Signatures in Remote Sensing, ISPRS, 723-730.
  17. Qin, Zhi-hao, Karnieli, A. & Berliner, P., 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.
  18. Rouse Jr, J., Haas, R.H., Schell, J.A. & Deering, D.W., 1973, Monitoring Vegetation Systems in the Great Plains with ERTS, NASA special publication, 351, 309.
  19. Singh, S.M., 1984, Removal of Atmospheric Effects on a Pixel by Pixel Basis from the Thermal Infrared Data from Instruments on Satellites, The Advanced Very High Resolution Radiometer (AVHRR). International Journal of Remote Sensing. Vol.5, pp. 161-183.
  20. Sobrino, JoseA, Coll, Cesar & Caselles, Vicente, 1991, Atmospheric Correction for Land Surface Temperature Using NOAA-11 AVHRR Channels 4 and 5, Remote sensing of environment, 38.1: 19-34.
  21. Weng, Q., 2003, Fractal Analysis of Satellite-Detected Urban Heat Island Effect, Photogrammetric engineering & remote sensing 69.5: 555-566.
  22. Yang, H. & Zhongdong, Y, 2006, A Modified Land Surface Temperature Split Window Retrieval Algorithm and Its Applications Over China, Global and Planetary Change 52.1: 207-215.
  23. Yang, J. & Wang, Y.Q., 2002, Estimation of Land Surface Temperature Using Landsat-7 ETM+ Thermal Infrared and Weather Station Data, Available on the