Retrieval of Land Surface Temperature Using Landsat 8 Images with Deep Neural Networks

Landsat 8 영상을 이용한 심층신경망 기반의 지표면온도 산출

  • Kim, Seoyeon (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University) ;
  • Lee, Soo-Jin (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University) ;
  • Lee, Yang-Won (Major of Spatial Information Engineering, Division of Earth Environmental System Science, Pukyong National University)
  • 김서연 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) ;
  • 이수진 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공) ;
  • 이양원 (부경대학교 지구환경시스템과학부 공간정보시스템공학전공)
  • Received : 2020.06.17
  • Accepted : 2020.06.22
  • Published : 2020.06.30


As a viable option for retrieval of LST (Land Surface Temperature), this paper presents a DNN (Deep Neural Network) based approach using 148 Landsat 8 images for South Korea. Because the brightness temperature and emissivity for the band 10 (approx. 11-㎛ wavelength) of Landsat 8 are derived by combining physics-based equations and empirical coefficients, they include uncertainties according to regional conditions such as meteorology, climate, topography, and vegetation. To overcome this, we used several land surface variables such as NDVI (Normalized Difference Vegetation Index), land cover types, topographic factors (elevation, slope, aspect, and ruggedness) as well as the T0 calculated from the brightness temperature and emissivity. We optimized four seasonal DNN models using the input variables and in-situ observations from ASOS (Automated Synoptic Observing System) to retrieve the LST, which is an advanced approach when compared with the existing method of the bias correction using a linear equation. The validation statistics from the 1,728 matchups during 2013-2019 showed a good performance of the CC=0.910~0.917 and RMSE=3.245~3.365℃, especially for spring and fall. Also, our DNN models produced a stable LST for all types of land cover. A future work using big data from Landsat 5/7/8 with additional land surface variables will be necessary for a more reliable retrieval of LST for high-resolution satellite images.


  1. Artis, D.A. and W.H. Carnahan, 1982. Survey of emissivity variability in thermography of urban areas, Remote Sensing of Environment, 12(4): 313-329.
  2. Avdan, U. and G. Jovanovska, 2016. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data, Journal of Sensors, 2016: 1-8.
  3. Coll, C., V. Caselles, J.A. Sobrino, and E. Valor, 1994. On the atmospheric dependence of the splitwindow equation for land surface temperature, International Journal of Remote Sensing, 15(1): 105-122.
  4. Cesar, C. and V. Caselles, 1997. A split-window algorithm for land surface temperature from advanced very high resolution radiometer data: Validation and algorithm comparison, Journal of Geophysical Research, 102(D14): 16697-16713.
  5. Cristobal, J., J.C. Jimenez-Munoz, A. Prakash, C. Mattar, D. Skokovic, and J.A. Sobrino, 2018. An improved single-channel method to retrieve land surface temperature from the Landsat-8 thermal band, Remote Sensing, 10(3): 431.
  6. Du, C., H. Ren, Q. Qin, J. Meng, and S. Zhao, 2015. A practical split-window algorithm for estimating land surface temperature from Landsat 8 data, Remote Sensing, 7(1): 647-665.
  7. Deng, Y., S. Wang, X. Bai, Y. Tian, L. Wu, J. Xiao, F. Chen, and Q. Qian, 2018. Relationship among land surface temperature and LUCC, NDVI in typical karst area, Scientific Reports, 8(1): 1-12.
  8. Fu, P. and Q. Weng, 2016. A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery, Remote Sensing of Environment, 175: 205-214.
  9. Goward, S.N., Y. Xue, and K.P. Czajkowski, 2002. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model, Remote Sensing of Environment, 79(2-3): 225-242.
  10. Geiger, R., R.H. Aron, and P. Todhunter, 2009. Influence of the Underlying Surface on the Adjacent Air Layer, In: The Climate Near the Ground, Rowman & Littlefield, Lanham, MD, USA, pp. 123-196.
  11. Hope, A.S. and T.P. McDowell, 1992. The relationship between surface temperature and a spectral vegetation index of a tallgrass prairie: effects of burning and other landscape controls, International Journal of Remote Sensing, 13(15): 2849-2863.
  12. He, J., W. Zhao, A. Li, F. Wen, and D. Yu, 2019. The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas, International Journal of Remote Sensing, 40(5-6): 1808-1827.
  13. 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: Atmospheres, 108(D22).
  14. Jacob, F., F. Petitcolin, T. Schmugge, E. Vermote, A. French, and K. Ogawa, 2004. Comparison of land surface emissivity and radiometric temperature derived from MODIS and ASTER sensors, Remote Sensing of Environment, 90(2): 137-152.
  15. Jin, M. and R.E. Dickinson, 2010. Land surface skin temperature climatology: Benefitting from the strengths of satellite observations, Environmental Research Letters, 5(4): 044004.
  16. Jimenez-Munoz, J.C., J.A. Sobrino, D. Skokovic, C. Mattar, and J. Cristobal, 2014. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data, IEEE Geoscience and Remote Sensing Letters, 11(10): 1840-1843.
  17. JAXA EORC, 2016. ALOS Global Digital Surface Model, 3d30/index.htm, Accessed on Feb. 28, 2020.
  18. Jee, J.B., B.Y. Kim, I.S. Zo, K.T. Lee, and Y.J. Choi, 2016. Retrieval of Land Surface Temperature based on High Resolution Landsat 8 Satellite Data, Korean Journal of Remote Sensing, 32(2): 171-183 (in Korean with English abstract).
  19. Kalma, J.D., T.R. McVicar, and M.F. McCabe, 2008. Estimating land surface evaporation: A review of methods using remotely sensed surface temperature data, Surveys in Geophysics, 29 (4-5): 421-469.
  20. Kim, T., W. Lee, and Y. Han, 2018. Analysis of thermal heat island potential by urbanization using Landsat-8 time-series satellite imagery, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 36(4): 305-316 (in Korean with English abstract).
  21. Kim, N., S.I. Na, C.W. Park, M. Huh, J. Oh, K.J. Ha, J. Cho, and Y.W. Lee, 2020. An artificial intelligence approach to prediction of corn yields under extreme weather conditions using satellite and meteorological data, Applied Sciences, 10: 3785.
  22. Lee, J.D., K.J. Bhang, and S.H. Han, 2012. Relationship Analysis between Topographic Factors and Land Surface Temperature from Landsat 7 ETM+ Imagery, The Journal of the Korea Contents Association, 12(11): 482-491 (in Korean with English abstract).
  23. Li, Z.L., B.H. Tang, H. Wu, H. Ren, G. Yan, Z. Wan, F.T. Isabel, and J.A. Sobrino, 2013. Satellitederived land surface temperature: Current status and perspectives, Remote Sensing of Environment, 131: 14-37.
  24. L3Harris Geospatial Solutions, 2015. Atmospheric Correction, docs/AtmosphericCorrection.html#Converti, Accessed on Mar. 11, 2020.
  25. Montanaro, M., A. Gerace, A. Lunsford, and D. Reuter, 2014. Stray light artifacts in imagery from the Landsat 8 thermal infrared sensor, Remote Sensing, 6(11): 10435-10456.
  26. Nemani, R.R., S.W. Running, R.A. Pielke, and T.N. Chase, 1996. Global vegetation cover changes from coarse resolution satellite data, Journal of Geophysical Research: Atmospheres, 101(D3): 7157-7162.
  27. Price, J.C., 1980. The potential of remotely sensed thermal infrared data to infer surface soil moisture and evaporation, Water Resources Research, 16(4): 787-795.
  28. Park, M.H., J.S. Lee, and J.I. Jung, 2008. A relationship analysis among land surface temperature and NDVI in hampyeong bay using landsat TM/ ETM+ satellite images, Journal of the Korean Cadastre Information Association, 10(2): 107-115 (in Korean with English abstract).
  29. Pal, S. and S.K. Ziaul, 2017. Detection of land use and land cover change and land surface temperature in English Bazar urban centre, The Egyptian Journal of Remote Sensing and Space Science, 20(1): 125-145.
  30. Parastatidis, D., Z. Mitraka, N. Chrysoulakis, and M. Abrams, 2017. Online global land surface temperature estimation from Landsat, Remote Sensing, 9(12): 1208.
  31. Quan, H.C. and B.G. Lee, 2009. Analysis of relationship between LST and NDVI using Landsat TM images on the city areas of Jeju island, Journal of Korean Society for Geospatial Information System, 17(4): 39-44 (in Korean with English abstract).
  32. Riley, S.J., S.D. DeGloria, and R. Elliot, 1999. Index that quantifies topographic heterogeneity, Intermountain Journal of Sciences, 5(1-4): 23-27.
  33. Running, S.W., R.R. Nemani, F.A. Heinsch, M. Zhao, M. Reeves, and H. Hashimoto, 2004. A continuous satellite-derived measure of global terrestrial primary production, Bioscience, 54(6): 547-560.[0547:ACSMOG]2.0.CO;2
  34. Rozenstein, O., Z. Qin, Y. Derimian, and A. Karnieli, 2014. Derivation of land surface temperature for Landsat-8 TIRS using a split window algorithm, Sensors, 14(4): 5768-5780.
  35. Saaroni, H. and B. Ziv, 2003. The impact of a small lake on heat stress in a Mediterranean urban park: the case of Tel Aviv, Israel, International Journal of Biometeorology, 47(3): 156-165.
  36. Sobrino, J.A., J.C. Jimenez-Munoz, and L. Paolini, 2004. Land surface temperature retrieval from LANDSAT TM 5, Remote Sensing of Environment, 90(4): 434-440.
  37. Sun, Q., Z. Wu, and J. Tan, 2012. The relationship between land surface temperature and land use/ land cover in Guangzhou, China, Environmental Earth Sciences, 65(6): 1687-1694.
  38. Tan, J., N. NourEldeen, K. Mao, J. Shi, Z. Li, T. Xu, and Z. Yuan, 2019. Deep Learning Convolutional Neural Network for the Retrieval of Land Surface Temperature from AMSR2 Data in China, Sensors, 19(13): 2987.
  39. Ulivieri, C.M.M.A., M.M. Castronuovo, R. Francioni, and A. Cardillo, 1994. A split window algorithm for estimating land surface temperature from satellites, Advances in Space Research, 14(3): 59-65.
  40. USGS, 2013. Landsat Level-1 Data Product,, Accessed on Mar. 17, 2020.
  41. 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.
  42. Wan, Z., Y. Zhang, Q. Zhang, and Z.L. Li, 2004. Quality assessment and validation of the MODIS global land surface temperature, International Journal of Remote Sensing, 25(1): 261-274.
  43. Yu, X., X. Guo, and Z. Wu, 2014. Land surface temperature retrieval from Landsat 8 TIRSComparison between radiative transfer equationbased method, split window algorithm and single channel method, Remote Sensing, 6(10): 9829-9852.
  44. Zhu, Z. and C.E. Woodcock, 2012. Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sensing of Environment, 118: 83-94.