Estimation and analysis of land surface temperature of Jubail Industrial City, Saudi Arabia, by using remote sensing and GIS technologies

  • Sheik Mujabar
  • Venkateswara Rao
Original Paper


Land surface temperature (LST) is one of the key parameter used for analyzing the heat energy balance and thermal flux of land surfaces. It is also useful for making urban heat transfer models, water resource management, climate change modeling, and environmental studies. This study is to find the surface temperature of Jubail Industrial City, which is one of the biggest industrial areas in the world. The study also aims to analyze the spatial and temporal variations of LST of the city. Landsat 8 Thermal Infrared Remote Sensor (TIRS) data has been used for this study and the surface temperature has been estimated by using single-channel (SC) method. The study reveals that the surface temperature is relatively low and ranging from 20 to 30 °C in January. However, the industrial area and some parts of the residential area have more temperature than the rest of the city. During the month of March, the temperature increases gradually and reaches high in June. During the summer, the surface temperature in the residential area of the city is around 40–50 °C. The temperature in the sub urban areas is moderate; however, high temperature (50–55 °C) has been recorded in the industrial area of the city. Significant heat islands of temperature more than 60 °C have also been noted near the iron and steel factories of the industrial area. In the month of September, the land surface temperature in most part of the city is lower than that of peak summer.


Remote sensing Heat Environment Geophysics Climate change 



The authors are thankful to the Managing Director, Jubail Industrial College, Jubail Industrial City, Saudi Arabia, for his kind support and encouragement to applied scientific research and development. The authors are also thankful to the Deputy Directors, Chairman, and Faculty members of the college for extending effective provisions, support, and encouragement for performing the work.


  1. Akhoondzadeh M, Saradjian MR (2008) Comparison of land surface temperature mapping using MODIS and ASTER images in semi-arid area. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVII(Part B8) BeijingGoogle Scholar
  2. Ali AR, Mohammed ES (2016) Impact of industrial activities on land surface temperature using remote sensing and GIS techniques—a case study in Jubail, Saudi Arabia. J Geogr Nat Disast S6:002. CrossRefGoogle Scholar
  3. Asmat A, Mansor S, Tai Hong W (2003) Rule based classification for urban heat island mapping. Proceedings of the 2nd FIG Regional Conference Marrakech, Morocco, 2–5 DecemberGoogle Scholar
  4. Becker F, Li ZL (1995) Surface temperature and emissivity at various scales: definition, measurement and related problems. Remote Sens Rev 12:225–253CrossRefGoogle Scholar
  5. Behrendt A, Wagner G, Petrova A, Shiler, M, Pal S, Schaberl T, Wulfmeyer V (2005) Modular LIDAR systems for high-resolution 4-dimensional measurements of water vapor, temperature, and aerosols SPIE 2005, 5653, 220Google Scholar
  6. Betts AK, Ball JH Beljaars ACM, Miller MJ, Viterbo PA (1996) The land surface-atmosphere interaction: a review based on observational and global modelling perspectives. J Geophys Res 101:7209–7225CrossRefGoogle Scholar
  7. Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62:241–252CrossRefGoogle Scholar
  8. Coll C, Caselles V (1997) A split-window algorithm for land surface temperature from advanced very high resolution radiometer data: validation and algorithm comparison. J Geophys Res-Atmos 102:16697–16713CrossRefGoogle Scholar
  9. Dickinson RE (1994) Satellite systems and models for future climate change. In: Henderson-Sellers A (ed) Future Climates of the World: A Modelling Perspective, 16th edn. World Survey of Climatology Elsevier, Amsterdam, p 27Google Scholar
  10. El-Nahry AH, Rashash A (2013) Impact of industrial on surface temperature using thermal infrared remote sensing and GIS techniques—a case study of Juabil City, KSA, The 8th National GIS Symposium in Saudi ArabiaGoogle Scholar
  11. Grimmond SUE (2007) Urbanization and global environmental change: local effects of urban warming. Geogr J 173:83–88CrossRefGoogle Scholar
  12. Hall FG, Huemmrich KF, Goetz SJ, Sellers PJ, Nickeson JE (1992) Satellite remote sensing of surface energy balance: success failures, and unresolved issues in FIFE. J Geophys Res 97:19,061–19,089CrossRefGoogle Scholar
  13. Jia LM, Su ZB, Li ZL, Djepa V, Wang JM (2001) Modelling sensible heat flux using estimates of soil and vegetation temperatures: the HEIFE and IMGRASS experiments. In: Beniston M, Verstraete M (eds) Remote sensing and climate modeling: synergies and limitations. Eds; Kluwer Academic Publishers, Berlin, pp 23–49CrossRefGoogle Scholar
  14. Kalma J, McVicar T, McCabe M (2008) Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surv Geophys 29:421–469CrossRefGoogle Scholar
  15. Kumar S, Bhasker U, Padmakumari K (2012) Estimation of land surface temperature to study urban heat island effect using Landsat ETM+ image. Int J Eng Sci Technol 4(02):771–777Google Scholar
  16. Kustas W, Anderson M (2009) Advances in thermal infrared remote sensing for land surface modeling. Agric For Meteorol 149:2071–2081CrossRefGoogle Scholar
  17. Li J, Wang X, Wang X, Ma W, Zhang H (2009) Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China. Ecol Complex 6(4):413–420CrossRefGoogle Scholar
  18. Li Z-L, Tang B-H, Hua W, Ren H, Yan G, Wan Z, Trigo IF, Sobrino JA (2013) Satellite-derived land surface temperature: current status and perspectives. Remote Sens Environ 131:14–37CrossRefGoogle Scholar
  19. Linh NT, Huy TQ, Jungwon H, Dongyeob H (2015) Land surface temperatures of industrial complexes in Jeonnam using Landsat 7 ETM+ satellite images. Journal of the KRSA 31(3):99–112Google Scholar
  20. Liu G, Zhang Q, Li G, Doronzo DM (2016) Response of land cover types to land surface temperature derived from Landsat-5 TM in Nanjing metropolitan region, China. Environ Earth Sci 75:1386CrossRefGoogle Scholar
  21. Moran MS, Rahman AF, Washburne JC, Goodrich DC, Weltz MA, Kustas WP (1996) Combining the Penman-Monteith equation with measurements of surface temperature and reflectance to estimate evaporation rates of semiarid grassland. Agric For Meteorol 80:87–109CrossRefGoogle Scholar
  22. Muro J, Canty M, Conradsen K, Hüttich C, Nielsen AA, Skriver H, Remy F, Strauch A, Thonfeld F, Menz G (2016) Short-term change detection in wetlands using Sentinel-1 time series. Remote Sens 8(10):795. CrossRefGoogle Scholar
  23. Musa T, Xu W, Hou W (2018) Terence Darlington Mushore (2018) Comparative analysis of responses of land surface temperature to long-term land use/cover changes between a coastal and Inland City: a case of Freetown and Bo town in Sierra Leone. Remote Sens 10:112. CrossRefGoogle Scholar
  24. Mushore TD, Mutanga O, Odindi J, Dube T (2017) Linking major shifts in land surface temperatures to long term land use and land cover changes: a case of Harare, Zimbabwe. Urban Climate 20:120–134CrossRefGoogle Scholar
  25. Nayak S, Mandal M (2012) Impact of land-use and land-cover changes on temperature trends over Western India. Curr Sci 102:1166–1173Google Scholar
  26. Pal S, Devara PA (2012) Wavelet-based spectral analysis of long-term time series of optical properties of aerosols obtained by LIDAR and radiometer measurements over an urban station in Western India. J Atmos Sol Terr Phys 84–85:75–87CrossRefGoogle Scholar
  27. Quattrochi DA, Luvall JC (2004) Thermal remote sensing in land surface processing. CRC Press, Boca RatonCrossRefGoogle Scholar
  28. Rajendran P, Mani K (2015) Estimation of spatial variability of land surface temperature using Landsat 8 imagery. Int J Eng Sci 4(11):19–23Google Scholar
  29. Salisbury JW, D’Aria DM (1992) Emissivity of terrestrial materials in the 8-14 pm atmospheric window. Remote Sens Environ 42:83–106CrossRefGoogle Scholar
  30. Schmugge TJ, Becker F, Li ZL (1991) Spectral emissivity variations observed in airborne surface temperature measurements. Remote Sens Environ 35:95–104CrossRefGoogle Scholar
  31. Sobrino José A, Jiménez-Muñoz JC, Sòria G, Romaguera M, Guanter L, Moreno J, Associate Member IEEE, Plaza A, Senior Member IEEE, Pablo MM (2008) Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE Trans Geosci Remote Sens 46(2)Google Scholar
  32. Sumit K, Rohit G, Nivedita K, Aneesh M (2018) Assessment of land surface temperature variation due to change in elevation of area surrounding Jaipur, India. The Egyptian Journal of Remote Sensing and Space Sciences 21:87–94CrossRefGoogle Scholar
  33. Tan KC, Lim HS, MatJafri MZ, Abdullah K (2010) Land surface temperature retrieval by using ATCOR3_T and normalized difference vegetation index methods in Penang Island. Am J Appl Sci 7(5):717–723CrossRefGoogle Scholar
  34. Vlassova L, Perez-Cabello F, Nieto H, Martín P, Riaño D, de la Riva J (2014) Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sens 6:4345–4368. CrossRefGoogle Scholar
  35. Wan Z (1999) MODIS land-surface temperature algorithm theoretical basis document. Institute for Computational Earth System Science University of California, Santa Barbara Santa Barbara, pp 93106–93060Google Scholar
  36. Wan Z, Dozier J (1996) A generalized split- window algorithm for retrieving land-surface temperature from space. IEEE Trans Geosci Remote Sens 34(4)Google Scholar
  37. Weng Q (2009) Thermal infrared remote sensing for urban climate and environmental studies: methods, applications, and trends. ISPRS J Photogramm Remote Sens 64:335–344CrossRefGoogle Scholar
  38. Yu X, Guo X, Zhaocong W (2014) Land surface temperature retrieval from Landsat 8 TIRS—comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sens 6:9829–9852. CrossRefGoogle Scholar
  39. Zaharaddeen ISA, Ibrahim I, Baba, Zachariah A (2016) Estimation of land surface temperature of Kaduna metropolis, Nigeria using Landsat images. Sci World J 11(3):36–42Google Scholar
  40. Zanter K (2016) LANDSAT 8 (L8) data users handbook. Department of the Interior U.S. Geological Survey Version 2.0Google Scholar
  41. Zareie S, Khosravi H, Nasiri A, Dastorani M (2016) Using Landsat thematic mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran. Solid Earth 7:1551–1564CrossRefGoogle Scholar
  42. Zhan X, Kustas WP, Humes KS (1996) An inter comparison study on models of sensible heat flux over partial canopy surfaces with remotely sensed surface temperature. Remote Sens Environ 58:242–256CrossRefGoogle Scholar
  43. Zhang F, Tiyip T, Kung H et al (2016) Dynamics of land surface temperature (LST) in response to land use and land cover (LULC) changes in the Weigan and Kuqa river oasis, Xinjiang, China. Arab J Geosci 9:499CrossRefGoogle Scholar
  44. Zhou J, Chen YH, Wang JF, Zhan WF (2011) Maximum night-time urban heat island (UHI) intensity simulation by integrating remotely sensed data and meteorological observations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4:138–146CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  1. 1.Department of General StudiesJubail Industrial CollegeJubail Industrial CityKingdom of Saudi Arabia

Personalised recommendations