Advertisement

Retrieval of Urban Surface Temperature Using Remote Sensing Satellite Imagery

Chapter

Abstract

Remote sensing observations provide local, regional, and global information in a holistic view as well as large spatial coverage. With the advancement of remote sensing technology, more instances of where remotely sensed data are used recently to investigate the terrestrial processes and global climate due to their high spatial and temporal resolution. In this regard, there are more studies using remotely sensed imagery for investigation of the Surface Urban Heat Island (SUHI) phenomenon and retrieval of urban surface parameters, e.g. surface temperature, surface albedo, energy fluxes. However, the complex geometric characteristics in urban areas pose great challenges for these retrievals. This chapter presents the Urban Surface Temperature (UST) retrieval with consideration to the urban geometric characteristics in different seasons, analyzing the effective emissivity and urban surface temperature. Emissivity is crucial for surface temperature retrieval. However, the cavity effects and thermal heterogeneity caused by complex buildings affects the effective emissivity over urban areas. In this study, the effective emissivity from ASTER products in different seasons were collected to study the thermal heterogeneity effects on the applications of Temperature and Emissivity Separation (TES) algorithm on the UST retrieval in Hong Kong. Thermal images of Landsat 5 in different seasons were collected for analyses, in which the retrieved USTs, with and without considerations to geometric effects, were compared and analyzed. Finally, SUHI estimates based on two sets of USTs and its impacts on SUHI intensity estimation at different seasons were also studied.

Keywords

Seasonal effects Remote sensing Urban geometry Urban surface temperature 

Notes

Acknowledgements

This work was supported in part by the grant of Early Career Scheme (project id: 25201614) and General Research Fund (project id: 515513) from the Research Grants Council of Hong Kong; the grant 1-ZE24 from the Hong Kong Polytechnic University. The authors thank the Hong Kong Planning Department, the Hong Kong Lands Department, the Hong Kong Civil Engineering and Development Department, and the Hong Kong Observatory for the planning, building GIS, weather and climate, and airborne LiDAR data, and NASA LP DAAC for the ASTER and Landsat satellite imagery.

References

  1. 1.
    Amiri R, Weng Q, Alimohammadi A, Alavipanah SK (2009) Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sens Environ 113(12):2606–2617CrossRefGoogle Scholar
  2. 2.
    Chakraborty SD, Kant Y, Bharath BD (2014) Study of land surface temperature in delhi city to managing the thermal effect on urban developments. Int J Adv Sci Tech Res 4(1):439–450Google Scholar
  3. 3.
    Chen L, Ng E, An X, Ren C, Lee M, Wang U, He Z (2012) Sky view factor analysis of street canyons and its implications for daytime intra-urban air temperature differentials in high-rise, high-density urban areas of Hong Kong: a GIS-based simulation approach. Int J Climatol 32(1):121–136CrossRefGoogle Scholar
  4. 4.
    Cheng KS, Su YF, Kuo FT, Hung WC, Chiang JL (2008) Assessing the effect of landcover changes on air temperature using remote sensing images—a pilot study in northern Taiwan. Landsc Urban Plan 85(2):85–96CrossRefGoogle Scholar
  5. 5.
    Dousset B, Gourmelon F (2003) Satellite multi-sensor data analysis of urban surface temperatures and landcover. ISPRS J Photogramm Remote Sens 58(1–2):43–54CrossRefGoogle Scholar
  6. 6.
    Gillespie A, Rokugawa S, Matsunaga T, Cothern JS, Hook S, Kahle AB (1998) A temperature and emissivity separation algorithm for advanced spaceborne thermal emission and reflection radiometer (ASTER) images. IEEE Trans Geosci Remote Sens 36(4):1113–1126CrossRefGoogle Scholar
  7. 7.
    Hu L, Brunsell NA (2013) The impact of temporal aggregation of land surface temperature data for surface urban heat island (SUHI) monitoring. Remote Sens Environ 134:162–174CrossRefGoogle Scholar
  8. 8.
    Kotthaus S, Smith TEL, Wooster MJ, Grimmond CSB (2014) Derivation of an urban materials spectral library through emittance and reflectance spectroscopy. ISPRS J Photogramm Remote Sens 94:194–212CrossRefGoogle Scholar
  9. 9.
    Krayenhoff ES, Voogt J (2007) A microscale three-dimensional urban energy balance model for studying surface temperatures. Bound-Layer Meteorol 123(3):433–461CrossRefGoogle Scholar
  10. 10.
    Lagouarde J-P, Hénon A, Irvine M, Voogt J, Pigeon G, Moreau P, Masson V, Mestayer P (2012) Experimental characterization and modelling of the nighttime directional anisotropy of thermal infrared measurements over an urban area: case study of Toulouse (France). Remote Sens Environ 117:19–33CrossRefGoogle Scholar
  11. 11.
    Lagouarde JP, Irvine M (2008) Directional anisotropy in thermal infrared measurements over Toulouse city centre during the CAPITOUL measurement campaigns: first results. Meteorol Atmos Phys 102(3–4):173–185CrossRefGoogle Scholar
  12. 12.
    Lai A, So AC, Ng S, Jonas D (2012) The territory-wide airborne light detection and ranging survey for the Hong Kong special administrative region, In: The 33RD Asian conference on remote sensing, pp 26–30Google Scholar
  13. 13.
    Li Z-L, Tang B-H, Wu H, 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
  14. 14.
    Liu L, Zhang Y (2011) Urban heat island analysis using the Landsat TM data and ASTER data: a case study in Hong Kong. Remote Sens 3(7):1535–1552CrossRefGoogle Scholar
  15. 15.
    Nichol JE, Fung WY, Lam K-S, Wong MS (2009) Urban heat island diagnosis using ASTER satellite images and ‘in situ’ air temperature. Atmos Res 94(2):276–284CrossRefGoogle Scholar
  16. 16.
    Oltra CR, Cubero-Castan M, Briottet X, Sobrino JA (2014) Analysis of the performance of the TES algorithm over urban areas. IEEE Trans Geosci Remote Sens 52(11):6989–6998CrossRefGoogle Scholar
  17. 17.
    Payan V, Royer A (2004) Analysis of temperature emissivity separation (TES) algorithm applicability and sensitivity. Int J Remote Sens 25(1):15–37CrossRefGoogle Scholar
  18. 18.
    Peng S, Piao S, Ciais P, Friedlingstein P, Ottle C, Bréon F-M, Nan H, Zhou L, Myneni RB (2012) Surface urban heat Island across 419 global big cities. Environ Sci Technol 46(2):696–703CrossRefGoogle Scholar
  19. 19.
    Revi A, Satterthwaite DE, Aragón-Durand F, Corfee-Morlot J, Kiunsi RB, Pelling M, Roberts DC, Solecki W (2014) Urban areas. Climate Change 535–612 Google Scholar
  20. 20.
    Soux A, Voogt JA, Oke T (2004) A model to calculate what a remote sensorsees’ of an urban surface. Bound-Layer Meteorol 111(1):109–132CrossRefGoogle Scholar
  21. 21.
    Voogt JA (2008) Assessment of an urban sensor view model for thermal anisotropy. Remote Sens Environ 112(2):482–495CrossRefGoogle Scholar
  22. 22.
    Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384CrossRefGoogle Scholar
  23. 23.
    Weng Q (2003) Fractal analysis of satellite-detected urban heat island effect. Photogramm Eng Remote Sens 69(5):555–566CrossRefGoogle Scholar
  24. 24.
    Yang J, Wong MS, Menenti M, Nichol J (2015) Modeling the effective emissivity of the urban canopy using sky view factor. ISPRS J Photogramm Remote Sens 105:211–219CrossRefGoogle Scholar
  25. 25.
    Yang J, Wong MS, Menenti M, Nichol J (2015) Study of the geometry effect on land surface temperature retrieval in urban environment. ISPRS J Photogramm Remote Sens 109:77–87CrossRefGoogle Scholar
  26. 26.
    Yang J, Wong MS, Menenti M, Nichol J, Voogt J, Krayenhoff ES, Chan PW (2016) Development of an improved urban emissivity model based on sky view factor for retrieving effective emissivity and surface temperature over urban areas. ISPRS J Photogramm Remote Sens 122:30–40CrossRefGoogle Scholar
  27. 27.
    Yang X, Li Y, Luo Z, Chan PW (2017) The urban cool island phenomenon in a high-rise high-density city and its mechanisms. Int J Climatol 37(2):890–904CrossRefGoogle Scholar
  28. 28.
    Yuan F, Bauer ME (2007) Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sens Environ 106(3):375–386CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Land Surveying and Geo-InformaticsThe Hong Kong Polytechnic UniversityKowloonHong Kong

Personalised recommendations