Satellite Based Estimation of Urban Surface Emissivity with the Use of Sub-Pixel Classification Techniques

Conference paper
Part of the Springer Atmospheric Sciences book series (SPRINGERATMO)

Abstract

Information about the spatial distribution of urban surface emissivity is essential for surface temperature estimation which is an important component of urban microclimate and it is critical in many applications, like turbulent sensible and latent heat fluxes estimation, energy budget, urban canopy modeling, bio-climatic studies and urban planning. The proposed method presents an improvement in emissivity estimation as compared with existing methods, such as the look-up table approach, wherein emissivity and other biophysical parameters are assigned to grid cells based on land cover types. The basic premise of this method is a sub-pixel classification of urban surface into vegetation, impervious and soil, based on spectral mixture analysis. The proposed approach was applied to Landsat-7 ETM + observations over the area of Athens, Greece. Spatial distributions of surface emissivity, as well as land surface temperature in the spectral region of 10.4–12.5 μm were derived. ASTER (Advanced Spectral Reflection and Emission Radiometer) emissivity and surface temperature products were used for evaluation.

References

  1. Baldridge AM, Hook SJ, Grove CI, Rivera G (2009) The ASTER spectral library version 2.0. Remote Sens Environ 113:711–715. doi:10.1016/j.rse.2008.11.007 CrossRefGoogle Scholar
  2. Chrysoulakis N (2003) Estimation of the all-wave urban surface radiation balance by use of ASTER multispectral imagery and in situ spatial data. J Geophys Res 108(D18):4582. doi:10.1029/2003JD003396 CrossRefGoogle Scholar
  3. Chrysoulakis N, Vogt R, Young D, Grimmond CSB, Spano D, Marras S (2009) ICT for urban metabolism: the case of BRIDGE. In: Proceedings of EnviroInfo2009: environmental informatics and industrial environmental protection: concepts, methods and tools, Hochschule für Technik und Wirtschaft, Berlin, pp 183–193Google Scholar
  4. Dash P, Gottsche FM, Olesen FS, Fischer H (2002) Land surface temperature and emissivity estimation from passive sensor data: theory and practice – current trends. Int J Remote Sens 23:2563–2594. doi:10.1080/01431160110115041 CrossRefGoogle Scholar
  5. 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 36:1113–1126. doi:10.1109/36.700995 CrossRefGoogle Scholar
  6. Mitraka Z, Chrysoulakis N, Kamarianakis N, Partsinevelos P, Tsouchlaraki A (2011) Improving the estimation of urban surface emissivity based on sub-pixel classification of high resolution satellite imagery. Remote Sens Environ 117:125–134. doi:10.1016/j.rse.2011.06.025 CrossRefGoogle Scholar
  7. Ridd MK (1995) Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. Int J Remote Sens 16:2165–2185. doi:10.1080/01431169508954549 CrossRefGoogle Scholar
  8. Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86:370–384. doi:10.1016/S0034-4257(03)00079-8 CrossRefGoogle Scholar
  9. Wuldera MA, Whitea JC, Masekb JG, Dwyerc J, Royd DP (2011) Continuity of Landsat observations: short term considerations. Remote Sens Environ 115(2):747–751. doi:10.1016/j.rse. 2010.11.002 CrossRefGoogle Scholar
  10. Yu Y, Privette JL, Pinheiro AC (2008) Evaluation of split-window land surface temperature algorithms for generating climate data records. IEEE Trans Geosci Remote Sens 46:179–192. doi:10.1109/TGRS.2007.909097 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Foundation for Research and Technology – Hellas, Institute of Applied and Computational MathematicsHeraklionGreece
  2. 2.European Space Agency, Directorate of Earth Observation Programmes, ESA/ESRINFrascatiItaly

Personalised recommendations