Retrieval of Urban Surface Temperature Using Remote Sensing Satellite Imagery



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.


Seasonal effects Remote sensing Urban geometry Urban surface temperature 



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.


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© 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

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