Theoretical and Applied Climatology

, Volume 123, Issue 3–4, pp 711–722 | Cite as

Assessing surface albedo change and its induced radiation budget under rapid urbanization with Landsat and GLASS data

  • Yonghong Hu
  • Gensuo Jia
  • Christine Pohl
  • Xiaoxuan Zhang
  • John van Genderen
Original Paper


Radiative forcing (RF) induced by land use (mainly surface albedo) change is still not well understood in climate change science, especially the effects of changes in urban albedo due to rapid urbanization on the urban radiation budget. In this study, a modified RF derivation approach based on Landsat images was used to quantify changes in the solar radiation budget induced by variations in surface albedo in Beijing from 2001 to 2009. Field radiation records from a Beijing meteorological station were used to identify changes in RF at the local level. There has been rapid urban expansion over the last decade, with the urban land area increasing at about 3.3 % annually from 2001 to 2009. This has modified three-dimensional urban surface properties, resulting in lower albedo due to complex building configurations of urban centers and higher albedo on flat surfaces of suburban areas and cropland. There was greater solar radiation (6.93 × 108 W) in the urban center in 2009 than in 2001. However, large cropland and urban fringe areas caused less solar radiation absorption. RF increased with distance from the urban center (less than 14 km) and with greater urbanization, with the greatest value being 0.41 W/m2. The solar radiation budget in urban areas was believed to be mainly influenced by urban structural changes in the horizontal and vertical directions. Overall, the results presented herein indicate that cumulative urbanization impacts on the natural radiation budget could evolve into an important driver of local climate change.


Urban Land Urban Heat Island Surface Albedo Radiative Force Urban Canopy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research was supported by the CAS Strategic Priority Research Program (XDA05090200), the National Natural Science foundation of China (41405064), and CMA/Henan Key Laboratory of Agrometeorological Support and Applied Technique (AMF 201406).


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Copyright information

© Springer-Verlag Wien 2015

Authors and Affiliations

  • Yonghong Hu
    • 1
  • Gensuo Jia
    • 2
  • Christine Pohl
    • 3
  • Xiaoxuan Zhang
    • 1
    • 4
  • John van Genderen
    • 5
  1. 1.Key Laboratory of Digital Earth Science, Institute of Remote sensing and Digital EarthChinese Academy of SciencesBeijingChina
  2. 2.Key Laboratory of Regional Climate-Environment for East Asia, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  3. 3.Institute of Geospatial Science and TechnologyUniversiti Teknologi MalaysiaJohor BahruMalaysia
  4. 4.College of GeosciencesChina University of PetroleumQingdaoChina
  5. 5.Geospatial Information Science Research CentreUniversiti Putra Malaysia43400 SerdangMalaysia

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