Downscaling of thermal images over urban areas using the land surface temperature–impervious percentage relationship

Intensive expansion and densification of urban areas decreases environmental quality and quality of urban life as exemplified by the urban heat island effect. For this reason, thermal information is becoming an increasingly important data source for integration in urban studies. It is expected that future spaceborne thermal sensors will provide data at appropriate spatial and temporal resolutions for urban studies. Until they become operational, research has to rely on downscaling algorithms increasing the spatial resolution of relatively coarse resolution thermal images albeit having a high temporal resolution. Existing downscaling algorithms, however, have been developed for sharpening images over rural and natural areas, resulting in large errors when applied to urban areas. The objective of this study is to adapt the DisTrad method for downscaling land surface temperature (LST) over urban areas using the relationship between LST and impervious percentage. The proposed approach is evaluated by sharpening aggregated LST derived from Landsat 7 ETM+ imagery collected over the city of Dublin on May 24th 2001. The new approach shows improved downscaling results over urban areas for all evaluated resolutions, especially in an environment with mixed land cover. The adapted DisTrad approach was most successful at a resolution of 480 m, resulting in a correlation of R2 = 0.84 with an observed image at the same resolution. Furthermore, sharpening using the adapted DisTrad approach was able to preserve the spatial autocorrelation present in urban environments. The unmixing performance of the adapted DisTrad approach improves with decreasing resolution due to the fact that the functional relationship between LST and impervious percentage was defined at coarse resolutions.