Abstract
Both land surface temperature (LST) and surface air temperature (SAT) are crucial spatiotemporal parameters in a wide range of environmental, ecological, and climatological studies from the local to global scales. It is a significant challenge for researchers to accurately determine their relationship for both SAT estimation from LST and LST validation using SAT. Bivariate correlation and regression analyses were performed to investigate the relationships between the LSTs retrieved from Landsat thermal infrared (TIR) images and the synchronous hourly SATs acquired at weather stations in this study. Two scenes of Landsat-5 images, both acquired at approximately 10:42A.M. (local time) in two different seasons, early spring 2008 and mid-fall 2009, respectively, were used to retrieve LSTs from their TIR band images. Both the 10A.M. and 11A.M. hourly SATs (Tmean, Tmax and Tmin) were obtained near-synchronously at the weather stations (approximately 100 values) in Shenzhen, South China. The results show that at the α = 0.05 level, (1) there is no statistically significant correlation between the LST and any of the SAT or height-corrected SAT variables for the two seasons; (2) it is likely that there is no prepositional or lagging effects for the correlation between the LST and any SAT or corrected SAT variable for the two seasons; (3) there is no pronounced linear regression trend between the LSTs and the SAT variables overall during both the spring 2008 and fall 2009 seasons; and (4) there is no distinct topography- (elevation or station height) and urbanization-induced land cover effects on the correlations between the LSTs and the SATs. The results suggest that it is unlikely and unfeasible to derive surface air temperatures directly from Landsat thermal infrared images in this case study due to their uncertain correlations at the α = 0.05 level.
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