Quantitative assessment and validation of the TM land surface temperature using synoptic weather stations

Land surface temperature (LST) is an essential parameter in ecological, hydrologic, climatic, and related studies. The objective of this study was to evaluate the performance of Artis and Sobrino algorithms for retrieving LST from 2009 Landsat TM thermal infrared band in Damaneh region of Isfahan province. The accuracy of LST extracted from geometrically corrected image was then assessed against field-based LST data recorded at 10 meteorological stations using linear regression analysis. The results showed that both algorithms were able to map LST spatial distribution in the region and they were significantly correlated (R>0.97), but the Artis algorithm performed slightly better than Sobrino one. This algorithm explained up to 72% of the variation in the field measurements of LST. According to this algorithm, bare lands and highly vegetated agricultural and rangeland areas had the highest (328k0) and lowest LST (291k0) in the region, respectively. As the results indicated here the decrease in vegetation cover corresponds with increase in temperature values, therefore, remotely-sensed LST information with their extensive coverage can have a key role in ecosystem management.