Robustness of Annual Cycle Parameters to Characterize the Urban Thermal Landscapes

Today, only few spaceborne sensors can deliver the thermal infrared information at medium scale required for urban heat island (UHI) modeling and assessment for applications in urban design. Despite recent advances in assessing urban thermal behavior, new methods for the estimation of UHI parameters are still needed. Relevant information about the thermal behavior is coded in the annual cycle of surface temperatures. In this letter, the robustness of parameters extracted from the annual cycle was tested. Therefore, a larger data set of Landsat surface temperatures was generated by cloud masking, and parameters from disjoint subsets were compared. Furthermore, a large number of virtual samples were generated to assess the accurateness of the extracted parameters. It was found that the mean and amplitude of the annual cycles in (black body) surface temperature can be estimated with an accuracy of approximately 1 K from 35 thermal infrared images. Hence, the multitemporal cycle parameters are an asset to characterize urban thermal landscapes.