Remote sensing thermal infrared (TIR) data have been widely used to retrieve land surface temperature (LST) (Quattrochi and Luvall, 1999; Weng et al., 2004). A series of satellite and airborne sensors, such as HCMM, Landsat TM/ETM+, AVHRR, ASTER, TIMS, have been developed to collect TIR data from the Earth’s surface. In addition to LST measurements, these TIR sensors may also be utilized to obtain emissivity data of different surfaces with varied resolutions and accuracies. LST and emissivity data have been used in urban climate and environmental studies, mainly for analyzing LST patterns and their relationship with surface characteristics, assessing urban heat island (UHI), and relating LSTs with surface energy fluxes for characterizing landscape properties, patterns, and processes (Quattrochi and Luvall, 1999). Remotely sensed TIR data are a unique source of information to define surface heat islands, which are related to canopy layer heat islands. In situ data (in particular, permanent meteorological station data) offer high temporal resolution and long-term coverage but lack spatial details. Moving observations overcome this limitation to some extent but do not provide a synchronized view over a city. Only remotely sensed TIR data can provide a continuous and simultaneous view of a whole city, which is of prime importance for detailed investigation of urban surface temperature. Generally speaking, the application of TIR data has been limited in urban surface energy modeling (Voogt and Oke, 2003). Previous works have focused on the methods for estimating variables related to energy driving forces, soil moisture availability, and vegetation-soil interaction from satellite remote sensing data, but little has been done to estimate surface atmospheric parameters (Schmugge et al., 1998). These parameters are measured in the traditional way in the network of meteorological stations or in situ field measurements.