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An important issue in urban thermal remote sensing is how to use pixel-based measurements of land surface temperature (LST) to characterize and quantify the urban heat island (UHI) observed at the mesoscale and macroscale. Characterization and modeling of UHIs must consider the inherent spatial nonstationarity within land surface variables. This study extended a kernel convolution modeling method for 2-D LST imagery to characterize and model the UHI in Indianapolis, IN, as a Gaussian process. To understand the UHI pattern over space and time, four Advanced Spaceborne Thermal Emission and Reflection Radiometer images of different seasons/years were acquired and analyzed. Furthermore, we employed linear spectral mixture analysis to extract subpixel urban biophysical variables [i.e., green vegetation (GV) and impervious surface (IS)] and developed new indexes of greenness and imperviousness based on the convoluted images of GV and IS fractions. These indexes were proposed to show the contrast in the urban-rural biophysical environmental conditions. Results indicate that the UHI intensity possessed a stronger correlation with both greenness and imperviousness indexes than with GV and IS abundance. Because this study utilized a smoothing kernel to characterize the local variability of urban biophysical parameters, including LST, characterized UHIs can therefore be examined as a scale-dependent process. To this end, we categorized the smoothing parameters into three groups, corresponding to the three scales that are suitable to studying the urban thermal landscape at the microscale, mesoscale, and regional scale, respectively. The identified scales can then be matched with various applications in urban planning and environmental management.