Mesoscale meteorological, urban dispersion and air quality simulation models applied at various horizontal scales require different levels of fidelity for specifying the characteristics of the underlying surfaces. As the modeling scales approach the neighborhood level (approx 1 km horizontal grid spacing), the representation of urban structures and surface cover properties requires much greater detail. To provide the most accurate surface characterization possible for an air quality modeling study of Houston, Texas, airborne LIDAR (Light Detection and Ranging) data were obtained at 1- m horizontal grid cell spacing for Harris County, Texas, an area of approximately 5800 km. The gridded dataset of full-feature elevation data was processed using GIS analysis techniques to determine more than 20 urban canopy parameters (UCPs) including building height statistics and histograms, height-to-width ratio, plan area density function, frontal area density function, roughness length, displacement height, mean orientation of streets, and sky view factor. In an effort to improve the efficiency and accuracy of the roughness length derivation, an alternative gridded dataset of roughness length was produced using satellite data collected by Synthetic Aperture Radar (SAR) instrumentation. The comparison of the SAR and morphometric (LIDAR) roughness lengths suggested an integration of the satellite and airborne LIDAR datasets may provide an efficient means to derive a more accurate roughness length gridded dataset. In this paper, we describe the high-resolution Houston UCP dataset, report on the variability of the UCPs across the Houston urban terrain, and present the comparison of the morphometric and SAR roughness lengths.