In order to obtain good regional hydroclimate simulation results, land surface properties such as leaf area index (LAI), albedo, emissivity, and roughness height are important for land surface hydrology modeling. A number of models have been proposed to describe the interaction between land surface and atmosphere during the last decade. The problem of these models is not taking account of spatial and temporal variability of the land surface properties within a computational grid. In an actual watershed, spatial distribution of vegetation is inhomogeneous, and its canopy varies seasonally. As such, it is necessary to consider the seasonal change of land surface properties in the land surface parameterization that is required for a regional hydroclimate model. The objective of this study is to understand the statistical characteristics of land surface properties. The satellite driven data from MODIS combined with local vegetation survey have been analyzed at a few selected watersheds in California in this study. The monthly spatial distributions of the land surface parameters have been computed. It was found that spatial distributions of the land surface parameters vary significantly in space and with seasons. This result indicates that non-stationarity as well as spatial variability of land surface parameters needs to be considered in land surface parameterization.