Characterization of North American land cover from NOAA-AVHRR data using the EOS MODIS land cover classification algorithm

Land cover is a key boundary condition in weather, climate, and terrestrial biogeochemical models. Until recently, such models have used maps depictin g potential vegetation, which are known to be of relatively poor quality, t o parameterize land surface properties. In this paper we describe the compi lation and assessment of a new map of North American land cover produced th rough the application of advanced pattern recognition techniques to multite mporal satellite data. This map was produced in a fully automated fashion u sing supervised classification methods that are robust, fully automated, an d repeatable. The processing flow described in this paper is a prototype of the algorithm to be used to generate maps of global land cover using data from EOS MODIS. The superior quality and timeliness of these maps should be very useful for a wide array of sub-continental to global-scale modeling a nd analysis activities.