In recent years, global scale multitemporal observations of land surface properties from satellites have provided a wealth of new information regarding the Earth's land surface properties and dynamics. Initial efforts in this domain were based on data derived from instruments such as the Advanced Very High Resolution Radiometer onboard the NOAA series of satellites, but were limited by the inadequate radiometric quality of these data. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA's Terra and Aqua spacecraft is now supplying high quality data sets that offer a wealth of new information and insights regarding the spatio-temporal dynamics in land surface properties. This information can be used to improve the representation of both the static and dynamic properties of land surfaces within weather and climate models at continental to global spatial scales, and seasonal to inter-annual time scales. In this paper, we describe new results from efforts to map land surface properties from MODIS, emphasizing land cover and vegetation dynamics. Specifically, we describe data sets that characterize the global distribution of vegetation and land cover types. Central to these efforts is the creation of a flexible database of land cover properties, which can be used to tailor land cover representations to the needs of individual users or models. The utility of this approach is demonstrated by showing how the global distribution of plant function types is being mapped in support of the NCAR Community Land Model (CLM). In addition, we present initial results from using two years of MODIS data to examine intra- and inter-annual variation in surface properties. In the former case, we show how MODIS data can be used to monitor continental to global scale vegetation phenology, emphasizing the identification of key intra-annual transition dates such as the onset of greenup and senescence. In the latter case, we present preliminary results from change vector analysis applied to two years of MODIS data, which reveal inter-annual variation in land surface biophysical conditions associated with variations in climate forcing.