NASA, Goddard Space Flight Center has developed the Land Information System infrastructure (LIS; http://lis.gsfc.nasa.gov) capable of simulating regional or global land-atmosphere interactions at spatial resolutions down to 1km. The LIS infrastructure builds on the North American and Global Land Data Assimilation Systems (LDAS; http://ldas.gsfc.nasa.gov ), developed jointly with NCEP, NWS, and university collaborators. The primary goal of Land Data Assimilation in the context of NWP applications is to provide optimal estimates of land surface state initial conditions such as soil moisture, snow and soil temperature, by integrating remotely sensed observations with land surface models. Accordingly, LIS consists of an ensemble of land surface models (e.g., Noah, CLM, VIC) run offline using satellite-based precipitation (e.g., TRMM MPA), radiation (e.g., GOES) and surface parameters, in addition to model-derived surface meteorology (e.g., GDAS, GEOS, ECMWF). Satellite-based surface parameters include AVHRR-based or MODIS-based land cover and Leaf Area Index (LAI). The high spatial resolution of LIS, enabled by the use of high performance computing and communications technologies, is capable of resolving mesoscale features, including urban areas, lakes, and agricultural fields. Results demonstrating LIS applied at ¼ degree, 5km and 1km resolutions will be presented. Several validation case studies conducted with LIS, including the Coordinated Enhanced Observing Period (CEOP) reference sites, demonstrate that (1) subgrid spatial heterogeneity at 1km yields significant differences in ¼ degree mean fluxes; and (2) using current land cover and LAI products from MODIS, rather than AVHRR-based climatologies, has significant impacts on predicted land surface temperatures and surface water and energy fluxes.