Accurate temporal and spatial estimation of land surface temperatures (LST) is important for modeling the hydrological cycle at field to global scales because LSTs can improve estimates of soil moisture and evapotranspiration. Using remote sensing satellites, accurate LSTs could be routine, but unfortunately the only instruments available to provide diurnal cycle observations have coarse spatial resolution (4 km). Examples are the Geostationary Environmental Satellites (GOES). These provide invaluable half hourly observations but have limited capacity to distinguish significantly different land surface types. This inability greatly constrains their utility since hydrological models respect differences in cover that the satellite data cannot provide. A technique that may help overcome the spatial resolution constraint is to disaggregate geostationary LST data using single time of day MODIS 1 km observations along with a diurnal-scale model. The resultant data are 1 km, hourly LSTs. Disaggregation procedures rely upon correlations between land cover types and LST. In studies using data for the U.S. Southwest, the most consistent and stable correlative estimators were obtained from 1 km MODIS emissivity data. An alternative estimator, MODIS Normalized Difference Vegetation Indices (NDVI) was more inconsistent and less correlative. Accuracies of LST estimates, investigated using 2002-2003 ground observations at Southern Great Plains and other surface sites will be discussed.