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). One approach that may help overcome the spatial resolution constraint is to disaggregate geostationary LST data using visible to thermal infrared information provided by single time of day MODIS 1 km observations. These higher-resolution observations are correlative with observations at 4-km scales, and thus can be used to estimate 1-km LST values throughout a day. Inamdar et al 2008, for example, showed how GOES 10 imager and MODIS data could be combined to produce accurate half-hourly, 1-km LST values. However, the method disaggregated coarse LST values using Normalized Difference Vegetation Index (NDVI) data and was sometimes highly inaccurate when considering heterogeneous terrain. This problem can be greatly reduced with an alternative approach, whereby MODIS land cover emissivity data sets supply the needed 1-km information. In a study of LST estimation over the US Southwest, diurnal disaggregation models using emissivity data were significantly more accurate than a comparable NDVI-based model. This alternative approach which directly employs 8-day composites of MODIS 1 km emissivity is a simple and fast method.