Daily evapotranspiration (ET) maps would significantly improve assessing crop water requirements, especially in the Texas High Plains (THP) where the supply of irrigation water is limited. Evapotranspireation maps derived from satellite data with daily coverage such as MODIS (Moderate Resolution Imaging Spectroradiometer) and GOES (Geostationary Operational Environmental Satellite) sensors are inadequate, because their thermal pixel size is larger than individual agricultural fields. However, there exists an opportunity to use simultaneously acquired high resolution visible, near-infrared, and shortwave-infrared images from MODIS, and thermal-infrared images from other high resolutions sensors such as LANDSAT 5 (Land Remote-Sensing Satellite) Thematic Mapper (TM) or ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)to improve spatial and temporal resolution of ET maps. Image downscaling methods are useful to improve spatial resolution by examining relationships between simultaneously acquired coarser thermal and finer non-thermal datasets. In this study, the TsHARP, an image downscaling technique, was evaluated for its capability to downscale land surface temperature (LST) images for ET mapping. The LANDSAT 5 TM images taken from a southern part of the THP area were utilized to implement TsHARP. For this purpose, we developed a synthetic image with a spatial resolution of 960x960 m using TM based 120x120 m LST image. The 960x960 m resolution was used to mimic a LST image derived from MODIS thermal data. The TsHARP was implemented to develop a LST image at 120x120 m resolution using a statistical relationship between LST and normalized difference vegetation index (NDVI). Comparison of downscaled 120x120 m LST image against original LST image from TM data yielded a correlation coefficient of 0.93. Results indicate that TsHARP has the potential to be used to downscale LST images with simultaneously acquired high resolution NDVI image derived from MODIS data.