Daily land surface temperatures (LST) of Moderate Resolution Imaging Spectroradiometer (MODIS) data were analyzed to determine how the data were correlated with climatic water budget variables. Using a climatic water budget program, four daily water budget factors were calculated at six weather stations across the state; percent soil moisture, AE/PE, water deficit, and water deficit/PE. Land surface temperature deviations standardized with air temperature were expected to have a significant correlation with the water budget factors. To do correlation analyses on a weekly basis, daily MODIS data were integrated into three different types of weekly composites, including maximum temperature, driest-day, and combination composite data sets. Results showed that the maximum composite data set had the highest, and the driest-day composite had the lowest, correlation with the climatic water budget on average. Percent soil moisture, AE/PE, and Def/PE consistently had a high correlation with the LST deviation, whereas water deficit values showed inconsistent relationships from place to place. Time-integrated, or cumulative values of the LST-meanTair showed even stronger relationships with the water budget factors, increasing the correlation coefficients by 33.4% on average. The absolute values of their correlation coefficients ranged from 0.618 to 0.823.