Five days of clear sky observations over Kansas and Nebraska are used to examine the statistical relationship between soil moisture and infrared surface temperature observations taken from a geosynchronous satellite. The approach relies on numerical model results to identify important variables other than soil moisture which have a significant effect on the surface temperature, and to define linear relationships between these variables and surface temperature. Linear regression is used to relate soil moisture to surface temperature and other variables that represent wind speed, vegetation cover, and low-level temperature advection. Results show good agreement between estimated and observed soil moisture features on each of the 5 days. The average coefficient of determination for five pseudo-independent tests in which the test day is held out of the regression is 0.71. When advection is neglected in these tests the average value of r2 drops to 0.57. It is shown that a depiction coefficient of 0.92... Abstract Five days of clear sky observations over Kansas and Nebraska are used to examine the statistical relationship between soil moisture and infrared surface temperature observations taken from a geosynchronous satellite. The approach relies on numerical model results to identify important variables other than soil moisture which have a significant effect on the surface temperature, and to define linear relationships between these variables and surface temperature. Linear regression is used to relate soil moisture to surface temperature and other variables that represent wind speed, vegetation cover, and low-level temperature advection. Results show good agreement between estimated and observed soil moisture features on each of the 5 days. The average coefficient of determination for five pseudo-independent tests in which the test day is held out of the regression is 0.71. When advection is neglected in these tests the average value of r2 drops to 0.57. It is shown that a depiction coefficient of 0.92...