The behavior of satellite footprint-scale surface soil moisture probability density functions (PDF) was analyzed using 50-km-scale samples taken from soil moisture images collected during the Southern Great Plains 1997 (SGP97) hydrology experiment. Under the observed wetness conditions, soil moisture variability generally peaked in the midrange of mean soil moisture content and decreased toward the wet and dry ends, while in the midrange it was more widely distributed. High variability in the midrange is attributed to the multimodality of soil moisture PDFs, which apparently results from fractional precipitation within the footprint-scale fields. Single Gaussian, single beta, and mixtures of two Gaussian distributions were utilized to fit observed footprint-scale soil moisture distributions. As a single-component density, the Gaussian PDF was shown to be a good choice, compared to the beta distribution, for representing spatial variability, particularly under wet conditions. The performance of the Gaussian PDF was greatly improved by using a mixture of two Gaussian distributions. Implications of this study for the validating spaceborne remotely sensed soil moisture estimates and for parameterization of subgrid-scale surface soil moisture content in land surface models are discussed.