Bayesian Maximum Entropy Approach to Mapping Soil Moisture at the Field Scale

The study of soil moisture spatial variability at the field scale is important to aid in modeling hydrological processes at the land surface. The Bayesian Maximum Entropy (BME) framework is a more general method than classical geostatistics and has not yet been applied to soil moisture spatial estimation. This research compares the effectiveness of BME versus kriging estimators for spatial prediction of soil moisture at the field scale. Surface soil moisture surveys were conducted in a 227 ha pasture at the Marena, Oklahoma In Situ Sensor Testbed (MOISST) site. Remotely sensed vegetation data will be incorporated into the soil moisture spatial prediction using the BME method. Soil moisture maps based on the BME and traditional kriging frameworks will be cross-validated and compared.