Field-Scale Soil Moisture Space-Time Geostatistical Modeling for Complex Palouse Landscapes in the Inland Pacific Northwest.

Estimating soil moisture content continuously over space and time using geostatistical techniques supports: (a) the calibration and validation of process-based hydrologic models; and (b) spatially modelling dynamic soil processes, such as moisture-mediated greenhouse gas emissions. In this study, we model soil profile volumetric moisture content for five agricultural fields with loess soils in the Palouse region of Eastern Washington and Northern Idaho. Using a combination of stratification and space-filling techniques, we selected 42 representative and distributed measurement locations in the Cook Agronomy Farm (Pullman, WA) and 12 locations each in four additional grower fields that span the precipitation gradient across the Palouse. At each measurement location, soil moisture was measured on an hourly basis at five different depths (30, 60, 90, 120, and 150 cm) using Decagon 5-TE/5-TM soil moisture sensors (Decagon Devices, Pullman, WA). This data was collected over three years for the Cook Agronomy Farm and one year for each of the grower fields. External covariates used in this analysis include topographic indices, remote sensing indices, and proximal soil sensing data (e.g. from electromagnetic induction and VisNIR penetrometer). The analysis of this data is in progress.