Identifying sampling locations for field-scale soil moisture estimation using K-means clustering

Identifying and understanding the impact of field-scale soil moisture patterns is currently limited by the time and resources required to do sufficient monitoring. This study uses K-means clustering to find critical sampling points to estimate field-scale near-surface soil moisture. Points within the field are clustered based upon topographic and soils data and the points representing the center of those clusters are identified as the critical sampling points. Soil moisture observations at 42 sites across the growing seasons of 4 years were collected several times per week. Using soil moisture observations at the critical sampling points and the number of points within each cluster, a weighted average is found and used as the estimated mean field-scale soil moisture. Field-scale soil moisture estimations from this method are compared to the rank stability approach (RSA) to find optimal sampling locations based upon temporal soil moisture data. The clustering approach on soil and topography data resulted in field-scale average moisture estimates that were as good or better than RSA, but without the need for exhaustive presampling of soil moisture. Using an electromagnetic inductance map as a proxy for soils data significantly improved the estimates over those obtained based on topography alone.