Spatial prediction of near surface soil water retention functions using hydrogeophysics and empirical orthogonal functions

The hydrological community often turns to widely available spatial datasets such as the NRCS Soil Survey Geographic database (SSURGO) to characterize the spatial variability of soil properties. When used to spatially characterize and parameterize watershed models, this has served as a reasonable first approximation when lacking localized or incomplete soil data. Within agriculture, soil data has been left relatively coarse when compared to numerous other data sources measured. This is because localized soil sampling is both expensive and time intense, thus a need exists in better connecting spatial datasets with ground observations. Given that hydrogeophysics is data-dense, rapid, non-invasive, and relatively easy to adopt, it is a promising technique to help dovetail localized soil sampling with spatially exhaustive datasets. In this work, we utilize two common near surface geophysical methods, cosmic-ray neutron probe and electromagnetic induction, to identify temporally stable spatial patterns of measured geophysical properties in three 65 ha agricultural fields in western Nebraska. This is achieved by repeat geophysical observations of the same study area across a range of wet to dry field conditions in order to evaluate with an empirical orthogonal function. Shallow cores were then extracted within each identified zone and water retention functions were generated in the laboratory. Using EOF patterns as a covariate, we quantify the predictive skill of estimating soil hydraulic properties in areas without measurement using a bootstrap validation analysis. Results indicate that sampling locations informed via repeat hydrogeophysical surveys, required only five cores to reduce the cross-validation root mean squared error by an average of 64% as compared to soil parameters predicted by a commonly used benchmark, SSURGO and ROSETTA. The reduction to five strategically located samples within the 65 ha fields reduces sampling efforts by up to ∼90% as compared to the common practice of soil grid sampling every 1 ha.