A linear mixed effect (LME) model for soil water content estimation based on geophysical sensing: a comparison of a LME model and kriging with external drift

Soil water content (SWC) is a critical attribute in Precision Irrigation. Direct measurements are costly and relative sparse, so there is interest in methods to predict SWC at unsampled sites from sampled data. The precision of such predictions can be improved, if covariates are incorporated into the predictor. To do that, two efficient ways are: a) linear mixed effect model (LME), in which the spatial processes is obtained by splitting the total variability in a systematic term or mean effect, a spatially correlated component and a random noise, and b) kriging with external drift (KED), a non-stationary geostatistical technique, assuming covariates to have a linear effect on target variable. Geoelectric sensors provide non-invasive information on soil. Their outputs are quite sensitive to SWC, therefore, they can be used as covariates in SWC predictor. The objective of this work was to compare LME and KED, based on geophysical sensing, to estimate shallow SWC. The surveys were conducted in a south-eastern Italy field, using GPR at two frequencies, 600 and 1600 MHz, EMI. Volumetric soil water contents were measured with a Time Domain Reflectometer at 96 locations. Three LMEs, using three correlation functions (spherical, exponential and Gaussian), were used and compared with KED using a set of cross validation criteria. The mixed models showed a quite similar behaviour even if exponential model outperformed the other two. The covariates used in the mixed models and in KED predictor were different and the results of cross-validation showed a slight out-performance of KED.