A Geostatistical Approach to Estimate Soil Moisture as a Function of Geophysical Data and Soil Attributes

Successful implementation of site-specific irrigation requires an understanding of within-field-variability of soil parameters. These parameters can be estimated by direct sampling or by indirect surveying using geophysical data. The geophysical outputs are quite sensitive to soil water content; therefore, they can be used as covariates in soil water content (SWC) estimation. The objectives of this study were to use geophysical and soil data as auxiliary variables in the estimation of soil water content through geostatistical techniques. The surveys were carried out in a test site at the agricultural experimental farm located in south-eastern Italy in dry and wet soil conditions. The plot was surveyed with an EMI sensor and two different mono-static GPR systems, one with central frequencies of 600/1600 MHz and the other with a central frequency of 250 MHz. Forty-eight soil cores were collected for laboratory analysis of textural properties. One hundred and sixteen soil samples up to 0.30m-depth were collected to measure the SWC with gravimetric method. Kriging with external drift (KED), a non-stationary geostatistical technique, was used to estimate SWC with EMI, GPR and soil data as covariates. Cross-validation test was used to assess the goodness of the estimates and compare KED with ordinary kriging. The results showed that the approach using the auxiliary variables can be preferred to univariate kriging in terms of correlation between true and estimated values and capability of interpretation of spatial variability. Kriging with external drift proved to be a valid tool in sensor data fusion and could be effectively applied in Precision Irrigation