To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0–1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed.

No abstract

No abstract

No abstract

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.\ud \ud 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.\ud \ud 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.\ud \ud 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 Irrigatio