On the use of geophysics to support and connect soil sensors and cosmic ray neutron sensing: a case study highlighting the relevance of soil heterogeneity

Precision agriculture directly points at both spatial and temporal variabilities, to be mapped and monitored with relevant technologies. With regard to the subsurface, soil sensors remain the foremost driver of precision agriculture. These sensors provide high temporal resolution information on key soil variables, including volumetric water content. However, their limited representativeness and high sensitivity to local and installation factors are intrinsic and well known issues. Cosmic ray neutron sensing (CRNS) is a newer technology that addresses these issues, with the water content information being integrated over a footprint of several tens of meters. Nonetheless, the integrated water information remains a one-dimensional time series. The interplay of different spatial scales of the measurements and unknown subsurface heterogeneity ultimately hinders the correct interpretation of the individual time series, and their discrepancies.

In this work we explore how geophysics-based soil heterogeneity supports the interpretation of time series from soil water sensors and cosmic ray neutron sensing. We present a case study from a vineyard in the Chianti region (Siena, Italy). We focus on the joint use of electrical resistivity tomography and frequency-domain electromagnetic induction. Two field campaigns, conducted in April and November 2024, highlight significant differences in both soil composition (clay content) and soil depth over the vineyard. Before the geophysical campaign, the soil water sensors were installed in a region with particularly deep and clayey soil. On the contrary, the cosmic ray was installed at the center of the vineyard and thus responds to regions with dominant water dynamics. The results show that the differences in water dynamics between the clay-rich area (with the soil sensors) and the surrounding areas coupled with the larger CRNS sensitivity to faster-draining regions lead to significant discrepancies. The geophysics-based spatial information qualitatively explains these discrepancies and supports CRNS numerical simulations (Uranos) that aim to provide a more quantitative understanding.