Mass and energy exchanges between soil, plants and atmosphere control a number of key environmental processes involving hydrology, biota and climate. The understanding of these exchanges also play a critical role for practical purposes e.g. in precision agriculture. In this paper we present a methodology based on coupling innovative data collection and models in order to obtain quantitative estimates of the key parameters of such complex flow system. In particular we propose the use of hydro-geophysical monitoring via "time-lapse" electrical resistivity tomography (ERT) in conjunction with measurements of plant transpiration via sap flow and evapotranspiration (ET) from eddy covariance (EC). This abundance of data is fed to spatially distributed soil models in order to characterize the distribution of active roots. We conducted experiments in an orange orchard in eastern Sicily (Italy), characterized by the typical Mediterranean semi-arid climate. The subsoil dynamics, particularly influenced by irrigation and root uptake, were characterized mainly by the ERT set-up, consisting of 48 buried electrodes on 4 instrumented micro-boreholes (about 1.2 m deep) placed at the corners of a square (with about 1.3 m long sides) surrounding the orange tree, plus 24 mini-electrodes on the surface spaced 0.1 m on a square grid. During the monitoring, we collected repeated ERT and time domain reflectometry (TDR) soil moisture measurements, soil water sampling, sap flow measurements from the orange tree and EC data. We conducted a laboratory calibration of the soil electrical properties as a function of moisture content and porewater electrical conductivity. Irrigation, precipitation, sap flow and ET data are available allowing for knowledge of the system's long-term forcing conditions on the system. This information was used to calibrate a 1-D Richards' equation model representing the dynamics of the volume monitored via 3-D ERT. Information on the soil hydraulic properties was collected from laboratory and field experiments. The successful results of the calibrated modelling exercise allow for the quantification of the soil volume interested by root water uptake (RWU). This volume is much smaller (with a surface area less than 2 m², and about 40 cm thick) than expected and assumed in the design of classical drip irrigation schemes that prove to be losing at least half of the irrigated water which is not taken up by the plants.

The surface wave method is a popular tool for geotechnical characterization because it supplies a cost-effective testing procedure capable of retrieving the shear wave velocity structure of the near-surface. Several acquisition and processing approaches have been developed to infer the Rayleigh wave dispersion curve which is then inverted. Typically, in active testing, single-component vertical receivers are used. In most cases, the inversion is carried out assuming that the experimental dispersion curve corresponds to a single mode, mostly the fundamental Rayleigh mode, unless clear evidence dictates the existence of a more complex response, e.g., in presence of low-velocity layers and inversely dispersive sites. A correct identification of the modes is essential to avoid serious errors. Here we consider the typical case of higher-mode misidentification known as “osculation” (“kissing”), where the energy peak shifts at low frequencies from the fundamental to the first higher mode. This jump occurs, with a continuous smooth transition, around a well-defined frequency where the two modes get very close to each other. Osculation happens generally in presence of strong velocity contrasts, typically with a fast bedrock underlying loose sediments. The practical limitations of the acquired active data affect the spectral and modal resolution, making it often impossible to identify the presence of two modes. In some cases, modes have a very close root and cannot be separated at the osculation point. In such cases, mode misidentification can create a large overestimation of the bedrock velocity and a large error on its depth. We examine the subsoil conditions that can generate this unwanted condition, and the common field acquisition procedures that can contribute to producing data having such deceptive Rayleigh dispersion characteristics. This mode misidentification depends strongly on the usual approach of measuring only the vertical component of ground motion, as the mode osculation is linked to the Rayleigh wave ellipticity polarization, and therefore we conclude that multicomponent data, using also horizontal receivers, can help discern the multimodal nature of surface waves. Finally, we introduce a priori detectors of subsoil conditions, based on passive microtremor measurements, that can act as warnings against the presence of mode osculation, and relate these detectors to the frequencies at which dispersion curves can be misidentified. Theoretical results are confirmed by real data acquisition tests.

The investigation of plant roots is inherently difficult and often neglected. Being out of sight, roots are often out of mind. Nevertheless, roots play a key role in the exchange of mass and energy between soil and the atmosphere, in addition to the many practical applications in agriculture. In this paper, we propose a method for roots imaging based on the joint use of two electrical noninvasive methods: electrical resistivity tomography (ERT) and mise-à-la-masse (MALM). The approach is based on the key assumption that the plant root system acts as an electrically conductive body, so that injecting electrical current into the plant stem will ultimately result in the injection of current into the subsoil through the root system, and particularly through the root terminations via hair roots. Evidence from field data, showing that voltage distribution is very different whether current is injected into the tree stem or in the ground, strongly supports this hypothesis. The proposed procedure involves a stepwise inversion of both ERT and MALM data that ultimately leads to the identification of electrical resistivity (ER) distribution and of the current injection root distribution in the three-dimensional soil space. This, in turn, is a proxy to the active (hair) root density in the ground. We tested the proposed procedure on synthetic data and, more importantly, on field data collected in a vineyard, where the estimated depth of the root zone proved to be in agreement with literature on similar crops. The proposed noninvasive approach is a step forward towards a better quantification of root structure and functioning.