Spatiotemporal monitoring of soil water content profiles in an irrigated field using probabilistic inversion of time-lapse EMI data

by D. Moghadas, K.Z. Jadoon, M.F. McCabe
Year: 2017 DOI: 10.1016/j.advwatres.2017.10.019

Extra Information

Advances in Water Resources, volume 110, pp. 238-248, (2017)


​Monitoring spatiotemporal variations of soil water content (θ) is important across a range of research fields, including agricultural engineering, hydrology, meteorology and climatology. Low frequency electromagnetic induction (EMI) systems have proven to be useful tools in mapping soil apparent electrical conductivity (σa) and soil moisture. However, obtaining depth profile water content is an area that has not been fully explored using EMI. To examine this, we performed time-lapse EMI measurements using a CMD mini-Explorer sensor along a 10 m transect of a maize field over a 6 day period. Reference data were measured at the end of the profile via an excavated pit using 5TE capacitance sensors. In order to derive a time-lapse, depth-specific subsurface image of electrical conductivity (σ), we applied a probabilistic sampling approach, DREAM(ZS), on the measured EMI data. The inversely estimated σ values were subsequently converted to θ using the Rhoades et al. (1976) petrophysical relationship. The uncertainties in measured σa, as well as inaccuracies in the inverted data, introduced some discrepancies between estimated σ and reference values in time and space. Moreover, the disparity between the measurement footprints of the 5TE and CMD Mini-Explorer sensors also led to differences. The obtained θ permitted an accurate monitoring of the spatiotemporal distribution and variation of soil water content due to root water uptake and evaporation. The proposed EMI measurement and modeling technique also allowed for detecting temporal root zone soil moisture variations. The time-lapse θmonitoring approach developed using DREAM(ZS) thus appears to be a useful technique to understand spatiotemporal patterns of soil water content and provide insights into linked soil moisture vegetation processes and the dynamics of soil moisture/infiltration processes.


Spatiotemporal monitoring