Estimation of soil hydraulic parameters by integrated hydrogeophysical inversion of time-lapse GPR data measured at Selhausen, Germany

Khan Jadoon, Lutz Weihermüller, Harry Verrecken, Sébastien Lambot

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present an integrated hydrogeophysical inversion approach that uses time-lapse off-ground ground-penetrating radar (GPR) data to estimate soil hydraulic parameters, and apply it to a dataset collected in the field. Off-ground GPR data are mainly sensitive to the near-surface water content profile and dynamics, and are thus related to soil hydraulic parameters, such as the parameters of the hydraulic conductivity and water retention functions. The hydrological simulator HYDRUS 1-D was used with a two-layer single- and dual-porosity model. To monitor the soil water content dynamics, time-lapse GPR and time domain reflectometry (TDR) measurements were performed, whereby only GPR data was used in the inversion. The dual porosity model provided better results compared to the single porosity model for describing the soil water dynamics, which is supported by field observations of macropores. Furthermore, the GPR-derived water content profiles reconstructed from the integrated hydrogeophysical inversion were in good agreement with TDR observations. These results suggest that the proposed method is promising for non-invasive characterization of the shallow subsurface hydraulic properties and monitoring water dynamics at the field scale.
Original languageEnglish (US)
Title of host publication2012 14th International Conference on Ground Penetrating Radar (GPR)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)9781467326636
DOIs
StatePublished - Jun 2012

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

Fingerprint

Dive into the research topics of 'Estimation of soil hydraulic parameters by integrated hydrogeophysical inversion of time-lapse GPR data measured at Selhausen, Germany'. Together they form a unique fingerprint.

Cite this