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ARS Home » Midwest Area » Columbus, Ohio » Soil Drainage Research » Research » Publications at this Location » Publication #357638

Research Project: Agricultural Water Management in Poorly Drained Midwestern Agroecosystems

Location: Soil Drainage Research

Title: Self-potential as a proximal soil sensing tool in agriculture

Author
item Wishart, Debonne - Central State University
item Allred, Barry

Submitted to: Fast Times: News for the Near Surface Geophysical Sciences
Publication Type: Trade Journal
Publication Acceptance Date: 1/1/2018
Publication Date: 1/15/2018
Citation: Wishart, D., Allred, B.J. 2018. Self-potential as a proximal soil sensing tool in agriculture. Fast Times: News for the Near Surface Geophysical Sciences. 22(4):75-80.

Interpretive Summary:

Technical Abstract: Agricultural lands are characterized by a wide variety of soils having variable permeability and drainage conditions. Self-potential (SP) is a non-invasive geophysical method used to measure the voltage differences of a naturally occurring electrical field in the Earth’s subsurface between any two points at the ground surface. SP signals when observed under laboratory and field-scale conditions are associated with the differential electric potential produced by electrically charged ions and particles streaming through fluid. Hence, SP signals exhibit a direct relationship to fluid flow in porous media. The acquisition of SP data is simple and easy compared to acquiring soil geophysical data using ground penetrating radar (GPR), electrical resistivity (ER) and electromagnetic induction (EM). Self-potential surveys can potentially provide the advantages of cost-effectiveness and quick turnaround time for the detection of subsurface drainage and preferential fluid flow pathways in soils. The objective of this article is to provide a brief overview of the self-potential method and two examples (flow to drainage pipes and bioelectric potentials between plants and soils) of how SP signals may be explored as indicators of soil health in agriculture. In addition, we suggest the integration of self-potential and multispectral imaging as a remote sensing platform could significantly improve our understanding of fluid movement at agricultural sites.