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Water WARRs

or more than four decades, radio wave velocity and soil water content have been known to be strongly coupled. By measuring radio wave velocity, an indirect measure of the water content of soil can be obtained. Such measurements have the potential to optimize agricultural irrigation.

Ground penetrating radar (GPR) maps the subsurface using radio waves. In the very early days of GPR, the factors controlling wave velocity were not well understood. Pioneering work in the late 1970s established the empirical relationship between water content and GPR velocity. In fact, use of time domain reflectometry (TDR), which is a close cousin to GPR, was used extensively to study controlled soil samples and establish the relationship.

Since that time, TDR devices have become a regular method for measuring the water content of soil. Numerous variations of the TDR approach now exist, and the method is commonly accepted. The TDR technique is limited because it requires a probe to be inserted into the soil; water content measurement is obtained over a limited area, so the method is not readily used to cover large areas. On the other hand, the method is great for monitoring the water content versus time at a localized position.

GPR has always offered the potential for providing a powerful means for rapid area coverage because the technique does not require direct contact with the soil. A GPR device can be moved over the surface quickly and large areas can be mapped. For many years development of GPR to complement discrete TDR measurements has been a goal.

Several GPR-based approaches are possible but all have seen limited success. Each approach can work effectively to obtain water content when a skilled researcher or GPR operator is engaged. Unfortunately, acquiring the desired result using a readily deployable GPR device with automated data analysis to a water content value has never been achieved.

One of the more effective ways of using GPR for estimating water content has been to use wide-angle reflection and refraction (WARR) soundings. These measurements have been complex to carry out with slow data acquisition and have only been limited to small areas. Further, an experienced operator is required to make the measurement and analyze the data.

Recently we have introduced the WARR Machine which is a novel new GPR instrument. This new system enables rapid profiling with virtually continuous acquisition of WARR soundings. The result has opened the door for doing large area soil moisture mapping.

spidar custom gpr
Figure 1
Collecting WARR Machine data at an agricultural test site.


The Forschungszentrum Jülich, a university in Germany, has been pioneering the use of GPR for many soil and ground water applications. Jülich has an extensive capability for examining agricultural problems and is developing several new and advanced applied geophysical methods. Well-controlled test sites enable technology testing for a wide variety of problem areas. Some unique time lapse studies are providing a greatly enhanced understanding of how ground water conditions change during the growing season.

Dr. Jan van der Kruk and his research team are currently pioneering the use of the WARR Machine for soil water content mapping. Research by PhD-candidate Manuela Kaufmann is demonstrating the viability of the new technology. A prototype system deployed at Jülich is shown in Figure 1. This WARR machine configuration unit is towed behind an ATV vehicle and is being developed to allow continuous profiling of field size areas to map variations in soil water content on a regular basis.

A profile across a controlled test field generated the preliminary results shown in Figure 2. Automated and manual data analysis were used to estimate water content from radio wave velocity and compared with a limited number of separate single-channel WARR measurement results. The development of a reliable automated data analysis tool will be key to the successful deployment of this new technology.

spidar custom gpr receiver
Figure 2
Preliminary Soil Water Content estimations from WARR Machine data
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