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Figure 3. Soil moisture maps generated by integrating PoLRa (turfRad)
data with digital job board (ASB taskTracker) for fairway 2 and 6 (a and
b, respectively) at Champions Golf Club (Jackrabbit Course) in Houston,
Texas, during a survey on August 14, 2023.
techniques. Calibration involves adjusting the sensor readings to
match those from reliable reference measurements, such as those
obtained from TDR sensors, to ensure the data collected are
accurate and reliable.
Factors influencing accuracy include soil moisture content,
leaf water levels, brightness temperature (how much microwave
radiation is reflected back to the sensor), temperature fluctuations,
and surface roughness (how smooth or uneven the surface is,
affecting the scattering of microwave signals). Different turfgrass
species may also affect readings due to variations in leaf water
content and surface characteristics. Therefore, site-specific
calibrations are essential for ensuring reliable measurements.
Significant effort is needed to develop and apply effective
calibration techniques to achieve precise and dependable results.
PRELIMINARY RESEARCH AT CHAMPIONS GOLF CLUB,
HOUSTON, TEXAS
On August 14, 2023, Texas A&M University researchers conducted
a study at Champions Golf Club (Jackrabbit Course) in Houston,
Texas, focusing on fairways 2, 6, and 13, which feature ‘Tifway 419’
hybrid bermudagrass in sandy loam soil.
Two methods to measure soil moisture were used: the PoLRa
(turfRad) microwave radiometer and handheld TDR 350 sensors
(FieldScout TDR 350 Soil Moisture Meter, Spectrum Technologies,
Inc., Plainfield, IL, USA).
The PoLRa was mounted on a fairway mower about 1 meter
above the ground (Figure 1a) and driven at speeds of 3.5-4.5 mph.
The mower made three passes per fairway—two near the edges
and one down the center—while data were collected at twelve
randomly chosen points per fairway. After each pass, the points
were flagged, and exact times were recorded using the ‘Unix
Time’ app.
Previous research has shown strong correlations between
TDR and gravimetric soil moisture measurements, especially in
coarse, non-conductive soils. Since TDR is practical for golf course
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• CGSA • GreenMaster
Superintendents, it provides reliable data for
calibrating the PoLRa sensor. After using PoLRa
to take measurements, soil moisture readings
were collected at marked points using handheld
TDR sensors at depths of 1.5, 3.0, and 4.8 inches
(Figure 1b). The PoLRa data were then matched
with the TDR readings based on the recorded
times to ensure accurate comparisons.
For calibration, we used ANCOVA regression,
a statistical method that helps understand the
relationship between different variables while
controlling for other factors. In this method, TDR
readings were treated as the dependent variable
(the outcome we are measuring), and the
brightness temperature from PoLRa’s vertical
polarization was the independent variable (the
factor we are testing to see its effect). This
method enabled more accurate estimation of soil
moisture levels. We assessed the model’s
performance using metrics such as R², which
indicates how well the model explains the
variation in soil moisture, and mean absolute
error (MAE), which shows the average size of the
prediction errors.
Our initial calibration using PoLRa’s off-the-
shelf (i.e., factory) settings showed an R² value of
0.60 (P < 0.01) and MAE of 0.06 (Figure 2a),
indicating that 60% of the variability in soil
moisture readings could be explained by PoLRa
data. While promising, these results highlighted
the need for further refinement to improve
accuracy. Using an advanced ANCOVA
calibration approach that incorporated additional
factors such as brightness temperature, the
model’s performance significantly improved. The
R² value increased to 0.78 (P < 0.01) (Figure 2b),
explaining 78% of the variability in soil moisture,
and the MAE was reduced to 0.03. These results
demonstrate the effectiveness of advanced
calibration techniques in enhancing the accuracy
of PoLRa’s soil moisture measurements.
FUTURE DIRECTION FOR ACCURACY AND
RELIABILITY
The improved soil moisture measurement
accuracy from using ANCOVA to calibrate
microwave radiometry technology highlights its
potential to improve golf course irrigation. While
effective, ANCOVA requires further refinement.
Future research should explore additional factors,
such as different soil types, turfgrass species and
varieties, management practices, and various
climatic environments. Considering temporal
factors, including seasonal variations, will also
help enhance the model’s year-round accuracy.
FINE-TUNING MICROWAVE RADIOMETRY
AND IMPROVING SOIL MOISTURE MAPPING
After improving soil moisture measurement
accuracy, the next step in optimizing precision
irrigation is enhancing soil moisture mapping
across large areas like fairways. These maps help
visualize soil moisture variability, as shown in
Figure 3, and hold great potential for precision
irrigation.