field calibration and monitoring of soil-water content with fiberglass electrical resistance sensors

Field Calibration and Monitoring of Soil-Water Contentwith Fiberglass Electrical Resistance Sensors

M. S. Seyfried*

ABSTRACTElectrical resistance sensors, combined with data acquisition sys-

tems, offer a relatively inexpensive means of continuously monitoringsoil-water content (0) at barely accessible remote sites. Fiberglass re-sistance sensors respond across the range of 0 but require calibration.With field calibration, site-specific soil conditions are implicitly ac-counted for, but calibration results have not been presented in theliterature. The objectives of this study were to determine the accuracyand precision of field-calibrated fiberglass resistance sensors and todemonstrate their application to monitoring at remote sites. Timedomain reflectometry (TDK) was used for calibration. Sixteen indi-vidual sensor-TDR calibrations and one overall calibration combiningall measurements showed a strong log-linear relationship between TDK-measured 0 and sensor-measured resistance. Individual calibration80% confidence intervals ranged from 0.02 to 0.045 m3 m-3. Cali-bration statistics did not appreciably drift during the study. Theseresults, and subsequent measurements, were unaffected by soil freez-ing, indicating that the sensors respond to liquid water content. Theoverall calibration 80% confidence interval was 0.065 m3 m~3, duelargely to high variability among sensors. However, changes in 0 couldbe estimated with reasonably accuracy. Most (73%) of the resistancemeasurements made the year after calibration were within ±0.05 m3

m~3 of the TDK-measured value. Sensor response time was shown tobe within the 1-h measurement interval. In this study, field-calibratedfiberglass resistance sensors provided reasonably accurate estimatesof 0 at a high level of spatial and temporal resolution.

FIELD SOIL-WATER CONTENT data can be difficult tocollect because the timing of critical rainfall and

snowmelt is unpredictable, accessibility to field sitesduring these events can be difficult, and major changesin 6 can occur very rapidly. For these reasons it isdesirable to have a system of continuous, automaticmonitoring. Remote sensing may provide this in thefuture, but that technology requires further develop-ment (Engman and Gurney, 1991). Two other meth-ods of 6 measurement amenable to continuousmonitoring are TDK and electrical resistance sensors.

Time domain reflectometry can provide uncali-brated, accurate, and precise measurements of 6 thatare relatively insensitive to variations in soil properties(Topp and Davis, 1985; Dalton and van Genuchten,1986). The primary disadvantage of TDR for remotesite applications is that expensive equipment must beleft unattended, subject to extremes in climatic con-ditions, and devoted to a single use for long periodsof time. Additional problems are the approximately 5-to 30-m usable radius around the TDR machine (Soil-moisture, 1989; Zegelin et al., 1992) and higher powerrequirements relative to electrical resistance sensors.

Fiberglass resistance sensors are a relatively oldmeasurement technique (Bouyoucous and Mick, 1940;Colman and Hendrix, 1949) that have been viewed asuseful in some contexts due partly to their low expense

USDA-ARS Northwest Watershed Center, 800 Park Blvd., PlazaIV, Boise, ID 83712. Received 31 Aug. 1992. *Correspondingauthor.

Published in Soil Sci. Soc. Am. J. 57:1432-1436 (1993).

and flexibility (Holmes et al., 1967; Schmugge et al.,1980). The relatively recent development of ruggeddata acquisition systems that can function in coldweather at remote sites has added a potential appli-cation of fiberglass resistance sensors that can be placedlarge distances from the data acquisition system andcan be powered by solar panels.

Fiberglass resistance sensors, as opposed to othercommonly used resistance sensors (e.g., gypsumblocks), were used in this study for two reasons: (i)they are relatively sensitive to changes in 6 at soilwater tensions near zero, where most soil water move-ment occurs (Palpant and Lull, 1953; Taylor et al.,1961), and (ii) they are relatively stable in humid soilregimes. The primary disadvantages of the sensors arethat they exhibit large variability among individualsensors, they are not nearly as precise or accurate asTDR, and they are sensitive to soil properties andtemperature. For these reasons they require individualcalibration for specific soil conditions.

Laboratory calibration is the easiest and most com-mon approach. Fiberglass resistance sensors are knownto be sensitive to changes in soil properties, however,such as texture and soluble salt content that often changewithin the soil profile and across different soils (Col-man and Hendrix, 1949). This makes comprehensivelab calibration a major undertaking. Some researchersrecommend field calibration (Reinhart, 1953; En-gland, 1965), but the accuracy and precision of thesecalibrations have not been published, so it is difficultfor researchers to assess the utility of these sensorsfor potential applications. Prior to the development ofTDR, field calibration required either destructivegravimetric sampling or the use of a neutron probe,which requires calibration of its own. Time domainreflectometry offers the possibility of nondestructive,uncalibrated 8 measurements for resistance sensor fieldcalibration. The objectives of this study were to de-termine the accuracy and precision of field-calibratedfiberglass resistance sensors using TDR, and to dem-onstrate the application of this approach to remotefield sites.

MATERIALS AND METHODSSite Description

Fiberglass sensor data was collected from a subwatershed inthe Reynold's Creek experimental watershed located in south-west Idaho. The site is at 1620-m elevation and has a meanannual precipitation of 361 mm, about one-half of which ar-rives as snow. The soil is classified as a fine-loamy, mont-morillonitic, frigid Typic Argixeroll. Some general features ofthe soil profile are a pronounced silt loam "cap" for the upper20 to 25 cm, which is underlain by a distinctive argillic horizon(40% clay). From the 60- to 90-cm depths there is a zone ofCaCO3 accumulation. Rock content increases with depth to amaximum at around 65-cm depth, where it is more than 50%by weight.

Measurements were made as a part of a study of surfaceAbbreviations: TDR, time domain reflectometry; 0, soil watercontent; PVC, polyvinyl chloride.

1432

SEYFRIED: SOIL WATER MONITORING WITH FIBERGLASS SENSORS 1433

runoff. A 2-m trench was dug immediately adjacent to a 3 by10 m runoff plot. Four vertical profiles of Colman1 fiberglassresistance sensors (Soiltest, Evanston, IL) and temperaturesensors, approximately 70 cm apart, were inserted into thetrench face at the following depths: 1, 10, 20, 40, 65, and 100cm. The temperature sensors used were YSI (Yellow SpringsInstruments, Yellow Springs, OH) two-thermistor compositethermolinear components accurate to ±0.15 °C between —30and 50 °C. These were connected to a CR10 datalogger(Campbell Scientific, Logan, UT) for monitoring. Measure-ments were stored each hour. A Trase TDK (Soilmoisture,Santa Barbara, CA) was used for 0 measurement. This systemused two parallel, 3-mm-diam. stainless steel rods placed 5cm apart. The TDR rods were placed adjacent to the fiberglasssensors 30 cm horizontally into the trench face at depths of10, 20, 40, and 100 cm. The 65-cm depth was omitted becauseis was impossible to insert the rods 30 cm through the rockyhorizon. The 1-cm depth was also omitted, due to the difficultyof placing and maintaining 30-cm-long horizontal rods so closeto the uneven soil surface. The TDR rods were bent at a 45°angle at the plot-trench interface and extended to the soil sur-face to permit access. That part of the rods from the trenchface to the surface was encased in PVC and filled with insu-lating foam. The design of the Trase TDR used was such thatthe rods had to come to the soil surface to access the balin.The trench was refilled after instrumentation. Additional 15-cm rods were placed vertically at the surface of each profile.The TDR readings were made at 1-wk intervals during thewinter and less frequently during the dry summer months.

CalibrationThe TDR-measured 0 was regressed against the log of the

fiberglass-sensor-measured resistance. The regression equationhad the form

Table 1. Regression parameters for calibration of fiberglasselectrical resistance soil moisture sensors.

7 = A + B x log(X) [1]where Y is the TDR-measured 6, A and B are constants, andX is the fiberglass-sensor-measured resistance (kfl). This ap-proach has been used by others (Colman and Hendrix, 1949;Palpant and Lull, 1953; Reynolds et al., 1987). An individualcalibration was established for each of the 16 individual sen-sors plus an overall calibration using data from all the sensors.The calibration data was collected between March 1990 andOctober 1991 resulting in sample sizes of about 31 per sensorand 500 overall. Sample sizes varied slightly between sensorsdue to instrumentation problems early in the study.

It was assumed in this analysis that the TDR-measured Bvalues were correct and all variability in measurement was dueto the resistance sensors. Laboratory column measurementsusing the manufacturer-supplied calibration (approximately equalto the Topp et al. [1980] calibration) were within ±0.009 m3

m~3 on average across a range in 0 of 0.06 to 0.41 m3 m~3.A linear temperature correction of — 0.0154 kfl/K for devia-tions from 273 K was used, which was determined from testsperformed at a 6 of 0.19 m3 m~3. Previous work has indicatedthat temperature corrections are not sensitive to soil type butcan vary with 6 (Colman and Hendrix, 1949). A single, inter-mediate 6 value was used because the temperature fluctuationswere relatively small and had a fairly small effect on the es-timated 6.

ApplicationsThree applications of the calibrated data were demonstrated:

(i) extrapolation of calibration statistics derived from a givenset of sensors to other, uncalibrated sensors in the field, (ii)temporal extrapolation from an initial calibration data set to

' Mention of manufacturers is for the convenience of the readeronly and implies no endorsement on the part of the author orUSDA.

Sensor

lOalOblOclOd20a20b20c20d40a40b40c40d

lOOalOOblOOclOOdAllH

B\m3 m"3 kft~'

-0.0520-0.0510-0.0423-0.0476-0.0542-0.0412-0.0602-0.0541-0.0513-0.0564-0.0661-0.0359-0.0330-0.0614-0.5900-0.0244-0.0435

A*m3m-3

0.30180.34780.28090.28250.30510.23520.33710.35500.34730.38980.44760.35110.32410.37270.34450.28110.3182

If

0.8270.8120.6460.8670.9170.7890.9380.9390.8500.8940.8810.5510.1520.2840.5920.0720.505

Range§m3 m"3

0.23260.21700.23060.22040.19780.17920.24850.19720.20730.20400.23630.20000.06910.07000.04500.06170.3624

t B in Eq. [1], the slope of the regression equation.i A in Eq. [1], the y intercept of the regression equation.§ The total range in water content (0) values observed for the sensor(s).H Result of calibration using all sensor data.

future measurements with the same sensors, and (iii) temporalinterpolation to estimate 0 between TDR readings.

The first application is for situations in which individualcalibration is not practical. Placement of sensors in rocky ho-rizons, near the soil surface, or in areas made difficult to accessdue to snow are some examples of conditions we have en-countered. It also allows a great reduction in effort since eachsensor does not require individual treatment. The response of1-cm-deep sensors, calibrated using the overall calibration, wasused to illustrate this application.

The second application is the more common calibration ap-proach where, once calibrated, the sensors are assumed to becorrect and monitored without further TDR measurement. TheTDR and sensor data collected in water-year 1992 (1 Oct.1991-1 Oct. 1992), using calibration statistics from measure-ments taken during the previous 18 mo, was used to illustratethis application.

The third application is for situations in which the site isvisited occasionally but with insufficient frequency to observemajor events of interest. Two critical issues for this applicationare the rapidity of sensor response and the temporal stabilityof the calibration. These issues are addressed using data fromthe previously described applications.

RESULTS AND DISCUSSIONCalibration Statistics

A total of 17 calibrations is reported, one for each ofthe 16 fiberglass sensor-TDR rod pairs, and one with allsensors combined (Table 1). A strong log-linear rela-tionship between the sensor-measured resistance and theTDR-measured B was observed. Calibration curves dis-play considerable scatter but little evidence of nonline-arity (Fig. 1-3). This is contrary to some previouslyreported calibrations (Colman and Hendrix, 1949; Pal-pant and Lull, 1953) but is consistent with others (Rein-hart, 1953; Reynolds et al., 1987).

Coefficient of determination values for the individualsensors range from excellent (for field calibrations) tovery poor (Table 1). The exceptionally low values forthe 100-cm-deep sensors probably resulted from the smallrange of 8 experienced at that depth so that there waslittle change in 0 across which to calibrate (Table 1).Other than at the 100-cm depth, no trends with depth

1434 SOIL SCI. SOC. AM. J., VOL. 57, NOVEMBER-DECEMBER 1993

0.40

0.30

0.20

0.10

0.00

20c' 9TOR

---- 80% C.I.Regression

0 1 2 3 4

Log Resistance (Kohm)

Fig. 1. Individual calibration curve for one year's dataillustrating a high R2 regression.

---- 80% C.I.Regression

0.001 2 3

Log Resistance (Kohm)

Fig. 3. Composite calibration curve including all resistanceand water content (0) measurements.

0.40 -

0.30 -cCD

j 0.20 r

0.10 -

0.00

20b' 9TDR

---- 80% C.I.—— Regression

2 3

Log Resistance (Kohm)

Fig. 2. Individual calibration curve for one year's dataillustrating a relatively low R2 regression.

12 0.35Average 1cm water contentPrecipitation

- 0.15 5

0.1012

Time (hr)

Fig. 4. Rainfall and 1-cm-deep fiberglass-sensor-estimated soil-water content (average of four) starting at 2000 h, 27 Sept.1991.

were observed, indicating that variability in calibrationstatistics was primarily the result of variability amongsensors and not soil variability. Particularly poor corre-lations were observed for Sites 40d and lOc. Sources ofdiscrepancy include: (i) differences in the sampling vol-ume between the TDR and the sensor, (ii) poor sensorresponse due to electronic or contact problems, and (iii)poor TDR measurements due to poor contact betweenthe rods and the soil. It is not clear which of these wasmost prevalent.

The 80% confidence intervals in Fig. 1 to 3 illustratethe precision obtained with the sensors. The 80% con-fidence interval, rather than the more commonly used 90or 95% confidence intervals, was used because of thehigh variability of measurement. For the individual cal-ibrations, the 80% confidence interval was between±0.025 and 0.04 m3 m~3 and was ±0.065 m3 m~3 forthe combined data.

Interestingly, the inclusion of measurements taken attemperatures below 0 °C in the calibration data set didnot alter the results. Patterson and Smith (1981) haveshown that the TDR measures the liquid (unfrozen) watercontent of frozen soils. An abrupt increase of resistancemeasured with electrical resistance sensors has been ob-served in previous studies (Colman and Hendrix, 1949;Harlan et al., 1971) but the correlation between liquidwater content and resistance has not been documented.

ApplicationsExtrapolation to Different Sensors

In general, it would be much preferable to use a singlecalibration for all sensors. This would entail less workand enable the use of sensors placed in locations that arevery difficult to calibrate, such as very rocky or shallowsoil horizons. Combinations of all sensors into a com-mon calibration curve resulted in a low R2 value andconsiderable scatter (Table 1, Fig. 3). This is consistentwith the relatively high 80% confidence interval of±0.065 m3 m~3. Others have also found a high degreeof variability among individual sensors (Palpant and Lull,1953; Collins, 1987). This lack of precision (the 95%confidence interval is almost 0.1 m3 m~3) renders theapplication of this calibration essentially qualitative innature. The methodology is much better, however, ifinterest is restricted to changes in 6. This is reasonablefor many hydrological applications related to water bal-ance. In that case, the 80% confidence interval arounda 0.10 m3 m~3 change in 6 is ±0.01 m3 m^3 and the95% confidence interval is about twice that.

The overall calibration has been applied to the 1-cm-deep sensors (Fig. 4). In this case, the cumulative rain-fall as measured at a rain gauge about 200 m from thesite on a chart recorder is compared with the average offour sensor measurements made hourly. As mentioned

SEYFRIED: SOIL WATER MONITORING WITH FIBERGLASS SENSORS 1435

0.40

0.30

0.20

0.10

0.00

0.30

E 0.20O

>§0.10

o(Ju 0.30-£"5* 0.20

' 0.10

0.00

0.30

0.20

0.10

0.00

10 cmi TDR

—— Block

20 cm

40 cm

N J F M A M J J A S O

Water Year (91-92)

Fig. 5. Comparison of average (n = 4) fiberglass-sensor-estimated and TDK-measured soil-water content for wateryear 1991 (1 Oct. 1991-1 30 Sept. 1992).

above, the actual water content is not accurately esti-mated but the estimated changes are much better. In thiscase, the initial and final values of 0.12 and 0.34 m3

m~3 are reasonable and the calculated difference of 0.22should be accurate within ±0.02 m3 m~3. Although theaccuracy is lower than other methods, this informationmay be useful in studies of water balance or where qual-itative knowledge of soil water dynamics is of interest.

Temporal ExtrapolationApplication of the calibration of 18 mo of data to the

following year's data is illustrated in Fig. 5. Plotted datarepresent the average of four TDR measurements on eachdate and the corresponding average sensor-estimated B.This data indicates, with few exceptions, reasonablequantitative agreement for individual measurements andexcellent qualitative agreement overall. Overall, 93% ofthe individual measurements were within ±0.5 m3 m~3,73% were within 0.025 m3 m-3, and 32% were within0.01 m3 m~3 of the TDR-measured value. Agreementwas slightly improved by the averaging used in Fig. 5.

Looking at the year for the 10-cm depth (Fig. 5), therewere two broad rises, one in November and the other inFebruary, followed by two more abrupt rises in May andJune. These rises are visible at 20 cm and all but theMay peak is evident at 40 cm. There was no apparentinfiltration to 100 cm. The first peak was the result offall rains. This was followed by freezing temperaturesfrom 12 Dec. 1990 to 1 Jan. 1991, which reduced the

liquid water content by about one-half. The second risewas the result of both soil thawing and snowmelt. Thesubsequent rises resulted from rainfall events. In all cases,there was reasonable agreement between the TDR- andsensor-measured 0.Interpolation

There are two main uses for this application: the cal-ibration drifts with time and the hydrologic events ofinterest occur more rapidly than the frequency of sam-pling. If the calibration drifts with time, it becomes nec-essary to update it. In the extreme case, the measurementand calibration data sets become the same. After 2.5 yrof measurement we have not observed substantial drift,although it has been observed by others in that time frame(England, 1965).

Evaluation of sensor response and equilibration timein general is complicated by the response and equil-ibration times of the soil itself, which is probably mostlimiting. Data presented in Fig. 4 give an indication ofsensor response rate during wetting, which is generallywhen 6 changes most rapidly. In that figure, rainfallbegan on about the fifth hour of measurement, or atabout 2400 h. No sensor response was evident after 1 hbut there had been only 0.5 mm of rain. By the sixthhour there had been about 2 mm of rain and a definitesensor response. Sensor response rose along with cu-mulative rainfall, but halted within 1 h of the end of therainfall. This indicates that the response time of the sen-sors is < 1 h.

CONCLUSIONSField calibration of fiberglass electrical resistance sen-

sors with TDR indicated a strong log-linear relationship.In general, use of individual calibrations resulted in rea-sonable accuracy and precision. Calibrations appliedequally well to above- and below-freezing soil temper-atures indicating that, like TDR, the fiberglass sensorsmeasure the liquid (unfrozen) water content. There isconsiderable variability among calibration statistics ofindividual sensors, making individual calibration pref-erable, although some useful information about changesin 6 may be obtained from averaged statistics. The re-sponse time of the sensors was more rapid than the 1-hmeasurement interval, indicating that good temporal res-olution was obtained. The small size of the sensors, com-bined with modem data acquisition systems, permits hightemporal and spatial resolution of changes in 0. This canbe useful in studies of variable soil hydrologic processessuch as root uptake of soil water, infiltration, andgroundwater recharge.

ACKNOWLEDGMENTSI express my gratitude to Mr. Mark Murdock and Dr. Gerald

Flerchinger for their efforts on this project. Mark was therefor the pick and shovel work, which was considerable, andhas spent many hours interpreting TDR graphs. Gerald hasbeen an advisor in both the design and monitoring phases ofthe project. They both have spent many hours braving wind,rain, and snow with me to collect the data presented.

1436 SOIL SCI. SOC. AM. J., VOL. 57, NOVEMBER-DECEMBER 1993