Calibration and validation of soil water reflectometers

Patrignani, Andres; Ochsner, Tyson; Feng, Li; Dyer, Don Wesley; Rossini, Pedro R.

doi:10.1002/vzj2.20190

Cited by 12 publications

(15 citation statements)

References 57 publications

(123 reference statements)

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“…(1980), (b) calibration Equation (Ledieu et al., 1986), (c) calibration Equation (Evett et al., 2005), and (d) calibration Equation (Kargas & Soulis, 2019). All coefficients used in implementing Equations , and were derived from a separate laboratory sensor calibration in an independent study (Patrignani et al., 2022). The dashed line represents the 1:1 line…”

Section: Resultsmentioning

confidence: 99%

“…However, it is well known that the Topp model overestimates volumetric water content when used with soil water reflectometers in fine‐textured soils at moderate‐to‐high volumetric water contents (Caldwell et al., 2018; Evett et al., 2005; Kargas & Soulis, 2019). A recent laboratory sensor calibration study spanning nine soil textural classes collected across Kansas and Oklahoma shows that alternative univariate and multivariate linear models that account for bulk electrical conductivity and temperature effects can result in improved accuracy of soil water reflectometers (Patrignani et al., 2022). The authors suggested that these alternative linear models may be acceptable for network‐wide calibration of soil water reflectometers without the need for laborious and time‐consuming site‐specific sensor calibrations.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, in this study, we compared the reflectometer readings at the time of soil sampling using the factory default function and the linear models used in the study by Patrignani et al. (2022) against the observed volumetric water content of the soil samples determined by the thermogravimetric method in our soil physical property database (average of both cores). The simplest univariate linear model is that proposed by Ledieu et al.…”

Section: Methodsmentioning

confidence: 99%

“…The authors suggested that these alternative linear models may be acceptable for network-wide calibration of soil water reflectometers without the need for laborious and time-consuming site-specific sensor calibrations. Thus, in this study, we compared the reflectometer readings at the time of soil sampling using the factory default function and the linear models used in the study by Patrignani et al (2022) against the observed volumetric water content of the soil samples determined by the thermogravimetric method in our soil physical property database (average of both cores). The simplest univariate linear model is that proposed by Ledieu et al (1986) (Equation 4):…”

Section: In Situ Validation Of Soil Water Contentmentioning

confidence: 99%

“…The objective of this study was to develop a site‐specific database of soil physical properties for the stations of the Kansas Mesonet equipped with soil moisture sensors. To illustrate an example application of the database presented herein, we conducted a network‐wide validation of the soil moisture sensors using sensor calibration models that were developed for a wide range of soil textural classes in a previous study (Patrignani et al., 2022). To emphasize the importance of developing a site‐specific soil physical properties database for in situ networks, we performed a networkwide evaluation between a set of selected properties from our in situ database and a commonly used gridded soil property database.…”

Section: Introductionmentioning

confidence: 99%

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A database of soil physical properties for the Kansas Mesonet

Parker

Kluitenberg

Redmond

et al. 2022

Soil Science Soc of Amer J

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This study developed a comprehensive database of soil physical properties to expand the potential applications of soil moisture observations across the Kansas Mesonet environmental monitoring network. The database comprises 14 site-and depthspecific soil hydraulic properties for 40 stations monitoring soil water content. The measured soil hydraulic properties include sand, clay, and silt contents; bulk density; particle density; total porosity; effective saturation; saturated hydraulic conductivity; and water retention at six matric potentials. The soil database spans eight soil textural classes, with silty clay loam, silt loam, and silty clay soils dominating the fine-textured soils, and sandy loam and sandy clay loam soils comprising the only coarse-textured soils. All the measured soil hydraulic properties showed low coefficient of variation (CV ≤10%) within soil textural classes, except for the saturated hydraulic conductivity, which varied from a median of 0.520 ± 308 cm d -1 in the clay soils to 47.1 ± 119 cm d -1 in the sandy loam soils. The uncertainty of the Kansas Mesonet soil water content observations was reduced by at least 29% using laboratory sensor calibration equations instead of the factory default sensor equation. Our soil physical property database offers new prospects to use historical and near realtime in situ soil water content observations across the Kansas Mesonet for diverse agricultural and hydrological applications, including drought monitoring, wildfire preparedness, and estimating potential drainage and groundwater recharge rates. INTRODUCTIONThe Kansas Mesonet is a mesoscale environmental monitoring network that was established in 1986 by Kansas State University Research and Extension, and currently consists of 75 automated stations distributed across the state of Kansas over an area of 213,000 km 2 . Each station is instrumented with research-grade sensors that record atmospheric (i.e., precipitation, air temperature, relative humidity, wind speed, solar radiation) and soil variables (i.e., soil moisture, soil Abbreviations: MBE, mean bias error; PAWC, plant available water capacity; RMSD, root mean square difference.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: In Situ Validation Of Soil Water Contentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A database of soil physical properties for the Kansas Mesonet

Parker

Kluitenberg

Redmond

et al. 2022

Soil Science Soc of Amer J

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A high‐resolution monitoring station for the in situ assessment of nitrate‐related redox processes at an agricultural site

et al. 2022

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Biogeochemical redox processes control the chemical behavior of many major and trace elements, making their comprehension crucial for predicting and protecting environmental health. Nitrogen (N) is especially susceptible to changes in soil redox conditions and affects the cycles of other redox‐sensitive species. Elevated N concentrations, in nitrate form, in agricultural soils and associated freshwater ecosystems constitute a problem in many parts of the world. Although a wide variety of measures have been adopted, their assessment through concentration measurements in groundwater and surface water of the different monitoring networks has shortcomings. Nitrate, as a non‐point pollutant, is subject to several processes (e.g., transformation and retardation) before it is detected, making it impossible to evaluate measurements’ effectiveness reliably. Thus, we designed and constructed a monitoring station featuring commercially available products and self‐manufactured components at an agricultural site for the in situ assessment of nitrate‐related processes by high‐resolution monitoring of hydraulic (soil water content, matric potential, groundwater head) and hydrogeochemical variables (oxidation‐reduction potential and groundwater and pore water chemistry) within the vadose zone and the shallow aquifer. The monitoring station has proven to be a reliable tool. Changes over depth and time of measured variables have been identified, allowing the detection of the transient behavior of the redox reactive zone and the interpretation of ongoing denitrification processes and other redox nitrate‐triggered phenomena, such as uranium roll‐front and selenium accumulation at the redox interface. Measuring both geochemical and soil water variables allows for the calculation of in situ solute inputs into the groundwater and their reaction rates.

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Calibration of TEROS 10 and TEROS 12 electromagnetic soil moisture sensors

Cominelli,

Rivera,

Brown

et al. 2024

Soil Science Soc of Amer J

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The TEROS 10 and TEROS 12 are widely used capacitance‐based sensors for measuring volumetric water content (), but few systematic studies of the measurement volume and accuracy of these sensors have been published. The objectives of this study were to (1) calibrate and validate these sensors in mineral soils, (2) determine the sensing volume of each sensor, and (3) evaluate the temperature sensitivity of the sensors. Both sensors were calibrated in the laboratory using columns of packed soil at multiple moisture levels from air‐dry to near saturation. Sensors were validated using additional laboratory and field measurements. The sensing volume was determined by quantifying the response of raw sensor outputs while increasing the level of oven‐dry sand, moist sand (0.100 cm3 cm−3), or water around the sensor in the radial and axial directions. Temperature sensitivity was tested in controlled conditions over a range of temperatures from 2°C to 40°C using encapsulated sensors in packed soil columns at constant . Linear calibration models resulted in mean absolute error ≤0.030 cm3 cm−3 for both sensors during laboratory and field validations. In moist sand, the sensing volume contributing 95% of the maximum sensor response was 439 cm3 for the TEROS 10 and 423 cm3 for the TEROS 12. Both sensors exhibited changes in of ≤0.02 cm3 cm−3 when subjected to a temperature change from 2°C to 40°C. For the soils considered here, both sensors demonstrated accuracy that is likely sufficient for a variety of agricultural and hydrological applications.

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Calibration and validation of soil water reflectometers

Cited by 12 publications

References 57 publications

A database of soil physical properties for the Kansas Mesonet

A database of soil physical properties for the Kansas Mesonet

A high‐resolution monitoring station for the in situ assessment of nitrate‐related redox processes at an agricultural site

Calibration of TEROS 10 and TEROS 12 electromagnetic soil moisture sensors

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