Matric Potential Sensors Research Articles

Soybean yield response to managed depletion irrigation regimes in a Mid-South silt loam soil

Soybean irrigation experiments were conducted for five years (2013–2017) on silt loam soil in Jackson, TN, a sub-humid region with high and variable precipitation. The purpose of the experiment was to investigate managed depletion irrigation (MDI) regimes in soybean to obtain the highest yield with minimal water applied on high water-holding capacity soil. Another goal was to find the best use of soil matric potential sensors and water balances to achieve MDI goals. Irrigation supplemented rainfall at a rate of 1.27, 2.54, and 3.81 cm wk−1 starting from first bloom (R1), first pod (R3), and first seed (R5) stages and these treatments were compared against a rainfed control. The results lead to the recommendation that delaying irrigation to R5 in most years and R3 in dry years produced yields at the highest significance level. The connection between irrigation rate and yield fluctuates yearly, but generally, higher rates result in greater crop yields, necessitating a high irrigation rate and rainfall for managed depletion after initiation. The water balance method best emulated optimum MDI results when irrigation was delayed until the soil moisture depletion reached the management allowable depletion (MAD) and then was followed by irrigation that maintained soil water at the MAD level. The irrigation trigger points were initially lowered for matric potential sensors to avoid excess watering in early growth, transitioning to higher levels for optimal yield later in the R3 stage. These results may be applicable in other regions with high water holding capacity soil and moderate to high rainfall rates during the growing season.

Effective irrigation scheduling to improve corn yield, net returns, and water use

AbstractIrrigated cropland continues to increase in Tennessee to sustain yield when seasonal temperatures are high and water deficit occurs. Corn (Zea mays L.) production is directly related to water availability. Inefficient irrigation scheduling can result in excessive or inadequate water applications. The objective of this study was to optimize profitable corn yield using soil water sensors to improve irrigation scheduling. Three irrigation regimes plus a rainfed/check were evaluated in a field study at the University of Tennessee Milan Research and Education Center in 2020 and 2021. Soil water level was monitored with Meter Inc. Teros 21 matric potential sensors and ZL6 soil‐water loggers. Irrigation‐scheduling decisions were based on rainfall, plant growth stages, and soil water sensors. An optimal irrigation schedule was determined within this study that increased yield by 13.0% and 3.0%, respectively, in 2020 and 2021 while also having the lowest cost among irrigation treatments. Results indicate that irrigation scheduling can improve yield, conserve water, and increase economic returns.

In-situ assessment of soil shrinkage and swelling behavior and hydro-thermal regimes with a thermo-time domain reflectometry technique

The shrinkage and swelling behaviors of clayey soils could lead to cracks and erosions inducing severe damages to agricultural lands and engineering projects. Thus, improving the understanding of shrink-swell characteristics is crucial to nonrigid soils. However, it remains difficult for in-situ monitoring of soil shrink-swell processes continuously and nondestructively. The objective of this study was to present a thermo-time domain reflectometry (thermo-TDR) technique for determination of soil shrinkage and swelling dynamics and hydro-thermal regimes in-situ during wet-dry cycles. A field experiment on two clayey soils (a paddy soil and an alluvial soil) was conducted to evaluate the performance of the thermo-TDR method for in-situ determination of soil volume changes and associated hydraulic and thermal properties during wet-dry cycles. The results showed that both soils experienced considerable shrinking and swelling processes with maximum vertical deformations of 2.4 and 1.8 cm for the paddy soil and alluvial soil, respectively, and with maximum horizontal deformations of 370 and 280 cm2 for the paddy soil and alluvial soil, respectively, during a 36-day period. The thermo-TDR method provided volume change values (shown as the bulk density) agreed well with independent core-sampling measurements with an average RMSE of 0.15 Mg m−3 and a R2 of 0.46. A better agreement was observed between thermo-TDR measured bulk densities and values from the ruler-image processing method with an average RMSE of 0.07 Mg m−3 and a R2 of 0.77. Besides, the thermo-TDR method gave consistent in-situ soil shrinkage curve and water retention surface measurements by combining with soil matric potential sensors. Unlike rigid soils, the paddy and alluvial soils showed complicated hydro-thermal regimes in response to the shrink-swell processes. We conclude that the thermo-TDR technique is a reliable tool for continuous in-situ determination of soil shrink-swell processes. Future studies should focus on development of comprehensive soil heat and water transport models accounting for both moisture content and volume changes.

LoRaWAN based internet of things (IoT) system for precision irrigation in plasticulture fresh-market tomato

Precision irrigation with sensors has proven to be effective for water saving in crop production. An Internet of Things (IoT) system is necessary for monitoring real-time data from sensors and automating irrigation systems. Long-range wide-area network (LoRaWAN), a type of low-power wide-area network (LPWAN), is a low-cost and easily implemented IoT system which can be used for precision crop irrigation. In this study, a LoRaWAN based IoT system was developed and evaluated as a precision irrigation tool on fresh-market tomato production in a plasticulture system. Four irrigation scheduling treatments were designed and tested, including irrigation based on crop evapotranspiration (ET), soil matric potential sensors (Watermark 200SS-5) at -60 kPa (MP60) or -40 kPa (MP40), and GesCoN fertigation decision support system (DSS). The treatments were arranged based on a randomized complete block design with four replicates. System feasibility, irrigation water use efficiency (iWUE), and crop yield were evaluated. Throughout the season, the overall water use efficiencies were 22.2, 26.5, 27.9, and 28.4 kg·m−3 for ET, MP60, MP40, and GesCoN, respectively. The results indicated that treatment MP60 and GesCoN had 15.2 and 22.1% higher marketable fruit yield than ET, while, MP40 had 12.5% lower marketable yield compared to ET. Overall, the LoRaWAN-based IoT system performed well in terms of power consumption, communication, sensor reading, and valve control. These results suggested that the IoT system can be implemented for precision and automatic irrigation operations for vegetable and other horticultural crops with enhancing crop's water use efficiency and sustainability.

Soil suction and temperature measurements in a heavy haul railway formation

Transportation foundations around the world exist predominantly in an unsaturated state. Support for the adoption of unsaturated soil mechanics in routine geotechnical engineering is ever-increasing. However, the measurement and characterization of soil suction is a potentially significant barrier thereto. To this end, the efficacy of various measurement techniques was investigated, with the aim to characterize the temporal variation in soil suction and temperature within the formation layers of a heavy haul railway formation. Tensiometers, fixed-matrix soil-water matric potential sensors and capacitance-type volumetric water content sensors were installed within the formation layers of a railway track and monitored for a period of 18 months. The average suction measured over the observational period was 25.6, 12.7, 3.5 and 4.2 kPa for the special subballast, subballast and two subgrade layers respectively. Suctions reached a minimum of ≈0 kPa at the end of the rainfall season across all layers and reached a maximum, ranging between 10 and 60 kPa across all layers at the end of the dry season. Soil temperatures for the observational period varied between 10 and 22 °C and displayed seasonality in-phase with the monitored air temperatures. The special subballast layer (uppermost layer) showed the greatest variation in both suction and temperature and is the layer which experienced the greatest stress changes as a result of seasonal variations in rainfall and air temperature.

Determination of irrigation scheduling thresholds based on HYDRUS-1D simulations of field capacity for multilayered agronomic soils in Alabama, USA

The use of soil matric potential (ψ) sensors to support irrigation decisions has become common practice among many producers. However, ψ values at which irrigation should be initiated (ψlim) based on a pre-defined irrigation depth is still lacking. The main objectives of this study were: (i) evaluate the impact of different negligible drainage flux on estimated ψ at field capacity (ψfc) using HYDRUS 1D simulations; (ii) identify ψlim values and its corresponded irrigation depth under different soil profile depth at representative soil types in Northwest and Southeast Alabama. The ψ-θ relation at field capacity (ψfc and θfc, respectively) were estimated by a numerical internal drainage flux experiment for multilayered soils using HYDRUS-1D software simulations. Among the different negligible drainage fluxes (qfc) tested, a qfc value of 0.01 and 0.025 cm d−1 yielded the best results for the soil located at Northwest and Southeast Alabama, respectively. For a soil water depletion of 35% and a soil profile depth of 0.6 m, the ψlim ranged from − 103 to − 133 kPa for the soils located at Northwest Alabama and − 38 to − 51 kPa for soils located in Southeast Alabama. It returned an irrigation depth varying from 20 to 24 mm for Northwest Alabama soils and 15–33 mm for Southeast Alabama soils. For a same irrigation depth, it was observed that the ψlim increased (became less negative) as soil profile depth considered for irrigation calculations increased. Additionally, if the same pre-defined irrigation depth is used during the entire growing season, there is a high change that plants could be under stress due to the high level of water deficit. Using the same irrigation depth during the entire crop growth season could be a flawed irrigation management strategy; therefore, irrigation depth should dynamically change over the growth season as the plant roots reach deeper soil layers.

Low-Cost Control and Measurement Circuit for the Implementation of Single Element Heat Dissipation Soil Water Matric Potential Sensor Based on a SnSe2 Thermosensitive Resistor.

A low-cost signal processing circuit developed to measure and drive a heat dissipation soil matric potential sensor based on a single thermosensitive resistor is demonstrated. The has a high thermal coefficient, from C in the 20 to 25 C to C in the 20 to 25 C. The thermosensitive resistor is encapsulated with a porous gypsum block and is used as both the heating and temperature sensing element. To control the power dissipated on the thermosensitive resistor and keep it constant during the heat pulse, a mixed analogue/digital circuit is used. The developed control circuit is able to maintain the dissipated power at mW when the resistor changes from to . When the gravimetric water content of the porous block changes from dry to saturated (), we measured a variation of in the thermosensitive resistor, which results in an end-point sensitivity of 130 m/%. The developed system can easily meet the standard requirement of measuring the gravimetric soil water content with a resolution of approximately , since the resistance is measured with a resolution of approximately , three orders of magnitude smaller than the sensitivity.

Potato Varieties Response to Soil Matric Potential Based Irrigation

Potato is one of the most cropped plants worldwide. Hundreds of different varieties are cultivated only in North America. Potato growers usually crop multiple varieties on their farms to answer the market demands for potato’s specific physical properties. However, few pieces of information are available regarding the optimal management of irrigation across potato varieties. Knowing that modern potatoes share genetics similarities, the optimal irrigation comfort zone for the potato crop might be the same for different groups of varieties. This study evaluates the effect of precision irrigation thresholds on the potato yields of three varieties (Envol: very early, Kalmia: early, and Red Maria: mid-late) with different maturity classes. In a greenhouse, a soil matric potential sensor network used in combination with a precise irrigation system allows the identification of a common optimal precision irrigation threshold, allowing optimal yields for the three varieties. This paper presents the first identification of an optimal irrigation threshold, −15 kPa, shared by different potato varieties. The optimal irrigation threshold identified in this study is not dependent on the maturity class, plant height or tuber potential production. The determination of an optimal precision irrigation threshold will allow potato growers to adapt their farm management processes to integrate more sustainable water management practices as they will be able to irrigate a field with multiple varieties with the same threshold.

Integrating Invasive and Non-invasive Monitoring Sensors to Detect Field-Scale Soil Hydrological Behavior

The Critical Zone Observatory (CZO) in the Alento River Catchment (ARC; southern Italy) has been collecting data for some decades and recently became part of the TERENO (TERrestrial ENvironmental Observatories) long-term ecosystem infrastructure network. In 2016, cosmic-ray neutron probes (CRNP, non-invasive proximal sensors) together with SoilNet wireless sensor networks (invasive ground-based sensors) were installed in two sub-catchments (MFC2 and GOR1), characterized by different weather, topographic, pedological, and land-use conditions. The SoilNet sensors are measuring soil apparent permittivity (converted into soil moisture), electrical conductivity, and temperature at two soil depths of 0.15 m and 0.30 m in 20 locations around the CRNP. The SoilNet sensor network includes also soil matric pressure potential sensors at the same soil depths and locations. A major aim of this study is to compare the hydrological response of the two experimental sites to climate forcings (rainfall minus potential evapotranspiration, hereby defined as rainfall deficit) through the use of the monthly Soil Moisture Index (SMI) computed from daily values of soil moisture measured by the CRNPs. For both sites, strong correlations are detected between monthly rainfall deficit and SMI, even if the cropland site (MFC2) is characterized by more extreme dry and wet conditions. For these two different sites, our investigations also aim at identifying the dominant controls governing the seasonal dynamics of spatial soil moisture patterns. The majority of the spatial variance in the cropland site is explained by terrain attributes, under both wet and dry conditions, whereas the second-order statistical moment of soil moisture in the forested site is mostly explained by topographic factors under wet conditions during the rainy season. For both sites, our data show that soil texture exerts a minimal impact on the spatial variations of soil moisture.

Infiltration efficiency and subsurface water processes of a sustainable drainage system and consequences to flood management

AbstractWith increased intensity rainfall events globally and urban expansion decreasing permeable surfaces, there is an increasing problem of urban flooding. This study aims to better understand rainfall infiltration into a Sustainable Drainage System (SuDS) permeable pavement, compared with an adjacent Green Area of made ground, in relationship to groundwater levels below both areas. Both areas were instrumented with soil water content and matric potential sensors and four shallow boreholes were instrumented with groundwater level sensors. Surface infiltration rates were measured using a double‐ring infiltrometer. Results showed that average infiltration rates of the SuDS (1,925 mm/hr) were significantly higher than the Green Area (56 mm/hr). The SuDS was well designed to transfer rainfall rapidly to the aquifer below, where groundwater levels rapidly rose within 1 hr of a 1 in 30 year event (32.8 mm/hr). In comparison, soil compaction of the made ground Green Area decreased infiltration rates, but still enabled the majority of rainfall events to infiltrate. The aquifer below the Green Area responded more slowly, as lower matrix potentials facilitated water retention in the soil profile, slowing water draining to the aquifer. This work reiterates the importance of ensuring a 1 m separation depth between the base of the SuDS infiltration zone and aquifer depth.

Canopy thermal response to water deficit of coffee plants under drip irrigation

AbstractTo quantify water stress on coffee trees, leaf water potential (LWP) measurements are commonly performed in a Scholander chamber; however, this methodology is restricted to research activities, due to the difficulty of measurement in the field: sampling hours at predawn and intensive work. A crop water stress index (CWSI) based on leaf temperature has been used as indicative of LWP in several crops, and thermal images can be a precious tool for mapping LWP under intensive drip‐irrigated coffee areas, in order to adjust irrigation requirements according to soil moisture spatial variability. This study aims to define the relationships between the water potential of coffee leaf based on CWSI, calculated by means of a portable infrared sensor and a thermal camera, analysing correlation, precision and accuracy indexes. The study was conducted in Piracicaba, SP, Brazil, using Coffea arabica species of the Catuaí Red variety IAC 144 that was drip irrigated using tensiometers as matric potential sensors. The observed CWSI and LWP presented good correlations in the linear and cubic models. It was concluded that the CWSI is a good indicator of the water potential of a coffee canopy and can be an alternative to measurements of LWP from the Scholander chamber. © 2020 John Wiley & Sons, Ltd.

Evaluation of Two Recurrent Neural Network Methods for Prediction of Irrigation Rate and Timing

HighlightsNARX and LSTM recurrent neural networks were evaluated for prediction of irrigation prescriptions.LSTM neural networks presented the best performance for irrigation scheduling using soil matric potential sensors.NARX neural networks had the best performance for predicting irrigation prescriptions using weather data.High performance for several time-ahead predictions using both recurrent neural networks, with R2 > 0.94.The results can be adopted as a decision-support tool in irrigation scheduling for fields with different types of soils.Abstract. The implementation of adequate irrigation strategies could be done through real-time monitoring of soil water status at several soil depths; however, this could also represent a complex nonlinear problem due to the plant-soil-weather relationships. In this study, two recurrent neural network (RNN) models were evaluated to estimate irrigation prescriptions. Data for this study were collected from an on-farm corn irrigation study conducted between 2017 and 2019 in Samson, Alabama. The study used hourly data of weather and soil matric potential (SMP) monitored at three soil depths from 13 sensor probes installed on a loamy fine sand soil and a sandy clay loam soil. Two neural network methods, i.e., a nonlinear autoregressive with exogenous (NARX) input system and long short-term memory (LSTM), were trained, validated, and tested with a maximum dataset of 20,052 records and a maximum of eight categorical attributes to estimate one-step irrigation prescriptions. The performance of both methods was evaluated by varying the model development parameters (neurons or blocks, dropout, and epochs) and determining their impact on the final model prediction. Results showed that both RNN models demonstrated good capability in the prediction of irrigation prescriptions for the soil types studied, with a coefficient of determination (R2) > 0.94 and root mean square error (RMSE) < 1.2 mm. The results of this study indicate that after training the RNNs using the dataset collected in the field, models using only SMP sensors at three soil depths obtained the best performance, followed by models that used only data of solar radiation, temperature, and relative humidity in the prediction of irrigation prescriptions. For future applicability, the RNN models can be extended using datasets from other places for training, which would allow the adoption of a unique data-driven soil moisture model for irrigation scheduling useful in a wide range of soil types. Keywords: Corn, Irrigation scheduling, Machine learning, Modeling, Soil matric potential sensor.

A Novel Frequency Domain Impedance Sensor with a Perforated Cylinder Coaxial Design for In-Situ Measuring Soil Matric Potential.

Soil matric potential is an important parameter for agricultural and environmental research and applications. In this study, we developed a novel sensor to determine fast and in-situ the soil matric potential. The probe of the soil matric potential sensor comprises a perforated coaxial stainless steel cylinder filled with a porous material (gypsum). With a pre-determined gypsum water retention curve, the probe can determine the gypsum matric potential through measuring its water content. The matric potential of soil surrounding the probe is inferred by the reading of the sensor after the soil reaches a hydraulic equilibrium with the gypsum. The sensor was calibrated by determining the gypsum water retention curve using a pressure plate method and tested in three soil samples with different textures. The results showed that the novel sensor can determine the water retention curves of the three soil samples from saturated to dry when combined with a soil water content sensor. The novel sensor can respond fast to the changes of the soil matric potential due to its small volume. Future research could explore the application for agriculture field crop irrigation.

Vadose Zone Modeling in a Small Forested Catchment: Impact of Water Pressure Head Sampling Frequency on 1D-Model Calibration

The characterization of vadose zone processes is a primary goal for understanding, predicting, and managing water resources. In this study, the issue of soil water monitoring on a vertical profile in the small forested Strengbach catchment (France) is investigated using numerical modeling with the long-term sequences 1D-Richards’ equation and parameter estimation through an inverse technique. Three matric potential sensors produce the observation data, and the meteorological data is monitored using an automatic weather station. The scientific questions address the selection of the calibration sequence, the initial starting point for inverse optimization and monitoring frequency used in the inverse procedure. As expected, our results show that the highly variable data period used for the calibration provides better estimations when simulating the long-term sequence. For the starting point of the initial parameters, handmade iterative initial parameters estimation leads to better results than a laboratory analysis or set of ROSETTA parameters. Concerning the frequency of monitoring, weekly and daily datasets provide efficient results compared to hourly data. As reported in other articles, the accuracy of the boundary conditions is important for estimating soil hydraulic parameters and accessing water stored in the layered profile.

Full-Scale Hydro-Mechanical Properties of Vertical Flow Bed Treatment Soils

A 15.25 m wide by 30.50 m long by 1.80 m thick, soil test section (test pad) was constructed to determine the hydraulic conductivity of a proof-of-concept vertical flow bed wetland water treatment system. Specifically, in situ and laboratory hydraulic conductivity tests were performed to confirm if the hydraulic conductivity of the treatment soil within the wetland was equal to or greater than the design requirement of 3.33 × 10−5 cm/s. The in situ hydraulic conductivity measurements were obtained by performing a sealed double ring infiltrometer tests and multiple two-stage borehole infiltration tests. Because of the soil conditions and the implementation of in situ sensors, these two tests were modified. Three types of in situ instrumentation including the time domain reflectrometry, heat dissipation water matric potential sensors, and irrometer tensiometers, were installed into the soil to monitor the movement of the water by measuring the changes of water content and suction as water flow through the soil. In addition, four flexible wall permeameter tests were conducted on Shelby-tube samples to validate the field hydraulic conductivity measurements. A comparison between field and laboratory testing results was completed. The laboratory hydraulic conductivity values were significantly lower than in situ hydraulic conductivity values, and also lower than the design requirement value. The in situ tests results met the 3.33 × 10−5 cm/s requirement, with the exception of a few of the values that were obtained from a borehole where highly plasticity clay was observed.

Water Infiltration into a New Three-Layer Landfill Cover System

AbstractOne of the main purposes of a landfill cover system is to minimize the migration of water into waste, known as percolation, and thereby reduce excessive leachate production. One possible way to achieve this goal is to use a two-layer cover with capillary barrier effects (CCBEs) for arid and semi-arid regions. For a humid climate or prolonged rainfall, the two-layer system with CCBEs is expected to lose its effectiveness for minimizing water percolation. A new three-layer landfill cover system is proposed and investigated for humid climates. This new system adds a fine-grained soil (i.e., clay) underneath a two-layer barrier with CCBE (i.e., a silt layer overlying a gravelly sand layer). The study is conducted by carrying out a one-dimensional (1D) water infiltration test in a soil column. The soil column was instrumented with tensiometers, heat dissipation matric potential sensors, and moisture probes to monitor the variations of pore-water pressure and water content with depth. The amount of wate...

Soil water monitoring in a vineyard and assessment of unsaturated hydraulic parameters as thresholds for irrigation management

Monitoring soil water status is a well-known method to efficiently control irrigation in order to optimally meet plant water requirements and at the same time avoid unproductive water losses through deep percolation. A common approach is to keep soil water status within a certain range that is defined via soil-specific unsaturated hydraulic parameters. In this study, water content and matric potential were monitored in a soil profile in a vineyard. The soil hydraulic properties required for irrigation control were determined by water retention analyses using a pressure plate apparatus, and estimated by means of pedotransfer functions. While the soil matric potential sensors delivered calibrated absolute values, their range was limited and soil water dynamics were not always reflected properly. The soil water content probe, on the other hand, properly illustrated soil water dynamics, but the readings were possibly inaccurate as no onsite calibration was executed. Furthermore, the determined unsaturated hydraulic parameters differed considerably depending on the applied method. Alternatively, a modified approach was applied. It was based on measurements of a sensor pair in a representative depth and should combine the advantages of both sensors types. The respective thresholds for irrigation management were determined based on sensor data using in-situ soil water retention functions. The main advantages were that neither field calibration of soil water content sensors nor laborious soil analyses were required. Furthermore, data interpretation was more plausible compared to the standard approach. Due to the reduced sensor setup and the omitted soil sampling and analyses, the modified approach represented a practical and economical alternative as basis for irrigation control.

Evaluation of Measurement Methodologies Used for Establishing Water Retention Characteristic Curve of Fly Ash

AbstractFly ash (FA) has potential applications in geotechnical and geoenvironmental projects, where its water retention characteristic curve (WRCC) is important. The main objective of this study was to establish the inherent range of measured suction of FAs using four different tools: tensiometer (TM), matric potential sensor, equitensiometer (EQT), and WP4 dew point potentiameter with a varying measurement range. Based on the measured results, the variability of WRCC equation parameters of four FAs collected from different thermal power plants were studied. Further, the sensitivity of a different range of measured suction on WRCC equation parameters of FA was investigated. The study indicated that the possible range of measurable suction for FA is 1 to 1000 kPa, which can be measured conveniently with an EQT. The parameters of FA were not influenced by high suction measurements (>1000 kPa) of WP4. The suction data (>1000 kPa) obtained using the WP4 can be appended with a low-suction-range instrument such as a TM for a better representation of the WRCC of FA. However, the study indicated that WRCC parameterization of FA is influenced more by the low range of suction.

11 Years of Evapotranspiration Cover Performance at the AA Leach Pad at Barrick Goldstrike Mines

The AA Leach Pad at Barrick Goldstrike Mine was reclaimed using an evapotranspiration (ET) cover designed to limit the infiltration of precipitation into the facility. Water content and matric potential sensor stations were installed in the cover and underlying leach material after cover system placement. Monitoring of the sensor nests continued for 11 years. Data indicates that the cover is performing well, limiting net percolation to less than 1 % of annual precipitation. The AA Leach Pad is the first large-scale closed mine waste facility that has been robustly monitored for a relatively long time in Nevada, USA. The results provide an understanding of ET cover system performance for closure of mine waste facilities and offer guidance for ET cover system requirements in other arid regions.

Performance of Hydra Probe and MPS-1 Soil Water Sensors in Topsoil Tested in Lab and Field

Soil water sensors are commonly used to monitor water content and matric potential in order to study hydrological processes such as evaporation. Finding a proper sensor is sometimes difficult, especially for measurements in topsoil, where changes of temperature and soil water dynamics occur generally with greater intensity compared to deeper soil layers. We assessed the perfor-mance of Hydra Probe water content sensors and MPS-1 matric potential sensors in topsoil in the laboratory and in the field. A common soil-specific calibration function was determined for the Hydra Probes. Measurement accuracy and sensor-to-sensor variation were within the manufacturer specification of ±0.03 m3·m-3. Hydra Probes can operate from dry to saturated conditions. Sensor-specific calibrations from a previous study were used to reduce sensor-to-sensor variation of MPS-1. Measurement accuracy can be expressed by a mean relative error of 10%. According to the manufacturer, the application range of matric potential readings is from -10 kPa to -500 kPa. MPS-1 delivered also values beyond this range, but they were not reliable. Sensor electronics of the MPS-1 were sensitive to ambient temperature changes. Beyond instrument effects, field measurements showed substantial temperature-driven fluctuations of soil water content and matric potential, which complicated data interpretation.