Optimizing the Positioning of Soil Moisture Monitoring Sensors in Winter Wheat Fields

Collecting accurate real-time soil moisture data in crop root zones is the foundation of automated precision irrigation systems. Soil moisture sensors (SMSs) have been used to monitor soil water content (SWC) in crop fields for a long time; however, there is no generally accepted guideline for determining optimal number and placement of soil moisture sensors in the soil profile. In order to study adequate positioning for the installation of soil moisture sensors in the soil profile, six years of field experiments were carried out in North China Plain (NCP). Soil water content was measured using the gravimetric method every 7 to 10 days during six growing seasons of winter wheat (Triticum aestivum L), and root distribution was measured using a soil core method during the key periods of winter wheat growth. The results from the experimental data analysis show that SWC at different depths had a high linear correlation. In addition, the values of correlation coefficients decreased with increasing soil depth; the coefficient of variation (CV) of SWC was higher in the surface layers than in the deeper layers (depths were 0–40 cm, 0–60 cm, and 0–100 cm during the early, middle, and last stages of winter wheat, respectively); wheat roots were mainly distributed in the surface layer. According to an analysis of CV for SWC and root distribution, the depths of planned wetted layers were determined to be 0–40 cm, 0–60 cm, and 0–100 cm during the sowing to reviving stages (the early stage of winter wheat), returning green and jointing stages (the middle stage of winter wheat), and heading to maturity stage (the last stage of winter wheat), respectively. The correlation and R-cluster analyses of SWC at different layers in the soil profile showed that SMSs should be installed 10 and 30 cm below the soil surface during the winter wheat growing season. The linear regression model can be built using SWC at depths of 10 and 30 cm to predict total average SWC in the soil profile. The results of validation showed that the developed model provided reliable estimates of total average SWC in the planned wetted layer. In brief, this study suggests that suitable positioning of soil moisture sensors is at depths of 10 and 30 cm below the soil surface.

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Soil Structure and Texture Effects on the Precision of Soil Water Content Measurements with a Capacitance- Based Electromagnetic Sensor

Transactions of the ASABE ◽

10.13031/trans.13496 ◽

2020 ◽

Vol 63 (1) ◽

pp. 141-152

Author(s):

Jasreman Singh ◽

Derek M. Heeren ◽

Daran R. Rudnick ◽

Wayne E. Woldt ◽

Geng Bai ◽

...

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Sensor Calibration ◽

Soil Water Depletion ◽

Undisturbed Soil ◽

Water Depletion ◽

Soil Moisture Sensors ◽

Electromagnetic Sensor

HighlightsCapacitance-based electromagnetic soil moisture sensors were tested in disturbed and undisturbed soils.The uncertainty in estimation of soil water depth was lower using the undisturbed soil sample calibrations.The uncertainty in estimation of soil water depletion was lower than the uncertainty in volumetric water content.Undisturbed calibration of water depletion quantifies water demand with better precision and avoids over-watering.Abstract. The physical properties of soil, such as structure and texture, can affect the performance of an electromagnetic sensor in measuring soil water content. Historically, calibrations have been performed on repacked samples in the laboratory and on soils in the field, but little research has been done on laboratory calibrations with intact (undisturbed) soil cores. In this study, three replications each of disturbed and undisturbed soil samples were collected from two soil texture classes (Yutan silty clay loam and Fillmore silt loam) at a field site in eastern Nebraska to investigate the effects of soil structure and texture on the precision of a METER Group GS-1 capacitance-based sensor calibration. In addition, GS-1 sensors were installed in the field near the soil collection sites at three depths (0.15, 0.46, and 0.76 m). The soil moisture sensor had higher precision in the undisturbed laboratory setup, as the undisturbed calibration had a better correlation [slope closer to one, R2undisturbed (0.89) > R2disturbed (0.73)] than the disturbed calibrations for the Yutan and Fillmore texture classes, and the root mean square difference using the laboratory calibration (RMSDL) was higher for pooled disturbed samples (0.053 m3 m-3) in comparison to pooled undisturbed samples (0.023 m3 m-3). The uncertainty in determination of volumetric water content (?v) was higher using the factory calibration (RMSDF) in comparison to the laboratory calibration (RMSDL) for the different soil structures and texture classes. In general, the uncertainty in estimation of soil water depth was greater than the uncertainty in estimation of soil water depletion by the sensors installed in the field, and the uncertainties in estimation of depth and depletion were lower using the calibration developed from the undisturbed soil samples. The undisturbed calibration of soil water depletion would determine water demand with better precision and potentially avoid over-watering, offering relief from water shortages. Further investigation of sensor calibration techniques is required to enhance the applicability of soil moisture sensors for efficient irrigation management. Keywords: Calibration, Capacitance, Depletion, Irrigation, Precision, Sensor, Soil water content, Structure, Uncertainty.

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Modelling leaching and recharge in a bare transitional red-brown earth ponded with low salinity water in summer

Australian Journal of Experimental Agriculture ◽

10.1071/ea9941085 ◽

1994 ◽

Vol 34 (7) ◽

pp. 1085 ◽

Cited By ~ 1

Author(s):

L Cai ◽

SA Prathapar ◽

HG Beecher

Keyword(s):

Hydraulic Conductivity ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Soil Profile ◽

Soil Surface ◽

Saturated Hydraulic Conductivity ◽

Rate Dependent ◽

Water And Salt Movement ◽

Winter Fallow

A modelling study was conducted to evaluate water and salt movement within a transitional red-brown earth with saline B horizon soil when such waters are used for ponding in summer. The model was calibrated using previously published experimental data. The calibrated model was used to evaluate the effect of depth to watertable, saturated hydraulic conductivity, and ponding water salinity on infiltration, water and salt movement within the soil profile, and recharge. The study showed that when initial soil water content and the saturated hydraulic conductivity (Ks) are low, infiltrating water will be stored within the soil profile even in the absence of a shallow watertable. Once the soil water content is high, however, recharge will be significant in winter, even if there is no net infiltration at the soil surface. Infiltration rates depend more on Ks than the depth to watertable if it is at, or below, 1.5 m from the soil surface. When Ks is high, recharge under ponding will be higher than that under winter fallow. Subsequent ponding in summer and fallow in winter tend to leach salts from the soil profile, the leaching rate dependent on Ks. During winter fallow, due to net evaporation, salts tend to move upwards and concentrate near the soil surface. In the presence of shallow watertables, leached salts tend to concentrate at, or near, the watertable.

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Laboratory calibration of different soil moisture sensors in various soil types

10.5194/egusphere-egu2020-5956 ◽

2020 ◽

Author(s):

Urša Pečan ◽

Damijana Kastelec ◽

Marina Pintar

Keyword(s):

Soil Moisture ◽

Water Content ◽

Bulk Density ◽

Soil Water ◽

Soil Water Content ◽

Soil Bulk Density ◽

Soil Samples ◽

Soil Types ◽

Soil Moisture Sensors ◽

Dry Soil

<p>Measurements of soil water content are particularly useful for irrigation scheduling. In optimal conditions, field data are obtained through a dense grid of soil moisture sensors. Most of the currently used sensors for soil water content measurements, measure relative permittivity, a variable which is mostly dependant on water content in the soil. Spatial variability of soil characteristics, such as soil texture and mineralogy, organic matter content, dry soil bulk density and electric conductivity can also alter measurements with dielectric sensors. So the question arises, whether there is a need for a soil specific calibration of such sensors and is it dependant on the type of sensor? This study evaluated the performance of three soil water content sensors (SM150T, Delta-T Devices Ltd, UK; TRIME-Pico 32, IMKO micromodultechnik GmbH, DE; MVZ 100, Eltratec trade, production and services d.o.o., SI) in nine different soil types in laboratory conditions. Our calibration approach was based on calibration procedure developed for undisturbed soil samples (Holzman et al., 2017). Due to possible micro location variability of soil properties, we used disturbed and homogenized soil samples, which were packed to its original dry soil bulk density. We developed soil specific calibration functions for each sensor and soil type. They ranged from linear to 5<sup>th</sup> order polynomial. We calculated relative and actual differences in sensor derived and gravimetrically determined volumetric soil water content, to evaluate the errors of soil water content measured by sensors which were not calibrated for soil specific characteristics. We observed differences in performance of different sensor types in various soil types. Our results showed measurements conducted with SM150T sensors were within the range of manufacturer specified measuring error in three soil types for which calibration is not necessary but still advisable for improving data quality. In all other cases, soil specific calibration is required to obtain relevant soil moisture data in the field.</p>

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Comparison of Sensor-Based and Weather-Based Irrigation Scheduling

Applied Engineering in Agriculture ◽

10.13031/aea.13678 ◽

2020 ◽

Vol 36 (3) ◽

pp. 375-386

Author(s):

Ruixiu Sui ◽

Earl D. Vories

Keyword(s):

Soil Moisture ◽

Water Balance ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Soil Texture ◽

Irrigation Scheduling ◽

Cotton Field ◽

List Type ◽

Soil Moisture Sensors

HighlightsSensor-based irrigation scheduling methods (SBISM) were compared with computerized water balance scheduling.Number and time of irrigation events scheduled using the SBISM were often different from those predicted by the computerized method.The highly variable soils at the Missouri site complicated interpretation of the sensor values.Both SBISM and computerized water balance scheduling could be used for irrigation scheduling with close attention to soil texture and effective rainfall or irrigation.Abstract. Sensor-based irrigation scheduling methods (SBISM) measure soil moisture to allow scheduling of irrigation events based on the soil-water status. With rapid development of soil moisture sensors, more producers have become interested in SBISM, but interpretation of the sensor data is often difficult. Computer-based methods attempt to estimate soil water content and the Arkansas Irrigation Scheduler (AIS) is one example of a weather-based irrigation scheduling tool that has been used in the Mid-South for many years. To aid producers and consultants interested in learning more about irrigation scheduling, field studies were conducted for two years in Mississippi and a year in Missouri to compare SBISM with the AIS. Soil moisture sensors (Decagon GS-1, Acclima TDR-315, Watermark 200SS) were installed in multiple locations of a soybean field (Mississippi) and cotton field (Missouri). Soil water contents of the fields were measured hourly at multiple depths during the growing seasons. The AIS was installed on a computer to estimate soil water content and the required data were obtained from nearby weather stations at both locations and manually entered in the program. In Mississippi, numbers and times of the irrigation events triggered by the SBISM were compared with those that would have been scheduled by the AIS. Results showed the number and time of irrigation events scheduled using the SBISM were often different from those predicted by the AIS, especially during the 2018 growing season. The highly variable soils at the Missouri site complicated the interpretation of the sensor values. While all of the sites were within the Tiptonville silt loam map unit, some of the measurements appeared to come from sandier soils. The AIS assumed more water entered the soil than the sensors indicated from both irrigations and rainfalls less than 25 mm. While the irrigation amounts were based on the pivot sprinkler chart, previous testing had confirmed the accuracy of the charts. Furthermore, the difference varied among sites, especially for rainfall large enough to cause runoff. The recommendations based on the Watermark sensors agreed fairly well with the AIS in July after the data from the sandiest site was omitted; however, the later irrigations called for by the AIS were not indicated by the sensors. Both the sensor-based irrigation scheduling method and the AIS could be used as tools for irrigation management in the Mid-South region, but with careful attention to soil texture and the effective portion of rainfall or irrigation. Keywords: Irrigation scheduling, Soil moisture sensor, Soil water content, Water management.

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Irrigation Scheduling Using Soil Moisture Sensors

Journal of Agricultural Science ◽

10.5539/jas.v10n1p1 ◽

2017 ◽

Vol 10 (1) ◽

pp. 1 ◽

Cited By ~ 6

Author(s):

Ruixiu Sui

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Management Practices ◽

Root Zone ◽

Weighted Average ◽

Irrigation Scheduling ◽

Time Interval ◽

Soil Moisture Sensors

Irrigation is required to ensure crop production. Practical methods of use sensors to determine soil water status are needed in irrigation scheduling. Soil moisture sensors were evaluated and used for irrigation scheduling in humid region of the Mid-South US. Soil moisture sensors were installed in soil at depths of 15 cm, 30 cm, and 61 cm belowground. Soil volumetric water content was automatically measured by the sensors in a time interval of an hour during the crop growing season. Soil moisture data were wirelessly transferred onto internet through a wireless sensor network (WSN) so that the data could be remotely accessed online. Soil water content measured at the three depths were interpreted using a weighted average method to reflect the status of soil water in plant root zone. A threshold to trigger an irrigation event was determined with sensor-measured soil water content. An antenna mounting device was developed for operation of the WSN. Using the antenna mounting device, the soil moisture measurement was not be interrupted by crop field management practices.

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Evaluation of SWAT Soil Water Estimation Accuracy Using Data from Indiana, Colorado, and Texas

Transactions of the ASABE ◽

10.13031/trans.13910 ◽

2020 ◽

Vol 63 (6) ◽

pp. 1827-1843

Author(s):

Ahmed A. Hashem ◽

Bernard A. Engel ◽

Gary W. Marek ◽

Jerry E. Moorhead ◽

Dennis C. Flanagan ◽

...

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Soil Profile ◽

Assessment Tool ◽

Swat Model ◽

List Type ◽

Water Assessment ◽

Soil And Water

HighlightsSWAT soil water assessment was performed using soil water measurements.Dryland SWAT model soil water content was greater than the irrigated SWAT model.Using SWAT soil water estimates for real-time (daily) irrigation management purposes with the existing SWAT soil water subroutines and available soils data is considered risky.The surface layer showed the greatest soil water variability compared to deeper layers.Abstract. Soil water content (SWC) is a challenging measurement at the field, watershed, and regional scales. Soil and Water Assessment Tool (SWAT) soil water estimates were evaluated at three locations: the St. Joseph River watershed (SJRW) in northeast Indiana, the USDA-ARS Conservation and Production Research Laboratory (CPRL) at Bushland, Texas, and the USDA-ARS Limited Irrigation Research Farm (LIFR) at Greeley, Colorado. The soil water estimates were evaluated under two scenarios: (1) for the defined soil profile, and (2) by individual layer. Each site’s soil water assessment was performed based on the existing management conditions during each experiment, whether dryland or irrigated, and for various periods depending on SWC measurement availability at each site. The SWAT soil water was evaluated as follows: the Indiana site was evaluated under dryland conditions using daily soil water observations for one year; the Texas site was evaluated for a ten-year period under irrigated and dryland conditions using weekly soil water observations from four lysimeters; and the Colorado site was evaluated under irrigated conditions for a four-year period. The simulated soil water was evaluated by comparing the model simulations with observed daily and weekly soil water measurements at the three sites. Based on the results, even though all the SWAT models were considered to perform as good models following calibration (streamflow, ET, etc.), the soil water simulations were unacceptable for the defined soil profile and for individual layers at the three sites. Deeper soil layers had observations greater than field capacity values, indicating poor soil parameterization. The dryland model had greater water content than the irrigated model, contradicting the soil water measurements. This greater soil water simulation with the dryland model is a result of SWAT model uncertainties with ET reduction under dryland conditions due to water stress. This study indicated that soil water estimation using the default SWAT soil water equations has many sources of uncertainties. Two apparent sources resulted in the SWAT model’s poor performance: (1) SWAT soil water routines that do not fully represent soil water moving between layers to meet plant demand and (2) uncertainty in soil parameterization. Keywords: Hydrologic modeling, Soil moisture, Soil moisture sensor, Soil water, Soil and Water Assessment Tool.

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Evaluation of SWAT Soil Water Estimation Accuracy Using Data from Indiana, Colorado, and Texas

Transactions of the ASABE ◽

10.13031/trans13910. ◽

2020 ◽

Vol 63 (6) ◽

pp. 1827-1843

Author(s):

Ahmed A. Hashem ◽

Bernard A. Engel ◽

Gary W. Marek ◽

Jerry E. Moorhead ◽

Dennis C. Flanagan ◽

...

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Soil Profile ◽

Assessment Tool ◽

Swat Model ◽

List Type ◽

Water Assessment ◽

Soil And Water

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Using soil surface gray level to determine surface soil water content

Science China Earth Sciences ◽

10.1007/s11430-010-4049-1 ◽

2010 ◽

Vol 53 (10) ◽

pp. 1527-1532 ◽

Cited By ~ 3

Author(s):

YuanJun Zhu ◽

YunQiang Wang ◽

MingAn Shao

Keyword(s):

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Surface Soil ◽

Soil Surface ◽

Gray Level

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Monitoring water content changes in a soil profile with TDR-probes at just three depths - How well does it work?

RBRH ◽

10.1590/2318-0331.252020190113 ◽

2020 ◽

Vol 25 ◽

Author(s):

Jens Hagenau ◽

Vander Kaufmann ◽

Heinz Borg

Keyword(s):

Time Delay ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

High Precision ◽

Soil Profile ◽

Reliable Data ◽

Short Term ◽

True Values ◽

Daily Changes

ABSTRACT TDR-probes are widely used to monitor water content changes in a soil profile (ΔW). Frequently, probes are placed at just three depths. This raises the question how well such a setup can trace the true ΔW. To answer it we used a 2 m deep high precision weighing lysimeter in which TDR-probes are installed horizontally at 20, 60 and 120 cm depth (one per depth). ΔW-data collected by weighing the lysimeter vessel were taken as the true values to which ΔW-data determined with the TDR-probes were compared. We obtained the following results: There is a time delay in the response of the TDR-probes to precipitation, evaporation, transpiration or drainage, because a wetting or drying front must first reach them. Also, the TDR-data are more or less point measurements which are then extrapolated to a larger soil volume. This frequently leads to errors. For these reasons TDR-probes at just three depths cannot provide reliable data on short term (e.g. daily) changes in soil water content due to the above processes. For longer periods (e.g. a week) the data are better, but still not accurate enough for serious scientific studies.

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Relating surface backscatter response from TRMM Precipitation Radar to soil moisture: results over a semi-arid region

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-6425-2009 ◽

2009 ◽

Vol 6 (5) ◽

pp. 6425-6454

Author(s):

H. Stephen ◽

S. Ahmad ◽

T. C. Piechota ◽

C. Tang

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Arid Regions ◽

Vegetation Index ◽

Temporal Trends ◽

Model Parameters ◽

Precipitation Radar ◽

Semi Arid

Abstract. The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using Variable Infiltration Capacity (VIC) model whereas measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.

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