scholarly journals Performance of Soil Moisture Sensors in Florida Sandy Soils

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 358 ◽  
Author(s):  
Rhuanito Soranz Ferrarezi ◽  
Thiago Assis Rodrigues Nogueira ◽  
Sara Gabriela Cornejo Zepeda
Keyword(s):  
Water Management ◽  
Soil Moisture ◽  
Water Content ◽  
Sandy Soils ◽  
Irrigation Scheduling ◽  
Management Efficiency ◽  
Soil Moisture Sensors ◽  
Citrus Production ◽  
Gravimetric Water Content ◽  
Calibration Equations

Soil moisture sensors can improve water management efficiency by measuring soil volumetric water content (θv) in real time. Soil-specific calibration equations used to calculate θv can increase sensor accuracy. A laboratory study was conducted to evaluate the performance of several commercial sensors and to establish soil-specific calibration equations for different soil types. We tested five Florida sandy soils used for citrus production (Pineda, Riviera, Astatula, Candler, and Immokalee) divided into two depths (0.0–0.3 and 0.3–0.6 m). Readings were taken using twelve commercial sensors (CS650, CS616, CS655 (Campbell Scientific), GS3, 10HS, 5TE, GS1 (Meter), TDT-ACC-SEN-SDI, TDR315, TDR315S, TDR135L (Acclima), and Hydra Probe (Stevens)) connected to a datalogger (CR1000X; Campbell Scientific). Known amounts of water were added incrementally to obtain a broad range of θv. Small 450 cm3 samples were taken to determine the gravimetric water content and calculate the θv used to obtain the soil-specific calibration equations. Results indicated that factory-supplied calibration equations performed well for some sensors in sandy soils, especially 5TE, TDR315L, and GS1 (R2 = 0.92) but not for others (10HS, GS3, and Hydra Probe). Soil-specific calibrations from this study resulted in accuracy expressed as root mean square error (RMSE) ranging from 0.018 to 0.030 m3 m−3 for 5TE, CS616, CS650, CS655, GS1, Hydra Probe, TDR310S, TDR315, TDR315L, and TDT-ACC-SEN-SDI, while lower accuracies were found for 10HS (0.129 m3 m−3) and GS3 (0.054 m3 m−3). This study provided soil-specific calibration equations to increase the accuracy of commercial soil moisture sensors to facilitate irrigation scheduling and water management in Florida sandy soils used for citrus production.

Comparison of Sensor-Based and Weather-Based Irrigation Scheduling

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.

scholarly journals Irrigation Scheduling Using Soil Moisture Sensors

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
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.

scholarly journals Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors

2020 ◽  
Vol 228 ◽  
pp. 105880 ◽  
Author(s):  
Jesús María Domínguez-Niño ◽  
Jordi Oliver-Manera ◽  
Joan Girona ◽  
Jaume Casadesús
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Irrigation Scheduling ◽  
Soil Moisture Sensors ◽  
Automated Algorithm

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

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.

scholarly journals Soil Moisture Sensor Test with Mississippi Delta Soils

2019 ◽  
Vol 62 (2) ◽  
pp. 363-370
Author(s):  
Ruixiu Sui ◽  
Horace C. Pringle ◽  
Edward M. Barnes
Keyword(s):  
Soil Moisture ◽  
Soil Temperature ◽  
Water Potential ◽  
Water Content ◽  
Soil Water ◽  
Measurement Accuracy ◽  
Mississippi Delta ◽  
Soil Water Potential ◽  
Gravimetric Method ◽  
Irrigation Scheduling

Abstract. One of the methods for irrigation scheduling is to use sensors to measure the soil moisture level in the plant root zone and apply water if there is a water shortage for the plants. The measurement accuracy and reliability of the soil moisture sensors are critical for sensor-based irrigation management. This study evaluated the measurement accuracy and repeatability of the EC-5 and 5TM soil volumetric water content (SVWC) sensors, the MPS-2 and 200SS soil water potential (SWP) sensors, and the 200TS soil temperature sensor. Six 183 cm × 183 cm × 71 cm wooden compartments were built inside a greenhouse, and each compartment was filled with one type of soil from the Mississippi Delta. A total of 66 sensors with 18 data loggers were installed in the soil compartments to measure SVWC, SWP, and soil temperature. Soil samples were periodically collected from the compartments to determine SVWC using the gravimetric method. SVWC measured by the sensors was compared with that determined by the gravimetric method. The SVWC readings from the sensors had a linear regression relationship with the gravimetric SVWC (r2 = 0.82). This relationship was used to calibrate the sensor readings. The SVWC and SWP sensors could detect the general trend of soil moisture changes. However, their measurements varied significantly among the sensors. To obtain accurate absolute soil moisture measurements, the sensors require individual and soil-specific calibration. The 5TM, MPS-2, and 200TS sensors performed well in soil temperature measurement tests. Individual temperature readings from these sensors were very close to the mean of all sensor readings. Keywords: Irrigation, Sensors, Soil types, Soil water content, Soil water potential.

Evaluation of Various Soil Moisture Sensors in Four Different Soil Types

2018 ◽  
Vol 34 (6) ◽  
pp. 963-971 ◽  
Author(s):  
Tonny José Araújo da Silva ◽  
Edna Maria Bonfim-Silva ◽  
Adriano Bicioni Pacheco ◽  
Thiago Franco Duarte ◽  
Helon Hébano de Freitas Sousa ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Soil Samples ◽  
Soil Types ◽  
Coefficient Of Determination ◽  
Regression Analyses ◽  
Drying Method ◽  
Soil Moisture Sensors ◽  
Calibration Equations

Abstract.Accurate measurements of soil moisture content can contribute to resource conservation in irrigated systems. The objective of this study was to evaluate various soil moisture sensors (a porous cup tensiometer, Diviner 2000, PR2, XH300, PM100, and ML3; the mention of model names does not constitute an implied endorsement) used in four different soil types. The experiment was conducted inside a greenhouse using a specially constructed box that contained the soil samples. The soil samples were first saturated and subsequently drained before starting the measurements. The soil moisture content was determined by the oven-drying method. Using the standard deviation of the sensor readings, regression analyses were performed, resulting in calibration equations and coefficient of determination (R2) values for each sensor and soil type combination. The porous cup tensiometer, Diviner 2000, PR2, and ML3 measurements resulted in excellent R2 values that exceeded 0.95 for the four soils. However, measurements with the XH300 and PM100 sensors resulted in R2 values of 0.37 to 0.86 and 0.61 to 0.94, respectively, limiting their scientific applicability for the studied soils. Therefore, the porous cup tensiometer, Diviner 2000, PR2, and ML3 estimated the soil moisture content with greater confidence than did the other sensors and with an error of less than 5%. Keywords: Calibration, Tensiometer, Volumetric water content.

Detecting changes in root zone soil moisture from radar vegetation backscatter

2020 ◽  
Author(s):  
Coleen Carranza ◽  
Tim van Emmerik ◽  
Martine van der Ploeg
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Hydrological Cycle ◽  
Root Zone ◽  
Water Status ◽  
Roughness Parameter ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Vegetation Water Content ◽  
Vegetation Water

<p>Root zone soil moisture (θ<sub>rz</sub>) is a crucial component of the hydrological cycle and provides information for drought monitoring, irrigation scheduling, and carbon cycle modeling. During vegetation conditions, estimation of θ<sub>rz</sub> thru radar has so far only focused on retrieving surface soil moisture using the soil component of the total backscatter (σ<sub>soil</sub>), which is then assimilated into physical hydrological models. The utility of the vegetation component of the total backscatter (σ<sub>veg</sub>) has not been widely explored and is commonly corrected for in most soil moisture retrieval methods. However, σ<sub>veg </sub>provides information about vegetation water content. Furthermore, it has been known in agronomy that pre-dawn leaf water potential is in equilibrium with that of the soil. Therefore soil water status can be inferred by examining  the vegetation water status. In this study, our main goal is to determine whether changes in root zone soil moisture (Δθ<sub>rz</sub>) shows corresponding changes in vegetation backscatter (Δσ<sub>veg</sub>) at pre-dawn. We utilized Sentinel-1 (S1) descending pass and in situ soil moisture measurements from 2016-2018 at two soil moisture networks (Raam and Twente) in the Netherlands. We focused on corn and grass which are the most dominant crops at the sites and considered the depth-averaged θ<sub>rz</sub> up to 40 cm to capture the rooting depths for both crops. Dubois’ model formulation for VV-polarization was applied to estimate the surface roughness parameter (H<sub>rms</sub>) and σ<sub>soil </sub>during vegetated periods. Afterwards, the Water Cloud Model was used to derive σ<sub>veg</sub> by subtracting σ<sub>soil</sub> from S1 backscatter (σ<sub>tot</sub>). To ensure that S1 only measures vegetation water content, rainy days were excluded to remove the influence of intercepted rainfall on the backscatter. The slope of regression lines (β) fitted over plots of Δσ<sub>veg</sub> against Δθ<sub>rz</sub> were used investigate the dynamics over a growing season. Our main result indicates that Δσ<sub>veg </sub>- Δθ<sub>rz</sub> relation is influenced by crop growth stage and changes in water content in the root zone. For corn, changes in β’s over a growing season follow the trend in a crop coefficient (K<sub>c</sub>) curve, which is a measure of crop water requirements. Grasses, which are perennial crops, show trends corresponding to the mature crop stage. The correlation between soil moisture (Δθ) at specific soil depths (5, 10, 20, and 40 cm) and Δσ<sub>veg </sub> matches root growth for corn and known rooting depths for both corn and grass. Dry spells (e.g. July 2018) and a large increase in root zone water content in between two dry-day S1 overpass (e.g. from rainfall) result in a lower β, which indicates that Δσ<sub>veg</sub> does not match well with Δθ<sub>rz</sub>. The influence of vegetation on S1 backscatter is more pronounced for corn, which translated to a clearer Δσ<sub>veg</sub> - Δθ<sub>rz</sub> relation compared to grass. The sensitivity of Δσ<sub>veg</sub> to Δθ<sub>rz</sub> in corn means that the analysis may be applicable to other broad leaf crops or forested areas, with potential applications for monitoring  periods of water stress.</p>

scholarly journals Evaluation of methods for water and non-volatile LNAPL content measurement in porous media

2019 ◽  
Vol 14 (No. 1) ◽  
pp. 47-56
Author(s):  
Ayele Teressa Chala ◽  
Svatopluk Matula ◽  
Kamila Báťková ◽  
František Doležal
Keyword(s):  
Porous Media ◽  
Soil Moisture ◽  
Water Content ◽  
High Standard ◽  
The Other ◽  
Gravimetric Analysis ◽  
Analysis Method ◽  
Soil Moisture Sensors ◽  
Phase Liquid ◽  
Proper Estimation

Proper characterization of contaminants in subsurface helps to clean up effectively the contaminated sites. In this study, different methods were used to quantify non-volatile light non-aqueous phase liquid (LNAPL) and water from sample columns subjected to different water to LNAPL ratios. The objective of the study was to evaluate methods for porous media water and LNAPL contents analysis. The liquids were sampled from the sample columns using activated carbon pellets (ACP). Sample columns water content was also measured using soil moisture sensors. Dielectric mixing model (DMM) was evaluated for the estimation of LNAPL content after water and LNAPL contents of the sample columns were determined through gravimetric analysis method. The result shows that it was possible to sample both water and LNAPL using ACP proportionally but with high standard deviations. It also shows that more liquid was sampled from sample columns subjected to only one liquid compared to sample columns subjected to two liquids. On the other hand, analysis of water and LNAPL using gravimetric analysis method gave the best result although the presence of LNAPL resulted in underestimation of water content at higher LNAPL contents. Meanwhile, the presence of LNAPL modified the bulk relative permittivity (ε<sub>a</sub>) of the sample columns and resulted in overestimation of water contents measured using soil moisture sensors at higher LNAPL content. The modification of ε<sub>a</sub> was used for the estimation of LNAPL using DMM. The evaluation of the model with known water and LNAPL contents and in estimating the LNAPL content of the other sample columns shows that the model could be used for the proper estimation of LNAPL in porous media.  

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