Time Domain Reflectometry Sensors Research Articles

The hysteretic response of a shallow pyroclastic deposit

Abstract. In the last decades, in Campania (southern Italy), steep slopes mantled by loose air-fall pyroclastic soils have been the seat of shallow, fast, rainfall-induced landslides. The occurrence of such events has been the result of the combination of critical rainstorms and of unfavourable initial conditions determined by antecedent infiltration, evaporation, and drainage processes. In order to understand the nature of the phenomena at hand and to clarify the role of all influencing factors, an automatic monitoring station has been installed in an area already subject to a recent killer flowslide (December, 1999). The paper reports data collected in 2011 about volumetric water content and suction (used to investigate the soil water retention features) and rainfall depth and temperature (providing the boundary conditions). In particular, the installation at the same depths of tensiometers and time domain reflectometry (TDR) sensors allowed us to recognise the hysteretic nature of the wetting and drying soil response to weather forcing and its influence on the slope stability conditions. The data reported in the paper are freely available at https://doi.org/10.5281/zenodo.4281166 (Comegna et al., 2020).

Sensitivity of Near-Infrared Permanent Laser Scanning Intensity for Retrieving Soil Moisture on a Coastal Beach: Calibration Procedure Using In Situ Data

Anthropogenic activities and climate change in coastal areas require continuous monitoring for a better understanding of environmental evolution and for the implementation of protection strategies. Surface moisture is one of the important drivers of coastal variability because it highly affects shoreward sand transport via aeolian processes. Several methods have been explored for measuring surface moisture at different spatiotemporal resolutions, and in recent years, light detection and ranging (LiDAR) technology has been investigated as a remote sensing tool for high-spatiotemporal-resolution moisture detection. The aim of the present study is the assessment of the performance of a permanent terrestrial laser scanner (TLS) with an original setting located on a high position and hourly scanning of a wide beach area stretching from a swash zone to the base of a dune in order to evaluate the soil moisture at a high spatiotemporal resolution. The reflectance of a Riegl-VZ2000 located in Noordwijk on the Dutch coast was used to assess a new calibration curve that allows the estimation of soil moisture. Three days of surveys were conducted to collect ground-truth soil moisture measurements with a time-domain reflectometry (TDR) sensor at 4 cm depth. Each in situ measurement was matched with the closest reflectance measurement provided by the TLS; the data were interpolated using a non-linear least squares method. A calibration curve that allowed the estimation of the soil moisture in the range of 0–30% was assessed; it presented a root-mean-square error (RMSE) of 4.3% and a coefficient of determination (R-square) of 0.86. As an innovative aspect, the calibration curve was tested under different circumstances, including weather conditions and tidal levels. Moreover, the TDR data collected during an independent survey were used to validate the assessed curve. The results show that the permanent TLS is a highly suitable technique for accurately evaluating the surface moisture variations on a wide sandy beach area with a high spatiotemporal resolution.

Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning

Root zone soil moisture (RZSM) estimation and monitoring based on high spatial resolution remote sensing information such as obtained with an Unmanned Aerial System (UAS) is of significant interest for field-scale precision irrigation management, particularly in water-limited regions of the world. To date, there is no accurate and widely accepted model that relies on UAS optical surface reflectance observations for RZSM estimation at high spatial resolution. This study is aimed at the development of a new approach for RZSM estimation based on the fusion of high spatial resolution optical reflectance UAS observations with physical and hydraulic soil information integrated into Automated Machine Learning (AutoML). The H2O AutoML platform includes a number of advanced machine learning algorithms that efficiently perform feature selection and automatically identify complex relationships between inputs and outputs. Twelve models combining UAS optical observations with various soil properties were developed in a hierarchical manner and fed into AutoML to estimate surface, near-surface, and root zone soil moisture. The addition of independently measured surface and near-surface soil moisture information to the hierarchical models to improve RZSM estimation was investigated. The accuracy of soil moisture estimates was evaluated based on a comparison with Time Domain Reflectometry (TDR) sensors that were deployed to monitor surface, near-surface and root zone soil moisture dynamics. The obtained results indicate that the consideration of physical and hydraulic soil properties together with UAS optical observations improves soil moisture estimation, especially for the root zone with a RMSE of about 0.04 cm3 cm−3. Accurate RZSM estimates were obtained when measured surface and near-surface soil moisture data was added to the hierarchical models, yielding RMSE values below 0.02 cm3 cm−3 and R and NSE values above 0.90. The generated high spatial resolution RZSM maps clearly capture the spatial variability of soil moisture at the field scale. The presented framework can aid farm scale precision irrigation management via improving the crop water use efficiency and reducing the risk of groundwater contamination.

Application of the TDR Sensor and the Parameters of Injection Irrigation for the Estimation of Soil Evaporation Intensity.

The objective of the study was to develop a precise method of determination of the evaporation rate in a soil irrigated with the use of a mobile injection irrigation system. Two methods of constructing functions approximating the value of evaporation have been developed. In the first method, the domain comprises the parameters of injection irrigation, i.e., the dose and the depth of injection, and in the second, the volumetric moisture of soil in the layer immediately below the soil surface, which was measured with time-domain reflectometry (TDR) sensors. For that purpose, a laboratory experiment was carried out, based on 12 physical models. The study was conducted on a natural soil material, with particle size distribution of its mineral parts corresponding to that of a loamy sand soil. It was demonstrated that evaporation intensity increases with irrigation and decreases with increase in the depth of water application. Using TDR sensors, it was also shown that evaporation intensity increases proportionally to the weighted arithmetic mean of the volumetric moisture. Comparison of the two methods indicates that the evaporation intensity of injection-irrigated soil can be estimated with higher accuracy when the domain of the approximating function is the injection depth and dose than when the domain of the function is the weighted mean of volumetric moisture of the surface horizon of the soil. However, the method using TDR sensors for the estimation of evaporation intensity of an injection-irrigated soil has a greater potential for the construction of universal approximating models. In addition, the advantage of the method based on the use of TDR sensors is that it uses arguments for the approximating function, , in real time.

Efficient Irrigation of Maize Through Soil Moisture Monitoring and Modeling

Agriculture is the major user of water resources, accounting for 70% of global freshwater demand. As the demand for clean water increases, so does the need to implement more efficient strategies for water management in irrigated agriculture. While the benefits of precision irrigation in high-value crops, such as cannabis, tomatoes, and potatoes, are fully recognized, there is still need to investigate and implement cheap and efficient irrigation strategies for widespread low-value crops such as maize. In this study, the soil moisture dynamics in a sprinkler-irrigated maize field in Veneto (Northeastern Italy) was monitored using six time domain reflectometry (TDR) probes for the entire growing season. The TDR sensors were positioned at different depths into two separate sites: an Uninformed Site irrigated based on the farmer's experience and an Informed Site in which a water balance irrigation strategy was applied based on soil moisture measurements. A parsimonious hydrological model was then implemented and calibrated to quantify the different water balance terms (precipitation, evapotranspiration, lateral fluxes, and deep percolation). The comparison between the water budget terms in the two sites highlights that soil moisture monitoring during agriculture activities leads to substantial savings in terms of irrigation water volumes requirements and cost, without compromising the productivity of the crop field. A simplified upscaling of the results at the regional scale, assuming average conditions as in this study site and growing season, reveals that potentially significant economic savings, compared to the total profits linked to maize crops, could be possible.

Determining sensor-based field capacity for irrigation scheduling

Irrigated agriculture is a major consumer of freshwater and acreage of irrigated land has increased as producers have become increasingly reliant on irrigation to ensure adequate yields and reduce production risks. Information about the soil’s field capacity, or the water content after a saturated soil has drained for at least 24 h, is needed to determine the soil’s readily available water, or the amount of water that can be safely removed by plants. To address the challenges associated with using soil water sensors in highly variable soils, soil water measurements were included in ongoing cotton irrigation studies during the 2017 through 2019 growing seasons at the University of Missouri Fisher Delta Research Center near Portageville. Sensor-based field capacity values were compared among multiple commercially available sensors. Time-domain reflectometry sensors (Acclima TDR 315) were installed at four depths and five locations each of the three years. In addition, resistance-type sensors (Irrometer Watermark 200SS) were installed at five depths and five locations for two years. The goal was to provide guidance on how best to use soil moisture sensor data for irrigation management. Although water continued to move in the profile for more than 24 h after saturation, the average change between 24 and 48 h at each observed depth was < 0.015 m3 m−3. Observed values for coarser textured soils were much wetter after 24 h than expected based on published field capacity values and in one case, even a small difference in sensor location had a large effect on the observed field capacity. Furthermore, soil apparent electrical conductivity was highly correlated to measured field capacity, which could result in a much faster and less expensive way to estimate field capacity compared to collecting and analyzing soil cores. These findings demonstrate that the application of soil moisture sensors for irrigation management is site specific, and differences can be observed over short distances within a field. The research is continuing to better meet the needs of agricultural producers, consultants, research and extension personnel, and others for information to improve irrigation management.

Soil apparent electrical conductivity and must carbon isotope ratio provide indication of plant water status in wine grape vineyards

Proximal sensing is being integrated into vineyard management as it provides rapid assessments of spatial variability of soils’ and plants’ features. The electromagnetic induction (EMI) technology is used to measure soil apparent electrical conductivity (ECa) with proximal sensing and enables to appraise soil characteristics and their possible effects on plant physiological responses. This study was conducted in a micro irrigated Cabernet Sauvignon (Vitis vinifera L.) vineyard to investigate the technical feasibility of appraising plant water status and its spatial variability using soil ECa and must carbon isotope ratio analysis (δ13C). Soil temperature and soil water content were monitored in-situ using time domain reflectometry (TDR) sensors. Soil ECa was measured with EMI at two depths [0–1.5 m (deep ECa) and 0–0.75 m (shallow ECa)] over the course of the crop season to capture the temporal dynamics and changes. At the study site, the main physical and chemical soil characteristics, i.e. soil texture, gravel, pore water electrical conductivity (ECe), organic carbon, and soil water content at field capacity, were determined from samples collected auguring the soil at equidistant points that were identified using a regular grid. Midday stem water potential (Ψstem) and leaf gas exchange, including stomatal conductance (gs), net carbon assimilation (An), and intrinsic water use efficiency (WUEi) were measured periodically in the vineyard. The δ13C of produced musts was measured at harvest. The results indicated that soil water content (relative importance = 24%) and texture (silt: relative importance = 22.4% and clay: relative importance = 18.2%) were contributing the most towards soil ECa. Deep soil ECa was directly related to Ψstem (r2 = 0.7214) and gs (r2 = 0.5007). Likewise, δ13C of must was directly related to Ψstem (r2 = 0.9127), gs (r2 = 0.6985), and An (r2 = 0.5693). Results from this work provided relevant information on the possibility of using spatial soil ECa sensing and δ13C analysis to infer plant water status and leaf gas exchange in micro irrigated vineyards.

Multifactor Analysis of Calibration and Service Quality of the Soil Moisture Sensor Applied in Subgrade Engineering

The soil is an important natural resource, and its moisture status plays a key role in the strength and stability of the soil structure. In civil industries including the agriculture engineering and the forestry engineering, moisture sensors are widely used to test and monitor the engineering properties and long‐term service performance of soil. The influence of multiple factors on the quality and the accuracy of the soil moisture sensor should be taken into account. Both laboratory and field tests were performed by the time‐domain reflectometry (TDR) method. The soil dielectric constant and magnetic permeability under different moisture contents, different temperatures, and different dry densities were comprehensively evaluated. At the same time, the sensitive factors in the service performance of the TDR sensor have been revealed by the real state of tested soil samples. Based on the testing principle of TDR, a calibration calculation model has been developed to evaluate accurately the moisture content of the subgrade slope. The results show that both the moisture content and the dry density had a significant influence on the dielectric constant and the magnetic permeability of soil. The developed calculation model has a good fitting effect and stability. Through the model, the influence of moisture content and temperature on the soil dielectric constant has been analyzed, and the fitting goodness is above 90%. After half a year of settlement, the soil was gradually drained and consolidated, and the moisture content of the subgrade section gradually decreased. It verified that the monitoring method determined by the laboratory calibration test was effective and accurate.

Real-time monitoring of deadwood moisture in forests: lessons learned from an intensive case study

Attributes of deadwood in forests, including quantity, landscape position, and state of decay, influence numerous ecosystem processes such as wildfire behavior, tree regeneration, and nutrient cycling. Attributes of deadwood that vary over subdiurnal time steps, including moisture, have not been routinely measured despite the profound effects they have on ecosystem processes. To improve our understanding of forest deadwood subdiurnal moisture dynamics, we installed an intensive time-domain reflectometry (TDR) sensor network in a log and surrounding soil within a northern hardwood forest in New England, United States. Intensive monitoring during a partial growing season indicated that deadwood moisture was dynamic but similar to that of surrounding soils at 15-min intervals, especially during wetting and drying events. Field results and bench analysis of the sample log revealed numerous challenges when attempting to monitor deadwood moisture with TDR such as heterogeneous and (or) advanced decay confounding TDR moisture measurements in logs. An efficient, high-frequency TDR sensor network was demonstrated to record deadwood and soil moisture fluctuations, which provides an opportunity to refine our understanding of deadwood dynamics in the context of global change such as changing precipitation regimes.

Advances in thermo‐time domain reflectometry technique: Measuring ice content in partially frozen soils

AbstractIce content (θi) is a critical parameter affecting soil thermal, mechanical, and hydraulic properties in cold regions. Few techniques are available for accurately determining θi in laboratory samples and in situ. A combined heat‐pulse and time domain reflectometry (thermo‐TDR) sensor, which measures soil thermal properties and electrical properties simultaneously, can be used to estimate θi. The thermo‐TDR method determines θi by using a heat‐capacity‐based (C‐based) approach or a thermal‐conductivity‐based (λ‐based) approach. Here, we describe the principles and procedures of such approaches. The C‐based thermo‐TDR approach is simple to use and provides reasonable θi values at temperatures below −5°C, but it fails at higher temperatures. The λ‐based approach, which solves for θi from thermo‐TDR measurements with an iterative method, gives more accurate θi estimates than does the C‐based approach and extends the thermo‐TDR measurement range to temperatures near the freezing point of water. Therefore, the λ‐based thermo‐TDR method is preferred for determining θi in partially frozen soils.

Non-Destruction Method for Detecting Corroded Underground Pipe Using Ground Penetrating Radar

Ground-penetrating radar (GPR) is a geophysical method that can accurately map the spatial extent of near-surface objects or changes in soil. It also produce radargram of those features. The demand for underground utility mapping in Malaysia is increasing rapidly for construction and non-construction activities. However, implementation of GPR in detecting corroded underground pipe in Malaysia can considered unpopular due to less exposure and exploration. Over the time, underground utility pipe will undergo corrosion process. Thus, is it relevant to detect old corroded pipe using GPR? On this basis, the aim of this experimental study was to validate the condition of corroded pipe by comparing its radagram profiles and to evaluate the significant signal reflectivity which were tested on three different pipe condition, namely new, average and old corroded pipe. The result of GPR calibration and data on GPR were processed using Reflex 2D software. In this study, with the aid of Time-Domain Reflectometry (TDR) sensor was used to obtain the soil moisture as it is one of the biggest in fluent to corrosion activity. In the GPR method, radar antennas moved along the ground in transects (B-scan), and two-dimensional profiles of a large number of periodic reflections (A-scan) were obtained. As conclusion, these GPR datasets and soil moisture from TDR reading are evaluated and been analysed thoroughly. At the end of this paper, it is suggested that GPR is efficiently can be used to detect corroded pipe.

Variation of Matric Suction as a Function of Gypseous Soil Dry Density

Gypseous soil is one of the most problematic types of collapsible soils which is affected by many geotechnical factors. The most important factors are the effect of loading and wetting and their relation to soil density, especially when the soil at unsaturation condition. Suction pressure is the main criteria in determining the deformation behaviour of unsaturated collapsible soil when these soils distributed in arid or semi-arid region. In this study, disturbed sample of sandy soil of more than 70% gypsum content is taken from Al-Ramadi city western of Iraq. This study interested to investigate the variation of matric suction with the dry density and their effects on deformation of gypseous soil. For this purpose, a soil-model device provided with high accurate Tensiometers and Time Domain Reflectometry sensors in addition to data logger is designed and manufactured. Tensiometer sensor is used to monitor and measure the matric suction, while the Time Domain Reflectometry is used to monitor and measure the volumetric water content in the soil mass. The results of the tests showed that there is a significant effect of soil dry density on the relationship between the matric suction and water content.

Time Domain Reflectometry flat sensor for non-invasive monitoring of moisture changes in building materials

Abstract The article presents the monitoring potential of a simplified surface Time Domain Reflectometry (TDR) sensor design. Its basic element is a polyvinyl chloride (PVC) plate with two rods placed along the plate edges. The paper describes typical, invasive TDR sensors and the attempts to build a non-invasive TDR sensor for the dielectric moisture determination. The newly developed flat TDR sensor presented in this article has a simple design, which makes it cheap, easy to carry and prospective for practical applications. Within the conducted examinations, the developed sensor specimens were calibrated and tested in an exemplary moisture transport experiment. The analysis of moisture changes in the sample of the examined material confirmed the usefulness of the developed TDR sensor and its applicability for the non-invasive moisture changes monitoring in hard porous materials.

Geoelectrical characterisation of CO2\u2013water systems in porous media: application to carbon sequestration

Carbon sequestration is a promising method for the reduction of carbon dioxide (CO2) emissions as it permits the storage of compressed CO2 in the subsurface. The carbon sequestration sites must be monitored to detect potential leaks; one possible method involves the monitoring of geoelectrical properties such as electrical conductivity (σb) and dielectric constant (εb). This investigation focuses on using a time domain reflectometry (TDR) sensor to determine the influence of different factors on the measurements of the electrical conductivity (σb) and dielectric constant (εb) of a porous rock reservoir in relation to the soil water saturation (Sw). The factors investigated were presence of surfactant, salt concentration, pH and rock type which are unique to a given storage site. A number of dynamic two-phase flow experiments were performed using gaseous CO2. It was found that salt concentration, rock type and presence of a surfactant had a notable effect on the σb–Sw and εb–Sw relationships. Higher salt concentrations were found to give higher values for σb and εb for given Sw values. Limestone was found to result in the highest values of both σb and εb for any given Sw, followed by silica and basalt samples. The presence of a surfactant resulted in higher values for σb at higher Sw values and lower values for σb at lower Sw values compared to the case when no surfactant was present. Surfactant presence also resulted in lower values for εb at given Sw values. Initial pH values (with silica sand) were found to have no significant effect on the σb–Sw and εb–Sw relationships. The measurements of σb and εb indicate that the use of TDR presents a viable monitoring option. Furthermore, statistical analysis using non-linear regression was carried out on the experimental results and the model shows a good reliability in the prediction of the monitoring process in geological carbon sequestration.

150 km φ-OTDR sensor based on erbium and Raman amplifiers

Extending the sensing range for monitoring the railways, pipelines, borders, long structures, etc., has great importance in distributed fiber optic sensors development. Phase-sensitive optical time-domain reflectometry (φ-OTDR) sensors are the best candidate for this purpose. Optical fiber amplifiers in φ-OTDR sensors have increased the sensing range. A φ-OTDR sensor using combination of Erbium and Raman amplifiers has been demonstrated with a sensing range of 128 km. In this paper, we presented a disturbance monitoring up to 150 km utilizing Erbium and bi-directional Raman amplifications. In the proposed sensor, disturbances at 30, 70 and 120 km distances are applied and Rayleigh backscattered signals are investigated.

Collapsibility of gypseous soil under suction control

Many geotechnical problems and foundation failures in engineering structures were recorded due to the presence of unsaturated gypseous soils. One of the main reasons, which introduced large volume change and collapse behaviour is the variation of suction pressure and water potential within the gypseous soil mass. In this study, Sandy gypseous soil from Al-Ramadi city (west of Iraq) with gypsum content of more than 70 % is used. In order to investigate the collapse deformation of gypseous soil under suction control by multi-steps wetting procedure, also the effect of sensors positions and applied vertical stress, a special soil-model device is designed and manufactured. In this device, high accurate and sensitive Tensiometers and Time Domain Reflectometry (TDR) sensors with an assistance of high quality Data logger are used for measuring and monitoring of matric suction and gravimetric water content of the soil sample during the deformation processes. The test results indicated that a large volume change and collapse deformation occurred upon reduction of matric suction. The void ratio dramatically reduced stepwise with reducing of matric suction by multi-steps wetting irrespective the value of applied vertical stress.

Evaluating time domain reflectometry and coaxial impedance sensors for soil observations by the U.S. Climate Reference Network

AbstractThe objective of this study was to evaluate the performance of two commercial‐grade electromagnetic sensors in measuring soil water content for the U.S. Climate Reference Network (USCRN). One sensor was a 50‐MHz coaxial impedance dielectric sensor (model HydraProbe, Stevens Water Monitoring Systems). The second sensor was a 1‐GHz time domain reflectometry (TDR) sensor (model TDR‐315L, Acclima). There was no substantial difference between both probes in daily mean volumetric soil water content and soil temperature measurements at a 0.1‐m depth in a customized uniform loamy soil testbed in Oak Ridge, TN, from 2016 to 2018, and at select USCRN stations with a variety of soils. However, the TDR‐315L provided more representative soil water content measurements for stations with high‐clay‐content soils than the HydraProbe. These results confirm the necessity of using site‐specific soil properties for soil sensors like the HydraProbe for estimating soil water content in different soil environments. They also suggest that TDR sensors may provide an opportunity to improve USCRN soil moisture measurements in higher‐clay‐content soils.

Calibration, measurement, and characterization of soil moisture dynamics in a central Amazonian tropical forest

AbstractSoil moisture plays a key role in hydrological, biogeochemical, and energy budgets of terrestrial ecosystems. Accurate soil moisture measurements in remote ecosystems such as the Amazon are difficult and limited because of logistical constraints. Time domain reflectometry (TDR) sensors are widely used to monitor soil moisture and require calibration to convert the TDR's dielectric permittivity measurement (Ka) to volumetric water content (θv). In this study, our objectives were to develop a field‐based calibration of TDR sensors in an old‐growth upland forest in the central Amazon, to evaluate the performance of the calibration, and then to apply the calibration to determine the dynamics of soil moisture content within a 14.2‐m‐deep vertical soil profile. Depth‐specific TDR calibration using local soils in a controlled laboratory setting yielded a novel Ka–θv third‐degree polynomial calibration. The sensors were later installed to their specific calibration depth in a 14.2‐m pit. The widely used Ka–θv relationship (Topp model) underestimated the site‐specific θv by 22–42%, indicating significant error in the model when applied to these well‐structured, clay‐rich tropical forest soils. The calibrated wet‐ and dry‐season θv data showed a variety of depth and temporal variations highlighting the importance of soil textural differentiation, root uptake depths, as well as event to seasonal precipitation effects. Data such as these are greatly needed for improving our understanding of ecohydrological processes within tropical forests and for improving models of these systems in the face of changing environmental conditions.

High-resolution mapping of in-depth soil moisture content through a laboratory experiment coupling a spectroradiometer and two hyperspectral cameras

A laboratory experiment is set up to study both surface and in-depth soil moisture content (SMC). For that purpose, an aquarium is filled successively with two soils, a clay loam and a sand. Reflectance spectra are acquired in the solar domain (400–2400 nm) on the soil surface using an ASD FieldSpec 3 HR spectroradiometer and in-depth through the aquarium glass wall using two hyperspectral cameras. Successive amounts of water ranging from low to heavy rainfall in a temperate region are uniformly poured into the aquarium. The MARMITforSMC method based on the MARMIT (MultilAyer Radiative Transfer Model for soIl reflecTance) model is applied to each reflectance spectrum to determine gravimetric SMC. In particular, vertical profiles of SMC are provided with unprecedented spatial accuracy (~0.287 mm). The results are compared with volumetric SMC measured by two time-domain reflectometry (TDR) sensors. The in-depth SMC image produced on the sand shows lower values within the first 2 cm (5%) than below (17%). In contrast, the SMC image produced on the clay loam shows evenly distributed values whatever the position in the aquarium, even 1 h after moistening. The difference in grain size between the soils explains this result.

A New Optical Remote Sensing Technique for High-Resolution Mapping of Soil Moisture.

The recently developed OPtical TRApezoid Model (OPTRAM) has been successfully applied for watershed scale soil moisture (SM) estimation based on remotely sensed shortwave infrared (SWIR) transformed reflectance (TRSWIR) and the normalized difference vegetation index (NDVI). This study is aimed at the evaluation of OPTRAM for field scale precision agriculture applications using ultrahigh spatial resolution optical observations obtained with one of the world's largest field robotic phenotyping scanners located in Maricopa, Arizona. We replaced NDVI with the soil adjusted vegetation index (SAVI), which has been shown to be more accurate for cropped agricultural fields that transition from bare soil to dense vegetation cover. The OPTRAM was parameterized based on the trapezoidal geometry of the pixel distribution within the TRSWIR-SAVI space, from which wet- and dry-edge parameters were determined. The accuracy of the resultant SM estimates is evaluated based on a comparison with ground reference measurements obtained with Time Domain Reflectometry (TDR) sensors deployed to monitor surface, near-surface and root zone SM. The obtained results indicate an SM estimation error between 0.045 and 0.057 cm3 cm−3 for the near-surface and root zone, respectively. The high resolution SM maps clearly capture the spatial SM variability at the sensor locations. These findings and the presented framework can be applied in conjunction with Unmanned Aerial System (UAS) observations to assist with farm scale precision irrigation management to improve water use efficiency of cropping systems and conserve water in water-limited regions of the world.