Search Results (32)

Soil moisture exhibits high spatio-temporal variability that necessitates dense monitoring networks, yet the cost of commercial sensors often limits widespread deployment. Despite the mass production of low-cost capacitive soil moisture sensors driven by IoT applications, significant gaps remain in their robust characterisation and in the availability of open-source, reproducible monitoring systems. This study pursues two primary objectives: (1) to develop an open-source, low-cost, off-grid soil moisture monitoring station (picoSMMS) and (2) to conduct a sensor-unit-specific calibration of a popular low-cost capacitive soil moisture sensor (LCSMS; DFRobot SEN0193) by relating its raw output to bulk static relative dielectric permittivity (${ϵ}_s$), with the additional aim of transferring technological gains from consumer electronics to hydrological monitoring while fostering community-driven improvements. The picoSMMS was built using readily available consumer electronics and programmed in MicroPython. Laboratory calibration followed standardised protocols using reference media spanning permittivities from 1.0 (air) to approximately 80.0 (water) under non-conducting, non-relaxing conditions at 25 ± 1 °C with temperature-dependency characterisation. Models were developed relating the sensor’s output and temperature to ${ϵ}_s$. Within the target permittivity range (2.5–35.5), the LCSMS achieved a mean absolute error of 1.29 ± 1.07, corresponding to an absolute error of 0.02 ± 0.01 in volumetric water content (VWC). Benchmarking revealed that the LCSMS is competitive with the ML2 ThetaProbe, and outperforms the PR2/6 ProfileProbe, but is less accurate than the SMT100. Notably, applying the air–water normalisation procedure to benchmark sensors significantly improved their performance, particularly for the ML2 ThetaProbe and PR2/6 ProfileProbe. A brief field deployment demonstrated the picoSMMS’s ability to closely track co-located HydraProbe sensors. Important limitations include the following: inter-sensor variability assessment was limited by the small sensor ensemble (only two units), and with a larger sample size, the LCSMS may exhibit greater variability, potentially resulting in larger prediction errors; the characterisation was conducted under non-saline conditions and may not apply to peat or high-clay soils; the calibration is best suited for the target permittivity range (2.5–35.5) typical of mineral soils; and the brief field deployment was insufficient for long-term validation. Future work should assess inter-sensor variability across larger sensor populations, characterise the LCSMS under varying salinity, and conduct long-term field validation. Full article

The electromagnetic induction (EMI) and frequency domain reflectometry (FDR) sensors, which measure the soil apparent electrical conductivity (ECa) in situ, have emerged as efficient and rapid tools for the indirect assessment of soil salinity, conventionally determined by the electrical conductivity of the saturated soil paste extract (ECe). However, the limitations of applying a single soil sensor and the ECa dependence on multiple soil properties, such as soil moisture and texture, can hinder the interpretation of ECe, whereas selecting the most appropriate set of sensors is challenging. To address these issues, this study explored the prediction ability of a noninvasive EM38-MK2 (EMI) and a capacitance dielectric WET-2 probe (FDR) in assessing topsoil salinity and texture within 0–30 cm depth across diverse soil and land-use conditions in Laconia, Greece. To this aim, multiple linear regression models of laboratory-estimated ECe and soil texture were constructed by the in situ measurements of EM38-MK2 and WET-2, and their performances were individually evaluated using statistical metrics. As was shown, in heterogeneous soils with sufficient wetness and high salinity levels, both sensors produced models with high adjusted coefficients of determination (adj. R² > 0.82) and low root mean square error (RMSE) and mean absolute error (MAE), indicating strong model fit and reliable estimations of topsoil salinity. For the EM38-MK2, model accuracy improved when clay was included in the regression, while for the WET-2, the soil pore water electrical conductivity (ECp) was the most accurate predictor. The drying soil surface was the greatest constraint to both sensors’ predictive performances, whereas in non-saline soils, the silt and sand were moderately assessed by the EM38-MK2 readings (0.49 < adj. R² < 0.51). The results revealed that a complementary use of the contemporary EM38-MK2 and the low-cost WET-2 could provide an enhanced interpretation of the soil properties in the topsoil without the need for additional data acquisition, although more dense soil measurements are recommended. Full article

Monitoring the water status of fruit orchards is required to optimize crop water management and determine irrigation scheduling. For this purpose, capacitance probes are commonly used to measure soil water content (θ_s). However, when these probes are not calibrated, the estimates of θ_s are, therefore, unreliable. Our objective was to relate the measurements of capacitance probes, without a site-specific calibration, with a reliable indicator of the water status (stem water potential at solar noon (Ψ_stem)) of rain-fed grapevines grown under contrasting soil management strategies (tillage and spontaneous vegetation) and of irrigated peach and pear trees. During the 2023 growing season, θ_s was monitored in a peach and a pear orchard and in a vineyard in northeast Spain using capacitance sensors at three depths: 0.15, 0.30, and 0.45 m. Correlation coefficients ranged from 0.75 to 0.87 in peach trees, from 0.53 to 0.56 in pear trees, and from 0.56 to 0.90 in grapevines, depending on soil depth. These relationships were significant for both peach trees and grapevines but not for pear trees. Under the conditions of this study, uncalibrated capacitance measurements of θ_s could be useful to assess grapevine and peach tree water status in real time but were limited for pear trees. Full article

The accurate measurement of soil suction is essential for understanding the behavior of unsaturated soils, particularly in soil–vegetation–atmosphere (SVA) interactions, where both energy and hydraulic gradients due to climatic action exhibit their maximum intensity. This study assesses the performance of the TEROS 21 probe, a capacitance-based water potential sensor, for measuring soil matric suction and temperature in clayey soils of the South Apennines, Italy. Laboratory tests were conducted on soil samples with varying moisture contents, and the results were compared with those obtained using the traditional filter paper (FP) method and high-capacity tensiometers (HCTs). The TEROS 21 (METER Group, Inc., Pullman, WA, USA) sensor demonstrated a reliable performance, especially at suction levels between 300 and 2000 kPa, though there was some dependency on the initial sensor conditions (wet or dry). The temperature data obtained from the TEROS 21 were verified by using a thermocouple, showing the high consistency of the readings. This study showed that the filter paper and sensor measurements aligned at a water content lower than 30% but diverged at higher levels due to method-specific accuracy limitations. The consistent sensor results confirmed the measurement’s reliability. The air-entry value (AEV) of the soil water retention data was identified at around 800 kPa, which is consistent with previous findings. Full article

This research examines and analyzes the measured electromagnetic characteristics of vegetal soil for Wireless Underground Sensor Networks applied to precision agriculture. For this, we used Wireless Underground Sensor Network (WUSN) technology, which consists of sensors that communicate through the soil to collect data on irrigation, such as temperature and humidity, for good plant growth. However, underground communication channels and signal transmission are required to travel through a dense and heterogeneous soil mixture. For the measurement results of the vegetal soil dielectric parameters, a precision domain sensing probe operating at 433 Mhz was used. Moreover, the different choices of capacitance, inductance, and varactor were included, with a reasonable estimation of the dielectric permittivity, ranging from 2 to 15, and an unlimited range of conductivities. Despite promising results in predicting the dielectric permittivities, several improvements were made to the mode for low permittivity values, and it was designed to accommodate a wide range of dielectric permittivities. Full article

In the context of smart agriculture, accurate soil moisture monitoring is crucial to optimise irrigation, improve water usage efficiency and increase crop yields. Although low-cost capacitive sensors are used to make monitoring affordable, these sensors face accuracy challenges that often result in inefficient irrigation practices. This paper presents a method for calibrating capacitive soil moisture sensors through data assimilation. The method was validated using data collected from a farm in Dos Hermanas, Seville, Spain, which utilises a drip irrigation system. The proposed solution integrates the Hydrus 1D model with particle filter (PF) and the Iterative Ensemble Smoother (IES) to continuously update and refine the model and sensor calibration parameters. The methodology includes the implementation of physical constraints, ensuring that the updated parameters remain within physically plausible ranges. Soil moisture was measured using low-cost SoilWatch 10 capacitive sensors and ThetaProbe ML3 high-precision sensors as a reference. Furthermore, a comparison was carried out between the PF and IES methods. The results demonstrate that the data assimilation approach markedly enhances the precision of sensor readings, aligning them closely with reference measurements and model simulations. The PF method demonstrated superior performance, achieving an 84.8% improvement in accuracy compared to the raw sensor readings. This substantial improvement was measured against high-precision reference sensors, confirming the effectiveness of the PF method in calibrating low-cost capacitive sensors. In contrast, the IES method showed a 68% improvement in accuracy, which, while still considerable, was outperformed by the PF. By effectively mitigating observation noise and sensor biases, this approach proves robust and practical for large-scale implementations in precision agriculture. Full article

A novel Portable L-band radiometer (PoLRa), compatible with tower-, vehicle- and drone-based platforms, can provide gridded soil moisture estimations from a few meters to several hundred meters yet its retrieval accuracy has rarely been examined. This study aims to provide an initial assessment of the performance of PoLRa-derived soil moisture at a spatial resolution of approximately 0.7 m × 0.7 m at a set of sampling pixels in central Illinois, USA. This preliminary evaluation focuses on (1) the consistency of PoLRa-measured brightness temperatures from different viewing directions over the same area and (2) whether PoLRa-derived soil moisture retrievals are within an acceptable accuracy range. As PoLRa shares many aspects of the L-band radiometer onboard NASA’s Soil Moisture Active Passive (SMAP) mission, two SMAP operational algorithms and the conventional dual-channel algorithm (DCA) were applied to calculate volumetric soil moisture from the measured brightness temperatures. The vertically polarized brightness temperatures from the PoLRa are typically more stable than their horizontally polarized counterparts across all four directions. In each test period, the standard deviations of observed dual-polarization brightness temperatures are generally less than 5 K. By comparing PoLRa-based soil moisture retrievals against the simultaneous moisture values obtained by a handheld capacitance probe, the unbiased root mean square error (ubRMSE) and the Pearson correlation coefficient (R) are mostly below 0.05 m³/m³ and above 0.7 for various algorithms adopted here. While SMAP models and the DCA algorithm can derive soil moisture from PoLRa observations, no single algorithm consistently outperforms the others. These findings highlight the significant potential of ground- or drone-based PoLRa measurements as a standalone reference for the calibration and validation of spaceborne L-band synthetic aperture radars and radiometers. The accuracy of PoLRa-yielded high-resolution soil moisture can be further improved via standardized operational procedures and appropriate tau-omega parameters. Full article

Soil water content (θ), matric potential (h) and hydraulic conductivity (K) are key parameters for hydrological and environmental processes. Several sensors have been developed for measuring soil θ–h–K relationships. The cost of such commercially available sensors may vary over several orders of magnitude. In recent years, some sensors have been designed in the framework of Internet of Things (i.e., IoT) systems to make remote real-time soil data acquisition more straightforward, enabling low-cost field-scale monitoring at high spatio-temporal scales. In this paper, we introduce a new multi-parameter sensor designed for the simultaneous estimation of θ and h at different soil depths and, due to the sensor’s specific layout, the soil hydraulic conductivity function via the instantaneous profile method (IPM). Our findings indicate that a second-order polynomial function is the most suitable model (R² = 0.99) for capturing the behavior of the capacitive-based sensor in estimating θ in the examined soil, which has a silty-loam texture. The effectiveness of low-cost capacitive sensors, coupled with the IPM method, was confirmed as a viable alternative to time domain reflectometry (TDR) probes. Notably, the layout of the sensor makes the IPM method less labor-intensive to implement. The proposed monitoring system consistently demonstrated robust performance throughout extended periods of data acquisition and is highly suitable for ongoing monitoring of soil water status. Full article

Dielectric properties are crucial in understanding the behavior of water within soil, particularly the soil water content (SWC), as they measure a material’s ability to store an electric charge and are influenced by water and other minerals in the soil. However, a comprehensive review paper is needed that synthesizes the latest developments in this field, identifies the key challenges and limitations, and outlines future research directions. In addition, various factors, such as soil salinity, temperature, texture, probing space, installation gap, density, clay content, sampling volume, and environmental factors, influence the measurement of the dielectric permittivity of the soil. Therefore, this review aims to address the research gap by critically analyzing the current state-of-the-art dielectric properties-based methods for SWC measurements. The motivation for this review is the increasing importance of precise SWC data for various applications such as agriculture, environmental monitoring, and hydrological studies. We examine time domain reflectometry (TDR), frequency domain reflectometry (FDR), ground-penetrating radar (GPR), remote sensing (RS), and capacitance, which are accurate and cost-effective, enabling real-time water resource management and soil health understanding through measuring the travel time of electromagnetic waves in soil and the reflection coefficient of these waves. SWC can be estimated using various approaches, such as TDR, FDR, GPR, and microwave-based techniques. These methods are made possible by increasing the dielectric permittivity and loss factor with SWC. The available dielectric properties are further synthesized on the basis of mathematical models relating apparent permittivity to water content, providing an updated understanding of their development, applications, and monitoring. It also analyzes recent mathematical calibration models, applications, algorithms, challenges, and trends in dielectric permittivity methods for estimating SWC. By consolidating recent advances and highlighting the remaining challenges, this review article aims to guide researchers and practitioners toward more effective strategies for SWC measurements. Full article

The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m³/m³. Full article

Soil profile moisture is a crucial parameter of agricultural irrigation. To meet the demand of soil profile moisture, simple fast-sensing, and low-cost in situ detection, a portable pull-out soil profile moisture sensor was designed based on the principle of high-frequency capacitance. The sensor consists of a moisture-sensing probe and a data processing unit. The probe converts soil moisture into a frequency signal using an electromagnetic field. The data processing unit was designed for signal detection and transmitting moisture content data to a smartphone app. The data processing unit and the probe are connected by a tie rod with adjustable length, which can be moved up and down to measure the moisture content of different soil layers. According to indoor tests, the maximum detection height for the sensor was 130 mm, the maximum detection radius was 96 mm, and the degree of fitting (R²) of the constructed moisture measurement model was 0.972. In the verification tests, the root mean square error (RMSE) of the measured value of the sensor was 0.02 m³/m³, the mean bias error (MBE) was ±0.009 m³/m³, and the maximum error was ±0.039 m³/m³. According to the results, the sensor, which features a wide detection range and good accuracy, is well suited for the portable measurement of soil profile moisture. Full article

A new type of soil moisture sensor using spatial frequency domain transmissometry (SFDT) was evaluated. This sensor transmits and receives ultrawideband (1 to 6 GHz) radio waves between two separated antennas and measures the propagation delay time in the soil related to the dielectric constant. This method is expected to be less affected by air gaps between the probes and the soil, as well as being less affected by soil electrical conductivity (EC), than typical commercial sensors. The relationship between output and volumetric water content ($\theta$), and the effects of air gaps and EC were evaluated through experiments using sand samples and the prototype SFDT sensor. The output of the SFDT sensor increased linearly with $\theta$ and was not affected by even a high salt concentration for irrigation water, such that the EC of the pore water was 9.2 dS·m⁻¹. The SFDT sensor was almost unaffected by polyethylene tapes wrapped around the sensor to simulate air gaps, whereas a commercially available capacitance sensor significantly underestimated $\theta$. Theoretical models of the SFDT sensor were also developed for the calibration equation and the air gaps. The calculation results agreed well with the experimental results, indicating that analytical approaches are possible for the evaluation of the SFDT sensor. Full article

Water is getting scarce and irrigation practices should become more efficient. Mango orchards require great quantities of water, and policies in developing countries are substituting surface gravity irrigation by pressurized systems. A commercial orchard having mature 25-year-old trees and a 10-year-old HD high-density section were irrigated with micro sprinklers using 100% ETc (crop evapotranspiration) and reduced deficit irrigation treatments of 75% and 50% ETc. Water soil measurements were made with EC-5 probes at 10 and 35 cm in depth to study the effect of the different irrigation treatments. After the 2020 harvest, mature trees were trimmed without achieving pruning severity greater than 1.3. Canopy volume, mango size, fruit yield and water-use efficiency WUE were analyzed during 2020 and 2021. Sporadic storms produced sprinkler watering problems as weeds proliferated within trees. A controller with a fuzzy algorithm optimized orchard management and saved water in trees without decreasing yield and fruit size. It was found that one year after mature trees were trimmed by taking away the larger internal branch, more light penetrated the canopy, increasing yield by 60%; pruning in HD trees presented a yield increase of 5.37%. WUE (water-use efficiency) also increased with pruning and its value increased to 87.6 when the fuzzy controller and the 50% DI treatments were used in mature trees. This value was 260% greater than the one obtained in pruned trees without the controller. HD trees presented a lower WUE and yield per hectare than mature trees. Full article

Potato (Solanum tuberosum L.) is a very sensitive crop to water stress and timely irrigation water management improves tuber yield and quality. The objectives of this study were to (1) investigate soil water dynamics under potato crops across their root zone and (2) estimate potato crop evapotranspiration (ETa) under sprinkler irrigation on the sandy loam soil. The field experiment was conducted during the 2018 and 2019 growing seasons at the Navajo Farms within the Navajo Agricultural Products Industry, Farmington, NM. Two irrigation scheduling methods were evaluated as FAO-56 approach evapotranspiration-based scheduling and soil moisture sensing irrigation scheduling. Sentek capacitance soil moisture probe was used across four commercial potato fields in each year after calibration to the soil texture just after installation. Crop Evapotranspiration values estimated by the water balance method and the two-step approach were compared to the satellite-based models used in OpenET. The results showed that the potato’s effective rooting zone is the upper 40 cm soil layer. Potato plants extracted more than 50% of total water from the upper 15 cm of the soil profile and about 85% from the upper 40 cm of the soil profile. Little water amount was extracted from the 40–60 cm soil water. Potato crop seasonal evapotranspiration averaged 580 to 645 mm in 2018 and 2019, respectively. The Two-step approach ETa values of 795.5 and 832.7 mm in 2018 and 2019, respectively, were higher than the soil water balance estimated ETa. The satellite modeled ETa varied with field and years and ranged from 437 to 759 mm and averaged 570.4 mm for the 2016–2020 period. Soil moisture probe-based irrigation scheduling improved irrigation water management and the irrigation water use of potatoes in the semiarid climate. Full article

Soil moisture is a key parameter of the climate system as it relates to plant transpiration and photosynthesis and impacts land–atmosphere interactions. Recent developments have seen an increasing number of electromagnetic sensors available commercially (EM) for soil volumetric water content (θ). Their use is constantly expanding, and they are becoming increasingly used for agricultural, ecological, and geotechnical applications and climate research, providing decision support and high-resolution data for models and machine-learning algorithms. In this study, a soil moisture sensor network consisting of 10 Sense Cap capacitance-based sensors is evaluated. Analytical performance of the sensors was determined based on laboratory and field measurements with dielectric permittivity (ε) standards and soil media substrates. Sensor response normalisation to standards of known ε was found to reduce intersensor variability and provide robust estimates of θ in soil samples with known θ. Cross-comparison with a time-domain reflectometry (TDR) instrument carried out in two soil media demonstrates good agreement between the two probes throughout the tested range. The data communication performance of the network was evaluated in terms of packet drop rate at different ranges and sampling frequencies. It was noticed that the drop rate increased with distance from the gateway, while sampling frequency had no effect. Sources of errors associated with probe installation were identified and recommendations are provided for sensor deployment. The off-the-shelf all-in-one solution provided by Sense Cap is low cost, user friendly and suitable for implementation at temporal and spatial scales once the identified shortcomings are addressed. The evaluation presented aims to aid stakeholders and users involved in soil and land management practices including crop production, soil conservation, carbon sequestration and pollutants transport. Full article