An official website of the United States government
Water resource has become one of the most precious resources in recent decades. Agriculture accounts for about 80\% of the total water usage in US. There is a demanding need for efficient irrigation and water management systems built for sustainable water utilization in smart agriculture. Real time in-situ soil moisture sensing is a vital part for smart agriculture. Traditional electromagnetic (EM) based soil moisture sensing relies on EM based wireless sensor or ground penetrating radar (GPR) system. Based on the receiving signal strength and delay, tomographic techniques are used to derive the dielectric parameters of the soil, which are then into soil moisture distribution using empirical model. However, the EM signal attenuate sharply during underground propagation because of high operating frequency and lossy medium. In order to counter the disadvantage for underground sensing, we propose a Magnetic Induction (MI) based large range soil moisture sensing scheme in inhomogeneous environments. Here, we present the topology of the sensing system and analyze the channel model. The sensing process is based on transformed model, the conductivity and permittivity distribution are derived using SIRT algorithm. Through COMSOL simulation and analytical results, our proposed soil moisture sensing method achieves a root mean square error (RMSE) of 0.06 m^3/m^3 in 40 m 2D scale inhomogeneous environment range.
more »
« less
Improving Field Moisture Monitoring of Recycled and Virgin Aggregates
The structural capacity of pavement foundation is altered by moisture conditions. Reliable moisture monitoring of geomaterials throughout pavement service life is critical for asset management by transportation agencies. Improved moisture sensing enables transportation officials and practitioners to better understand performance of complex recycled materials under frequent extreme rain events. Although moisture sensing for geomaterials has improved significantly, there are still challenges when using sensors in recycled materials that may contain unhydrated cement, aged asphalt, or both. Challenges include the development of calibration functions that account for the presence of recycled materials and robust installation procedures, as technology was developed mostly for agricultural practices. In this study, a series of experiments were conducted to suggest improvements for installation techniques and data interpretation of soil moisture and water potential sensors. Suggested installation guidelines minimize wash-out and erosion potential at the soil–sensor interface. Experimental results indicate a strong bilinear relation between dielectric of recycled materials and water content, with a region of relatively small change governed by dielectric permittivity of air and a region of rapid change controlled by dielectric permittivity of water. Moreover, it was found that dielectric permittivity is not significantly affected by aggregate internal structure as dielectric for a specific moisture content for different compaction degrees is relatively similar. Furthermore, soil water characteristic curves obtained using the water potential sensor and improved installation technique compare reasonably well with laboratory results obtained with traditional equipment. Reliability of both moisture and water potential sensors was improved with the suggested guidelines.
more »
« less
- Award ID(s):
- 1947009
- PAR ID:
- 10430189
- Date Published:
- Journal Name:
- Transportation Research Record: Journal of the Transportation Research Board
- ISSN:
- 0361-1981
- Page Range / eLocation ID:
- 036119812311657
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Meteorological and subsurface factors influence pavement’s response to cold fronts. Prediction of pavement temperature, particularly icing, is important to winter pavement maintenance which relies on an estimated time for the formation of ice. However, the prediction development is limited by test data on pavement icing. A model column consisting of soil samples and a concrete pavement slab retrieved from the Dallas Fort Worth airport was used to replicate the airport pavement structure, including subgrade. The soil was classified using USCS, tested for optimum moisture content and compacted in lifts in the column. Thermistors and moisture sensors were placed at different depths. The pavement slab was fitted with temperature sensors throughout. The system was installed in a freezer box,wrapped in insulation and plastic. Three cold front scenarios were selected from observed airport weather data and simulated in the freezer box using varying rates of temperature decrease and precipitation. The formation of ice on a pavement surface was observed at 10–20 min after the start of precipitation. This time frame is not affected by the freezer box cooling rate. The icing time found from this study is useful for the development of prediction models for icing on pavements.more » « less
-
Surface and subsurface hydrogeochemical measurements collected from three sites in the Midtre Lovenbreen (ML) glacier forefield (Svalbard), from 31 Jul 20211 - 05 Oct 2022. Measurements consist of soil volumetric water content (meter^3/meter^3), soil temperature in Celsius (deg C), soil electric conductivity in microsiemens per centimeter (uS/cm), soil dielectric permittivity (m^3/m^3) at multiple sensor depths, surface snow depth in millimeters (mm), and near-surface air temperature at 2m (deg C). Subsurface time domain reflectometry (TDR) measurements are repeated for 24 sensors at sites 1 and 2, with four boreholes of six sensors each, with the following convention: sensorOne is the uppermost sensor in borehole 1, sensorSix is the lowermost sensor in borehole 1, sensorSeven is the uppermost sensor in borehole 2, sensorTwelve is the lowermost sensor in borehole 2. Site 3 has only 1 borehole with six sensors.Installation depths for the sensors can be found in the borehole notes file. Snow depth sensor measurements contain error from damaged sensors and are unreliable.more » « less
-
Continuous monitoring of soil nitrate levels is essential for effective soil nutrient management. However, limited soil pore water at low soil water content levels hinders miniaturized soil sensor surfaces from efficiently interacting with nutrient ions. To address this, we introduce a nanofibrous mat designed to enhance nitrate detection by increasing connectivity between miniature sensors and the soil solution. Composed of polysulfone, polymethylmethacrylate, and polyvinyl alcohol, this mat is fabricated using electrospinning and electrospray methods to balance water absorbency, mechanical durability, and ease of manufacturing. When wrapped around an ion-selective electrode-based nitrate sensor, the mat improves access to soil pore water, acts as a filter, prevents direct sensor-soil particle contact, and reduces the impact of soil particle surface charges on sensor measurements. Continuous nitrate monitoring with both mat-wrapped and bare sensors was conducted in controlled and field environments. Linear regression analysis indicates that the mat-wrapped sensor has a stronger correlation with conventional salt extract methods for measuring soil nitrate levels. T-tests confirm statistically significant differences between sensor measurements and the salt extraction method. Additionally, Bland-Altman analysis reveals that mat-wrapping reduces mean bias and narrows the limits of agreement, demonstrating improved agreement with the conventional method. Notably, the mat-wrapped sensor performs consistently across varying soil moisture conditions. These findings suggest that the water-absorbing mat improves the ability of the sensor to monitor nitrate continuously by accommodating varying soil moisture levels over time, making the mat-wrapped soil nitrate sensor a viable improvement for in-field measurements of soil solution chemistry.more » « less
-
T. Matthew Evans, Ph.D. Nina Stark (Ed.)Soil water characteristic curve (SWCC) which describes the relationship between water content and matric suction is important to analyzing unsaturated soil behavior. Because of the degree of uncertainty in field conditions due to climatic variability and soil heterogeneities, it becomes necessary to probabilistically characterize the SWCC. A satisfactory probabilistic characterization of field-based SWCCs requires a substantial data pair of water content and suction and their distribution characteristics. In this study, the kernel density estimate (KDE) approach was applied to water content and suction data measured from field-installed co-located sensors of a compacted clay bed to (1) determine the modality of water content and suction distribution and their constitutive relationship at variable weather conditions and (2) demonstrate the importance of probabilistic analysis of SWCC. The Gaussian function was used in the KDE analysis. A moisture sensor and soil water potential sensor were juxtaposed at 0.3 m depth of the 3 m × 3 m compacted clay bed to collect the water content and suction data and determine their distribution under the field condition. The density plots of both water content and suction at 0.3 m depth exhibited multimodal distribution due to the uneven distribution of climatic events. The KDE reasonably identified the air entry value, saturated moisture content, and residual moisture content in the field conditions, which were validated with field-based SWCC plots. The study showed that probabilistic analysis better interprets the realistic scenarios of field unsaturated soil behavior.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/03611981231165758
