Thesis (M.S.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.

Soil moisture and temperature are two important soil parameters that influence many vital agronomic, environmental, engineering processes within the soil. Due to the difficulties arising when measuring these parameters in the field as well as the cost of instrumentation, many models that yield accurate and timely estimation of these parameters on a large scale have been developed as reliable and efficient alternatives. The Versatile Soil Moisture Budget model can be used to stimulate the vertical, one dimensional, water balance in a soil profile. Originally the model was designed to use air temperature and precipitation data to simulate soil water content within the root zone of a cereal crop. It has since undergone modifications and the model can now output, potential evapo-transpiration, actual evapo-transpiration, and surface temperature. The temperature algorithm simulates temperature at the soil surface and has not been rigorously tested for cropping systems. In this study, a simple empirical equation that simulates soil temperature at depth of up to 90 cm was introduced into the model. The model was evaluated and the accuracy of predicted soil moisture and temperature under both perennial and annual cropping systems were tested using two years of data collected at the University of Manitoba Research Station at Carman using soil water and temperature probes. The model’s accuracy in simulating soil moisture was also tested. Observed R2 comparing modelled temperature with observed was greater than 0.90 at the soil surface but decreased to about 0.40 at soil depth greater than 30-45 cm layer. The model was shown to be better at estimating soil temperature than soil moisture. The accuracy of the model was also shown to decrease with depth. These results can be used to improve soil temperature modeling at depth as well as improve farm management planning, irrigation schedules, nutrient management, fertilizer application and drought monitoring. / October 2015

Spatial variability of porous media often prevents precise physical characterization of the system. In order to model moisture and solute transport through this media, certain sacrifices in precision must be made. Physical characteristics of the system must be averaged over large scales, lumping the small scale variability into the large scale characterization. Although this precludes a precise definition of the small scale flow characteristics, parameterization is much more attainable. This study addresses methods for determining effective hydraulic conductivity of unsaturated porous media. Effective conductivity is used to describe the large scale behavior of the system. Different methods for calculating the effective conductivity are presented and compared. Results indicate that the unit mean gradient method produces good estimates of the effective conductivity and can be applied using limited field data. The zone of correlation of the hydraulic parameters can be used in experimental design to minimize the errors associated with estimation of the mean pressure. An inverse method for evaluating the optimum effective hydraulic parameters is presented. Results indicate the optimization procedure is more sensitive to wetting than to drying conditions. Because of interaction between the hydraulic parameters, concurrent optimization of more than two of the parameters based on soil pressure data alone is not advised. Anisotropy in an unsaturated soil was found to be a function of the profile mean soil pressure. Results indicate the effective conductivity for flow parallel to soil layering can be estimated from the arithmetic mean of the unsaturated conductivity values for each of the layers and is between the harmonic and geometric means of these data for flow perpendicular to the layering. Estimates of the effective unsaturated hydraulic conductivity obtained through stochastic analysis agreed well with simulation results. Deviations between the stochastic predictions and simulation results are larger when the variability of the soil profile is greater and begin to deviate significantly when the variance of ln K(ψ₀) exceeds 5.0 and the variance of a exceeds 0.02 1/cm².

Increasing irrigation efficiency on turfgrass could help reduce water consumption on large turf facilities. Two experiments were conducted using perennial ryegrass (Lolium perenne (L.) Derby) to evaluate the potential of using remote sensing to estimate turf water status, predict daily evapotranspiration (ET), and estimate turf biomass. In the first experiment a crop water stress index, utilizing remotely sensed canopy temperature, were used to schedule irrigations on 6 of 10 drainage lysimeters. Three of the remaining lysimeters were irrigated used on meteorological estimates of ET calculated using a modified Penman equation. The results of this experiment were inconclusive due to inconsistent lysimeter drainage characteristics. The second experiment was conducted on a turf green with multiple heights to evaluate the potential for using canopy radiance to estimate turf biomass. These results showed that turf biomass could be estimated from a vegetative index (Red Ratio = Near Infrared/Red radiance) obtained through measurements of canopy radiance (r2 = 0.91).

published_or_final_version / Civil Engineering / Master / Master of Philosophy

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Existing techniques measure soil moisture either at a point or over a large area many kilometers across. To bridge these two scales, we used the mobile cosmic-ray probe, or cosmic-ray rover, an instrument similar to the recently developed COSMOS probe, but bigger and mobile. This study explores the challenges and opportunities for making maps of soil moisture over large areas using the cosmic-ray rover. In 2012, soil moisture was mapped 22 times in a 25 km x 40 km survey area of the Tucson Basin at 1 km² resolution, i.e., at a scale comparable to that of a pixel for the Soil Moisture and Ocean Salinity (SMOS) satellite mission. The soil moisture distribution is influenced mainly by climatic variations, notably by the North American monsoon, which resulted in a systematic change in the regional variance as a function of the mean soil moisture.

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