Lysimeter experimental studies were conducted in a greenhouse to investigate canola (Brassica Napus) plant water use, growth and yield parameters under three different water table depths of 30, 60, and 90 cm. Additionally, control experiments were conducted and only irrigation was applied to these lysimeters without water table limitation. Canola plant’s tolerance level to shallow groundwater was determined. Results showed that groundwater contributions to canola plant were 97, 71, and 68%, while the average grain yields of canola were 4.5, 5.3, and 6.3 gr for the treatments of 30, 60, and 90 cm water table depths, respectively. These results demonstrated that 90 cm water table depth is the optimum depth for canola plant to produce high yield with the least amount of water utilization.

The Global L-band Observatory for Water Cycle Studies (GLOWS) is designed as a follow-on to the Soil Moisture Active Passive (SMAP) observatory launched in 2015. While GLOWS is essentially copying many aspects of the SMAP mission, a key change has been made in the antenna technology. SMAP uses a reflector antenna and to reduce mission costs GLOWS uses a metamaterial lens antenna. This type of antenna is less efficient, so it must be proven that GLOWS can achieve the same uncertainty levels in soil moisture measurements as SMAP. In this work, a unified framework for modeling and analyzing GLOWS' ability to meet all mission and measurement requirements is developed. A model for the uncertainty effects of the lens antenna is developed and used to show that so long as the lens efficiency is above a threshold determined by the accuracy of the lens physical temperature knowledge, GLOWS will also be able to achieve all measurement requirements. It is shown that GLOWS is able to copy the design parameters of SMAP and achieve the same mission requirements.

Weather stations provide key information related to soil moisture and have been used by farmers to decide various field operations. We first evaluated the discrepancies in soil moisture between a weather stations and nearby field; due to soil texture, crop residue cover, crop type, growth stage and duration of temporal dependency to recent rainfall and evaporation rates using regression analysis. The regression analysis showed strong relationship between soil moisture at the weather station and the nearby field at the late vegetative and early reproductive stages. The correlation thereafter declines at later growth stages for corn and wheat. We can adduce that the regression coefficient of soil moisture with four-day cumulative rainfall slightly increased with an increase in the crop residue resulting in a low root mean square error (RMSE) value. We then investigated the effectiveness of machine learning techniques such as random forest regression (RFR), boosted regression trees (BRT), support vector regression, and artificial neural network to predict soil moisture in nearby fields based on RMSE of a 30% validation dataset and to determine the relative importance of predictor variables. The RFR and BRT performed best over other machine learning algorithms based on the lower RMSE values of 0.045 and 0.048 m3 m-3, respectively. The Classification and Regression Trees (CART), RFR and BRT models showed soil moisture at nearby weather stations had the highest relative influence for moisture prediction, followed by the four-day cumulative rainfall and Potential Evapotranspiration (PET), and subsequently followed by bulk density and Saturated Hydraulic Conductivity (Ksat). We then evaluated the integration of weather station data, RFR machine learning, and remotely sensed satellite imagery to predict soil moisture in nearby fields. Soil moisture predicted with an RFR algorithm using OPtical TRApezoidal Model (OPTRAM) moisture values, rainfall, standardized precipitation index (SPI) and percent clay showed high goodness of fit (r2=0.69) and low RMSE (0.053 m3 m-3). This research shows that the integration of weather station data, machine learning, and remote sensing tools can be used to effectively predict soil moisture in the Red River Valley of the North among a large diversity of cropping systems.

Failure of viable seed to germinate results in poor stands and often in lower yields. Some of the more important factors that affect germination of seed are temperature, moisture, aeration, and alkalinity.

A mathematical expression was developed and tested which describes the relation between evapotranspiration and soil moisture. A general premise of this mathematical model is that the evapotranspiration-soil moisture relationship is determined by interaction of climatic, soil and plant factors. The basic model is dETa/dYs = -ke[1-(ETa/ETmx)] in which ETa is the actual evapotranspiration, Ys is the total soil water potential, k is a proportionality coefficient , E is the soil moisture extraction capacity of the atmosphere , and ETmx is the evapotranspiration that would occur from a particular crop-soil unit when soil moisture was not limiting. From this model the following expression was derived: ETa = (1/1+(Ys/Ys’)^[2.56/log (Ymi/Ymx)])g E0 where Ymx', Ys', Ymi are the soil potentials at which ETa is equal to 95%, 50% and 5% of ETmx' respectively; E 0 is the evaporation from a free water surface and expressed the evaporative demands of the atmosphere. The term g is a proportionality coefficient equal to ETmx/E0. A second formula was developed that expresses the same relationship in terms of soil water content, and was derived from the former by assuming a hyperbolic relationship between soil water potential and water content. These formulas, as well as various other models which are described in the literature, were tested using experimental data covering a wide range of climatic, soil and plant variables. It was concluded that: (a) Most models advocated in the literature are only adequate to describe the relation between evapotranspiration and soil moisture under particular climatic , soil and plant conditions. (b) The formulas derived from the proposed model provide a good fit for the evapotranspiration-soil moisture relationship under widely different circumstances. If proper values are chosen for the coefficients, these formulas yield relations that are similar to several of the models taken from the literature. Consequently, the proposed mathematical expression appears to be a general model of the manner in which plants use soil water under different vegetative and environmental conditions. (c) It seems possible to predict in a comparative way the pattern of soil water utilization in a soil-plant-atmosphere system . This may be done from a knowledge of the relations between the coefficients of the formulas and climatic, soil and plant factors influencing evapotranspiration.

This thesis introduces the implementation of different supervised learning techniques for producing accurate estimates of soil moisture content using empirical information, including meteorological and remotely sensed data. The models thus developed can be extended to be used by the personal remote sensing systems developed in the Center for Self-Organizing Intelligent Systems (CSOIS). The dfferent models employed extend over a wide range of machine-learning techniques starting from basic linear regression models through models based on Bayesian framework. Also, ensembling methods such as bagging and boosting are implemented on all models for considerable improvements in accuracy. The main research objective is to understand, compare, and analyze the mathematical backgrounds underlying and results obtained from dfferent models and the respective improvisation techniques employed.

During 1966 and 1967, a range plant clipping study was conducted at two locations in central Utah's Ephraim Canyon. It was found that extreme clipping treatment resulted in a significant increase in soil moisture due to a presumed decrease in transpiration during 1967. At the lower location, 7,100 feet elevation, a difference of 5.4 inches over a 5 foot soil profile was noted between the extreme and control clipping treatments in 1967. At the upper location, 10,000 feet in elevation, a savings of 3.6 inches soil moisture was observed over the 5 foot soil profile in 1967. Other clipping intensities also showed water savings in terms of reduced depletion values over the control plots, although these differences were not in all cases statistically significant.

The movement of water in soils is of great importance to all of us but especially to agriculturalists. If it were not for this movement plants would not be able to survive in soil. If the moisture moves too freely in the soil insufficient water can be stored to supply plants during dry periods. The movement of water through soil may remove plant nutrients or accumulate salts in soil horizons.

The object of current efforts at investigating catchment response is to derive a physically based stochastic model of the watershed. Recent studies have, however, indicated that a limiting factor in deriving such models is the dependence of hydrologic response on initial soil moisture. The dependence affects the distributions and moments of the hydrological processes being investigated. A stochastic model of soil moisture dynamics is developed in the form of a pair of stochastic differential equations (SDE's) of the Ito type. The sources of stochasticity are linked to the random inputs of rainfall and evapotranspiration (ET). One of the SDE's describes the "surplus" case, in which sufficient infiltration always occurs to allow for moisture depletion by the processes of drainage through and ET out of the root zone. The other SDE represents the "deficit" case, in which lack of adequate moisture leads only to an ET-controlled depletion process. Sample functions and moments of moisture evolution are obtained from the SDE's. From the general model of soil moisture, a specific model of initial soil moisture (the moisture at the beginning of a rainstorm event) is developed and its moments are derived. Furthermore, the probability distribution of initial moisture is postulated to permit the assessment of how initial moisture affects the estimation of hydrologic response. The moisture dynamics model reveals that the stochastic properties of moisture ae sensitive to initial conditions in the watershed only for less permeable soils under the "surplus" state but are practically insensitive to such conditions for more permeable soils. The stochastic properties are also less sensitive to initial conditions for all soil types whenever under the "deficit" state. These results suggest that hydrologic processes, such as precipitation excess and infiltration, depend on initial moisture only in regions where the soils are generally less permeable and where the climate tends to sustain a "wet" environment, whereas in arid or semi-arid regions, such processes would not depend on initial moisture. These conclusions imply that, in arid regions, an effective value of initial moisture such as the mean can be used to estimate the properties of the hydrologic processes, whereas in "wet" environments, more accurate values of the properties must be "weighted" based on the probability distribution of initial soil moisture.

With over 10,000 acres burned in wildland fires in 2014 in Mississippi, accurate fire hazard prediction is of great importance. Soil moisture, fuel moisture, and fire hazard are inextricably linked. Remote estimation of soil moisture in the Southeastern United States for fire hazard modeling is hampered by the use of models engineered for other physiographic regions and the prevalence of deep, fast-draining sands underneath heavy vegetation. United States Geologic Service hydrographs were investigated and compared to nearby soil moisture and precipitation readings in an attempt to identify the links between stream gauge readings and watershed soil moisture. Stream gauge peaks corresponded within a three day window of soil moisture peaks 73.3% of the time, with 43.8% of peaks occurring simultaneously, thus verifying the indicative nature of local hydrographs. With further study, this easily accessed proxy variable could enhance currently used soil moisture models and drought indices.