Degree Type


Date of Award


Degree Name

Master of Science


Agricultural and Biosystems Engineering

First Advisor

Amy Kaleita


Identifying and understanding the impact of within-field soil moisture patterns is currently limited by the time and resources required to do sufficient monitoring. The spatial and temporal variance of soil moisture complicates the ability to monitor and effectively predict soil moisture values. Remote sensing offers non-invasive techniques to measure soil moisture, but the resolution is too coarse to be of immediate value in many of the applications requiring soil moisture information. Obtaining high resolution soil moisture data requires dense sensor networks to adequately monitor changing spatial and temporal soil moisture patterns. The aim of this study is to develop methods to estimate soil moisture values at the field scale without the need for exhaustive pre-sampling. This is achieved by finding critical sampling locations within the field based upon topographic and soils data that can adequately predict field scale soil moisture. Given these sampling locations and values for soil moisture at those points, an interpolation method is developed that is independent of the spatial relationship between the sampling locations and the points to be interpolated. Ultimately, these approaches can be used as a method to find critical sampling points and interpolate field-scale soil moisture values based upon topographic and soils data that can be collected in a one pass operation and thus eliminate the need for extensive soil moisture monitoring.


Copyright Owner

Zachary James Van Arkel



Date Available


File Format


File Size

89 pages