Katherine R. Grote

Mapping the volumetric soil water content of a California vineyard using high-frequency GPR ground wave data

Water distribution in the top 1 m of the earths surface soil layer often controls the success of agricultural crops. In this near-surface zone, large spatial and temporal variations in soil water content are associated with soil heterogeneities, topography, land cover, evapotranspiration, and precipitation. Conventional techniques of measuring soil water content for agricultural purposes—e.g., time domain reflectometry (TDR), neutron probe, or gravimetric techniques, are intrusive and provide information at a point scale only, which is often inadequate for capturing the variations in soil water content with sufficient resolution. Both passive and active remote sensing methods have also been investigated as a tool to provide soil water content in the top 0–5 cm of the subsurface over large spatial areas and in a rapid manner. However, it is still a challenge to obtain information about soil water content from remote sensing data in the presence of a mature crop cover. At the spatial and temporal scales ne...

Characterization of Soil Water Content Variability and Soil Texture using GPR Groundwave Techniques

Characterization of Soil Water Content Variability and Soil Texture using GPR Groundwave Techniques Katherine Grote 1 , Cale Anger 2 , Bridget Kelly 3 , Susan Hubbard 4 and Yoram Rubin 5 Department of Geology, University of Wisconsin - Eau Claire, Eau Claire, WI 54702 Email: grotekr@uwec.edu Department of Geology and Geophysics, University of Minnesota, Minneapolis, MN 55455 Email: ange0075@umn.edu Department of Geosciences, University of Nebraska – Lincoln, Lincoln, NE 68588 Email: bridget_kelly@gmail.com Geophysics Department, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 Email: sshubbard@lbl.gov (Note: Hubbard’s contribution is supported as part of the Subsurface Science Scientific Focus Area funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Number DE-AC02-05CH11231.) Civil and Environmental Engineering, University of California – Berkeley, Berkeley, CA 94720-1710 Email: rubin@ce.berkeley.edu ABSTRACT Accurate characterization of near-surface soil water content is vital for guiding agricultural management decisions and for reducing the potential negative environmental impacts of agriculture. Characterizing the near-surface soil water content can be difficult, as this parameter is often both spatially and temporally variable, and obtaining sufficient measurements to describe the heterogeneity can be prohibitively expensive. Understanding the spatial correlation of near-surface soil water content can help optimize data acquisition and improve understanding of the processes controlling soil water content at the field scale. In this study, ground penetrating radar (GPR) methods were used to characterize the spatial correlation of water content in a three acre field as a function of sampling depth, season, vegetation, and soil texture. GPR data were acquired with 450 MHz and 900 MHz antennas, and measurements of the GPR groundwave were used to estimate soil water content at four different times. Additional water content estimates were obtained using time domain reflectometry measurements, and soil texture measurements were also acquired. Variograms were calculated for each set of measurements, and comparison of these variograms showed that the horizontal spatial correlation was greater for deeper water content measurements than for shallower measurements. Precipitation and irrigation were both shown to increase the spatial variability of water content, while shallowly-rooted vegetation decreased the variability. Comparison of the variograms of water content and soil texture showed that soil texture generally had greater small-scale spatial correlation than water content, and that the variability of water content in deeper soil layers was more closely correlated to soil texture than were shallower water content measurements. Lastly, cross-variograms of soil texture and water content were calculated, and co-kriging of water content estimates and soil texture measurements showed that geophysically-derived estimates of soil water content could be used to improve spatial estimation of soil texture. Introduction Accurate estimates of soil water content are important for maximizing crop yield, efficiently applying irrigation, and minimizing the potential environmental impacts of farming. Crop yield is partially influenced by soil water content; crop yield will decrease if the soil water content is below a crop-specific range (van Wijk, 1988; Williams et al., 1990; Dry et al., 2000). Crop yield is also affected by fertilization, and the soil must have a favorable water content to allow plants to fully absorb the nutrients in fertilizers and to achieve high nutrient efficiency (Fageria, 1992). Thus, crop yield can be maximized and nutrients can be applied most efficiently when the soil water content is well characterized across a field. In addition to crop yield, the quality

GPR Monitoring of Volumetric Water Content in Soils Applied to Highway Construction and Maintenance

Detailed knowledge of the subsurface water content is important for highway design, maintenance, and repair. Transportation engineers can monitor the water content of subasphalt soils to estimate the soil stiffness as an index of the likely performance of a pavement and to evaluate the need for subsurface drainage retrofits. Conventional approaches for measuring water content include gravimetric sampling, time-domain reflectometry (TDR), and neutron probes, all of which are time-consuming and invasive. Additionally, each of these methods provides only point measurements; because soil moisture content can vary greatly over space and time, point measurements are of limited value when surveying over a large area and over a period of time. An alternative to these conventional methods is ground-penetrating radar (GPR), which can be used to quickly collect continuous, high-resolution water content estimates. GPR techniques can be used to estimate water content due to the sensitivity of electromagnetic velocity ...

Characterization of soil water content variability and soil texture using GPR groundwave techniques

Characterization of Soil Water Content Variability and Soil Texture using GPR Groundwave Techniques Katherine Grote 1 , Cale Anger 2 , Bridget Kelly 3 , Susan Hubbard 4 and Yoram Rubin 5 Department of Geology, University of Wisconsin - Eau Claire, Eau Claire, WI 54702 Email: grotekr@uwec.edu Department of Geology and Geophysics, University of Minnesota, Minneapolis, MN 55455 Email: ange0075@umn.edu Department of Geosciences, University of Nebraska – Lincoln, Lincoln, NE 68588 Email: bridget_kelly@gmail.com Geophysics Department, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 Email: sshubbard@lbl.gov (Note: Hubbard’s contribution is supported as part of the Subsurface Science Scientific Focus Area funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award Number DE-AC02-05CH11231.) Civil and Environmental Engineering, University of California – Berkeley, Berkeley, CA 94720-1710 Email: rubin@ce.berkeley.edu ABSTRACT Accurate characterization of near-surface soil water content is vital for guiding agricultural management decisions and for reducing the potential negative environmental impacts of agriculture. Characterizing the near-surface soil water content can be difficult, as this parameter is often both spatially and temporally variable, and obtaining sufficient measurements to describe the heterogeneity can be prohibitively expensive. Understanding the spatial correlation of near-surface soil water content can help optimize data acquisition and improve understanding of the processes controlling soil water content at the field scale. In this study, ground penetrating radar (GPR) methods were used to characterize the spatial correlation of water content in a three acre field as a function of sampling depth, season, vegetation, and soil texture. GPR data were acquired with 450 MHz and 900 MHz antennas, and measurements of the GPR groundwave were used to estimate soil water content at four different times. Additional water content estimates were obtained using time domain reflectometry measurements, and soil texture measurements were also acquired. Variograms were calculated for each set of measurements, and comparison of these variograms showed that the horizontal spatial correlation was greater for deeper water content measurements than for shallower measurements. Precipitation and irrigation were both shown to increase the spatial variability of water content, while shallowly-rooted vegetation decreased the variability. Comparison of the variograms of water content and soil texture showed that soil texture generally had greater small-scale spatial correlation than water content, and that the variability of water content in deeper soil layers was more closely correlated to soil texture than were shallower water content measurements. Lastly, cross-variograms of soil texture and water content were calculated, and co-kriging of water content estimates and soil texture measurements showed that geophysically-derived estimates of soil water content could be used to improve spatial estimation of soil texture. Introduction Accurate estimates of soil water content are important for maximizing crop yield, efficiently applying irrigation, and minimizing the potential environmental impacts of farming. Crop yield is partially influenced by soil water content; crop yield will decrease if the soil water content is below a crop-specific range (van Wijk, 1988; Williams et al., 1990; Dry et al., 2000). Crop yield is also affected by fertilization, and the soil must have a favorable water content to allow plants to fully absorb the nutrients in fertilizers and to achieve high nutrient efficiency (Fageria, 1992). Thus, crop yield can be maximized and nutrients can be applied most efficiently when the soil water content is well characterized across a field. In addition to crop yield, the quality