Showing papers by "Marcel G. Schaap published in 2004"

Calibration of Capacitance Probe Sensors using Electric Circuit Theory

Abstract: Capacitance probe sensors are an attractive electromagnetic technique for estimating soil water content. There is concern, however, about the influence of soil salinity and soil temperature on the sensors. We present an electric circuit model that relates the sensor frequency to the permittivity of the medium and that is able to correct for dielectric losses due to ionic conductivity and relaxation. The circuit inductance L is optimized using sensor readings in a modified setup where ceramic capacitors replace the sensor's capacitance plates. The three other parameters in the model are optimized using sensor readings in a range of nonconductive media with different permittivities. The geometric factor for the plastic access tube gp is higher than the geometric factor for the medium gm, indicating that most of the electromagnetic field does not go beyond the access tube. The effect of ionic conductivity on the sensor readings is assessed by mixing salts in three of the media. The influence is profound. The sensor frequency decreases with increasing conductivity. The effect is most pronounced for the medium with the lowest permittivity. The circuit model is able to correct for the conductivity effect on the sensors. However, as the dielectric losses increase, the frequency becomes relatively insensitive to permittivity and small inaccuracies in the measured frequency or in the sensor constants result in large errors in the calculated permittivity. Calibration of the capacitance sensors can be simplified by fixing two of the constants and calculating the other two using sensor readings in air and water.

Topographical attributes to predict soil hydraulic properties along a hillslope transect

Abstract: [1] Basic soil properties have long been used to predict unsaturated soil hydraulic properties with pedotransfer function (PTFs). Implementation of such PTFs is usually not feasible for catchment-scale studies because of the experimental effort that would be required. On the other hand, topographical attributes are often readily available. This study therefore examines how well PTFs perform that use both basic soil properties and topographical attributes for a hillslope in Basilicata, Italy. Basic soil properties and hydraulic data were determined on soil samples taken at 50-m intervals along a 5-km hillslope transect. Topographical attributes were determined from a digital elevation model. Spearman coefficients showed that elevation (z) was positively correlated with organic carbon (OC) and silt contents (0.62 and 0.59, respectively) and negatively with bulk density (ρb) and sand fraction (−0.34 and −0.37). Retention parameters were somewhat correlated with topographical attributes z, slope (β), aspect (cosϕ), and potential solar radiation. Water contents were correlated most strongly with elevation (coefficient between 0.38 and 0.48) and aspect during “wet” conditions. Artificial neural networks (ANNs) were developed for 21 different sets of predictors to estimate retention parameters, saturated hydraulic conductivity (Ks), and water contents at capillary heads h = 50 cm and 12 bar (103 cm). The prediction of retention parameters could be improved with 10% by including topography (RMSE = 0.0327 cm3 cm−3) using textural fractions, ρb, OC, z, and β as predictors. Furthermore, OC became a better predictor when the PTF also used z as predictor. The water content at h = 50 cm could be predicted 26% more accurately (RMSE = 0.0231 cm3cm−3) using texture, ρb, OC, z, β, and potential solar radiation as input. Predictions of ANNs with and without topographical attributes were most accurate in the wet range (0 < h < 250 cm). Semivariograms of the hydraulic parameters and their residuals showed that the ANNs could explain part of the (spatial) variability. The results of this study confirm the utility of topographical attributes such as z, β, cosϕ, and potential solar radiation as predictors for PTFs when basic soil properties are available. A next step would be the use of topographical attributes when no or limited other predictors are available.

Comparison of Models for Indirect Estimation of Water Retention and Available Water in Surface Soils

Abstract: Quantitative knowledge of the unsaturated soil hydraulic properties is required in most studies involving water flow and solute transport in the vadose zone. Unfortunately, direct measurement of such properties is often difficult, expensive and time-consuming. Pedotransfer functions (PTFs) offer a means to estimate soil hydraulic properties based on predictors like texture, bulk density, and other soil variables. In this study, we focus on PTFs for water retention and show that systematic errors in five existing PTFs can be reduced by using water content–based objective functions, instead of parameter value–based objective functions. The alternative analysis was accomplished by establishing offset and slope coefficients for each estimated hydraulic parameter. Subsequently we evaluated these and six other PTFs for estimating water retention parameters using the NRCS soils database. A total of 47435 records containing 113970 observed water contents were used to test the PTFs for mean errors and root mean square errors. No overall superior model was found. Models with many calibration parameters or more input variables were not necessarily better than more simple models. All models underestimated water contents, with values ranging from −0.0086 to −0.0279 cm 3 cm −3 . Average root mean square errors ranged from 0.0687 cm 3 cm −3 for a PTF that provided textural class average parameters to 0.0315 cm 3 cm −3 for a model that also used two water retention points as predictors. Available soil water content for vegetation was estimated with errors ranging from 0.058 to 0.080 cm 3 cm −3 , depending on the model and the definition of available water.

Data mining and exploration techniques

Abstract: Publisher Summary The data mining and exploration methods introduce algorithms that automate predictor and equation selections. This chapter describes three methods: artificial neural networks, group method of data handling (GMDH), and the regression tree that have recently been used in the pedotransfer function (PTF) development. Each of these methods has its advantages and disadvantages. For example, the advantage of regression trees is the transparency of results, whereas the advantage of neural networks is the ability to mimic practically any relationship. The disadvantage of all these techniques as compared to statistical regression is the heuristic element involved so that the rigorous statistical judgment is hard to make. The three techniques practically produce identical PTF accuracy. The database exploration is a useful step that may generate PTFs that are either sufficient for the intended application or may suggest further applications of more rigorous or more flexible PTF-building techniques.

Accuracy and uncertainty in PTF predictions

Abstract: Publisher Summary This chapter discusses optimization, accuracy, and uncertainty in pedotransfer functions (PTFs). Optimization criteria are the objective functions used to calibrate empirical parameters in PTFs. Accuracy criteria relate to the statistical methods used to test the performance of indirect methods, preferably using data distinct from those used for calibration. Uncertainty estimates provide information about the probability distribution of estimated hydraulic quantities. Thus, a PTF can make accurate estimates (i.e., it produces the correct values on average), but it may not be reliable sometimes; for example, real-world variability is larger than estimated with the PTF. Alternatively, a PTF can be deemed inaccurate when it produces estimates that, on average, differ systematically from observations.

Graphic user interfaces for pedotransfer functions

Abstract: Publisher Summary Several pedotransfer function (PTF) implementations have emerged in recent years. This chapter discusses four codes that can be easily obtained through the World Wide Web: soil-water characteristics from texture (SWCT), soilpar, Rosetta, and Neuropack. SWCT is part of the Soil Plant Atmosphere Water Field and Pond Hydrology (SPAW) package. The windows-based SPAW package is targeted at farmers and resource managers interested in water and nutrient budgeting in soil and ponds. SPAW uses SWCT to estimate soil hydraulic data, such as wilting point, field capacity, and available water content. The Soilpar program implements 10-point PTFs and 4 parametric PTFs and provides a wide range of output data. Required input data and estimated output data depend on the model used and include soil texture, organic carbon, soil pH, and cation-exchange (CEC) capacity. The program also allows fitting retention data to four types of retention equations. Rosetta is a windows-based program that implements artificial neural network results. The program implements five PTFs in a hierarchical approach. This approach maximizes the accuracy of the PTF estimates, given a particular data availability. The Neuropack software package is primarily intended to develop PTFs using neural network-based techniques using the data that are supplied by the user. The Neuropack package comes with a complete technical guide and user manual. The technical guide describes the scientific background of both programs and includes an ANN primer, an explanation of the Bootstrap Method, and a description of optimization and error criteria. The package consists of two separate programs: NeuroPath and NeuroMan.

Dielectric Constant Measurements on Lunar Soils and Terrestrial Minerals

Abstract: The return to the Moon has ignited the need to characterize the lunar regolith using in situ methods. An examination of the lunar regolith samples collected by the Apollo astronauts indicates that only a few minerals (silicates and oxides) need be considered for in situ resource utilization (ISRU). This simplifies the measurement requirements and allows a detailed analysis using simple methods. Characterizing the physical properties of the rocks and soils is difficult because of many complex parameters such as soil temperature, mineral type, grain size, porosity, and soil conductivity. In this presentation, we will show that the dielectric constant measurement can provide simple detection for oxides such as TiO2, FeO, and water. Their presence is manifest by an unusually large imaginary permittivity.

An evaluation of permeability of statistically reconstructed three-dimensional pore structures with lattice boltzmann simulations

Abstract: Water flow and related processes at the pore scale essentially occur in three dimensions (3D). Unfortunately, it is often difficult and expensive to obtain reliable “images” of the 3D pore structure. Several techniques are available to statistically generate 3D pore structures from spatial information derived from 2D microscope images of thin sections of rock and soil. The question remains, however, whether the reconstructed media are functionally identical to the 3D originals. In this study we try to answer this question by reconstructing pore structures of idealized media. Lattice Bolzmann simulations are carried out to compare the permeabilities of the original and reconstructed 3D media.

Autonomous In-Situ Resources Prospector

Abstract: This presentation will describe the concept of an autonomous, intelligent, rover-based rapid surveying system to identify and map several key lunar resources to optimize their ISRU (In Situ Resource Utilization) extraction potential. Prior to an extraction phase for any target resource, ground-based surveys are needed to provide confirmation of remote observation, to quantify and map their 3-D distribution, and to locate optimal extraction sites (e.g. ore bodies) with precision to maximize their economic benefit. The system will search for and quantify optimal minerals for oxygen production feedstock, water ice, and high glass-content regolith that can be used for building materials. These are targeted because of their utility and because they are, or are likely to be, variable in quantity over spatial scales accessible to a rover (i.e., few km). Oxygen has benefits for life support systems and as an oxidizer for propellants. Water is a key resource for sustainable exploration, with utility for life support, propellants, and other industrial processes. High glass-content regolith has utility as a feedstock for building materials as it readily sinters upon heating into a cohesive matrix more readily than other regolith materials or crystalline basalts. Lunar glasses are also a potential feedstock for oxygen production, as many are rich in iron and titanium oxides that are optimal for oxygen extraction. To accomplish this task, a system of sensors and decision-making algorithms for an autonomous prospecting rover is described. One set of sensors will be located in the wheel tread of the robotic search vehicle providing contact sensor data on regolith composition. Another set of instruments will be housed on the platform of the rover, including VIS-NIR imagers and spectrometers, both for far-field context and near-field characterization of the regolith in the immediate vicinity of the rover. Also included in the sensor suite are a neutron spectrometer, ground-penetrating radar, and an instrumented cone penetrometer for subsurface assessment. Output from these sensors will be evaluated autonomously in real-time by decision-making software to evaluate if any of the targeted resources has been detected, and if so, to quantify their abundance. Algorithms for optimizing the mapping strategy based on target resource abundance and distribution are also included in the autonomous software. This approach emphasizes on-the-fly survey measurements to enable efficient and rapid prospecting of large areas, which will improve the economics of ISRU system approaches. The mature technology will enable autonomous rovers to create in-situ resource maps of lunar or other planetary surfaces, which will facilitate human and robotic exploration.