GEOSTATISTICAL METHODS FOR ESTIMATING SOIL PROPERTIES (KRIGING, COKRIGING, DISJUNCTIVE).
AuthorYATES, SCOTT RAYMOND.
KeywordsSoil mineralogy -- Arizona -- Tucson Region.
Soils -- Arizona -- Tucson Region -- Statistical methods.
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PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractGeostatistical methods were investigated in order to find efficient and accurate means for estimating a regionalized random variable in space based on limited sampling. The random variables investigated were (1) the bare soil temperature (BST) and crop canopy temperature (CCT) which were collected from a field located at the University of Arizona's Maricopa Agricultural Center, (2) the bare soil temperature and gravimetric moisture content (GMC) collected from a field located at the Campus Agricultural Center and (3) the electrical conductivity (EC) data collected by Al-Sanabani (1982). The BST was found to exhibit strong spatial auto-correlation (typically greater than 0.65 at 0⁺ lagged distance). The CCT generally showed a weaker spatial correlation (values varied from 0.15 to 0.84) which may be due to the length of time required to obtain an "instantaneous" sample as well as wet soil conditions. The GMC was found to be strongly spatially dependent and at least 71 samples were necessary in order to obtain reasonably well behaved covariance functions. Two linear estimators, the ordinary kriging and cokriging estimators, were investigated and compared in terms of the average kriging variance and the sum of squares error between the actual and estimated values. The estimate was obtained using the jackknifing technique. The results indicate that a significant improvement in the average kriging variance and the sum of squares could be expected by using cokriging for GMC and including 119 BST values in the analysis. A nonlinear estimator in one variable, the disjunctive kriging estimator, was also investigated and was found to offer improvements over the ordinary kriging estimator in terms of the average kriging variance and the sum of squares error. It was found that additional information at the estimation site is a more important consideration than whether the estimator is linear or nonlinear. Disjunctive kriging produces an estimator of the conditional probability that the value at an unsampled location is greater than an arbitrary cutoff level. This latter feature of disjunctive kriging is explored and has implications in aiding management decisions.
Degree ProgramSoils, Water and Engineering