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Russo, D., Div. of Soil Physics, Agric. Res. Organis., Volcani Center, Bet Dagan, Israel
Jury, W.A., Div. of Soil Physics, Agric. Res. Organis., Volcani Center, Bet Dagan, Israel
The effect of the sampling network on the estimates of covariance functions of two-dimensional, isotopic, second-order stationary processes, characterized by different underlying correlation scales, was analyzed. Estimates of the covariance function based on a standard systematic sampling network were compared with estimates of the covariance functions based on a modified sampling network. Results of these comparisons showed that the modified sampling network was better suited than the systematic network to resolving the statistical properties of the underlying stochastic process at small separation distances. This may result in an improvement in structure identification. Analyses of the effect of the sampling network used for estimating the covariance function on the results of conditional inference procedures such as kriging showed that the kriging estimates were essentially insensitive to the geometric configuration of the sampling network. Uncertainties about these estimates were reduced when using the modified sampling network. -from Authors
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Effect of the sampling network on estimates of the covariance function of stationary fields
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Russo, D., Div. of Soil Physics, Agric. Res. Organis., Volcani Center, Bet Dagan, Israel
Jury, W.A., Div. of Soil Physics, Agric. Res. Organis., Volcani Center, Bet Dagan, Israel
Effect of the sampling network on estimates of the covariance function of stationary fields
The effect of the sampling network on the estimates of covariance functions of two-dimensional, isotopic, second-order stationary processes, characterized by different underlying correlation scales, was analyzed. Estimates of the covariance function based on a standard systematic sampling network were compared with estimates of the covariance functions based on a modified sampling network. Results of these comparisons showed that the modified sampling network was better suited than the systematic network to resolving the statistical properties of the underlying stochastic process at small separation distances. This may result in an improvement in structure identification. Analyses of the effect of the sampling network used for estimating the covariance function on the results of conditional inference procedures such as kriging showed that the kriging estimates were essentially insensitive to the geometric configuration of the sampling network. Uncertainties about these estimates were reduced when using the modified sampling network. -from Authors
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