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| Titel |
A linear mixed model, with non-stationary mean and covariance, for soil potassium based on gamma radiometry |
| VerfasserIn |
K. A. Haskard, B. G. Rawlins, R. M. Lark |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 7, no. 7 ; Nr. 7, no. 7 (2010-07-02), S.2081-2089 |
| Datensatznummer |
250004889
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| Publikation (Nr.) |
 copernicus.org/bg-7-2081-2010.pdf |
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| Zusammenfassung |
| In this paper we present a linear mixed model for the potassium content of
soil across a large region of eastern England in which the mean is modelled
as a linear function of the passive gamma-ray emissions of the earth surface
in the energy interval commonly associated with potassium decay.
Non-stationary models are proposed for the random effect, which is the
variation not captured by this regression. Specifically, we assume that the
local spectrum of the standardized random effect can be obtained by tempering
a common (stationary) spectrum, that is to say raising its values to a power,
the tempering parameter, which is itself modelled as a linear function of the
radiometric data. This allows the "smoothness" of the random effect to vary
locally. In addition the local spatially correlated variance and "nugget"
variance (apparently uncorrelated given the resolution of the sampling) can
also be modelled as a function of the radiometric data. Using the radiometric
signal as a covariate gave some improvement in the precision of predictions
of soil potassium at validation sites. In addition, there was evidence that
non-stationary models for the random effect fitted the data better than
stationary models, and this difference was statistically significant.
Non-stationary models also appeared to describe the error variance of
predictions at the validation sites better. Further work is needed on
selection among alternative non-stationary models, since simple procedures
used here, based on comparing log-likelihood ratios of nested models and the
Akaike information criterion for non-nested models, did not identify the
model which gave the best account of the prediction error variances at
validation sites. |
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