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How should a spatial-coverage sample design for a geostatistical soil survey be supplemented to support estimation of spatial covariance parameters?

Lark, Murray; Marchant, B.P.

Authors

MURRAY LARK MURRAY.LARK@NOTTINGHAM.AC.UK
Professor of Geoinformatics

B.P. Marchant



Abstract

We use an expression for the error variance of geostatistical predictions, which includes the effect of uncertainty in the spatial covariance parameters, to examine the performance of sample designs in which a proportion of the total number of observations are distributed according to a spatial coverage design, and the remaining observations are added at supplementary close locations. This expression has been used in previous studies on numerical optimization of spatial sampling, the objective of this study was to use it to discover simple rules of thumb for practical geostatistical sampling. Results for a range of sample sizes and contrasting properties of the underlying random variables show that there is an improvement on adding just a few sample points and close pairs, and a rather slower increase in the prediction error variance as the proportion of sample points allocated in this way is increased above 10 to 20% of the total sample size. One may therefore propose a rule of thumb that, for a fixed sample size, 90% of sample sites are distributed according to a spatial coverage design, and 10% are then added at short distances from sites in the larger subset to support estimation of spatial covariance parameters.

Journal Article Type Article
Publication Date Jun 1, 2018
Journal Geoderma
Print ISSN 0016-7061
Electronic ISSN 1872-6259
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 319
APA6 Citation Lark, M., & Marchant, B. (2018). How should a spatial-coverage sample design for a geostatistical soil survey be supplemented to support estimation of spatial covariance parameters?. Geoderma, 319, https://doi.org/10.1016/j.geoderma.2017.12.022
DOI https://doi.org/10.1016/j.geoderma.2017.12.022
Keywords Spatial sampling; Prediction variance; Geostatistics
Publisher URL https://www.sciencedirect.com/science/article/pii/S0016706117309187?via%3Dihub
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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