Cite as:
Lie&szlig;, M. (2015): <b>Sampling for regression-based digital soil mapping: Closing the gap between statistical desires and operational applicability</b>. <i>Spatial Statistics</i> <b>13</b>, 106-122.

Resource Description

Title: Sampling for regression-based digital soil mapping: Closing the gap between statistical desires and operational applicability
FOR816dw ID: 1409
Publication Date: 2015-06-17
License and Usage Rights: IPR Ließ<br/> Copyright Elsevier
Resource Owner(s):
Individual: Mareike Ließ
With respect to sampling for regression-based digital soil mapping<br/> (DSM), the above all aim is to ensure that the spatial variability<br/> of the soil is well-captured without introducing any bias, while<br/> the design remains feasible according to operational constraints<br/> such as accessibility, man power and cost. Representativeness of<br/> the sample concerning the population to be sampled needs to be<br/> guaranteed in any regression-based modelling approach. Four selected<br/> sampling designs were adapted to show that basically any<br/> design may be optimised to represent the n-dimensional predictor<br/> space of a particular area, while selecting points is only permitted<br/> from a small accessible sub-area or from outside the area. Sampling<br/> efficiency may be evaluated based on the representation of<br/> the predictor space. However, not only each predictor’s probability<br/> function but also the interaction between predictors may have to<br/> be considered, to select a representative sample. Instead of sampling<br/> a previously un-sampled area with limited accessibility, the<br/> four sampling designs may also be used to subsample an existing<br/> dataset and, thereby, optimise a suboptimal dataset based on the<br/> predictor space of the area which shall be mapped by DSM.
| sampling design | digital soil mapping | regression |
Literature type specific fields:
Journal: Spatial Statistics
Volume: 13
Page Range: 106-122
Publisher: Elsevier
Metadata Provider:
Individual: Mareike Ließ
Online Distribution:
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