In previous chapters, the use of geostatistical modelling for soil mapping was addressed. We learnt that one of the advantages of kriging is that it not only produces a map of predictions but that it also quantifies the uncertainty about the predictions, through the kriging standard deviation. In this chapter we will look into this in more detail. We will also examine another way to assess the accuracy of soil prediction maps, namely, through independent validation. This approach has the advantage that it is model-free and hence makes no assumptions about the structure of the spatial variation and relationships between the target soil property and covariates. Finally, we will examine how uncertainties in soil maps propagate through environmental models and spatial analyses. Throughout this chapter we will use the Allier data set and case study, Limagne rift valley, central France, to illustrate concepts and methods. We will only consider soil properties that are measured on a continuous-numerical scale. Many of the concepts presented can also be extended to categorical soil variables, but this is more complicated and beyond the scope of this chapter.
|Title of host publication||Pedometrics|
|Editors||A.B. McBratney, B. Minasny, U. Stockmann|
|Publication status||Published - 2018|
|Name||Progress in Soil Science|
Heuvelink, G. B. M. (2018). Uncertainty and uncertainty propagation in soil mapping and modelling. In A. B. McBratney, B. Minasny, & U. Stockmann (Eds.), Pedometrics (Progress in Soil Science). Springer.