Geostatistics for soil mapping

This tutorial shows how to map a soil property with geostatistical (kriging) approaches using the statistical software R. A sample dataset from the Geul river valley in the south of the Netherlands is used for this purpose. The tutorial consists of five parts:

1. ‘Analyzing and modelling spatial variation’: creating spatial data plots and fitting a semivariogram.

2. ‘Ordinary kriging’: spatial interpolation using ordinary kriging.

3. ‘Spatial stochastic simulation’: creating multiple realizations of a soil variable using Gaussian simulation.

4. ‘Regression kriging’: spatial interpolation using regression kriging

5. ‘Universal kriging’: spatial interpolation using universal kriging, a more elegant way to use predictors (covariates) in a kriging framework.

Each section contains a number of questions to stimulate learning. Answers to these questions can be found in section 6.

After completing this tutorial you will be able to:

• model spatial variation with a semivariogram.
• create soil maps with different forms of kriging and assess the uncertainty of the predictions.
• generate possible realities with spatial stochastic simulation.

Materials

• Tutorial
  
• Scripts and datasets

License: This tutorial is released under the GNU GPL v3.0 license. GNU GPL v3.0 is a strong copyleft license. This means that you may use the code and change/modify the code. If you distribute copies or modifications of the code, you are required to release these updates under the GPL v3 license.

Disclaimer: This tutorial is provided without warranty. ISRIC is not obliged to provide updates or “bug fixes” of any kind. ISRIC will not provide user support for this tutorial.

Even though this tutorial is created with utmost care, ISRIC cannot be held liable for any damage caused by using this tutorial or any content therein in whatever form, whether or not caused by possible errors or faults nor for any consequences thereof.