Project properties

Title Digital Soil Mapping: Random Forest spatial interpolation
Group Soil Geography and Landscape
Project type thesis
Credits 36
Supervisor(s) Gerard Heuvelink
Examiner(s) Gerard Heuvelink
Contact info
Begin date 2022/01/03
End date
Description The thesis research will begin with a literature review of machine learning for soil mapping. Next you will study the RFSI algorithm and apply it to a test dataset. This is all done in R, using existing scripts that need slight modification. Once this is completed you will test the method for a real-world digital soil mapping case study. This involves the selection of a study area and preparing covariates and soil point observations, provided by ISRIC - World Soil Information. Application to the case study must also include a cross-validation of results, comparison with conventional random forest, and optimisation of hyperparameters, such as the number of nearest observations that should be included as covariates. The results of this MSc research will likely be very useful to the ISRIC SoilGrids project (
Used skills Statistics, machine learning, R scripting, GIS
Requirements - Solid background in statistical modelling, such as obtained through the Spatial Modelling and Statistics course
- Experience with programming in R