Module 3 · Digital soil mapping

Modelling and mapping soil properties with Quantile Regression Forest: covariate selection (Boruta), training, accuracy assessment and tiled prediction with uncertainty.

Format: 2 sessions · Level: Intermediate · Runs in: Google Colab (R) · Material: SoilFER Training Manual + Training Resources

Read the manual chapter

What you will learn

• build the regression matrix from soil points and covariates
• select covariates and tune a Quantile Regression Forest
• assess accuracy with cross-validation
• predict mean and uncertainty maps by tiles and mosaic them

Notebooks

Each notebook opens in Google Colab with the R runtime. Run the Setup cell first: it downloads the training project, installs the R packages and gets the course data. Then run the cells in order.

Notebook
Helper · Soil property classes (pH and clay × pH)	Open in Colab.ipynb
Modelling and mapping SOC with Quantile Regression Forest	Open in Colab.ipynb

TipRun time

The Kansas example runs in a free Colab session. For a whole country, reduce the area or the resolution, or use a Colab Pro runtime with more memory.

First time in Colab? Read Getting started.