This workshop is an extension to the submitted oral talk “Probability based stratified sampling for both mapping and estimating the population parameters of the soil health indicators at field scale”. In the beginning of the workshop, the participants will become familiar with the appropriate (fit-for-purpose) sampling designs for monitoring the soil health indicators at the field based on the presence/absence of the legacy sampling data and environmental covariates. The brief introduction will be followed by practical coding secession in R using premade computational notebooks. The participants will compare the novel probability based balanced stratified sampling algorithm with 1) simple random sampling, and 2) feature space coverage sampling algorithm using legacy data. In the first experiment, the aim will be to optimize the number of sampling locations of the classic grid sampling performed in a real-world field using the available auxiliary Earth observation environmental layers from the Soil Health Data Cube (https://shdc.ai4soilhealth.eu/). In the second experiment, we will use the available soil maps from the datacube to design a new optimized sampling network for spatiotemporal predictive modeling. The target group of the workshop are non experts in the topic, i. e. soil managers, advisors, young researchers or soil scientists.