Sampling Design Optimization for Soil Mapping with Random Forest

Machine learning techniques are widely employed to generate digital soil maps. The map accuracy is partly determined by the number and spatial locations of the measurements used to calibrate the machine learning model. However, determining the optimal sampling design for mapping with machine learning techniques has not yet been considered in detail in digital soil mapping studies. In this paper, we investigate sampling design optimization for soil mapping with random forest. A design is optimized using spatial simulated annealing by minimizing the mean squared prediction error (MSE). We applied this approach to mapping soil organic carbon for a part of Europe using subsamples of the LUCAS dataset. The optimized subsamples are used as input for the random forest machine learning model, using a large set of readily available environmental data as covariates. We also predicted the same soil property using subsamples selected by simple random sampling, conditioned Latin Hypercube sampling (cLHS), spatial coverage sampling and feature space coverage sampling. Distributions of the estimated population MSEs are obtained through repeated random splitting of the LUCAS dataset, serving as the population of interest, into subsets used for validation, testing and selection of calibration samples, and repeated selection of calibration samples with the various sampling designs. The differences between the medians of the MSE distributions were tested for significance using the non-parametric Mann-Whitney test. The process was repeated for different sample sizes. We also analyzed the spread of the optimized designs in both geographic and feature space to reveal their characteristics. Results show that optimization of the sampling design by minimizing the MSE is worthwhile for small sample sizes. However, an important disadvantage of sampling design optimization using MSE is that it requires known values of the soil property at all locations and as a consequence is only feasible for subsampling an existing dataset. For larger sample sizes, the effect of using an MSE optimized design diminishes. In this case, we recommend to use a sample spread uniformly in the feature (i.e. covariate) space of the most important random forest covariates. The results also show that for our case study, cLHS sampling performs worse than the other sampling designs for mapping with random forest. We stress that comparison of sampling designs for calibration by splitting the data just once is very sensitive to the data split that one happens to use if the validation set is small.