Digital mapping of soil particle size fractions across Taiwan

Soil particle size fractions (PSF) directly affect the movement and retention of water, nutrients, and air, which are critical for providing optimal conditions for crop growth. This study applies three log-ratio transformation techniques for compositional data: additive log-ratio (ALR), centered log-ratio (CLR), and isometric log-ratio (ILR). Digital soil mapping (DSM) combined environmental covariates (satellite images, terrain features, and climatic data) and machine learning (ML) models (Cubist and random forest (RF)) to create a predictive map for soil particle size distribution across Taiwan. Model accuracy was evaluated using R², root mean squared error (RMSE), Aitchison’s distance (AD), and the right ratio of the predicted soil texture types (RR). The analysis revealed that CLR transformation combined with RF (RF_CLR) had the best performance, with the highest R² values (sand: 0.59, silt: 0.29, clay: 0.51), as well as the lowest RMSE (sand: 16.51%, silt: 10.65%, clay: 8.45%) and AD (0.08). The accuracy of RR (45%) was consistent across different log-ratio transformation methods. The various sampling sizes influence the computational efficiency of the data. Therefore, different sampling sizes were tested for the best-performing combination (RF_ CLR). When the sampling size was less than 50% of the original sampling size (N = 22,000), the prediction performance of PSF showed a significant decline. Our findings can serve as a valuable reference for soil management and crop cultivation planning in Taiwan.