Jeonghwan Choi

Comparing uncertainty quantification methods for Random Forest-based digital soil mapping

Abstract. Uncertainty quantification is essential for using machine learning-based soil maps in decision-making, but the uncertainty communicated to users can depend strongly on the method used. This is especially relevant for soil organic carbon (SOC), where spatial predictions are used in carbon accounting, land mana...