A mixed model for landscape soil organic carbon prediction across continuous profile depth in the mountainous subtropics

Abstract Due to the spatial variability of soil resources in rapidly changing landscapes, such as rubber expansion areas in mountainous South East Asia, landscape based soil organic carbon (SOC) stock assessments need new approaches to obtain cost effective high-resolution soil maps. 3D modelling presents the opportunity to model changes of soil properties with soil depth and in space in one single model. While most 3D models make use of spatial autocorrelation to create soil maps, it might be feasible for upscaling to neglect the spatial autocorrelation and only model autocorrelation within the soil profiles. We propose a “mixed model over continuous depth” (MMCD), which uses a linear and quadratic term to model changes of soil properties with depth and predicts the spatial distribution of soil properties at the landscape level. As the study area of 43 km2 in South West China was subject to multiple constraints such as sparse road networks, steep terrain, and poor infrastructure, we applied the cost-constrained conditioned Latin hypercube sampling (CCLHS) scheme for soil sampling at 120 locations to a depth of 1 m. The MMCD provides information on the most important drivers of selected soil properties, and their relative importance. In this study, SOC was strongly linked to an interaction of elevation with mean horizon depth (p