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[Display omitted] Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a high-performance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties (pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately 5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate (Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development. [ABSTRACT FROM AUTHOR]

In this study, a hybrid method combining 3D kriging and depth function considering heterogeneity (3DK_DF) was proposed to enhance the accuracy and reliability of 3D spatial interpolation for soil heavy metals. Soil samples collected at varied depth intervals in a mining city in China were used as a case dataset. First, the parameters of a logarithmic depth function model for every horizontal soil sample site were fitted on the basis of the observed values of soil Cd collected at varied depth intervals. Second, the 3D trend of soil Cd was obtained on the basis of the spatial distributions of the parameters of the logarithmic depth function model. Third, 3D kriging was used to generate the 3D spatial distribution of residual Cd. Finally, the 3D spatial distribution of the soil Cd was obtained by combining the 3D trend and residual results. The interpolation accuracy of 3DK_DF improved by 29.71% and 48.9% compared with those of the 3D kriging without a trend analysis and the 3D kriging with a polynomial trend model, respectively. The proposed hybrid 3D interpolation method could be of great significance for the comprehensive assessment of soil heavy metal pollution. [Display omitted] • Model a 3D trend of soil Cd using depth function with spatial heterogeneity. • A hybrid technique improved the 3D spatial interpolation accuracy. • 3D distribution generated by the hybrid method best preserve the spatial variation. [ABSTRACT FROM AUTHOR]