Predictive Mapping of Organic Carbon Content in Soils of Russia Using Ensemble Machine Learning

The study reflects an understanding of individual factors regulating and controlling the content of organic carbon of soils, and shows a modern quantitative assessment of the content of organic carbon of soils in Russia, taking into account its huge variability. Paper presents the results of three-dimensional modeling of the organic carbon content of soils with 500 m spatial resolution at several standard depths (0–5, 5–15) to the territory of the Russian Federation using ensemble machine learning. Automated predictive mapping was based on 4 961 soil horizons from 863 soil profiles, and an extensive set of spatial information, including bioclimatic variables, a digital elevation model and its derivatives, and long-term averaged time series of MODIS data. The results of spatial cross-validation show lower (when compared with randomized) accuracy: the coefficient of determination is 0.46, CCC 0.63, RMSE 1.41 g/kg. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.