• DOI 10.31509/2658-607x-202361-120
  • УДК 631.4


N.V. Gopp1, J. L. Meshalkina2, A. N. Narykova3, A. S. Plotnikova3, O. V. Chernova4


1Institute of Soil Science and Agrochemistry of the Siberian Branch of the Russian Academy of Sciences pr. Akademika Lavrentieva 8/2, Novosibirsk, 630099, Russian Federation

2Lomonosov Moscow State University
Leninskie Gory 1 bldg. 12, Moscow, 119234, Russian Federation

3Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Profsoyuznaya st. 84/32 bldg. 14, Moscow, 117997, Russian Federation


4A. N. Severtsov Institute of Ecology and Evolution

Leninskii pr. 33, Moscow, 119071, Russian Federation

E-mail: gopp@issa-siberia.ru

Received 04.02.2023

Revised: 18.03.2023

Accepted: 20.03.2023

This paper provides an overview of scientific publications in Russia and other countries devoted to the soil organic carbon (SOC) content and stocks mapping at regional and local levels. The analysis showed that the cartographic assessment of the SOC content and stocks was conducted using various approaches that the choice depends on the multiple factors: the size of the territory (continental, national, regional, local levels); the cartographic basis availability (maps of soil types, of landscapes, of vegetation formations, remote sensing data, etc.) and laboratory and field surveys data. Two main approaches were generally used for SOC content and stocks mapping: (1) based on available thematic maps; (2) digital soil mapping. The review also provides the analysis of all spatial predictors that were used in collected papers in concordance with the SCORPAN model widely used in digital soil mapping. Spatial terrain data was one of the most commonly used predictors, followed by the vegetation and climate variables. The accuracy of predictive maps significantly increased by using soil maps. The reviewed studies showed that climate variables had a significant impact on the spatial variation of the SOC content and stocks at the regional level, while at the local level the influence of climatic variables was less significant. The analysis showed that the most common methods used in digital mapping were machine learning algorithms. Random Forest method often showed the best results. Results were cross-validated almost in all studies. Tests of the map’s accuracy using an external independent validation dataset were rare, although this was the most important stage of digital soil mapping. R was the most popular software, that was used for modeling the SOC content and stocks. SAGA GIS, QGIS, ArcGIS, and cloud platform Google Earth Engine (GEE) were most commonly used to prepare predictors.


Key words: digital soil mapping, soil predictors, machine learning, Random Forest, Regression Kriging, Support Vector Machine, cross-validation, bootstrap, Gradient Boosting, monitoring



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