Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes.

The agricultural use of soils is limited by their contamination with various compounds and low contents of nutrients. We aimed to study the unique soils of the Yamal Experimental Station to determine their contamination with heavy metals and assess their potential fertility. Established in 1932, the Yamal Experimental Station (Salekhard, Russia) has bred new varieties of vegetable crops in open and protected ground. In August 2021, we made a soil section and 40 pits in a 0–10 cm layer. X-ray fluorescence was used to determine 11 metals and oxides. The qualitative assessment was based on the total soil pollution, soil pollution, and geoaccumulation indexes. Finally, we determined the contents of nutrients. The metals and metal oxides showed regressive-accumulative distribution along the soil profile. The concentrations of all ecotoxicants (except for arsenic) were within the maximum/approximate permissible values. Since arsenic has a high regional background content, its elevated concentrations make the soil suitable for agricultural use if proper quality control is in place. The total soil pollution index classified the level of pollution as “acceptable”. The geoaccumulation index showed the soils as mostly “unpolluted” with metals. The soil pollution index had values below 1, which indicated the absence of pollution. The fallow soils of the Yamal Experimental Station have a high level of potential fertility and are suitable for agricultural reuse according to the soil quality indexes applied. They can also serve as a local geochemical standard that has a long history of agrogenic transformation in cryogenic ecosystems. Taking into account increased concentrations of arsenic, we recommend primary quality control of agricultural products to identify its possible migration in the soil-plant system.

This study aimed to map soil organic carbon under erosion processes on an arable field in the Republic of Bashkortostan (Russia). To estimate the spatial distribution of organic carbon in the Haplic Chernozem topsoil, we applied Sentinel-2A satellite data and the linear regression method. We used 13 satellite bands and 15 calculated spectral indices for regression modelling. A regression model with an average prediction level has been created (R2 = 0.58, RMSE = 0.56, RPD = 1.61). Based on the regression model, cartographic materials for organic carbon content have been created. Water flows and erosion processes were determined using the calculated Flow Accumulation model. The relationship between organic carbon, biological activity, and erosion conditions is shown. The 13C-NMR spectroscopy method was used to estimate the content and nature of humic substances of different soil samples. Based on the 213C-NMR analysis, a correlation was established with the spectral reflectivity of eroded and non-eroded soils. It was revealed that the effect of soil organic carbon on spectral reflectivity depends not only on the quantity but also on the quality of humic substances and soil formation conditions.

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