The problem of salinization/spreading of saline soils is becoming more urgent in many regions of the world, especially in context of climate change. The monitoring of salt-affected soils’ properties is a necessary procedure in land management and irrigation planning and is aimed to obtain high crop harvest and reduce degradation processes. In this work, a machine learning method was applied for modeling of the spatial distribution of topsoil (0–20 cm) properties—in particular: soil organic carbon (SOC), pH, and salt content (dry residue). A random forest (RF) machine learning approach was used in combination with environmental variables to predict soil properties in a semi-arid area (Trans-Ural steppe zone). Soil, salinity, and texture maps; topography attributes; and remote sensing data (RSD) were used as predictors. The coefficient of determination (R²) and the root mean square error (RMSE) were used to estimate the performance of the RF model. The cross-validation result showed that the RF model achieved an R² of 0.59 and an RMSE of 0.68 for SOM; 0.36 and 0.65, respectively, for soil pH; and 0.78 and 1.21, respectively for dry residue prediction. The SOC content ranged from 0.8 to 2.8%, with an average value of 1.9%; soil pH ranged from 5.9 to 8.4, with an average of 7.2; dry residue varied greatly from 0.04 to 16.8%, with an average value of 1.3%. A variable importance analysis indicated that remote sensing variables (salinity indices and NDVI) were dominant in the spatial prediction of soil parameters. The importance of RSD for evaluating saline soils and their properties is explained by their absorption characteristics/reflectivity in the visible and near-infrared spectra. Solonchak soils are distinguished by a salt crust on the land surface and, as a result, reduced SOC contents and vegetation biomass. However, the change in saline and non-saline soils over a short distance with mosaic structure of soil cover requires high-resolution RSD or aerial images obtained from unmanned aerial vehicle/drones for successful digital mapping of soil parameters. The presented results provide an effective method to estimate soil properties in saline landscapes for further land management/reclamation planning of degraded soils in arid and semi-arid regions.

It has been revealed that, in the years when the freezing depth of leached chernozems on slopes in the southern Cis-Ural region is in the range of 30–38 cm, no surface runoff is observed in the spring regardless of the snow cover, soil wetting, or vegetation; the critical freezing depth under forest is half that on the open surfaces. When the freezing depth is deeper, the critical volume of the spring runoff is mainly determined by the meteorological conditions, the water permeability of the soil, and its protection by plants.

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 R 2, the best predictive models were obtained for content of nitrogen (R 2 = 0.74), SOC (R 2 = 0.66), and potassium (R 2 = 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 effect of rainfall intensity on the erosion of residual calcareous agrogray soils and clay-illuvial agrochernozems in the Southern Cis-Ural region on slopes of different inclination and vegetation type has been studied by simulating with a small-size sprinkler. It has been shown that soil loss linearly depends on rainfall intensity (2, 4, and 6 mm/min) and slope inclination (3° and 7°). When the rainfall intensity and duration, and the slope inclination increase, soil loss by erosion from agrogray soils increases higher than from agrochernozems. On the plowland with a slope of 3°, runoff begins 12, 10, and 5 min, on the average, after the beginning of rains at these intensities. When the slope increases to 7°, runoff begins earlier by 7, 6, and 4 min, respectively. After the beginning of runoff and with its increase by 1 mm, the soil loss from slopes of 3° and 7° reaches 4.2 and 25.7 t/ha on agrogray soils and 1.4 and 4.7 t/ha on agrochernozems, respectively. Fallow soils have higher erosion resistance, and the soil loss little depends on the slope gradient: it gradually increases to 0.3–1.0 t/ha per 1 mm of runoff with increasing rainfall intensity and duration. The content of physical clay in eroded material is higher than in the original soils. Fine fractions prevail in this material, which increases their humus content. The increase in rainfall intensity and duration to 4 and 6 mm/min results in the entrapment of coarse silt and sand by runoff.

This article aims to explore the use of machine learning (ML) methods for mapping the distribution of mercury (Hg) content in topsoil, using the city of Ufa (Russia) and adjacent areas as an example. For this purpose, a soil dataset of 250 points sampled from a 0–20 cm depth on different land uses, including residential, industrial and undisturbed (forests and parks), was used. Random Forest (RF), Extreme Gradient Boosting (XGboost), Cubist and k-Nearest Neighbor (kNN) ML techniques were employed to model and map the Hg concentrations. We used remote sensing data (RSD) and topographic attributes as explanatory variables. ML models were calibrated and validated using the leave-one-out cross-validation approach. The Hg content varied from 0.005 to 0.58 mg/kg and was characterized by very high variability. According to the MAE and RMSE metrics, the RF method resulted in the most accurate spatial prediction for the Hg content (0.029 and 0.065 mg/kg, respectively), while the XGBoost approach showed the lowest prediction efficiency (0.032 and 0.073 mg/kg, respectively). The results showed that the slope map, spectral index MSI and Sentinel-2A band B11 were the key variables in explaining the variability of Hg content. We found that higher uncertainty values of soil Hg were found in croplands, urban residential and industrial areas, which supports the view that spatial modelling of HM in urban landscapes is challenging. The present study provides insights into the potential of digital soil mapping techniques in combination with RSD and terrain variables for identifying areas at risk of Hg contamination in urban areas, which can inform land-use planning and management strategies to protect human health and the environment.