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.

The effect of long-term (about 25 years) use of different farming practices on a set of soil properties and development of erosion in Lower Austria has been studied. Three tillage systems—zero, or no-till (NT); minimum, or conservation (CS); and conventional (CV)—are compared. The comparison demonstrates that the properties of Typic Argiudols (Luvic Phaeozems), formed on steep (13.2%) slopes, change depending on both the tillage type and the position on the slope. Unlike the CV tillage, the soil-saving technologies provide higher contents of nutrients, silt, and clay, as well as better water permeability and water stability of soil aggregates. Despite an almost doubled amount of lumpy fractions (>10 mm), the soil aggregate states after NT and CS tillage are estimated as “excellent”. Independently of the tillage system, all agrochemical, electrophysical, and hydrophysical parameters (except for pH and bulk density) increase downward the slope, which is associated with erosion, namely, the washout of suspended sediments by water flows. The C_org content in the soil tightly correlates with the water stability of soil aggregates (r = 0.91), the concentration of soluble humic substances and fine solids (SAK; r = 0.76), and electroconductivity (r = 0.75). An anti-erosion efficiency of tillage practice increases in the series CV–CS–NT. The NT or CS systems are recommended for the erosion-prone slopes of Alpine foothills.

This study analyzes the 137Cs behavior in the ponds of Okuma Town from 2015 to 2019 in the Fukushima Dai-ichi nuclear power plant (FDNPP) exclusion zone. A decline in both particulate and dissolved 137Cs activity concentrations was revealed. The decline rate constants for the particulate 137Cs activity concentration were found to be higher than for the dissolved 137Cs activity concentration. In terms of seasonality the dissolved 137Cs concentrations were higher from June to October, depending on the specific pond and year, most likely due to temperature dependence of 137Cs desorption from frayed edge sites of micaceous clay minerals. The apparent Kd(137Cs) in the studied ponds, in absolute value, appeared to be much higher than that for closed and semi-closed lakes of the Chernobyl contaminated area; however, these were comparable to the values characteristic of the rivers and reservoirs of the FDNPP contaminated area. The apparent Kd(137Cs) in the suspended sediment-water system was observed to decrease over time. It was hypothesized that this trend was associated with the decomposition of glassy hot particles. Relying on the theory of selective sorption and fixation, the exchangeable radiocesium interception potential, RIPex(K) was estimated using data on 137Cs speciation in the surface bottom-sediment layer and its distribution in the sediment-water system. For the studied ponds, RIPex(K) was on the average 2050 mEq/kg, which is within the range of values measured in laboratory studies reported in the literature.

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.