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The study was conducted on the territory of the "Nasibash" site of the Eurasian carbon polygon, where 9 vegetation communities were identified. The approach to estimation and construction of soil temperature distribution maps using Landsat 8-9 space images is presented. Evaluation of vegetation response to soil temperatures was carried out using vegetation indices: NDVI, GNDVI, EVI, CVI. The use of statistics of the obtained rasters made it possible to reveal the dependence of the values of vegetation indices on soil temperature. GNDVI demonstrated the greatest relationship: r = 0.82, R2 = 0.65. Further, the effect of soil temperature on individual plant communities was evaluated. Plots with 4 (r=0.74, R2 = 0.55) and 3 stages (r=0.47, R2 = 0.23) of pine (Pinus sylvesrtis L.) overgrowth and hayfield (r=, 0.54, R2 = 0.29) showed the highest correlations.

The state of the microbial community is an essential factor determining the processes of soil restoration of disturbed soils. Quarrying and mining complexes are valuable sites for studying soil formation processes. Four different quarries (gypsum, sand–gravel mixture, sand, and clay) of the Republic of Bashkortostan were surveyed to determine the status of the microbial community of post-technogenic soils. Amplicon libraries of the 16s rDNA gene were analyzed. It was shown that the microbial communities of different soil horizons of quarries differ significantly. According to the results of the analysis, quarry communities demonstrate differences in the microbial composition of different horizons, while in some cases, the upper horizon is richer.

Understanding the dynamics of SOM in different land use types is critical for effective land management and climate mitigation strategies. In this study, we investigated the differences in soil organic matter (SOM) content between forest and grassland landscapes in the mountainous zone of Republic of Bashkortostan, Russia. We collected soil samples from multiple locations under both land use types and analysed them for SOM content using standard laboratory techniques. The results showed that the SOM content varied from 2.2 to 15.3% under forest landscape with an average of 6.7% and was characterized by high variability (51.8%). The SOM concentrations under grassland ranged from 2.1 to 6.5% with an average of 3.2%, while the coefficient of variation was 31.8%. According to the classification, the average SOM value in forest soils was classified as “high”, while in grassland soils it was classified as “low”. Overall, the variability in SOM content within forested landscapes can be influenced by a range of factors, including topography, erosion, and redeposition of soil. These factors contributed to the complex patterns of SOM accumulation and decomposition observed in forest soils, and should be considered when evaluating the potential impacts of land use change on SOM. Our study highlights the importance of understanding the factors that influence SOM content in soils, and the need for careful management of land use systems to maintain or enhance soil fertility and productivity.

Land salinization is an up-to-date issue being broadly studied all over the world. In Russia, salinization processes are predominantly observed in the southern regions, where the main areas of arable land are situated. This research is devoted to mapping of saline lands with the help of satellite data. The study was performed on a 100-hectare plot in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). A correlation was determined between the level of soil salinity and the main spectral indices associated with Sentinel-2A satellite data. Regression models used 5 salinity indices, vegetation index NDVI, and values of soil conductivity. Linear, quadratic, and logarithmic functions were used. By calculation, the salinity index 5 (G×R)/B demonstrated the best correlation values with the salinity level of (R=0.88, R2=0.77) while using the quadratic function. The vegetation index NDVI revealed no correlation, owing to the poor development or dried-up condition of vegetation. On the basis of the developed regression models, salinity maps are drawn, in which the areas of solonchak complexes are defined.