As a consequence of global population growth and increasing demand for agricultural commodities, vast areas of cultivable land have been brought under cultivation, while natural ecosystems are being converted for agricultural use. This transformation has led to various forms of soil degradation, with excessive salinity emerging as a critical concern. Drought-prone ecosystems are particularly vulnerable to salinization processes, resulting in an annual reduction of arable land by approximately 1% to 2%. In the Kazakhstan, salt-affected soils account for approximately 43% of all agricultural land. The aim of this study is to assess the extent and spatial distribution of soil salinity in the Talas district of Kazakhstan and to identify the dominant chemical characteristics of soil salts across different land types. To achieve this, soil samples were collected from river valleys, desert pastures, irrigated lands, and sandy areas. The analysis focused on salinization processes, ion toxicity thresholds, and the chemical composition of soil salts. Results reveal that salt accumulation varies between the 30 cm and 80 cm soil layers in river valleys and desert pastures, whereas the highest concentrations are observed in the upper layers of irrigated soils. The greatest variation in average salt content by soil texture was ?0.81% in loamy soils and ?0.62% in silty loam soils. These findings highlight the urgent need for ecologically sound land management strategies to mitigate soil salinization, especially in irrigated areas. They also offer valuable insights for enhancing irrigation efficiency and preserving soil fertility in Talas and other comparable regions.

This study employs the robust capabilities of Google Earth Engine (GEE) to analyze and forecast land cover and land use changes in the Talas District, situated within the Zhambyl region of Kazakhstan, for a period spanning from 2000 to 2030. The methodology involves thorough image selection, data filtering, and classification using a Random Forest algorithm based on Landsat imagery. This study identifies significant shifts in land cover classes such as herbaceous wetlands, bare vegetation, shrublands, solonchak, water bodies, and grasslands. A detailed accuracy assessment validates the classification model. The forecast for 2030 reveals dynamic trends, including the decline of herbaceous wetlands, a reversal in bare vegetation, and concerns over water bodies. The 2030 forecast shows dynamic trends, including a projected 334.023 km² of herbaceous wetlands, 2271.41 km² of bare vegetation, and a notable reduction in water bodies to 24.0129 km². In quantifying overall trends, this study observes a decline in herbaceous wetlands, bare vegetation, and approximately 67% fewer water bodies from 2000 to 2030, alongside a rise in grassland areas, highlighting dynamic land cover changes. This research underscores the need for continuous monitoring and research to guide sustainable land use planning and conservation in the Talas District and similar areas.

Differences in resolution, spectral response, and spatial support complicate the joint use of UAV and Sentinel-2 data for mapping soil electrical properties. We developed a scale-matched machine learning framework for in situ soil electrical conductivity (EC) across four irrigated sites in southern Kazakhstan. Random Forest (RF) and Elastic Net (EN) were evaluated using nested leave-one-site-out cross-validation. For UAV data, RF with median aggregation within a 5 m buffer performed best (RMSE = 0.211 dS m⁻¹, Q² = 0.367) and retained positive Q² at all withheld sites. Spatial-support matching increased cross-sensor associations, whereas low Lin’s concordance coefficients (0.105–0.135) indicated limited numerical agreement. For Sentinel-2, EN showed the best independent predictive performance and was retained as the primary mapping model, while RF was retained for secondary comparison. Model-specific Areas of Applicability covered 66.08% of the mapped area for EN and 71.92% for RF. All EN predictions exceeding the observed field maximum occurred outside its AOA. Field EC was strongly associated with laboratory-determined total water-soluble salts (Spearman’s ρ = 0.911). Because ECe was not measured, the maps represent spatial variability of in situ EC rather than standard agronomic salinity classes. Sentinel-2 supports regional screening, while UAV imagery resolves finer spatial variability. These findings contribute to sustainable irrigated agriculture by supporting soil salinity monitoring and informed land management decisions aimed at reducing land degradation risks.