The problem of saving soil resources and their reclamation measures under current climate change conditions attracts the world community’s close attention. It is relevant in the Syr Darya River’s lowlands, where the secondary soil salinization processes have intensified. The demand for robust methods to assess soil salinity is high, and the primary purpose of this study was to develop a quantitative analysis method for soil salinity estimation. We found a correspondence between the sum of salts in a topsoil layer to the Landsat 8 data in the Tasseled cap transformation of the image values. After testing several methods, we built a prediction model. The K-nearest neighborhood (KNN) model with a coefficient of determination equal to 0.96 using selected predictors proved to be the most appropriate for soil salinity assessment. We also performed a quantitative assessment of soil salinity. A significant increase in a salt-affected area and the mean soil sum expressing an intensification of secondary soil salinization from 2018 to 2021 was found. The increasing temperature values, decreasing soil moisture, and agricultural use affect the extension of salt-affected ground areas in the study area. Thus, the soil moisture trend in the Qazaly irrigation zone is negative and declining, with the highest peaks in early spring. The maximum temperature has a mean value of 15.6 °C (minimum = −15.1 °C, maximum = 37.4 °C) with an increasing trend. These parameters are evidence of climate change that also affects soil salinization. PCA transformation of the Landsat-8 satellite images helped to remove redundant spectral information from multiband datasets and map soil salinity more precisely. This approach simultaneously extends mapping opportunities involving visible and invisible bands and results in a smaller dataset.

Soil degradation in arid ecosystems is a major threat to sustainable development and food security, especially under accelerating climate change. Kazakhstan, where more than 70% of agricultural land suffers from salinisation, erosion, and humus loss, offers a representative case for studying climate-driven degradation. This study quantitatively assessed the influence of air temperature, precipitation, aridity index, and extreme climatic events on soil properties in the arid regions of western Kazakhstan (Atyrau and Mangystau). The analysis integrated long-term meteorological time series (1941–2023) with field and laboratory data (1967–2024) into a harmonised dataset of 1330 records. Principal component analysis (PCA) identified four degradation gradients explaining 73.6% of total variance, while Random Forest and SHAP algorithms quantified variable importance. Mean annual temperature, frequency of arid years, and aridity index were the strongest predictors of humus, salinity, pH, and CO₂ parameters, with climate factors accounting for up to 30% of soil variability. The findings demonstrate that climatic stressors are the main drivers of soil degradation in arid zones, with climate factors explaining up to 30% of the variability in key soil properties (humus, salinity, pH, and CO₂)—a substantial proportion that underscores their dominant role relative to local geochemical and anthropogenic influences. The proposed hybrid PCA—Random Forest/SHAP framework provides a robust tool for analysing climate–soil interactions and supports the design of adaptive land-use strategies to achieve Land Degradation Neutrality (LDN) in Kazakhstan and other arid regions.

Soil salinization poses a persistent threat to irrigated agroecosystems, yet remote sensing-based salinity assessment remains predominantly calibrated against bulk electrical conductivity without fully integrating crop physiological variability. This review examines the evolution of remote sensing approaches for soil salinity mapping (1994–2024), with particular emphasis on the role of crop salt tolerance in shaping spectral interpretation and mapping accuracy. A systematic synthesis of 58 peer-reviewed studies retrieved from the Scopus database was conducted using bibliometric analysis and structured full-text thematic classification to evaluate methodological trends and conceptual integration across soil, crops, and spectral domains. The results reveal substantial technological advancement, including multispectral and hyperspectral sensing, machine learning frameworks, and multi-source data integration. However, most approaches remain surface-oriented and statistically calibrated, with limited operationalization of crop-specific tolerance thresholds, root-zone salinity dynamics, and hydrochemical variability. The findings indicate that crop salt tolerance functions as a mediating factor within the soil–plant–spectral continuum, influencing the stability and transferability of spectral–salinity relationships. Integrating physiological tolerance parameters and subsurface processes into modeling frameworks is essential for improving agronomic interpretability and supporting more reliable salinity management in irrigated systems.

Accurate assessment of long-term soil organic carbon (SOC) change requires repeated field measurements and explicit treatment of temporal differences in soil mass. We quantified the net 2004–2024 SOC stock difference at 20 permanent monitoring sites in the semi-arid chestnut-soil zone of Central Kazakhstan using fixed-depth (FD) and equivalent soil mass (ESM) calculations, together with Landsat NDVI and hydroclimatic summaries. At 0–50 cm, FD SOC stock increased from 69.70 ± 17.43 to 79.39 ± 25.00 Mg C ha⁻¹, giving a mean paired difference of +9.69 Mg C ha⁻¹ (95% CI: 2.78 to 16.60; p = 0.008). After ESM adjustment to the site-specific 2004 reference soil mass, the 2024 mean was 75.43 ± 25.24 Mg C ha⁻¹ and the paired difference was +5.73 Mg C ha⁻¹ (95% CI: −0.78 to 12.25; p = 0.081). None of the five depth-specific FD comparisons remained significant after Holm adjustment. Positive annual-composite NDVI slopes occupied approximately 94.7% of the mapped area, but the site-level association between ΔNDVI and ESM-corrected ΔSOC was weak and non-significant (Pearson r = 0.259, R² = 0.067, p = 0.270; Spearman ρ = 0.241, p = 0.307). Mean aridity index declined from 0.345 in 2004–2008 to 0.308 in 2020–2024 and was interpreted as hydroclimatic context rather than a demonstrated driver of SOC change. Management–soil combinations were examined only as an exploratory site-level comparison because most combinations were unreplicated. These results show that ESM correction materially affects inference and that a two-date resampling design supports interpretation of net endpoint differences rather than continuous SOC change.

The purpose of the research is to study changes in the morphological, physicochemical properties of irrigated and rainfed soils of different types on the foothill plains of the Zhambyl region in the conditions of their intensive use with the identification of the main factors leading to their transformation. The object of the study is the soils of the Zhambyl region, which are undergoing anthropogenic transformation in the conditions of agricultural use. On the basis of a comparative analysis of paired sections of virgin and arable lands, a study of agrochemical and physicochemical indicators for chestnut, serozem, meadow and meadow-serozem soils was carried out. It has been established that degradation has a complex nature and is expressed in dehumification, a decrease in the content of mobile phosphorus and potassium, and a change in the acid-base regime. The most intensive degradation processes are recorded in chestnut and serozem soils, where humus losses reach 50–55%, and phosphorus - 30–35%. In meadow and floodplain-meadow soils, salinization is observed.

Long-term soil organic carbon (SOC) monitoring provides a rare opportunity to test the performance of process-based models against real data. We evaluated RothC using 20 georeferenced soil profiles sampled in 2004 and 2024 in the semi-arid steppe of Central Kazakhstan. We used the 2004 SOC stocks for equilibrium initialization, whereas independently measured 2024 stocks were used for endpoint evaluation. A baseline-conditioned, endpoint-uncalibrated forward simulation (S1) was driven by profile-specific climate, NPP-scaled plant-C inputs, vegetation cover, and reconstructed management; a constrained inverse analysis (S2) estimated the non-negative effective C-input multiplier required to reproduce the observed endpoint, and a zero-input simulation (S3) defined the lower feasibility boundary. S1 preserved the broad between-profile variation in absolute 2024 SOC stocks (R² = 0.692), but agreement for the observed 2004–2024 SOC change was negligible (R² < 0.001; Lin’s CCC = 0.002). The direction of SOC change was reproduced at 9 of the 20 profiles, with an RMSE of 12.49 Mg C ha⁻¹ and a mean bias of −4.41 Mg C ha⁻¹. We obtained physically admissible inverse solutions for 17 profiles, with M_required ranging from 0.42 to 4.62 (mean = 2.04; median = 1.96). Profiles 295, 299, and 313 remained below the zero-input boundary under all management-timing scenarios. Together, these results show that adjusting plant-C input can reconcile modeled and observed SOC at most profiles. However, no non-negative adjustment of plant-C input could reproduce some observed endpoints. These cases indicate that other processes or sources of uncertainty should be considered.