Satellite monitoring of agricultural crops plays a crucial role in ensuring food security and in the sustainable management of agricultural resources, particularly in regions dominated by rainfed farming, such as the Turkestan region of Kazakhstan. Many satellite monitoring tasks rely on remote identification of different types of cultivated crops. In developing the proposed method, we accounted for the temporal characteristics of crop growth and development in various climatic zones of rainfed agriculture, analyzed the dynamics of the Normalized Difference Vegetation Index (NDVI) together with ground-based data, and identified effective time periods and patterns for successful crop recognition. This study aims to develop and comparatively assess two methods for the automatic identification of cultivated crops in rainfed zones using Sentinel-2 satellite data for the years 2018 and 2022. The first method is based on detailed classification of pre-digitized field boundaries, providing high accuracy in satellite-based mapping. The second method represents a fully automated approach applied to large rainfed areas, emphasizing operational efficiency and scalability. The results obtained from both methods were validated against official national statistics, ground-based field surveys, and farm-level data. The findings indicate that the field-boundary-based method delivers significantly higher accuracy (average accuracy of 91.1%). While the automated rainfed-zone approach demonstrates lower accuracy (78%), it still produces acceptable results for large-scale monitoring, confirming its suitability for rapid assessment of sown areas. This research highlights the trade-off between the accuracy achieved through detailed field boundary digitization and the efficiency provided by an automated, scalable approach, offering valuable tools for agricultural production management.

Kazakhstan’s pasture, as a spatially extended agricultural resource for sustainable animal husbandry, requires effective monitoring with connected rational uses. Ranking number nine globally in terms of land size, Kazakhstan, with an area of about three million square km, requires proper assessment technologies for climate change and anthropogenic impact to track the pasture lands’ degradation. Remote sensing (RS)-based adaptive approaches for assessing pasture load, combined with field cross-checking of pastures, have been applied to evaluate the quality of vegetation cover, economic potential, service function, regenerative capacity, pasture productivity, and changes in plant species composition for five pilot regions in Kazakhstan. The current stages of these efforts are presented in this project report. The pasture lands in five regions, including Pavlodar (8,340,064 ha), North Kazakhstan (2,871,248 ha), Akmola (5,783,503 ha), Kostanay (11,762,318 ha), Karaganda (19,709,128 ha), and Ulytau (18,260,865 ha), were evaluated. Combined RS data were processed and the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Fraction of Vegetation Cover (FCover), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Chlorophyll Content (CCC), and Canopy Water Content (CWC) indices were determined, in relation to the herbage of pastures and their growth and development, for field biophysical analysis. The highest values of LAI, FCOVER, and FARAR were recorded in the Akmola region, with index values of 18.5, 126.42, and 53.9, and the North Kazakhstan region, with index values of 17.89, 143.45, and 57.91, respectively. The massive 2024 spring floods, which occurred in the Akmola, North Kazakhstan, Kostanay, and Karaganda regions, caused many problems, particularly to civil constructions and buildings; however, these same floods had a very positive impact on pasture areas as they increased soil moisture. Further detailed investigations are ongoing to update the flood zones, wetlands, and swamp areas. The mapping of proper flood zones is required in Kazakhstan for pasture activities, rather than civil building construction. The related sustainable permissible grazing husbandry pasture loads are required to develop also. Recommendations for these preparation efforts are in the works.

Land degradation issues are getting complicated worldwide. Kazakhstan’s land use has sharply deteriorated over several decades, necessitating comprehensive assessment and restoration. Farmlands in Kazakhstan are grappling with multiple challenges related to climate change, intense anthropogenic disturbances, and aggressive industrial agricultural practices involving monoculture crop production. Soil depletion is widespread in Kazakhstan due to flood erosion and drought expansion, causing desertification. The land sustainability of farmland improvement, including the soil, geology, and water retention assessment, is currently under investigation through our project activities in North Kazakhstan. Nature-based methods for forest plantation along contour strips and topography-based design landscapes are rarely applied or are absent in many rural areas these days. The land use issues have resulted in the loss of the soil moisture protective functions and a reduction in agricultural efficiency. Geodesy geomatics tools were applied for a topography investigation with digital elevation, digital terrain model preparation, and potential retention ponds’ location identification for managed aquifer recharge introduction. The combination of effective water accumulation methods, considering topography, with the development of protective forest shelterbelts should enhance the land use strategies for sustainable development. This strategy is expected to reduce soil erosion, promote moisture accumulation, by improving the soil’s quality as a sponge in water collection, and increase crop yields. Alongside this, a system for developing the retention ponds with managed aquifer recharge locations for proper water collection to improve the agrolandscapes was presented.

Floods are among the most frequent and destructive natural hazards in Kazakhstan, particularly in the Abai Region, Kazakhstan, where topographic, hydrological, and climatic factors strongly influence flood occurrence. This study presents a comprehensive spatial assessment of flood susceptibility in the Abai Region using a multi-criteria Geographic Information System (GIS) approach. The analysis integrates twelve flood-conditioning factors representing hydrological, topographic, environmental, and anthropogenic variables. The relative importance of these factors was determined using the Analytical Hierarchy Process (AHP). The results indicate that distance to rivers (20%) and precipitation (16%) are the most influential drivers of flood susceptibility, followed by Height Above Nearest Drainage (HAND) (11%) and drainage density (9%). The resulting flood susceptibility map classifies the study area into five susceptibility levels. Approximately 56.6% of the study area falls within the moderate susceptibility class, while 25.0% is categorized as high susceptibility, mainly concentrated in low-lying floodplains and foothill regions. Low-susceptibility areas account for 18.1% of the region, whereas the very high and very low susceptibility classes together represent less than 1% of the territory. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis, yielding an Area Under the Curve (ROC–AUC) value of 0.893, indicating strong agreement between predicted susceptibility patterns and observed flood occurrences. Additional validation metrics derived from the confusion matrix show an overall accuracy of 83.3%, precision of 0.75, recall of 1.0, and a Kappa coefficient of 0.67, confirming reliable predictive performance. Sensitivity analysis with ±10% variation in factor weights further demonstrated the spatial stability of the susceptibility results. The resulting susceptibility map provides an important spatial basis for infrastructure planning, flood mitigation, and disaster preparedness in the Abai Region and offers a transferable framework for flood-susceptibility assessment in other semi-arid regions of Central Asia.

This study analyzed precipitation and air temperature in the Zhambyl, Almaty, Zhetysu, Abay, and East Kazakhstan regions of Kazakhstan using data from the national meteorological network of the RSE Kazhydromet. The purpose of the study was to reveal the climatic changes and their spatial distribution throughout the study area. A modified Mann–Kendall test and Sen’s Slope estimator were applied to analyze aridity conditions in combination with the drought indices SPEI and Selyaninov hydrothermal coefficient, enabling analysis of the magnitude and statistical significance of trend changes from April to September for the period 1981 to 2023. The magnitude of the observed trends of the mean growing-season temperature increased by 0.211 °C decade⁻¹, while precipitation declined by 2.074 mm decade⁻¹, which indicates a decrease in moisture availability for crops in the southeast and east of Kazakhstan. The results of this study may be of interest to agricultural specialists, ecologists, the Ministry of Emergency Situations, and hydrologists to develop activities aimed at preventing threats and mitigating the effects of climate change in Kazakhstan. The use of the above statistical methods in combination with drought indices is relevant in the context of climate change and worsening food security and can serve as a good indicator for determining when significant changes in climatic parameters occurred, which will be valuable information for making management decisions.

This study addresses the issue of accurate and rapid aboveground biomass estimation in rangeland ecosystems, as traditional grazing methods are labor-intensive, while modern remote sensing techniques often require expensive equipment and controlled conditions. The goal of this work is to develop an efficient and accessible approach for biomass estimation of natural pastures based on ground-level RGB images captured with smartphones. For this purpose, a dataset consisting of 1196 field images and corresponding biomass values collected from 40 districts in southern Kazakhstan was used, and a wide + tiles architecture based on the DINOv3 model of Vision Transformer was proposed. The model utilized attention pooling and feature fusion mechanisms to integrate both global and local features, and various preprocessing and augmentation strategies were comparatively examined. Experimental results demonstrated that the proposed method exhibits high accuracy (with the best result being R² = 0.733, MAE ≈ 0.779 c/ha), where the DINOv3 model showed clear advantages over ConvNeXtV2. Furthermore, the impact of preprocessing strategies was minimal, and the importance of high-resolution images was clearly established. The obtained results show that the proposed method performs consistently under heterogeneous field conditions and allows for reliable biomass estimation without the need for specialized equipment. This makes it a practical tool for monitoring pastures, planning forage supply, and supporting agronomic decision-making.