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.

Farmers in Kazakhstan face unreliable water resources. This includes water scarcity in the summer, high fluctuations in precipitation levels, and an increase in extreme weather events such as snow, rain, floods, and droughts. Wheat production is regulated and subsidized by the Kazakh government to strengthen food security. The proper monitoring of crop production is vital to government agencies, as well as insurance and banking structures. These organizations offer subsidies through different levels support. Some farmers already use farmland soil monitoring combined with adaptive combinations of different crops. These include winter–spring plowing crop programs. Winter wheat crops are generally more adaptive and may survive summer droughts. Kazakhstan is a large country with large plots of farmland, which are complicated to monitor. Therefore, it would be reasonable to adapt more efficient technologies and methodologies, such as remote sensing. This research work presents a method for identifying winter wheat crops in the foothills of South Kazakhstan by employing multi-temporal Sentinel-2 data. Here, the researchers adapted and applied a Plowed Land Index, derived from the Brightness Index. The methodology encompasses satellite data processing, the computation of Plowed Land Index values for the swift recognition of plowed fields and the demarcation of winter wheat crop sowing regions, along with a comparative analysis of the acquired data with ground surveys.

This study analyzes the interannual dynamics of fallow land extent in the North Kazakhstan Region during 2021–2025 using Sentinel-2 data. Fallow fields were identified through a rule-based multi-temporal approach in which the adapted Plowed Land Index (PLI) was used as the principal indicator of mechanically processed soil surfaces, while the NDVI was applied as a supporting indicator to exclude actively vegetated fields and control for vegetation overgrowth. Annual fallow masks were generated within a unified seasonal observation window and subsequently analyzed at the district level. The results revealed pronounced interannual variability in fallow land extent, including both reductions and partial recovery over the study period, together with substantial spatial heterogeneity among districts. The largest fallow area was recorded in 2021, whereas the minimum was observed in 2024. Comparison with district-level hydrothermal coefficient (HTC) values showed that lower moisture availability generally corresponded to larger fallow areas, while relatively more favorable hydrothermal conditions were associated with their reduction. At the same time, district-specific trajectories indicate that interannual dynamics were controlled not only by agroclimatic variability but also by differences in land-use structure and agricultural management. The proposed approach confirms the applicability of Sentinel-2 time series for regional monitoring of fallow lands and demonstrates the methodological value of the adapted PLI for identifying mechanically processed fallow surfaces under the heterogeneous agricultural conditions of North Kazakhstan.

In the article features of temperature regime and heat resources in the North-Kazakhstan region are considered on the basis of long-term meteorological data. Spatial and temporal analysis of solar radiation, temperature regime, climate continentality, date of onset and duration of seasons of the year, climate favorability for recreation, heat availability of vegetation period, frosts and unfavorably hot days on the territory of North-Kazakhstan region has been carried out. On basis of heat resources the agricultural crops provided with heat are determined. The obtained results are of practical value for assessment of climatic risks, planning of agricultural activity and development of adaptation measures in conditions of changing climate of North-Kazakhstan region.