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.

The territory of the Degelen mountain massif is located within territory of the former Semipalatinsk nuclear test site and it is an area of ecological disaster. Currently there is a process of secondary radioactive contamination that is caused by geodynamic processes activated at the Degelen array, violation of underground hydrological cycles and as a consequence, water seepage into the tunnels. One of the methods of monitoring of geodynamic processes is the modern technology of geographic information systems (GIS), methods of satellite radar interferometry and high accuracy satellite navigation system in conjunction with radioecological methods. This paper discusses on the creation of a GIS-project for the Degelen array, facilitated by quality geospatial analysis of the situation and simulation of the phenomena, in order to maximize an objective assessment of the radiation situation in this protected area.

The results of the project presented in this article were obtained within the framework of the implementation of the Republican budget program 010 "Ensuring the safety and expansion of the use of space infrastructure» from the Aerospace Committee of the Ministry of Defense and the aerospace industry of the Republic of Kazakhstan. Since 2018, the National Operator of the Earth Remote Sensing Space System (JSC NC Kazakhstan Gharysh Sapary) has begun work on studying geographic information systems for conducting space-based monitoring and forest management of the world. In Kazakhstan, forest area occupies less than 5% of the total area of the territory and therefore the question of monitoring of forest resources is particularly relevant: changes in forest area, number of loggings, number of burnt areas, fire danger forecast for forest cover, etc. Given the importance of forest as a climate and resource-forming factor, forest resources, changes in forest resources are estimated as one of the priority of scientific and practical directions. The article presents the results of using the Earth Remote Sensing data and geo-information technologies in the assessment of forest cover of Kazakhstan, the legality of loggings and estimation of the burnt areas, etc. As the result of space-based monitoring, according to the Earth remote sensing data, also confirmed by field surveys in key areas, for the first time ever all forests of Kazakhstan were decrypted, digital maps of forests of Kazakhstan were created with the detailed attribute information up to the species composition of forests, age and other specific information provided by the State Enterprise «Kazakh Forest Management enterprise», several thousand of logging were detected including illegal loggings, burnt areas were also decrypted and all data were uploaded to the industrial geoportal with up-to-date attribute information.

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.

The results of researches presented in article were obtained within implementation of the scientific and technical program for program-targeted financing of applied scientific research on space activities for 2018-2020 by the Aerospace Committee of the Ministry of Defense and Aerospace Industry of the Republic of Kazakhstan. Considering the high production significance of soil resources for Kazakhstan as the main means of production, soil fertility assessment, methods of rational use of land resources is estimated as a priority scientific direction. The article shows the application of the remote sensing (RS) data and GIS-technologies for the assessment of the soil fertility of the main soils types in Northern Kazakhstan. Kazakhstan is the ninth biggest country in the world, the production resource of arable lands takes more than 18.3 million hectares. Operational assessment of such data is possible only with use of RS methods. Kazakhstan, having own space system of ERS, has the powerful tool for effective management of land resources, operative and reliable monitoring of agricultural production and economic activity. It is established that the pre-sowing content of basic nutrients on the main soil types have the considerable impact on development of cultural plants biomass which is registered by satellites through vegetation indices. On the basis of empirical dependencies using the universal equation by reporting year the particular dependence of significance of some soil nutrients with derivates of vegetation indices according to the RS data is revealed. Results of space monitoring and also the confirmed ground-truth observations noted high anthropogenic influence on the leading soil-forming processes, high coefficient of familiarity of agricultural grounds and the developing processes of soil cover degradation in Northern Kazakhstan.