Аңдатпа
This study explores the use of Unmanned Aerial Vehicles “UAVs” and Remote Sensing “RS” data to assess the productivity of agricultural lands in various regions of Kazakhstan. The primary focus is on analyzing vegetation indicators such as the Normalized Difference Vegetation Index “NDVI” and their relationship with the agrochemical properties of the soil. A comparative analysis was performed between satellite multispectral imaging and UAV-based aerial photography to determine the most accurate methods for predicting spring barley yield. The integration of RS technologies with traditional agricultural methods has proven to reduce environmental impact, minimize soil degradation, and enhance the stability of agroecosystems. Field experiments took place in three different soil-climatic zones of Kazakhstan, with spring barley cultivated under controlled conditions. The study included multispectral imaging from Landsat-8–9 satellites and UAV-based multispectral imaging. The results indicate that differentiated fertilization strategies, based on the spatial distribution of soil nutrients, contribute to increased crop productivity and reduced excessive nitrogen application. The findings of this study highlight that the implementation of precision agricultural technologies can significantly improve yield prediction accuracy and resource efficiency. Moreover, integrating UAV-based monitoring with GIS mapping enables real-time decision-making in farm management. These approaches promote sustainable agricultural practices by reducing soil degradation and enhancing long-term productivity through environmentally friendly farming methods. The results of this study are applicable to the development of sustainable agricultural strategies aimed at enhancing productivity while conserving natural resources, thereby ensuring long-term food security and environmental stability.