UDC 631.42
UDC 631.471
The study explores the possibility of creating detailed maps of soil cover structure and sustainable intra-field heterogeneity using the principles of calculating coefficients of the multitemporal soil line for precision agriculture systems. The research employs various methods, including neural network filtering of remote sensing data, constructing open soil surface maps based on the spectral vicinity of the soil line, field soil surveys, biological yield measurements, and laboratory agrochemical analysis of soil samples. The study area comprises fields located in northern Kazakhstan, characterized by moderately dry steppe conditions and minimal relief variations. The primary soil types in this region are southern and ordinary chernozems, which exhibit different fertility potentials and soil formation conditions. The results demonstrate that the open soil surface map, derived from multitemporal remote sensing data, effectively highlights soil heterogeneity. Southern chernozems showed lower biological yield due to the presence of dense carbonate horizons, while ordinary chernozems exhibited higher fertility and yield potential. The study also revealed a strong linear relationship (R² = 0.95) between spectral reflectance and organic carbon content, enabling accurate mapping of humus content and soil types. The practical application of these maps in precision agriculture resulted in an economic benefit of approximately 1100 rubles per hectare.
open soil surface, precision planting, soil cover structure, neural-network satellite data filtering
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