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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 5, pp. 129-144

Application of hyperspectral remote sensing data and geostatistical methods to nitrogen status management in grain crops

V.P. Yakushev 1 , O.A. Mitrofanova 1 , V.M. Bure 1 , E.P. Mitrofanov 1 , A.A. Smolina 1 , Ya.B. Pankratova 1 , V.V. Yakushev 2 
1 Saint Petersburg State University, Saint Petersburg, Russia
2 Saint Petersburg State Agrarian University, Pushkin, Saint Petersburg, Russia
Accepted: 21.07.2025
DOI: 10.21046/2070-7401-2025-22-5-129-144
The relevance of the study is due to the need for operational and non-destructive monitoring of nitrogen supply to grain crops for effective agricultural management. Modern remote sensing technologies (hyperspectral and multispectral imaging using unmanned aerial vehicles and satellites) provide extensive data, but require the development of analysis methods to identify informative features and substantiate fertilization strategies. The aim of the work is to develop methodological approaches to statistical and variogram analysis of data on the search for informative channels in multi- and hyperspectral imaging of agricultural crops using the example of the problem of managing the nitrogen regime of grain crops. The experimental part of the study was conducted in the fields of the Leningrad Region (2022–2024). The most informative for assessing the nitrogen status of wheat were the vegetation indices ChlRI (Chlorophyll Reflectance Index), SIPI (Structure Insensitive Pigment Index), GNDVI (Green Normalized Difference Vegetation Index), NDVI761 (Normalized Difference Vegetation Index 761), NDVI850 (Normalized Difference Vegetation Index 850) and NDVI780 (Normalized Difference Vegetation Index 780), demonstrating significant correlations with nitrogen levels. At the same time, the hypothesis about the inefficiency of spectral methods in conditions of high contamination of fields was confirmed: correlations between remote sensing data and nitrogen availability turned out to be statistically insignificant in an experiment with high contamination. A key tool for choosing agricultural technology was variogram analysis, which makes it possible to estimate the proportion of random micro-components (ξ) in the spatial heterogeneity of the field. It was found that at ξ > 0.5 (for example, ξ = 0.64 in the 2022 experiment), the use of differentiated technologies is impractical due to the predominance of uncontrolled variability. To calculate the doses of agrochemicals, the use of neural network models (U-Net modifications) has been recognized as a promising direction, providing accuracy of up to 99.96 % when combining RGB, near-infrared data and vegetation indices. The proposed three-stage methodology integrates spectral data analysis, spatial heterogeneity assessment and technological decision-making, increasing the efficiency of nitrogen nutrition management for crops.
Keywords: hyperspectral imaging, remote sensing data, nitrogen regime, wheat, variogram analysis, precision agriculture, neural network technologies
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