Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 6, pp. 204-213
A method for identifying informative spectral channels for determining the sulfur level in wheat crops based on remote sensing data
D.I. Bikbulatov
1 , V.P. Yakushev
1 , Ya.B. Pankratova
1 , A.F. Petrushin
1 , O.A. Mitrofanova
1 , V.V. Yakushev
2 , A.A. Fedotov
3 , A.B. Terent'ev
4 1 Saint Petersburg State University, Saint Petersburg, Russia
2 Saint Petersburg State Agrarian University, Pushkin, Saint Petersburg, Russia
3 Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia
4 All-Russian Institute of Plant Protection, Pushkin, Saint Petersburg, Russia
Accepted: 23.09.2025
DOI: 10.21046/2070-7401-2025-22-6-204-213
The article discusses a method for determining the sulfur content in spring wheat crop based on Earth remote sensing data. A field experiment is described, in which test plots with different sulfur content were established, and plant and soil samples were collected. Spectral imaging of the test plots was carried out during various phenological phases of plant development using unmanned aerial vehicles equipped with multi- and hyperspectral cameras. Threshold clustering was performed on averaged hyperspectral imaging data based on simple wavelength ratios. The obtained data were processed using various statistical methods. A significant volume of data was analyzed, including 127,200 optical measurements, as well as their various ratios. Verification of the analysis results was carried out using the nonparametric Kruskal–Wallis test, the parametric pairwise Student’s t-test for equality of means, Shapiro–Wilk test, and Levene’s test. A set of the most informative spectral channels has been identified, and a method for determining the sulfur level in spring wheat crop, which is most effective during the early stages of vegetation, has been found. A strong correlation between the SR (Simple Ratio) index and the NDVI (Normalized Difference Vegetation Index), especially during the tillering phase, has been demonstrated. The results of the work can be used in precision agriculture for monitoring plant nutrition and optimizing the application of sulfur-containing fertilizers.
Keywords: remote sensing, data clustering, statistical methods, hyperspectral imaging, unmanned aerial vehicles, precision agriculture, sulfur, trace elements, spring wheat, fertilization
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