Plant and Soil Science

Varietal spectral differences in winter wheat plants according to satellite monitoring data

Natalia Pasichnyk, Olha Dmytrenko, Maksym Petrenko
Download article
Abstract

The paper reflected the results of investigating the spectral features of winter wheat plants of various varieties, which was a component of the implementation of conventional remote monitoring technologies in precision agricultural production. It was based on empirical data for determining the characteristic spectral characteristics of different varieties and evaluating them using remote sensing data (RS). The purpose of the study was to establish the possibility of identifying varietal differences of winter wheat based on the spectral characteristics of plants obtained from open sources of satellite monitoring data. For this purpose, open data from the Sentinel-2 satellite was used, which provided relatively high spatial and spectral resolution. The analysis covered key wheat growing periods, and spectral differences were investigated using the RGB additive colour model and vegetation indices SAVI (soil-adjusted vegetation index), NDVI (normalised difference vegetation index), ARVI (atmospherically resistant vegetation index). The results of the study indicated the presence of significant spectral differences between winter wheat varieties due to their genetic diversity and response to agroecological conditions. It was found that spectral profiles and indices of photosynthetic activity of different varieties correlated with indicators of productivity and resistance to biotic and abiotic stresses. The obtained data helped not only to differentiate wheat varieties by spectral characteristics, but also to use them to build models for predicting yield and evaluating adaptive properties in climate change conditions. The informative value of satellite data for optimising winter wheat cultivation technologies and improving the efficiency of breeding programmes was shown. Such studies were promising in the development of precision agricultural production technologies, ensuring prompt and objective assessment of the state of crops over large areas, tracking the phenology of a particular wheat variety, and in breeding research

Keywords

winter grain crops; varietal differences; remote monitoring of agrophytocoenoses; vegetation indices; spectral characteristics of plants

Suggested citation
Pasichnyk, N., Dmytrenko, O., & Petrenko, M. (2025). Varietal spectral differences in winter wheat plants according to satellite monitoring data. Plant and Soil Science, 16(4), 57-70. https://doi.org/10.31548/plant4.2025.57
References
  1. Akumaga, U., Gao, F., Anderson, M., Dulaney, W.P., Houborg, R., Russ, A., & Hively, W.D. (2023). Integration of remote sensing and field observations in evaluating DSSAT model for estimating maize and soybean growth and yield in Maryland, USA. Agronomy, 13(6), article number 1540. doi: 10.3390/agronomy13061540.  
  2. Aranguren, M., Castellón, A., & Aizpurua, A. (2020). Crop sensor based non-destructive estimation of nitrogen nutritional status, yield, and grain protein content in wheat. Agriculture, 10(5), article number 148. doi: 10.3390/agriculture10050148.
  3. Cai, W., Tian, J., Li, X., Zhu, L., & Chen, B. (2022). A new multiple phenological spectral feature for mapping winter wheat. Remote Sensing, 14(18), article number 4529. doi: 10.3390/rs14184529.
  4. Camino, C., González-Dugo, V., Hernández, P., Sillero, J.C., & Zarco-Tejada, P.J. (2018). Improved nitrogen retrievals with airborne-derived fluorescence and plant traits quantified from VNIR-SWIR hyperspectral imagery in the context of precision agriculture. International Journal of Applied Earth Observation and Geoinformation, 70, 105-117. doi: 10.1016/j.jag.2018.04.013.
  5. Campoy, J., Campos, I., Villodre, J., Bodas, V., Osann, A., & Calera, A. (2023). Remote sensing-based crop yield model at field and within-field scales in wheat and barley crops. European Journal of Agronomy, 143, article number 126720. doi: 10.1016/j.eja.2022.126720.
  6. Cann, D.J., Hunt, J.R., Porker, K.D., Harris, F.A.J., Rattey, A., & Hyles, J. (2023). The role of phenology in environmental adaptation of winter wheat. European Journal of Agronomy, 143, article number 126686. doi: 10.1016/j.eja.2022.126686.
  7. Convention on Biological Diversity. (1992, June). Retrieved from https://www.cbd.int/doc/legal/cbd-en.pdf.
  8. Convention on the Trade in Endangered Species of Wild Fauna and Flora. (1976, March). Retrieved from https://treaties.un.org/doc/publication/unts/volume%20993/volume-993-i-14537-english.pdf.
  9. EOS data analytics. (n.d.). Retrieved from https://eos.com.
  10. Google Earth. (n.d.). Retrieved from https://earth.google.com/web/.
  11. Hu, N., Li, W., Du, C., Zhang, Z., Gao, Y., Sun, Z., Yang, L., Yu, K., Zhang, Y., & Wang, Z. (2021). Predicting micronutrients of wheat using hyperspectral imaging. Food Chemistry, 343, article number 128473. doi: 10.1016/j.foodchem.2020.128473.
  12. Liu, L., Cao, R., Chen, J., Shen, M., Wang, S., Zhou, J., & He, B. (2022). Detecting crop phenology from vegetation index time-series data by improved shape model fitting in each phenological stage. Remote Sensing of Environment, 277, article number 113060. doi: 10.1016/j.rse.2022.113060.
  13. Liu, X., Wu, X., Peng, Y., Mo, J., Fang, S., Gong, Y., Zhu, R., Wang, J., & Zhang, C. (2023). Application of UAV-retrieved canopy spectra for remote evaluation of rice full heading date. Science of Remote Sensing, 7, article number 100090. doi: 10.1016/j.srs.2023.100090.
  14. Lyu, X., Du, W., Zhang, H., Ge, W., Chen, Z., & Wang, S. (2024). Classification of different winter wheat cultivars on hyperspectral UAV imagery. Applied Sciences, 14(1), article number 250. doi: 10.3390/app14010250.
  15. Ma, J., Zheng, B., & He, Y. (2022). Applications of a hyperspectral imaging system used to estimate wheat grain protein: A review. Frontiers in Plant Science, 13, article number 837200. doi: 10.3389/fpls.2022.837200.
  16. Memon, M.S., Chen, S., Niu, Y., Zhou, W., Elsherbiny, O., Liang, R., Du, Z., & Guo, X. (2023). Evaluating the efficacy of Sentinel-2B and Landsat-8 for estimating and mapping wheat straw cover in rice-wheat fields. Agronomy, 13(11), article number 2691. doi: 10.3390/agronomy13112691.
  17. Opryshko, O., Pasichnyk, N., Kiktev, N., Dudnyk, A., Hutsol, T., Mudryk, K., Herbut, P., Łyszczarz, P., & Kukharets, V. (2024). European green deal: Satellite monitoring in the implementation of the concept of agricultural development in an urbanized environment. Sustainability, 16(7), article number 2649. doi: 10.3390/su16072649.
  18. Pancorbo, J.L., Quemada, M., Raya-Sereno, M.D., Gioli, B., Beck, P.S.A., & Camino, C. (2025). Integrating artificial neural network-PROSAIL with Sentinel-2 to monitor crop traits dynamics and nitrogen status. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, 17770-17786. doi: 10.1109/JSTARS.2025.3585080.
  19. Revill, A., Florence, A., MacArthur, A., Hoad, S.P., Rees, R.M., & Williams, M. (2019). The value of Sentinel‑2 spectral bands for the assessment of winter wheat growth and development. Remote Sensing, 11(17), article number 2050. doi: 10.3390/rs11172050.  
  20. Shi, Y., Han, L., González-Moreno, P., Dancey, D., Huang, W., Zhang, Z., Liu, Y., Huang, M., Miao, H., & Dai, M. (2023). A fast Fourier convolutional deep neural network for accurate and explainable discrimination of wheat yellow rust and nitrogen deficiency from Sentinel-2 time series data. Frontiers in Plant Science, 14, article number 1250844. doi: 10.3389/fpls.2023.1250844.
  21. Song, G., et al. (2022). Monitoring leaf phenology in moist tropical forests by applying a superpixel-based deep learning method to time-series images of tree canopies. ISPRS Journal of Photogrammetry and Remote Sensing, 183, 19-33. doi: 10.1016/j.isprsjprs.2021.10.023.
  22. Sun, D., Zhang, L., Li, H., Lan, W., Tu, K., Liu, J., & Pan, L. (2025). Non-destructive prediction of the moisture content of individual wheat kernels combining hyperspectral imaging and WGAN data augmentation algorithm. Food Research International, 212, article number 116498. doi: 10.1016/j.foodres.2025.116498.
  23. Tarariko, O.G., Syrotenko, O.V., Ilyenko, T.V., & Kuchma, T.L. (2019). Agroecological satellite monitoring. Kyiv: Agricultural Science. doi: 10.5281/zenodo.3492936.  
  24. Vincke, D., Mercatoris, B., Eylenbosch, D., Baeten, V., & Vermeulen, P. (2022). Assessment of kernel presence in winter wheat ears at spikelet scale using near-infrared hyperspectral imaging. Journal of Cereal Science, 106, article number 103497. doi: 10.1016/j.jcs.2022.103497.
  25. Wang, Y., Ou, X., He, H.-J., & Kamruzzaman, M. (2024). Advancements, limitations and challenges in hyperspectral imaging for comprehensive assessment of wheat quality: An up-to-date review. Food Chemistry: X, 21, article number 101235. doi: 10.1016/j.fochx.2024.101235.
  26. Xie, Y., Plett, D., Evans, M., Garrard, T., Butt, M., Clarke, K., & Liu, H. (2024). Hyperspectral imaging detects biological stress of wheat for early diagnosis of crown rot disease. Computers and Electronics in Agriculture, 217, article number 108571. doi: 10.1016/j.compag.2023.108571.
  27. Zhang, R., et al. (2024). PhenoNet: A two-stage lightweight deep learning framework for real-time wheat phenophase classification. ISPRS Journal of Photogrammetry and Remote Sensing, 208, 136-157. doi: 10.1016/j.isprsjprs.2024.01.006.
  28. Zvonar, A. (2020). Influence of weather conditions of the year and variety features on nitrogen consumption and formation of winter wheat grain quality. Ukrainian Black Sea Region Agrarian Science, 24(3), 87-95. doi: 10.31521/2313-092X/2020-3(107)-11.