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Estimation of temporal and spatial characteristics of oat development parameters using Sentinel-1 backscatter data

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dc.contributor.authorFedoniuk, T.
dc.contributor.authorPyvovar, P.
dc.contributor.authorTopolnytskyi, P.
dc.contributor.authorBezvershuk, I.
dc.contributor.authorTereshchuk, V.
dc.contributor.authorPuleko, I.
dc.date.accessioned2025-09-12T10:57:19Z
dc.date.available2025-09-12T10:57:19Z
dc.date.issued2025
dc.descriptionReceived: May 9th, 2025 ; Accepted: July 20th, 2025 ; Published: September 5th, 2025 ; Correspondence: tanyavasiluk2015@gmail.comeng
dc.description.abstractThe implementation of precision agriculture is an urgent priority for Ukraine's agricultural sector under climate change and restricted use of unmanned aerial vehicles in border regions. This study aims to clearly define and evaluate the potential of Sentinel-1 radar data in identifying temporal and spatial variations in oat crop density and structure during the growing season under field conditions in Ukraine. The technique encompassed the acquisition of Sentinel1 satellite images in VV and VH polarizations, data processing by SNAP, field assessments of height, plant density, and phenological development, along with statistical analysis of the association between satellite data and land observations. The study demonstrated that the reflectance coefficient values in VV and VH polarizations fluctuate according to the oat development phase: a reduction in backscattering was noted at the onset of the growing season, followed by an increase during the stem formation and earing phases. The VH/VV ratio is responsive to variations in moisture, plant biomass, and stress conditions. The modelling demonstrated a substantial correlation among planting rate, herbicide application, and polarization markers. The findings validate the efficacy of Sentinel-1 for monitoring crop structure irrespective of weather conditions. This method enables farmers to obtain dependable information for making decisions regarding crop management, timely fertilizer application, or harvesting. The regression model demonstrated a consistent association with a R²=0.61, suggesting the potential for further research utilizing multi-year data to develop integrated yield forecasting models.eng
dc.identifier.citationFedoniuk, T., Pyvovar, P., Topolnytskyi, P., Bezvershuk, I., Tereshchuk, V., & Puleko, I. (2025). Estimation of temporal and spatial characteristics of oat development parameters using Sentinel-1 backscatter data. https://doi.org/10.15159/AR.25.088en
dc.identifier.issn2228-4907
dc.identifier.publicationAgronomy Research, 2025, vol. 23, no. 2, pp. 1060–1073eng
dc.identifier.urihttp://hdl.handle.net/10492/10133
dc.identifier.urihttps://doi.org/10.15159/ar.25.088
dc.publisherEstonian University of Life Scienceseng
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)eng
dc.rightsinfo:eu-repo/semantics/openAccesseng
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectprecision agricultureeng
dc.subjectremote sensingeng
dc.subjectherbicideeng
dc.subjectagricultureeng
dc.subjectplant densityeng
dc.subjectpolarizationeng
dc.subjectarticleseng
dc.titleEstimation of temporal and spatial characteristics of oat development parameters using Sentinel-1 backscatter dataeng
dc.typeArticleeng

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2025, Vol. 23, No. 2