Qualitative evaluation and within-field mapping of winter wheat crop condition using multispectral remote sensing data

Eugenia Roumenina1, Georgi Jelev1, Petar Dimitrov1, Lachezar Filchev1, Ilina Kamenova1, Alexander Gikov1, Martin Banov2, Veneta Krasteva2, Milena Kercheva2 and Viktor Kolchakov2
1 Bulgarian Academy of Sciences, Space Research and Technology Institute, Department of Remote Sensing and GIS, 1113 Sofia, Bulgaria
2 Agricultural Academy, Institute of Soil Science, Agrotechnologies and Plant Protection “Nikola Poushkarov”, 1331 Sofia, Bulgaria


Roumenina, E., Jelev, G., Dimitrov, P., Filchev, L., Kamenova, I., Gikov, A., Banov, M.,   Krasteva, V., Kercheva, M. & Kolchakov, V. (2020). Qualitative evaluation and within-field mapping of winter wheat crop condition using multispectral remote sensing data. Bulg. J. Agric. Sci., 26 (6), 1129–1142

This study presents a method for evaluation and mapping of winter wheat crop condition using a set of crop variables, e.g. leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fAPAR), fraction of vegetation cover (fCover), fresh above ground biomass (AGBf), and Nitrogen uptake derived from multispectral imagery. First, the crop condition is assessed with respect to each variable using a qualitative, three-grade scale. In a second step, these individual assessments are combined to produce assessment map of the crop’s general condition, discriminating between three possible conditions – Good, Fair, or Poor. The method was tested on winter wheat fields in Bulgaria in two agricultural years – 2016/2017 at phenological growth stage (FGS) Z31 to Z34 and 2017/2018 at FGS Z30. The results presented were based on Sentinel-2 satellite imagery (at 20 m spatial resolution) and imagery from Specialized Unmanned Aerial Vehicle (SUAV) sense FlyeBee Ag, equipped with Parrot Sequoia camera (resampled to 10 m spatial resolution). The remotely sensed crop condition was validated against independent ground-based assessments in a number of elementary sampling units (ESUs). The proposed approach proved to be effective and the crop condition was accurately determined in 87% – 94% of the ESUs depending on the FGS/agricultural year and the imagery type. We observed only minor differences in the areas of the three crop conditions when mapped with Sentinel-2 and Parrot Sequoia data.

Keywords: winter wheat; crop condition; assessment map; Sentinel-2; Parrot Sequoia camera

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