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

Eugenia Roumenina, Georgi Jelev, Petar Dimitrov, Lachezar Filchev, Ilina Kamenova, Alexander Gikov, Martin Banov, Veneta Krastev

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

Date published: 2021-02-17