WITHIN-FIELD MAPPING OF WINTER WHEAT BIOPHYSICAL VARIABLES USING MULTISPECTRAL IMAGES FROM UAV

The paper presents the results from a study aiming to map the dynamic of biophysical variables of winter wheat crops in different phenological growth stages (PGSs) using multispectral camera data acquired by Unmanned Aerial Vehicle (UAV). The studied biophysical variables are Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and fraction of vegetation cover (fCover). During agricultural year 2016/2017, 4 field campaigns (FCs) were carried out in 6 farmer-managed fields sown with two winter wheat varieties. During the FCs, 8 UAV flight missions were accomplished. Linear and exponential regression models were designed and evaluated to derive predictive equations for the biophysical variables of the crops based on a set of vegetation indices (VIs). The best predictor for all biophysical variables was OSAVI (RMSE was 0.90 m(2)/m(2), 0.07 and 0.08 for LAI, fAPAR, and fCover respectively). The chosen models were used to compose maps of LAI, fAPAR, and fCover of the studied fields. The maps correspond well with the spatial distribution of the values of the respective biophysical variables measured during the respective field campaign.

Automated summary

This paper presents the results of a study that aimed to map the dynamic of biophysical variables of winter wheat crops during different growth stages using multispectral camera data from an Unmanned Aerial Vehicle (UAV). Linear and exponential regression models were used to predict the biophysical variables, and the best predictor was found to be the OSAVI index. The chosen models were then used to create maps of the biophysical variables for the studied fields.