Open AccessWithin-Field Mapping of Winter Wheat Biophysical Variables Using Multispectral Images from UAV
Author(s) -
Georgi Jelev,
Petar Dimitrov,
Eugenia Roumenina
Publication year - 2022
Publication title -
aerospace research in bulgaria
Language(s) - English
Resource type - Journals
eISSN - 2367-9522
pISSN - 1313-0927
DOI - 10.3897/arb.v34.e02
Subject(s) - multispectral image , photosynthetically active radiation , vegetation (pathology) , phenology , leaf area index , remote sensing , precision agriculture , environmental science , linear regression , field (mathematics) , regression analysis , mathematics , geography , agriculture , statistics , agronomy , botany , biology , medicine , photosynthesis , archaeology , pathology , pure mathematics
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 m2/ m2, 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.