The potential of UAV multispectral imagery to estimate chlorophyll content of vine leaves

As is known, chlorophyll is an important biophysical parameter used to monitor the overall physiological status of plants. The aim of this research was to study the potential of UAV multispectral images for estimating the contents of leaf chlorophyll in vineyards. For this purpose, a UAV flight was conducted (eBee SQ with Parrot Sequoia multispectral camera) and simultaneously in-situ measurements of leaf chlorophyll content of vine were performed using MC-100 Chlorophyll Meter. A total of 51 samples were collected: each sample representing the average of 5 measurements from the top of a single plant. Pearson correlation analysis was applied to test the relationships between spectral reflectance, Normalized Difference Vegetation Index (NDVI), Normalized Difference RedEdge Index (NDRE) and in-situ measured leaf chlorophyll content. A Partial Least Squares Regression (PLSR) model was also applied to predict chlorophyll content using 6 predictor variables: NDVI, NDRE and green, red, red edge, near infrared bands. The results showed that among spectral reflectance, the red band was most sensitive to chlorophyll variations (r = -0.46). Positive correlation between chlorophyll content and NDVI/NDRE also was found (r = 0.67 and r = 0.57, respectively). Promising results were obtained for the PLSR model (R2Val = 0.49, RMSEVal = 43.68), which proves the high potential of multispectral UAV imagery for chlorophyll monitoring in vineyards.