Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops

The recent emergence of unmanned aerial vehicles (UAV) has opened a new horizon in vegetation remote sensing, especially for agricultural applications. However, the benefits of UAV centimeter-scale imagery are still unclear compared to coarser resolution data acquired from satellites or aircrafts. This study aims (i) to propose novel methods for retrieving canopy variables from UAV multispectral observations, and (ii) to investigate to what extent the use of such centimeter-scale imagery makes it possible to improve the estimation of leaf and canopy variables in sugar beet crops (Beta vulgaris L.). Five important structural and biochemical plant traits are considered: green fraction (GF), green area index (GAI), leaf chlorophyll content (Cab), as well as canopy chlorophyll (CCC) and nitrogen (CNC) contents. Based on a comprehensive data set encompassing a large variability in canopy structure and biochemistry, the results obtained for every targeted trait demonstrate the superiority of centimeter-resolution methods over two standard remote-sensing approaches (i.e., vegetation indices and PROSAIL inversion) applied to average canopy reflectances. Two variables (denoted GF(GREENPIX) and VICAB) extracted from the images are shown to play a major role in these performances. GFGREENPix is the GF estimate obtained by thresholding the Visible Atmospherically Resistant Index (VARI) image, and is shown to be an accurate and robust (e.g., against variable illumination conditions) proxy of the structure of sugar beet canopies, i.e., GF and GAL VICAB is the mND(blue) index value averaged over the darkest green pixels, and provides critical information on C-ab. When exploited within uni-or multivariate empirical models, these two variables improve the GF, GAL C-ab, CCC and CNC estimates obtained with standard approaches, with gains in estimation accuracy of 24, 8, 26, 37 and 8%, respectively. For example, the best CCC estimates (R-2 = 0.90) are obtained by multiplying C-ab and GAI estimates respectively derived from VICAB and a log-transformed version of GF(GREENPIX), log(1-GF(GREENPIX)). The GF(GREENIPIX) and VICAB variables, which are only accessible from centimeter-scale imagery, contributes to a better identification of the effects of canopy structure and leaf biochemistry, whose influences may be confounded when considering coarser resolution observations. Such results emphasize the strong benefits of centimeter-scale UAV imagery over satellite or airborne remote sensing, and demonstrate the relevance of low-cost multispectral cameras to retrieve a number of plant traits, e.g., for agricultural applications.