Extraction of chlorophyll concentration maps from AOTF hyperspectral imagery

Abstract

Remote mapping of chlorophyll concentration in leaves is highly important for various biological and agricultural applications. Multiple spectral indices calculated from reflectance at specific wavelengths have been introduced for chlorophyll content quantification. Depending on the crop, environmental factors and task, indices differ. To map them and define the most accurate index, a single multi-spectral imaging system with a limited number of spectral channels is insufficient. When the best chlorophyll index for a particular task is unknown, hyperspectral imager able to collect images at any wavelengths and map multiple indices is in need. Due to precise, fast and arbitrary spectral tuning, acousto-optic imagers provide highly optimized data acquisition and processing. In this study, we demonstrate the feasibility to extract the distribution of chlorophyll content from acousto-optic hyperspectral data cubes. We collected spectral images of soybean leaves of 5 cultivars in the range 450–850 nm, calculated 14 different chlorophyll indices, evaluated absolute value of chlorophyll concentration from each of them via linear regression and compared it with the results of well-established spectrophotometric measurements. We calculated parameters of the chlorophyll content estimation models via linear regression of the experimental data and found that index CIRE demonstrates the highest coefficient of determination 0.993 and the lowest chlorophyll content root-mean-square error 0.66 μg/cm2. Using this index and optimized model, we mapped chlorophyll content distributions in all inspected cultivars. This study exhibits high potential of acousto-optic hyperspectral imagery for mapping spectral indices and choosing the optimal ones with respect to specific crop and environmental conditions.