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Leaf area index (LAI) is an important phenotypic trait closely related to plant vigor and biomass. It is also a key parameter used in crop growth modeling. However, manually measuring LAI in the field can be slow and labor intensive. High resolution remote sensing, such as unmanned aircraft systems (UAS), has been explored for LAI estimation but with limited data sources, usually RGB and multispectral imagery. As UAS-based thermal infrared (TIR) imaging becoming readily available in agriculture, it is worth investigating the potential of its role in improving LAI estimation. In this study we evaluated the importance of canopy temperature measured by UAS-based TIR and multispectral imagery on maize LAI quantification within a breeding context (23 genotypes). Five plot-level features (canopy temperature, structure and two common vegetation indices) were extracted from the images, and used as inputs of machine learning models for the LAI estimation. The performance of the estimation was evaluated with a 5-fold cross validation with 30 random repeats for 162 samples. Results showed that, canopy temperature, together with canopy structure as model predictors, slightly improved LAI estimation (root mean square error, RMSE of 0.853 m2/m2 and coefficient of determination, R2 of 0.740) than those models without temperature difference (RMSE of 0.917 m2/m2 and R2 of 0.706) for the various genotypes included in this study. In addition, canopy temperature showed moderate and more stable significance in estimating LAI than plant height and image uniformity. Its contribution to the estimation was comparable or even higher than those from vegetation indices when being modeled with random forest in this study. These relationships may be changed with a single or less genotypes which can be explored in future studies.
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