Comparison of PROSAIL Model Inversion Methods for Estimating Leaf Chlorophyll Content and LAI Using UAV Imagery for Hemp Phenotyping

Unmanned aerial vehicle (UAV) remote sensing was used to estimate the leaf area index (LAI) and leaf chlorophyll content (LCC) of two hemp cultivars during two growing seasons under four nitrogen fertilisation levels. The hemp traits were estimated by the inversion of the PROSAIL model from UAV multispectral images. The look-up table (LUT) and hybrid regression inversion methods were compared. The hybrid methods performed better than LUT methods, both for LAI and LCC, and the best accuracies were achieved by random forest for the LAI (0.75 m(2) m(-2) of RMSE) and by Gaussian process regression for the LCC (9.69 mu g cm(-2) of RMSE). High-throughput phenotyping was carried out by applying a generalised additive model to the time series of traits estimated by the PROSAIL model. Through this approach, significant differences in LAI and LCC dynamics were observed between the two hemp cultivars and between different levels of nitrogen fertilisation.

Automated summary

This study utilized unmanned aerial vehicle remote sensing to estimate the leaf area index (LAI) and leaf chlorophyll content (LCC) of two hemp cultivars under four levels of nitrogen fertilization. Hybrid regression inversion methods were found to be more accurate than the look-up table (LUT) methods, and significant differences in LAI and LCC dynamics were observed between the hemp cultivars and nitrogen fertilization levels.