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

Abstract

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 m2 m−2 of RMSE) and by Gaussian process regression for the LCC (9.69 µg 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.

Plant phenotyping relevance

UAV画像からPROSAILモデル反転と回帰法でLAI・葉クロロフィル含量を推定し、複数手法を比較評価することが中心であり、植物表現型取得手法の検証・応用に該当する。

Code and data availability

The supplied blocks describe UAV multispectral imagery, field LAI/LCC measurements, PROSAIL inversion, and GAM phenotyping, but contain no data availability statement, public dataset deposit, or author code repository. The only URLs mentioned are generic R package references (hsdar, caret, caretEnsemble, mgcv, scmamp),

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