UAV-based estimates of corn LAI using hyperspectral and EnMAP spectral resolutions

Abstract

Accurate estimation of the Leaf Area Index (LAI) is essential for assessing vegetation health and managing agricultural productivity. This study examines the application of Unmanned Aerial Vehicle (UAV)-based hyperspectral imaging and convolved EnMAP spectral data for estimating corn LAI, utilizing machine learning (ML) models to improve prediction accuracy. Various ML models, including k-nearest Neighbors (KNN), Support Vector Machines (SVM), Partial Least Squares Regression (PLS), and Random Forests (RF), were assessed to predict LAI from hyperspectral, EnMAP, and vegetation index features. Results demonstrate that PLS models consistently outperformed other ML approaches, achieving coefficients of determination (R²) ranging from 0.79 to 0.82. Notably, for the top two performing models (PLS and SVM) spectral indices such as NDRE, GNDVI, and NDVI proved more effective for LAI prediction than individual spectral bands. Interestingly, no matter the incorporation of hyperspectral wavelengths or EnMAP bands, the models predicting LAI were comparable. Feature importance analysis reinforced the dominance of vegetation indices as key predictors. The findings emphasize the benefits of high-resolution UAV hyperspectral imaging, convolved satellite spectral data, and machine learning, particularly PLS, for scalable and accurate LAI estimation in agroecosystems.

Plant phenotyping relevance

UAVハイパースペクトル画像と機械学習を用いてトウモロコシのLAIという植物形質を推定し、複数モデルと特徴量の性能を比較・評価しているため、形質取得手法が中心である。

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