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

• LAI can be modeled via hyperspectral sensors and machine learning techniques. • PLS models achieved the highest accuracy in predicting LAI (R 2 up to 0.823). • Raw spectral bands outperformed incorporation of vegetation indices for best models. • Vegetation indices did aid some models, though the increase was not substantial. 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 ( R 2 ) 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.