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

Authors: Flynn, Kyle; CHINHAYI, H - Orise Fellow; BAATH, GURJINDER - Texas A&M Agrilife; SAPKOTA, BALA - Texas A&M University; Delhom, Christopher; Smith, Douglas

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/19/2026
Publication Date: 1/23/2026
Citation: Flynn, K.C., Chinhayi, H.K., Baath, G.S., Sapkota, B.R., Delhom, C.D., Smith, D.R. 2026. UAV-based estimates of corn LAI using Hyperspectral and EnMAP spectral resolutions. Computers and Electronics in Agriculture. https://doi.org/10.1016/j.compag.2026.111469.
DOI: https://doi.org/10.1016/j.compag.2026.111469

Interpretive Summary: 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. Notably, spectral indices such as NDRE, GNDVI, and NDVI proved more effective for LAI prediction than individual spectral bands across all models. Hyperspectral imagery also yielded higher model accuracy than EnMAP data alone, and combining indices with spectral bands further improved predictions. The findings emphasize the benefits of high-resolution UAV hyperspectral imaging, convolved satellite spectral data, and machine learning for scalable and accurate LAI estimation in agroecosystems.

Technical 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 (PLSR), and Random Forests (RF), were assessed to predict LAI from hyperspectral, EnMAP, and vegetation index features. Results demonstrate that RF models consistently outperformed other ML approaches, achieving coefficients of determination (R²) ranging from 0.77 to 0.82. Notably, spectral indices such as NDRE, GNDVI, and NDVI proved more effective for LAI prediction than individual spectral bands across all models. Hyperspectral imagery also yielded higher model accuracy than EnMAP data alone, and combining indices with spectral bands further improved predictions. 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 RF, for scalable and accurate LAI estimation in agroecosystems.