Estimating height and leaf area index of ‘Tifton 85’ bermudagrass using UAV-derived vegetation indices under heterogeneous tropical field conditions

Purpose Unmanned aerial vehicle (UAV)-based remote sensing can support non-destructive and spatial pasture monitoring, particularly in heterogeneous tropical systems. This study assessed the potential of UAV-derived vegetation indices (VIs) from the visible and near-infrared (NIR) spectrum to estimate canopy height and leaf area index (LAI) of ‘Tifton 85’ bermudagrass under conditions characterized with high spatial variability. Methods Mean canopy height and LAI were measured at 55 georeferenced sampling points during five field campaigns between May 15 and June 22, 2023. Multispectral UAV imagery was used to derive 10 VIs. Relationships between VIs and observed variables were assessed using Spearman’s correlation coefficient (R s ). Simple nonlinear models were fitted and assessed using the coefficient of determination (R²) and the standard error of estimate (SEE), and tested with an independent dataset using R², root mean square error (RMSE), and the modified Willmott index (d mw ). Results Near-infrared (NIR)-based indices showed the strongest relationships with pasture parameters. Similar to LAI, height was strongly correlated with some VIs (R s > 0.80) and good fit to the nonlinear models (R² > 0.64). However, height model performance testing was poor (R² < 0.25, d mw < 0.49, and nRMSE > 40%) and lower than that obtained for LAI (R² < 0.72, d mw < 0.64, and nRMSE > 22%). Overall, the best performances were obtained with polynomial models using Normalized Difference Vegetation Index (NDVI) or Visible Atmospherically Resistant Index (VARI) for height and exponential or power models using Green Normalized Difference Vegetation Index (GNDVI) for LAI. Conclusion UAV-derived multispectral VIs showed potential for estimating and spatially modeling ‘Tifton 85’ parameters, especially LAI, under heterogeneous tropical field conditions.