UAV‐based RGB and multispectral vegetation indices as alternatives to light box‐derived dark green color index for turfgrass color assessment

Abstract Traditionally, turfgrass color has been assessed through visual ratings or light box‐based digital image analysis, methods that are either subjective or labor‐intensive. In this study, we evaluated the potential of unmanned aerial vehicle (UAV)‐based multispectral and red‐green‐blue (RGB) imagery as a high‐throughput alternative for capturing variation in turfgrass color. Turfgrass color was assessed by capturing variation driven by genotype, fertilizer regimes, and environmental conditions across six species. Multiple vegetation indices derived from RGB and multispectral imagery were compared against the dark green color index obtained from light box imaging (DGCI_LB). Green normalized difference vegetation index and chlorophyll index green were the most reliable indicators of turfgrass color, showing strong correlations with DGCI_LB ( R 2 ≥ 0.93) across species, nitrogen rates, and locations. Normalized difference red edge showed slightly lower associations (quadratic R 2 = 0.84) but outperformed other indices under diseased or non‐uniform canopies. Partial least squares regression models incorporating either the six top‐performing multispectral vegetation indices (M1) or the full set of raw spectral bands (M4) further improved predictive accuracy, achieving R 2 = 0.97 with root mean square error (RMSE) = 0.015 and R 2 = 0.96 with RMSE = 0.017, respectively. RGB‐based indices also correlated with DGCI_LB, but their performance was slightly weaker than multispectral indices and less stable across conditions, with DGCI_UAV in particular showing high sensitivity to environmental variation. These results demonstrate that UAV‐based multispectral and RGB imagery offer an objective, reliable, and scalable solution for turfgrass color assessment, providing a faster and less labor‐intensive alternative to traditional methods.