Cover Crop Types Influence Biomass Estimation Using Unmanned Aerial Vehicle-Mounted Multispectral Sensors

Accurate cover crop biomass estimation is critical for evaluating their ecological benefits. Traditional methods, like destructive sampling, are labor-intensive and time-consuming. This study assesses unmanned aerial vehicle-mounted multispectral sensors to estimate biomass across oats, Austrian winter peas (AWP), turnips, and a mix of all three replicated in six experimental plots. Five spectral images were collected at two growth stages, analyzing band reflectance, nine vegetation indices, and canopy height models (CHMs) for biomass estimation. Results indicated that most vegetation indices were effective during mid-growth stages but showed reduced accuracy later. Stepwise multiple linear regression revealed that combining normalized difference red-edge (NDRE), vegetation index (NDVI), and CHM provided the best biomass model before termination (R2 = 0.90). For bitemporal images, green reflectance, CHM, near-infrared (NIR)/red ratio, and green normalized difference vegetation index (GNDVI) achieved optimal performance (R2 = 0.86). Cover crop species influenced the model performance. Oats were best modeled with enhanced vegetation index (EVI) (R2 = 0.86), AWP with red-edge reflectance (R2 = 0.71), turnips with NIR, GNDVI, and CHM (R2 = 0.95), and mixed species with NIR and blue band reflectance (R2 = 0.93). These findings demonstrate the potential of high-resolution multispectral imaging for efficient biomass assessment in precision agriculture.