Integrating UAV-Derived Canopy Cover and Satellite LAI with AquaCrop for Daily Farm-Scale Maize Biomass Estimation

Abstract Assessing crop condition and biomass throughout the growth period provides valuable insights into plant performance relative to optimal (stress-free) growth conditions and supports precise agricultural management. In this study, biomass estimation was conducted using vegetation indices (LAI and CC) derived from both UAV (Unmanned Aerial Vehicle) and satellite-based remote sensing in combination with the AquaCrop model. For this purpose, crop images were acquired with a digital camera under four different imaging setups during the growing season to determine field-scale canopy cover (CC). UAV imagery was also collected on the same dates at two flight altitudes (214 and 430 m). In addition, destructive plant sampling was carried out at 11 stages during the maize growing season to measure LAI and aboveground biomass. Evaluation of different canopy cover (CC) determination models showed that CC derived from GreenCrop Tracker (GCT) from bottom-up digital camera imagery without a fisheye lens (BU-NF) provided the most accurate farm yield estimation, with an error of 3.29%. GCT also demonstrated the best performance in determining CC at both UAV flight altitudes of 214 m and 430 m above ground level. Moreover, Sentinel-2, owing to its favourable spatial and temporal resolution, performed well in estimating LAI and daily maize biomass. In cases where Sentinel-2 imagery is unavailable, applying CC derived from GCT at 214 m UAV altitude or CC estimated by ENVI using top-down imaging without a fisheye lens within AquaCrop is recommended for reliable biomass monitoring.