Enhancing maize aboveground biomass estimation through time-series UAV-based multispectral and thermal imagery and improved FAO66 model

Accurate and efficient estimation of aboveground biomass (AGB) is essential for precision agriculture and sustainable crop management. Traditional methods for AGB estimation rely on destructive sampling, which is time-consuming and impractical for frequent, large-scale monitoring. Although remote sensing techniques offer non-destructive alternatives, many existing models lack integration with crop physiological processes. The study introduced an optimized FAO66 approach combined time-series unmanned aerial vehicle (UAV) multispectral and thermal imagery to improve the accuracy of maize AGB model. We proposed and compared water stress coefficients derived from stomatal conductance (Ksg), canopy temperature (Ksr), and soil moisture (Ksw). The accumulated product of the basal crop coefficient (Kcb), temperature stress coefficient (Kst), and water stress coefficients was used as input variables for AGB modelling using stepwise regression, random forest regression (RFR), support vector regression, and convolutional neural networks. Across multiple irrigation treatments and growth stages, the model utilizing the accumulated Kcb×Ksr×Kst achieved the highest accuracy among the FAO66-based AGB models (R2 = 0.80, RMSE = 254.8 g m−2 in 2018 and 2019). Machine learning models, particularly RFR (R2 = 0.84, RMSE = 242.5 g m−2 in both years), improved robustness by capturing nonlinear relationships among multi-temporal features, while the canopy temperature-based water stress coefficient offered a practical alternative to soil moisture – based metrics, reducing fieldwork without sacrificing accuracy. The proposed UAV-based framework provided a scalable, high-resolution solution for field-scale AGB mapping to support precision irrigation and water management.