Spatiotemporal Characterization of Wheat Development Using UAV LiDAR Structure–Intensity Fusion with Multispectral and Thermal Data

Abstract. This study presents the first integration of UAV LiDAR structure (canopy height (CH), multi-layer gap fraction (GF)) and intensity features with multispectral (MS) and thermal infrared (TIR) data for aboveground biomass (AGB) estimation in winter wheat. A shallow artificial neural network (ANN), trained on a limited but high-quality destructive dataset, enabled direct integration of multi-sensor features without complex parameterization, supporting systematic evaluation of individual and combined sensor performance. Among single-sensor inputs, LiDAR features were most effective. LiDAR alone, combining all of its features such as CH, multi-layer GF, and INT, achieved a testing RMSE of 1.73 t/ha (18.27% error) and R² = 0.87, surpassing the common reliance on CH or MS features in UAV-based AGB studies. Multi-layer GF also improved accuracy compared to conventional ground-return GF and was successfully used as a direct ANN input. Fusion with other sensors further enhanced performance, with the best model (LiDAR INT + MS + TIR) reaching a testing RMSE of 1.47 t/ha (16.3% error) and R² = 0.91. Notably, this outperformed fusion models that included LiDAR CH or GF, indicating that INT is a particularly information-rich predictor likely encoding both structural and physiological canopy properties. Furthermore, sensor contributions varied seasonally, with CH and GF most informative during early growth and canopy closure, while MS and TIR became dominant during senescence and stress, with rankings providing practical guidance for sensor selection based on monitoring periods or economic constraints. Results from nitrogen treatments indicated that UAV data captured management effects more effectively than destructive sampling, highlighting the value of spatially comprehensive observations, an advantage that can be further enhanced through the fusion of emerging UAV sensor products. Overall, the findings position LiDAR’s dual structural and spectral information, particularly INT, as a promising breakthrough for improving UAV-based AGB monitoring, with strong potential to advance multi-sensor fusion approaches as algorithms and crop applications broaden.