Purpose <p>The leaf area index (LAI) is a crucial parameter for crop growth management. While UAV remote sensing has been utilized to estimate LAI at the plot scale, its application to complex farmland environments—characterized by heterogeneous backgrounds (e.g., soil, residue, and weeds)— has been less explored.</p> Method <p>This study employed UAV-mounted hyperspectral and RGB sensors to gather data from both experimental plots and farmland environments. Data from diverse rapeseed cultivars and growth stages were used as the calibration dataset, while farmland-level data validated the models. The study compared three models: the PROSAIL model, an empirical model incorporating canopy spectral and morphological parameters without differentiating canopy cover types, and the proposed canopy morphological parameters (CMP) model. The CMP model estimated LAI using fractional vegetation cover (FVC) for sparse canopies and canopy height for closed canopies.</p> Result <p>Despite challenges such as UAV image resolution and the limited availability of spatial data, the CMP model showed strong performance, with an R² of 0.779 and RMSE of 0.732. Although its R² was similar to that of the empirical spectral–morphological (ESM) model (R² = 0.780), the CMP approach achieved a notably lower RMSE (0.732 vs. 0.814). This improvement stems from its canopy-aware design, which adaptively uses fractional vegetation cover for sparse canopies and canopy height for closed canopies. Such differentiation enhances model stability and generalization in heterogeneous farmland scenes—conditions in which background interference and structural variability often degrade empirical models. In comparison, the PROSAIL model performed less accurately (R² = 0.618, RMSE = 1.094).</p> Conclusion <p>These results highlight that the CMP model provides a robust and cost-effective solution for LAI estimation, supporting crop growth assessment and management in real farmland.</p>