Accurate Inversion of Rice LAI Using UAV-Based Hyperspectral Data: Integrating Days After Transplanting and Meteorological Factors

The leaf area index (LAI) is a key physiological parameter characterizing rice canopy structure and growth status. To face the limits of traditional destructive sampling, which is time-consuming, labor-intensive, and difficult to achieve large-scale dynamic detection, this study proposes a precise UAV-based hyperspectral inversion method for rice LAI using the fusion of Days After Transplantation and Meteorological Factors data (DATaMF). The study framework consisted of three key components: spectral preprocessing (smoothing-RSG, resampling-RRS, first derivative transformation-RFD), spectral feature selection (SPA, CARS, Relief-F), and the construction and assessment of LAI inversion models (RF, ELM, XGBoost) that integrated DATaMF. The results show that (1) the three-level data preprocessing procedure—comprising RSG, RRS, and RFD—coupled with the feature subset selected by the CARS method, demonstrates strong performance in LAI inversion; (2) the incorporation of DATaMF significantly improves rice LAI estimation, leading to improved model accuracy and robustness; and (3) the optimal LAI inversion model is achieved with the RF-based CARS-RFD-DATaMF approach, yielding test set R2, RMSE, and RPD values of 0.8015, 0.5745, and 2.2857, respectively. In conclusion, the hyperspectral LAI inversion method developed in this study, which integrates DATaMF, significantly enhances the model’s accuracy and stability under small-sample conditions. This approach provides reliable technical support for efficient, precise, and dynamic monitoring of rice growth.