Rice LAI estimation using UAV-based multi-parameter fusion at the booting stage.

to 0.901, reducing RMSE to 0.273, and raising RPD to above 3.0. These findings demonstrate that TFIs significantly enhance the spectral-spatial representation capability of multispectral data, thereby improving model accuracy. The combined use of CIs and VIs across different sensors compensates for the inherent limitations between spectral and spatial information, while the integration of multi-resolution TIs and TFIs effectively overcomes the constraints of single-source data. Overall, the proposed approach provides a robust and efficient solution for high-precision LAI estimation during critical growth stages of rice, offering strong support for precision agricultural management.