An Approach for Joint Estimation of Grassland Leaf Area Index and Leaf Chlorophyll Content from UAV Hyperspectral Data

Leaf area index (LAI) and leaf chlorophyll content (Cab) are two important indicators of vegetation growth. Due to the high-coupling spectral signals content, simultaneous retrieval LAI Cab from remotely sensed date is always challenging. In this paper, an approach for joint estimation grassland unmanned aerial vehicle (UAV) hyperspectral data was proposed. Firstly, based on a PROSAIL model, 15 typical indices (VIs) were calculated analyzed identify optimal VIs estimation. Secondly, four pairs established their discreteness also building two-dimension matrix. Thirdly, two-layer VI matrix generated determine relationship with values values. Finally, jointly retrieved according cells The reduced cross-influence between Cab. Compared empirical model single-layer matrix, accuracy UAV significantly improved (for LAI: R2 = 0.73, RMSE 0.91 m2/m2 u(SD) 0.82 m2/m2; Cab: 0.79, 11.7 μg/cm2 10.84 μg/cm2). proposed method has potential rapid data. As similar look-up table, can be used directly without need prior measurements training.