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

Abstract

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