Application of multi-layer discrete anisotropic radiative transfer model in vertical distribution inversion of maize leaf area index - DONG Zhen; YANG Guijun; SUN Lin; YANG Hao; ZHU Yaohui; LEI Lei; CHEN Riqiang; ZHANG Chengjian; LIU Miao (浙江大学学报. 农业与生命科学版)
In order to more accurately monitor the vertical distribution of the leaf area index (LAI) of maize, we propose a conditionally constrained LAI vertical distribution inversion method based on the simulation dataset constructed by the discrete anisotropic radiative transfer (DART) model. First, the simulation effects of DART model on canopy reflectance and photosynthetically active radiation (PAR) were evaluated based on the three-layer vertical distribution scenario, and constructed the corresponding simulation dataset with PAR. Second, a single parameter inversion model for LAI and PAR was built based on the simulated dataset. Finally, using the single parameter inversion model as a priori knowledge, the inversion of the vertical distribution of maize canopy LAI based on the hyperspectral vegetation index was realized by solving the constraint problem. The results showed that the accuracy of the constraint optimization inversion model was higher than that of the single parameter inversion model. The coefficient of determination (R2) of LAI inversion results for top layer of maize increased by 0.022, root-mean-square error (RMSE) decreased by 0.016 m2/m2, and normalized root-mean-square error (NRMSE) decreased by 1.3%. The R2 of LAI inversion results for middle layer of maize increased by 0.08, RMSE decreased by 0.219 m2/m2, and NRMSE decreased by 10.1%. The R2 of LAI inversion results for bottom layer of maize increased by 0.069, RMSE decreased by 0.041 m2/m2, and NRMSE decreased by 4.6%. Therefore, it can be concluded that the inversion of vertical distribution of LAI in maize canopy using the conditional constraint optimization method can effectively improve the inversion accuracy.