Retrieval of the Leaf Area Index from MODIS Top-of-Atmosphere Reflectance Data Using a Neural Network Supported by Simulation Data

The leaf area index (LAI), a key parameter used to characterize the structure and function of the vegetation canopy, is crucial to simulations of the carbon, nitrogen, and water cycles of Earth's system. In this paper, a neural network (NN) method coupled with vegetation canopy and atmospheric radiative transfer (RT) simulations is proposed to realize LAI retrieval without prior data support and complex atmospheric corrections. The look-up table (LUT) of the top-of-atmosphere (TOA) reflectance and associated input variables was simulated by 6S (6S simulation) based on the top-of-canopy (TOC) reflectance LUT simulated by PROSAIL. This was then used to train the NN to obtain the LAI inversion model. This method has been successfully applied to MODIS L1B data (MOD021KM), and the LAI retrieval of the vegetation canopy was realized. The estimated LAI was compared with the MODIS LAI (MOD15A2H) using mid-latitude summer data from 2000 to 2017 in the DIRECT 2.0 ground database. The experiments indicated that the LAI retrieved by the TOA reflectance (r = 0.7852, RMSE = 0.5191) was not much different from the LAI retrieved by the TOC reflectance (r = 0.8063, RMSE = 0.7669), and the accuracy was better than the MODIS LAI (r = 0.7607, RMSE = 0.8239), which proves the feasibility of this method.