An LUT-Based Inversion of DART Model to Estimate Forest LAI from Hyperspectral Data

The efficient inversion of complex, three-dimensional (3-D) radiative transfer models (RTMs), such as the discrete anisotropy radiative transfer (DART) model, can be achieved using a look-up table (LUT) approach. A pressing research priority in LUT-based inversion for a 3-D model is to determine the optimal LUT grid size and density. We present a simple and computationally efficient approach for populating an LUT database with DART simulations over a large number of spectral bands. In the first step, we built a preliminary LUT using model parameters with coarse increments to simulate reflectance for six broad bands of Landsat Thematic Mapper (TM). In the second step, the preliminary LUT was compared with the TM reflectance, and the optimal input ranges and realistic parameter combinations that led to simulations close to Landsat spectra were then identified. In the third step, this information was combined with a sensitivity study, and final LUTs were built for the full spectrum of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) narrow bands and six Landsat broad bands. The final LUT was inverted to estimate leaf area index (LAI) in northern temperate forests from AVIRIS and TM data. The results indicate that the approach used in this study can be a useful strategy to estimate LAI accurately by DART model inversion.