학술논문

An LUT-Based Inversion of DART Model to Estimate Forest LAI from Hyperspectral Data
Document Type
Periodical
Source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of. 8(6):3147-3160 Jun, 2015
Subject
Geoscience
Signal Processing and Analysis
Power, Energy and Industry Applications
Table lookup
Remote sensing
Earth
Satellites
Biological system modeling
Computational modeling
Solid modeling
Hyperspectral remote sensing
imaging spectrometer
inversion methods
Landsat
look-up-table (LUT)
radiative transfer
Language
ISSN
1939-1404
2151-1535
Abstract
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.