Nonlinear spectral mixing theory to model multispectral signatures

Conference · Thu Feb 01 04:00:00 EST 1996

OSTI ID:195673

Borel, C C ^[1]

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1. Los Alamos National Lab., NM (United States). Astrophysics and Radiation Measurements Group

Nonlinear spectral mixing occurs due to multiple reflections and transmissions between discrete surfaces, e.g. leaves or facets of a rough surface. The radiosity method is an energy conserving computational method used in thermal engineering and it models nonlinear spectral mixing realistically and accurately. In contrast to the radiative transfer method the radiosity method takes into account the discreteness of the scattering surfaces (e.g. exact location, orientation and shape) such as leaves and includes mutual shading between them. An analytic radiosity-based scattering model for vegetation was developed and used to compute vegetation indices for various configurations. The leaf reflectance and transmittance was modeled using the PROSPECT model for various amounts of water, chlorophyll and variable leaf structure. The soil background was modeled using SOILSPEC with a linear mixture of reflectances of sand, clay and peat. A neural network and a geometry based retrieval scheme were used to retrieve leaf area index and chlorophyll concentration for dense canopies. Only simulated canopy reflectances in the 6 visible through short wave IR Landsat TM channels were used. The authors used an empirical function to compute the signal-to-noise ratio of a retrieved quantity.

• Research Organization: Los Alamos National Lab., NM (United States)
• Sponsoring Organization: National Aeronautics and Space Administration, Washington, DC (United States)
• DOE Contract Number: W-7405-ENG-36
• OSTI ID: 195673
• Report Number(s): LA-UR--95-4186; CONF-960203--1; ON: DE96005599
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
ALGORITHMS
CANOPIES
CLIMATIC CHANGE
DATA ANALYSIS
LANDSAT SATELLITES
MULTISPECTRAL PHOTOGRAPHY
NEURAL NETWORKS
P CODES
PLANTS
REMOTE SENSING
S CODES
SOILS
TERRESTRIAL ECOSYSTEMS