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Proceedings Paper

Combined hyperspectral and thermal imaging for improved land surface flux estimation
Author(s): James Alan Smith; Jeffrey A. Pedelty
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Paper Abstract

We present a new approach for estimating land surface fluxes using remote sensing optical and thermal IR observations. We employ an artificial neural network and train it with a radiosity reflectance model. We then apply the network without retraining to extract geometrical view factors from AVIRIS imagery. We use the retrieved view factors and in- situ meteorological data to drive a surface energy balance model. Theoretical directional view factors were retrieved with an average absolute error of 15 percent. Hemispherical view factors were retrieved with a root mean square error of 6 percent. Surface net radiation estimated using the AVIRIS imagery and the surface energy balance model varied form 520 W m-2 to 650 W m-2 and are consistent with tower measurements. The retrieved view factors may also be used to model mixed pixel response for directional thermal IR data.

Paper Details

Date Published: 4 August 1997
PDF: 8 pages
Proc. SPIE 3071, Algorithms for Multispectral and Hyperspectral Imagery III, (4 August 1997); doi: 10.1117/12.280588
Show Author Affiliations
James Alan Smith, NASA Goddard Space Flight Ctr. (United States)
Jeffrey A. Pedelty, NASA Goddard Space Flight Ctr. (United States)

Published in SPIE Proceedings Vol. 3071:
Algorithms for Multispectral and Hyperspectral Imagery III
A. Evan Iverson; Sylvia S. Shen, Editor(s)

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