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4 August 1997 Combined hyperspectral and thermal imaging for improved land surface flux estimation
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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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James Alan Smith and Jeffrey A. Pedelty "Combined hyperspectral and thermal imaging for improved land surface flux estimation", Proc. SPIE 3071, Algorithms for Multispectral and Hyperspectral Imagery III, (4 August 1997); https://doi.org/10.1117/12.280588
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