Atmospheric correction of hyperspectral data in terms of the determination of plant parameters

Heike Bach; Wolfram Mauser

doi:10.1117/12.197245

21 December 1994 Atmospheric correction of hyperspectral data in terms of the determination of plant parameters

Heike Bach, Wolfram Mauser

Proceedings Volume 2318, Recent Advances in Remote Sensing and Hyperspectral Remote Sensing; (1994) https://doi.org/10.1117/12.197245
Event: Satellite Remote Sensing, 1994, Rome, Italy

Abstract

The procedure PULREF, which converts optical remote sensing data to absolute reflectance values, is presented. This procedure can be applied to different hyperspectral (AVIRIS, CASI,...) and multispectral sensors (TM, SPOT,...) and for a wide range of atmospheric and flight conditions. It is based on the radiative transfer code LOWTRAN-7, which contains all important absorption features and also includes multiple scattering calculations. PULREF corrects the adjacency effect by considering the environment's influence on the signal measured by a sensor. The easy use of PULREF makes it suitable for application oriented users. The necessity of converting remote sensing data to absolute reflectance values is demonstrated in an example. On the basis of a multitemporal and multisensoral dataset, a regression between the arNDVI (NDVI based on absolute reflectance values) and the plant parameter LAI was determined. The relationship is valid for different vegetation periods and regions in Germany. The regression between the LAI and the arNDVI of airborne and spaceborne sensors coincides well with results from ground based radiometer measurements. The atmospheric correction scheme PULREF can therefore serve as a prerequisite for time and location independent extraction of land surface parameters from optical remote sensing data.

Citation Download Citation

Heike Bach and Wolfram Mauser "Atmospheric correction of hyperspectral data in terms of the determination of plant parameters", Proc. SPIE 2318, Recent Advances in Remote Sensing and Hyperspectral Remote Sensing, (21 December 1994); https://doi.org/10.1117/12.197245

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