Improving the analysis of Hyperion red-edge index from an agricultural area

David L. B. Jupp; Bisun Datt; Tim R. McVicar; Tom G. Van Niel; Jay S. Pearlman; Jenny L. Lovell; Edward A. King

doi:10.1117/12.472696

11 June 2003 Improving the analysis of Hyperion red-edge index from an agricultural area

David L. B. Jupp, Bisun Datt, Tim R. McVicar, Tom G. Van Niel, Jay S. Pearlman, Jenny L. Lovell, Edward A. King

Proceedings Volume 4898, Image Processing and Pattern Recognition in Remote Sensing; (2003) https://doi.org/10.1117/12.472696
Event: Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2002, Hangzhou, China

Abstract

The benefits of EO-1 data, and especially Hyperion hyperspectral data, are being studied at sites in the Coleambally Irrigation Area of Australia where a seasonal time series has been developed. Hyperion can provide effective measures of agricultural performance through the use of spectral indices if systematic and random noise is managed and such noise management methods have been established for Coleambally. Among the sources of noise specific to Hyperion is the spectral “smile” which affects the location of the red-edge -- an important index in agricultural assessment. We show how this phenomenon, which arises from the pushbroom technology of Hyperion, affects the data and discuss how its effects can be overcome to provide stable and accurate measures of the red-edge and related indices. HyMap airborne data are used to evaluate the results of the methods studied. This paper also shows how future pushbroom instruments should consider the wavelength sampling step in their design if it is intended to remove the “smile” effects by a systematic software processing.

Citation Download Citation

David L. B. Jupp, Bisun Datt, Tim R. McVicar, Tom G. Van Niel, Jay S. Pearlman, Jenny L. Lovell, and Edward A. King "Improving the analysis of Hyperion red-edge index from an agricultural area", Proc. SPIE 4898, Image Processing and Pattern Recognition in Remote Sensing, (11 June 2003); https://doi.org/10.1117/12.472696

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