Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes

The benefits of Hyperion hyperspectral data to agriculture have been studied at sites in the Coleambally Irrigation Area of Australia. Hyperion can provide effective measures of agricultural performance through the use of established spectral indexes if systematic and random noise is managed. The no...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2003-06, Vol.41 (6), p.1246-1259
Main Authors: Datt, B., McVicar, T.R., Van Niel, T.G., Jupp, D.L.B., Pearlman, J.S.
Format: Article
Language:English
Subjects:
Agriculture
Atmospheric measurements
Australia
Data smoothing
Hyperspectral imaging
Image processing
Irrigation
Noise
Noise measurement
Noise reduction
Smoothing methods
Spectroscopy
Studies
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Summary:The benefits of Hyperion hyperspectral data to agriculture have been studied at sites in the Coleambally Irrigation Area of Australia. Hyperion can provide effective measures of agricultural performance through the use of established spectral indexes if systematic and random noise is managed. The noise management strategy includes recognition of "bad" pixels, reducing the effects of vertical striping, and compensation for atmospheric effects in the data. It also aims to reduce compounding of these effects by image processing. As the noise structure is different for Hyperion's two spectrometers, noise reduction methods are best applied to each separately. Results show that a local destriping algorithm reduces striping noise without introducing unwanted effects in the image. They also show how data smoothing can clean the data and how careful selection of stable Hyperion bands can minimize residual atmospheric effects following atmospheric correction. Comparing hyperspectral indexes derived from Hyperion with the same indexes derived from ground-measured spectra allowed us to assess some of these impacts on the preprocessing options. It has been concluded that preprocessing, which includes fixing bad and outlier pixels, local destriping, atmospheric correction, and minimum noise fraction smoothing, provides improved results. If these or equivalent preprocessing steps are followed, it is feasible to develop a consistent and standardized time series of data that is compatible with field-scale and airborne measured indexes. Red-edge and leaf chlorophyll indexes based on the preprocessed data are shown to distinguish different levels of stress induced by water restrictions.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2003.813206