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.

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The spectral reflectance of leaves from several Eucalyptus species was measured over the 400–2500 nm wavelengths with a laboratory spectroradiometer. The relationship of reflectance with the gravimetric water content and equivalent water thickness (EWT) of the leaves was analysed. The results showed that EWT was strongly correlated with reflectance in several wavelength regions. No significant correlations could be obtained between reflectance and gravimetric water content. It was also possible to confirm theoretically that reflectance changes of leaves could be directly linked to changes in EWT but not to changes in gravimetric water content. Several existing reflectance indices were evaluated for estimation of leaf water content and some new indices were developed and tested. Two semi-empirical indices developed in this study, (R850 - R2218)/(R850 - R1928) and (R850 - R1788)/(R850 - R1928), were found to show significantly stronger correlations with EWT than all other indices tested. It was also shown that these new indices were least sensitive to the effects of radiation scatter. The indices (R850 - R2218)/(R850 - R1928) and (R850 - R1788)/(R850 - R1928) are therefore proposed as two new indices for the remote estimation of vegetation water content.