Journal ArticleDOI
Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes
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Preprocessing, which includes fixing bad and outlier pixels, local destriping, atmospheric correction, and minimum noise fraction smoothing, provides improved results and it is feasible to develop a consistent and standardized time series of data that is compatible with field-scale and airborne measured indexes.Abstract:
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.read more
Citations
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The use of remote sensing in soil and terrain mapping — A review
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Hyperion, a space-based imaging spectrometer
TL;DR: The Hyperion Imaging Spectrometer was the first imaging spectrometer to routinely acquire science-grade data from Earth orbit and met or exceeded predictions including continued operation well beyond the planned one-year program.
References
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I and i
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
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AERONET-a federated instrument network and data archive for aerosol Characterization
Brent N. Holben,Thomas F. Eck,Ilya Slutsker,Didier Tanré,J. P. Buis,Alberto Setzer,Eric Vermote,John A. Reagan,Yoram J. Kaufman,Teruyuki Nakajima,François Lavenu,I. Jankowiak,Alexander Smirnov +12 more
TL;DR: The operation and philosophy of the monitoring system, the precision and accuracy of the measuring radiometers, a brief description of the processing system, and access to the database are discussed.
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NDWI--a normalized difference water index for remote sensing of vegetation liquid water from space.
TL;DR: The normalized difference water index (NDWI) as discussed by the authors was proposed for remote sensing of vegetation liquid water from space, which is defined as (ϱ(0.86 μm) − ϱ(1.24 μm)) where ϱ represents the radiance in reflectance units.
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A transformation for ordering multispectral data in terms of image quality with implications for noise removal
TL;DR: In this paper, a transformation known as the maximum noise fraction (MNF) transformation is presented, which always produces new components ordered by image quality, and it can be shown that this transformation is equivalent to principal components transformations when the noise variance is the same in all bands and that it reduces to a multiple linear regression when noise is in one band only.
Journal ArticleDOI
Imaging Spectrometry for Earth Remote Sensing
TL;DR: The initial results show that remote, direct identification of surface materials on a picture-element basis can be accomplished by proper sampling of absorption features in the reflectance spectrum.