Preprocessing eo-1 hyperion hyperspectral data to support the application of agricultural 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.

Satellite Retrievals of Aerosol Optical Depth over a Subtropical Urban Area: The Role of Stratification and Surface Reflectance

“…Moreover, the ratio between 660 nm and 2130 nm BRDF derived surface reflectance (mean of ~0.68 with standard deviation of 0.03) is considerably higher than the MOD04 surface reflectance ratio (mean of ~0.5 with standard deviation of 0.02). Similar differences in the 660/2130 nm ratio of surface reflectance in an urban context have been found by Oo et al (2010) who compare the surface reflectance 600/2130 ratio of the MOD04 algorithm with the one derived from a high spectral resolution Hyperion dataset (Datt et al, 2003).…”

Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery

“…To facilitate the development of indices, these values were converted to apparent surface reflectance using ACORN 4.10 software (Analytical Imaging and Geophysics LLC 2002). Prior to this conversion, the following pre-processing steps were implemented: re-calibration, band selection, destreaking, and repair of 'bad' (non-responsive) pixel values (figure 2) (Apan and Held 2002;Datt et al, 2003). A minimum noise fraction (MNF) transformation smoothing was applied to the post-atmospheric correction reflectance image to minimize uncorrelated spatial noise.…”

Narrowband Bio-Indicator Monitoring of Temperate Forest Carbon Fluxes in Northeastern China

“…This technology advancement provides some opportunities for assessing additional narrowband indices, including PRI (photochemical reflectance index), NDNI (normalized difference nitrogen index), a series of related biochemical component and red edge indices (e.g., leaf chlorophyll index, chlorophyll vegetation index, canopy chlorophyll content index, and Dmax, the maximum derivative of reflectance in 650-750 nm). The hyperspectral data are primarily derived from space-borne sensors (e.g., EO-1 Hyperion) [25,26], airborne sensors (e.g., AVIRIS, Carnegie Airborne Observatory AToMS) [27,28], and ground vegetation spectral measurements collected from FluxNet EC towers [29]. Although it is underutilized in the monitoring of C fluxes, hyperspectral remote sensing has much to offer toward the improved accuracy of global carbon exchange models, e.g., monitoring light use efficiency (LUE) [30].…”