Hyperion data collection: Performance assessment and science application

Barry, P.S.; Segal, C.; Pearlman, Jay; Jarecke, Peter J.; Folkman, Mark A.

doi:10.1109/aero.2002.1035287

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…With respect to a 30% uniform albedo target (Pearlman et al 2000), these ratios yield an estimated noise equivalent delta reflectance in the VNIR equal to or less than about 0.2%. In addition, a series of lunar and solar calibration collections found VNIR repeatability ,1% (Barry et al 2002). Both measures are well below the 2% upper limit estimated for detecting a few senescing tallow trees.…”

Section: Study Area and Image Collectionmentioning

confidence: 96%

“…We restricted analyses to wavelengths below 1000 nm and, with the exception of nine bands surrounding the spectral overlap region, used only bands of visibly high quality (table 1). Pre-launch and on-orbit calibrations have reported the signal to noise ratio (SNR) in the VNIR ranged from about 140 to 190 (Barry et al 2002). With respect to a 30% uniform albedo target (Pearlman et al 2000), these ratios yield an estimated noise equivalent delta reflectance in the VNIR equal to or less than about 0.2%.…”

Section: Study Area and Image Collectionmentioning

confidence: 96%

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A whole image approach using field measurements for transforming EO1 Hyperion hyperspectral data into canopy reflectance spectra

Ramsey

Nelson

2005

International Journal of Remote Sensing

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To maximize the spectral distinctiveness (information) of the canopy reflectance, an atmospheric correction strategy was implemented to provide accurate estimates of the intrinsic reflectance from the Earth Observing 1 (EO1) satellite Hyperion sensor signal. In rendering the canopy reflectance, an estimate of optical depth derived from a measurement of downwelling irradiance was used to drive a radiative transfer simulation of atmospheric scattering and attenuation. During the atmospheric model simulation, the input whole-terrain background reflectance estimate was changed to minimize the differences between the model predicted and the observed canopy reflectance spectra at 34 sites. Lacking appropriate spectrally invariant scene targets, inclusion of the field and predicted comparison maximized the model accuracy and, thereby, the detail and precision in the canopy reflectance necessary to detect low percentage occurrences of invasive plants. After accounting for artifacts surrounding prominent absorption features from about 400 nm to 1000 nm, the atmospheric adjustment strategy correctly explained 99% of the observed canopy reflectance spectra variance. Separately, model simulation explained an average of 88%¡9% of the observed variance in the visible and 98%¡1% in the near-infrared wavelengths. In the 34 model simulations, maximum differences between the observed and predicted reflectances were typically less than ¡1% in the visible; however, maximum reflectance differences higher than ¡1.6% (,¡2.3%) at more than a few wavelengths were observed at three sites. In the near-infrared wavelengths, maximum reflectance differences remained less than ¡3% for 68% of the comparisons (¡1 standard deviation) and less than ¡6% for 95% of the comparisons (¡2 standard deviation). Higher reflectance differences in the visible and near-infrared wavelengths were most likely associated with problems in the comparison, not in the model generation.

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Section: Study Area and Image Collectionmentioning

confidence: 96%

Section: Study Area and Image Collectionmentioning

confidence: 96%

A whole image approach using field measurements for transforming EO1 Hyperion hyperspectral data into canopy reflectance spectra

Ramsey

Nelson

2005

International Journal of Remote Sensing

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“…The most important of these challenges include the time of day of measurements and solar inclination angle, relief characteristics, extent and type of vegetation, and atmospheric obstacles such as humidity and cloud cover. Anticipating this, NASA included many ground truth verification locations in Hyperion's initial paths to ensure effective calibration [58,59] Because of the presence of visible light wavelengths in the examined spectra, the time of day of satellite measurement is certainly a restrictive condition in terms of method applicability. Future analysis should be conducted to test the effectiveness of only using infrared wavelengths to detect arsenic contamination; if the resulting accuracies are reasonably similar to current ones, the impact of time of day can be greatly reduced because of infrared's relative independence from time of day.…”

Section: Limiting Factors Of the Satellite Approachmentioning

confidence: 99%

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Agrawal¹,

Petersen

2021

Toxics

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Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression (R2=0.840 and normalized root mean squared error (re-scaled to [0,1]) = 0.122) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

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“…The performance characteristics of Hyperion are shown in Table 3 . The scientists’ range of interests that can achieve good benefit from Hyperion data include: (1) geology; (2) agriculture monitoring; (3) volcanic temperature measurement; (4) study of reef and coral bay health; (5) glaciological applications (Barry et al, 2002 ). Hyperion data have a signal-to-noise ratio about 161/1 in the visible and near infrared and 40/1 in the shortwave infrared region that somewhat limited the scientific applications of Hyperion data (Folkman et al, 2001 ; Thome et al, 2003 ).…”

Section: Remote Sensorsmentioning

confidence: 99%

ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration

Pour

Hashim

2014

SpringerPlus

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This paper provides a review of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and Hyperion data and applications of the data as a tool for ore minerals exploration, lithological and structural mapping. Spectral information extraction from ASTER, ALI, and Hyperion data has great ability to assist geologists in all disciplines to map the distribution and detect the rock units exposed at the earth’s surface. The near coincidence of Earth Observing System (EOS)/Terra and Earth Observing One (EO-1) platforms allows acquiring ASTER, ALI, and Hyperion imagery of the same ground areas, resulting accurate information for geological mapping applications especially in the reconnaissance stages of hydrothermal copper and gold exploration, chromite, magnetite, massive sulfide and uranium ore deposits, mineral components of soils and structural interpretation at both regional and district scales. Shortwave length infrared and thermal infrared bands of ASTER have sufficient spectral resolution to map fundamental absorptions of hydroxyl mineral groups and silica and carbonate minerals for regional mapping purposes. Ferric-iron bearing minerals can be discriminated using six unique wavelength bands of ALI spanning the visible and near infrared. Hyperion visible and near infrared bands (0.4 to 1.0 μm) and shortwave infrared bands (0.9 to 2.5 μm) allowed to produce image maps of iron oxide minerals, hydroxyl-bearing minerals, sulfates and carbonates in association with hydrothermal alteration assemblages, respectively. The techniques and achievements reviewed in the present paper can further introduce the efficacy of ASTER, ALI, and Hyperion data for future mineral and lithological mapping and exploration of the porphyry copper, epithermal gold, chromite, magnetite, massive sulfide and uranium ore deposits especially in arid and semi-arid territory.

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Hyperion data collection: Performance assessment and science application

Cited by 6 publications

References 12 publications

A whole image approach using field measurements for transforming EO1 Hyperion hyperspectral data into canopy reflectance spectra

A whole image approach using field measurements for transforming EO1 Hyperion hyperspectral data into canopy reflectance spectra

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration

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