2003
DOI: 10.3133/ofr03395
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USGS Digital Spectral Library splib05a

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Cited by 174 publications
(208 citation statements)
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“…We identified ferric oxide and some clay minerals by analyzing reflectance spectra using the Material Identification and Characterization Algorithm (MICA), a module of the PRISM software system (Kokaly, 2011), which uses continuum removal to isolate diagnostic absorption features and linear regression to compare spectral features. In this way, the spectra of Bodélé samples are compared with reference spectra of minerals and other materials (Clark et al, 2007). In this study, we are interested in the iron-bearing minerals with electronic absorption features centered primarily in the visible and near-infrared wavelengths (0.4 to 1.0 μm).…”
Section: Reflectance Spectroscopymentioning
confidence: 99%
See 1 more Smart Citation
“…We identified ferric oxide and some clay minerals by analyzing reflectance spectra using the Material Identification and Characterization Algorithm (MICA), a module of the PRISM software system (Kokaly, 2011), which uses continuum removal to isolate diagnostic absorption features and linear regression to compare spectral features. In this way, the spectra of Bodélé samples are compared with reference spectra of minerals and other materials (Clark et al, 2007). In this study, we are interested in the iron-bearing minerals with electronic absorption features centered primarily in the visible and near-infrared wavelengths (0.4 to 1.0 μm).…”
Section: Reflectance Spectroscopymentioning
confidence: 99%
“…We also determined the type of ferric oxide mineral, whether primarily hematite (-Fe 2 O 3 ) or goethite (-FeOOH), which are the two most common forms in desert soils and surfaces (hereafter collectively referred to as iron oxides or ferric oxides), and which have different optical properties. Reflectance spectroscopy can be used to identify different ferric oxide minerals (e.g., Clark et al, 2007), as can certain magnetic methods (Maher et al, 2004;Guyodo et al, 2006;Carter-Stiglitz et al, 2006;Liu et al, 2006;Maher, 2011). Mössbauer spectroscopy was also employed to identify with high specificity various iron phases, including amounts of hematite and goethite as well as the fraction that occur as nanoparticles, in a sample.…”
Section: Introductionmentioning
confidence: 99%
“…In this work, we expand the knowledge base by building a spectral database of 137 microorganisms from 0.35 to 2.5 μm (currently data for three such organisms are available in the literature; see ref. 4). There are several advantages of choosing this wavelength range.…”
Section: Discussionmentioning
confidence: 99%
“…Although there is a considerable knowledge base of the spectral properties of land plants (4,5), very little information is present in the literature on the reflectance properties of microorganisms. Land plants are widespread on present-day Earth and are easily detected from high-resolution spacecraft observations (6).…”
mentioning
confidence: 99%
“…Hyperspectral endmember-spectra are randomly chosen from a spectral library measured from 0.4 to 2.5 µm with 420 wavelengths and compiled by the United States Geological Survey (USGS) [14]. The number of endmembers is varied from 2 to 10 in our tests.…”
Section: Tested Data and Experiments Designmentioning
confidence: 99%