White mica wavelength position data were a derivative dataset produced from Corescan© reflectance data. Corescan Hyperspectral Core Imager Mark III (HCI-III) system data were acquired for hand samples, and subsequent billets made from the hand samples, collected during the U.S. Geological Survey (USGS) 2014, 2015, and 2016 field seasons in the Nabesna area of the eastern Alaska Range. This area contains exposed porphyry deposits and hand samples were collected throughout the region in support of the HyMap imaging spectrometer survey (https://doi.org/10.5066/F7DN435W) (Kokaly and others, 2017a). The HCI-III system consists of three different components. The first is an imaging spectrometer which collects reflectance data with a spatial resolution of approximately 500 nanometers (nm) for 514 spectral channels covering the 450-2,500 nm wavelength range of the electromagnetic spectrum (Martini and others, 2017). The second is a spectrally calibrated RGB camera that collects high resolution imagery of the samples with a 50 micrometer (μm) pixel size. The third component is a three-dimensional (3D) laser profiler that measures sample texture, surface features and shape with a vertical resolution of 20 μm (Martini and others, 2017). A total of 63 hand samples and four billets were analyzed using the HCI-III system in three scans. The imaging spectrometer raw data was collected with an average bandpass of approximately 6 nm across the Short Wave Infrared (SWIR) but smoothing functions applied by Corescan during the conversion of raw data to reflectance result in a relative bandpass of approximately 13 nm in the data delivered to the USGS. Wavelength evaluations of the imaging spectrometer data revealed that the supplied wavelength values should be shifted and, thus, adjustments were made to the wavelength positions (Kokaly and others, 2017c). The wavelength and bandpass evaluation results are provided in the 'Calibration' section of this data release and were used to adjust the Corescan reflectance data. The calibrated Corescan data were combined into a reflectance data cube mosaic and are provided in the 'HyperspectralCalibrated' section. Calibrated reflectance data from Corescan were processed using the Material Identification and Characterization Algorithm (MICA), a module of the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011). MICA identifies the spectrally predominant mineral(s) in each pixel of imaging spectrometer data by comparing continuum-removed spectral features in the pixel’s reflectance spectrum to continuum-removed absorption features in reference spectra of minerals and other materials. For each pixel, the reference spectrum with the highest fit value identifies the predominant mineral class. White mica wavelength position was computed for each pixel with spectrally predominant muscovite or illite. The computation was made using a function of the USGS PRISM software (Kokaly, 2011). The white mica wavelength values were output as a classification image, with classes in 1 nm increments.

A map of the wavelength position of the white mica 2,200 nanometer (nm) Al-OH absorption feature was compiled for a region of Nabesna, Alaska, using HyMap™ reflectance data provided and described in this data release. White mica wavelength position was computed for each pixel with spectrally predominant muscovite or illite. The computation was made using a function of the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011), programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). The PRISM function applies linear continuum-removal (Clark and Roush, 1984) to the 2,200 nm feature and fits a parabola to three channels: the channel with the minimum value in continuum-removed reflectance and one channel on either side (Kokaly, 2011). PRISM uses the wavelength position of the axis of symmetry from the fitted parabola as the definition of wavelength value. The white mica wavelength values were output as a classification image, with classes in 1-nm (0.001-micron) increments.

Approximately 1,900 square kilometers of imagery were collected from July 14 to July 21, 2014 using a HyMap™ sensor (Cocks and others, 1998) mounted on a modified Piper Navajo aircraft. The survey area covered parts of the Wrangell and Nutzotin Mountains in the eastern Alaska Range near Nabesna, Alaska. The aircraft was flown at an altitude of approximately 5,050 meters (m) (3,480 m above the mean ground surface elevation of 1570 m) resulting in average ground spatial resolution of 6.7 m. HyMap measured reflected sunlight in 126 narrow channels that cover the wavelength region of 455 to 2,483 nanometers (nm). Data were delivered by the operators of the sensor (HyVista Corp., Australia) in units of radiance (Kokaly and others, 2017). Radiance data were converted to reflectance with procedures adapted from Kokaly and others (2013). They are described and documented in this data release. Reflectance data from HyMap were processed using the Material Identification and Characterization Algorithm (MICA), a module of the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011), programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). MICA identifies the spectrally predominant mineral(s) in each pixel of imaging spectrometer data by comparing continuum-removed spectral features in the pixel’s reflectance spectrum to continuum-removed absorption features in reference spectra of minerals, vegetation, water, and other materials. For each pixel, the reference spectrum with the highest fit value identifies the predominant mineral class. A map of the wavelength position of the white mica 2,200 nm Al-OH absorption feature, elsewhere referred to more concisely as white mica, was also compiled. White mica wavelength position was computed for each pixel with spectrally predominant muscovite or illite. The computation was made using a function of the USGS PRISM software (Kokaly, 2011). The white mica wavelength values were output as a classification image, with classes in 1 nm increments. Each of these three datasets (reflectance, mineral predominance, and white mica wavelength position) are documented and described as part of this U.S. Geological Survey data release.

Approximately 1,900 square kilometers of imagery were collected from July 14 to July 21, 2014 using a HyMap™ sensor (Cocks and others, 1998) mounted on a modified Piper Navajo aircraft. The survey area covered parts of the Wrangell and Nutzotin Mountains in the eastern Alaska Range near Nabesna, Alaska. The aircraft was flown at an altitude of approximately 5,050 meters (m) (3,480 m above the mean ground surface elevation of 1570 m) resulting in average ground spatial resolution of 6.7 m. HyMap measured reflected sunlight in 126 narrow channels that cover the wavelength region of 455 to 2,483 nanometers (nm). Data were delivered by the operators of the sensor (HyVista Corp., Australia) in units of radiance (Kokaly and others, 2017). Radiance data were converted to reflectance with procedures adapted from Kokaly and others (2013). They are described and documented in this data release. Reflectance data from HyMap were processed using the Material Identification and Characterization Algorithm (MICA), a module of the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011), programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). MICA identifies the spectrally predominant mineral(s) in each pixel of imaging spectrometer data by comparing continuum-removed spectral features in the pixel’s reflectance spectrum to continuum-removed absorption features in reference spectra of minerals, vegetation, water, and other materials. For each pixel, the reference spectrum with the highest fit value identifies the predominant mineral class. A map of the wavelength position of the white mica 2,200 nm Al-OH absorption feature, elsewhere referred to more concisely as white mica, was also compiled. White mica wavelength position was computed for each pixel with spectrally predominant muscovite or illite. The computation was made using a function of the USGS PRISM software (Kokaly, 2011). The white mica wavelength values were output as a classification image, with classes in 1 nm increments. Each of these three datasets (reflectance, mineral predominance, and white mica wavelength position) are documented and described as part of this U.S. Geological Survey data release.

Measurements of reference materials were made on the Corescan© HCI-III to evaluate the supplied channel wavelength positions and bandpass values. Wavelength position and bandpass of channels in a spectrometer, referred to as full-width half max (FWHM) in the contractor's documentation (Corescan_Product_MetaData_v3.pdf), are two fundamental spectral characteristics that need to be known in order to spectrally identify minerals by comparison to a spectral library, like the Material Identification and Characterization Algorithm (MICA) analysis used to generate the mineral predominance maps. Spectrometers with finer bandpass can reveal greater spectral detail that can be related to a material’s chemical composition or physical structure: for example, kaolinite crystallinity. Imprecise knowledge of wavelength positions of channels could interfere with interpreting a material’s composition from its spectral feature positions: for example, interpreting Al composition in white mica from wavelength shifts in the absorption feature centered near 2,200 nm. Because, push broom imaging spectrometers, like the Corescan HCI-III, can sometimes have variable spectral characteristics across the field of view, the channel wavelength positions across the field of view were also evaluated. The imaging spectrometer raw data were collected with an average bandpass of approximately 6 nm across the Short Wave Infrared (SWIR) but smoothing functions applied by Corescan during the conversion of raw data to reflectance result in a relative bandpass of approximately 13 nm in the data delivered to the U.S. Geological Survey (USGS). Wavelength evaluations of the imaging spectrometer data revealed that the supplied wavelength values should be shifted and, thus, adjustments were made to the wavelength positions (Kokaly and others, 2017). The wavelength and bandpass evaluation results are provided in this section of the data release and were used to adjust the Corescan reflectance data.

Corescan© Hyperspectral Core Imager Mark III (HCI-III) system data were acquired for hand samples, and subsequent billets made from the hand samples, collected during the U.S. Geological Survey (USGS) 2014, 2015, and 2016 field seasons in the Nabesna area of the eastern Alaska Range. The HCI-III system consists of three different components. The first is an imaging spectrometer which collects reflectance data with a spatial resolution of approximately 500 nanometers (nm) for 514 spectral channels covering the 450-2,500 nm wavelength range of the electromagnetic spectrum (Martini and others, 2017). The second is a spectrally calibrated RGB camera that collects high resolution imagery of the samples with a 50 micrometer (μm) pixel size. The third component is a three-dimensional (3D) laser profiler that measures sample texture, surface features and shape with a vertical resolution of 20 μm (Martini and others, 2017). Corescan reflectance data were provided for a total of 63 hand samples and four billets analyzed using the HCI-III system in three scans.

Corescan© Hyperspectral Core Imager Mark III (HCI-III) system data were acquired for hand samples, and subsequent billets made from the hand samples, collected during the U.S. Geological Survey (USGS) 2014, 2015, and 2016 field seasons in the Nabesna area of the eastern Alaska Range. This area contains exposed porphyry deposits and hand samples were collected throughout the region in support of the HyMap imaging spectrometer survey (https://doi.org/10.5066/F7DN435W) (Kokaly and others, 2017a). The HCI-III system consists of three different components. The first is an imaging spectrometer which collects reflectance data with a spatial resolution of approximately 500 nanometers (nm) for 514 spectral channels covering the 450-2,500 nm wavelength range of the electromagnetic spectrum (Martini and others, 2017). The second is a spectrally calibrated RGB camera that collects high resolution imagery of the samples with a 50 micrometer (μm) pixel size. The third component is a three-dimensional (3D) laser profiler that measures sample texture, surface features and shape with a vertical resolution of 20 μm (Martini and others, 2017). A total of 63 hand samples and four billets were analyzed using the HCI-III system in three scans. The imaging spectrometer raw data were collected with an average bandpass of approximately 6 nm across the Short Wave Infrared (SWIR) but smoothing functions applied by Corescan during the conversion of raw data to reflectance result in a relative bandpass of approximately 13 nm in the data delivered to the USGS. Wavelength evaluations of the imaging spectrometer data revealed that the supplied wavelength values should be shifted and, thus, adjustments were made to the wavelength positions (Kokaly and others, 2017b). The wavelength and bandpass evaluation results are provided in the 'Calibration' section of this data release and were used to adjust the Corescan reflectance data. The calibrated Corescan data were combined into a reflectance data cube mosaic and are described and provided in this section.

Corescan© Hyperspectral Core Imager Mark III (HCI-III) system data were acquired for hand samples, and subsequent billets made from the hand samples, collected during the U.S. Geological Survey (USGS) 2014, 2015, and 2016 field seasons in the Nabesna area of the eastern Alaska Range. This area contains exposed porphyry deposits and hand samples were collected throughout the region in support of the HyMap imaging spectrometer survey (https://doi.org/10.5066/F7DN435W) (Kokaly and others, 2017a). The HCI-III system consists of three different components. The first is an imaging spectrometer which collects reflectance data with a spatial resolution of approximately 500 nanometers (nm) for 514 spectral channels covering the 450-2,500 nm wavelength range of the electromagnetic spectrum (Martini and others, 2017). The second is a spectrally calibrated RGB camera that collects high resolution imagery of the samples with a 50 micrometer (μm) pixel size. The third component is a three-dimensional (3D) laser profiler that measures sample texture, surface features and shape with a vertical resolution of 20 μm (Martini and others, 2017). A total of 63 hand samples and four billets were analyzed using the HCI-III system in three scans. The imaging spectrometer raw data were collected with an average bandpass of approximately 6 nm across the Short Wave Infrared (SWIR) but smoothing functions applied by Corescan during the conversion of raw data to reflectance result in a relative bandpass of approximately 13 nm in the data delivered to the USGS. Wavelength evaluations of the imaging spectrometer data revealed that the supplied wavelength values should be shifted and, thus, adjustments were made to the wavelength positions (Kokaly and others, 2017b). The wavelength and bandpass evaluation results are provided in the 'Calibration' section of this data release and were used to adjust the Corescan reflectance data. The calibrated Corescan data were combined into a reflectance data cube mosaic and are described and provided in this section.

Approximately 1,900 line square kilometers of imagery were collected using a HyMap™ sensor (Cocks and others, 1998) mounted on a modified Piper Navajo aircraft. The aircraft was flown at an altitude of approximately 5,050 m (3,480 m above the mean ground surface elevation of 1570 m) resulting in average ground spatial resolution of 6.7 m. Solar elevation and azimuth angles ranged from 42.0-48.3° (average 46.2°) and 134.2-182.4° (average 155°), respectively. HyMap measured reflected sunlight in 126 narrow channels that cover the wavelength region of 455 to 2,483 nm. Data were delivered by the operators of the sensor (HyVista Corp., Australia) in units of radiance (data are available in Kokaly and others, 2017). Radiance data were converted to reflectance with procedures adapted from Kokaly and others (2013). First, the radiance data were converted to apparent surface reflectance using a radiative transfer program, Atmospheric and Topographic Correction for airborne imagery (ATCOR-4), in rugged terrain mode (ReSe Applications, Zurich, Switzerland). The ATCOR-4 rugged terrain mode utilizes a surface elevation model to adjust illumination levels. Apparent surface reflectance values from the ATCOR-4 processing were empirically adjusted using ground-based reflectance measurements from calibration sites measured with an Analytical Spectral Devices FieldSpec® 4 (ASD FS4; ASD Inc., a Malvern PANalytical Company, Longmont, Colorado) standard resolution field spectrometer. Following the procedures adapted from Clark and others (2002), ASD FS4 data were collected from four sites in broad alluvial-fluvial gravel bars that were minimally vegetated and mostly lichen-free. The four sites covered areas of 0.31, 0.36, 0.51 and 0.76 hectares (ha). In the HyMap data, 86, 101, 143, and 210 HyMap pixels covered these areas, respectively. Although the rocks in these areas were mixed and varied at the fine spatial scale, at the HyMap 6 m pixel scale the calibration areas were spectrally homogeneous. The bare fiber optic of the ASD was held at shoulder height (~1.4 m) while walking around the calibration site and recording measurements of reflected sunlight relative to a Spectralon® white reference panel. The integration times for dark current and white reference panel were set to 10 and 24 seconds, respectively. The ASD was configured for 6 second averages for each recording of surface reflectance. A great number of ASD recordings were made in each calibration site: 455, 319, 420, and 310, respectively. Subsequently, the relative reflectance measurements at each site were averaged. The average relative reflectance was converted to absolute reflectance by correcting for the absorption properties of Spectralon (see the discussion of processing ASD spectra in Kokaly and Skidmore, 2015). Furthermore, offsets in reflectance between the three ASD detectors were rectified using a procedure in the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011) programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). PRISM functions were also used to compute multiplicative correction factors to convert HyMap apparent surface reflectance to ground-calibrated surface reflectance. Because flight lines were designed with substantial overlap, the four calibration sites could be used to directly calibrate eight of the nine flight lines. For the remaining flight line, the cross-calibration procedure of Kokaly and others (2013) was used to compute an empirical correction factor using a non-vegetated and topographically flat area overlapping with an adjacent flight line. Each flight line was geometrically-corrected using data provided by HyVista Corp. (see files provided in Kokaly and others, 2017). The images were mosaicked together using the mosaic function in ENVI (ENvironment for Visualizing Images; Harris Geospatial Solutions, Broomfield, Colorado). To improve the quality of the mosaic image,

Approximately 1,900 line square kilometers of imagery were collected using a HyMap™ sensor (Cocks and others, 1998) mounted on a modified Piper Navajo aircraft. The aircraft was flown at an altitude of approximately 5,050 m (3,480 m above the mean ground surface elevation of 1570 m) resulting in average ground spatial resolution of 6.7 m. Solar elevation and azimuth angles ranged from 42.0-48.3° (average 46.2°) and 134.2-182.4° (average 155°), respectively. HyMap measured reflected sunlight in 126 narrow channels that cover the wavelength region of 455 to 2,483 nm. Data were delivered by the operators of the sensor (HyVista Corp., Australia) in units of radiance (data are available in Kokaly and others, 2017). Radiance data were converted to reflectance with procedures adapted from Kokaly and others (2013). First, the radiance data were converted to apparent surface reflectance using a radiative transfer program, Atmospheric and Topographic Correction for airborne imagery (ATCOR-4), in rugged terrain mode (ReSe Applications, Zurich, Switzerland). The ATCOR-4 rugged terrain mode utilizes a surface elevation model to adjust illumination levels. Apparent surface reflectance values from the ATCOR-4 processing were empirically adjusted using ground-based reflectance measurements from calibration sites measured with an Analytical Spectral Devices FieldSpec® 4 (ASD FS4; ASD Inc., a Malvern PANalytical Company, Longmont, Colorado) standard resolution field spectrometer. Following the procedures adapted from Clark and others (2002), ASD FS4 data were collected from four sites in broad alluvial-fluvial gravel bars that were minimally vegetated and mostly lichen-free. The four sites covered areas of 0.31, 0.36, 0.51 and 0.76 hectares (ha). In the HyMap data, 86, 101, 143, and 210 HyMap pixels covered these areas, respectively. Although the rocks in these areas were mixed and varied at the fine spatial scale, at the HyMap 6 m pixel scale the calibration areas were spectrally homogeneous. The bare fiber optic of the ASD was held at shoulder height (~1.4 m) while walking around the calibration site and recording measurements of reflected sunlight relative to a Spectralon® white reference panel. The integration times for dark current and white reference panel were set to 10 and 24 seconds, respectively. The ASD was configured for 6 second averages for each recording of surface reflectance. A great number of ASD recordings were made in each calibration site: 455, 319, 420, and 310, respectively. Subsequently, the relative reflectance measurements at each site were averaged. The average relative reflectance was converted to absolute reflectance by correcting for the absorption properties of Spectralon (see the discussion of processing ASD spectra in Kokaly and Skidmore, 2015). Furthermore, offsets in reflectance between the three ASD detectors were rectified using a procedure in the USGS PRISM (Processing Routines in IDL for Spectroscopic Measurements) software (Kokaly, 2011) programmed in Interactive Data Language (IDL; Harris Geospatial Solutions, Broomfield, Colorado). PRISM functions were also used to compute multiplicative correction factors to convert HyMap apparent surface reflectance to ground-calibrated surface reflectance. Because flight lines were designed with substantial overlap, the four calibration sites could be used to directly calibrate eight of the nine flight lines. For the remaining flight line, the cross-calibration procedure of Kokaly and others (2013) was used to compute an empirical correction factor using a non-vegetated and topographically flat area overlapping with an adjacent flight line. Each flight line was geometrically-corrected using data provided by HyVista Corp. (see files provided in Kokaly and others, 2017). The images were mosaicked together using the mosaic function in ENVI (ENvironment for Visualizing Images; Harris Geospatial Solutions, Broomfield, Colorado). To improve the quality of the mosaic image,