DOI: 10.5061/dryad.37pvmcvpw
title: Seasonal surface reflectance mosaics of Blackhawk Island, Wisconsin May-October 2018

description: This dataset contains high-resolution hyperspectral surface reflectance
 mosaics collected over Blackhawk Island, Wisconsin, USA. Images were
 collected at eight dates during the 2018 growing season (May – October)
 using a VNIR-SWIR (400–2400nm) HySpex airborne imaging system at 1m
 spatial resolution.
description: Imaging spectroscopy data was collected using a HySpex airborne
 imaging system (Norsk Elektro Optikk As, Skedsmokorset, Norway). The
 system consists of two cameras, a VNIR-1800 camera, which measures
 radiation between 400 and 997 nm across 186 channels with a spectral
 sampling interval of 3.26 nm, and a SWIR-384 camera, which covers 975–2500
 nm and measures radiation at 288 channels with a spectral sampling
 interval of 5.45 nm. The cameras were mounted on a vibration-dampening
 platform with an iTraceRT F400-E GPS/IMU (iMAR Navigation GmbH, St.
 Ingbert, Germany). The imaging system was flown aboard a Cessna 180 at a
 nominal altitude of 700 m above ground level, resulting in a spatial
 resolution of 0.5 m for the VNIR camera and 1.0 m for the SWIR camera.
 Each overflight consisted of nine flightlines with 60% sidelap. A total of
 eight overflights were flown between 16 May and 17 October 2018, and all
 flights were conducted +/− 2 h of solar noon. Raw image
 data were converted to radiance using manufacturer-provided calibration
 coefficients. Wavelength centers were estimated following Guanter et al.
 (2009). Camera alignment and geometric registration were performed using
 PARGE 6.0 orthorectification software (RESE, Wil SG, Switzerland). A
 secondary geometric adjustment was performed using a correlation-based
 image-matching algorithm (Gao et al., 2009), using a 2015 National
 Agriculture Imagery Program aerial image as a reference image. Calculation
 of apparent surface reflectance from at-sensor radiance was performed
 using an inverse algebraic atmospheric correction algorithm with the
 ‘libRadtran’ radiative transfer code (Emde et al., 2016) based on the
 method of Adler-Golden et al. (1999). Total column water vapor was
 estimated using the depth of the water vapor feature at 940 nm (Carrere
 and Conel, 1993). Visibility, which was high during all overflights, was
 set to a constant of 50 km. Next, a bidirectional reflectance distribution
 function (BRDF) correction was applied to remove brightness gradients
 resulting from varying sun and sensor geometry using the approach
 described in Chlus et al. (2020). Briefly, using sensor and sun geometry,
 we modeled the volumetric, geometric and isometric scattering components
 using the Ross and Li scattering kernels (Schläpfer et al., 2014). For
 each date, we pooled data across all flightlines and generated a single
 set of BRDF correction coefficients by regressing the resulting kernels
 against the uncorrected reflectance data for each wavelength. The VNIR
 imagery was aggregated and averaged to 1 m to match the spatial resolution
 of the SWIR camera. Image data from both cameras were combined at 980 nm
 to create a single full range (400–2500 nm) image for each flightline. The
 SWIR spectrum tail (&gt;2400 nm) and water absorption bands were
 excluded from analysis due to low signal-to-noise ratio (SNR). Individual
 flightlines were merged to create a mosaic of the island for each date; in
 overlapping regions, the pixel with the smallest viewing zenith angle
 (i.e., closest to nadir) was used. To improve SNR we averaged bands
 pairwise, resulting in a total of 187 bands with nominal spectral sampling
 intervals of 7 nm and 10 nm in the VNIR and SWIR, respectively. Finally,
 to suppress residuals in the reflectance spectra we calculated per-date
 multiplicative correction factors, using a sand bar as a smooth reference
 surface (Thompson et al., 2015). References
 Adler-Golden, S. M.,
 Matthew, M. W., Bernstein, L. S., Levine, R. Y., Berk, A., Richtsmeier, S.
 C., Acharya, P. K., Anderson, G. P., Felde, J. W., Gardner, J. A., Hoke,
 M. L., Jeong, L. S., Pukall, B., Ratkowski, A. J., & Burke, H.-hua
 K. (1999). Atmospheric correction for shortwave spectral imagery based on
 MODTRAN4. Imaging Spectrometry V. Carrere, V., & Conel, J. E. (1993).
 Recovery of atmospheric water vapor total column abundance from imaging
 spectrometer data around 940 nm—Sensitivity analysis and application to
 airborne visible/infrared imaging spectrometer (AVIRIS) data. Remote
 Sensing of Environment, 44(2-3), 179-204. Chlus, A., Kruger, E. L., &
 Townsend, P. A. (2020). Mapping three-dimensional variation in leaf mass
 per area with imaging spectroscopy and lidar in a temperate broadleaf
 forest. Remote Sensing of Environment, 250, 112043. Emde, C., Buras-Schnell, R., Kylling, A.,
 Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C.,
 Dowling, T., & Bugliaro, L. (2016). The libRadtran software
 package for radiative transfer calculations (version 2.0.1). Geoscientific
 Model Development, 9(5), 1647–1672. Gao, F., Masek, J. G., & Wolfe, R.
 E. (2009). Automated registration and orthorectification package for
 Landsat and Landsat-like data processing. Journal of Applied Remote
 Sensing, 3(1), 033515. Guanter, L., Segl, K., Sang, B., Alonso,
 L., Kaufmann, H., & Moreno, J. (2009). Scene-based spectral
 calibration assessment of high spectral resolution imaging
 spectrometers. Optics Express, 17(14), 11594-11606. Schläpfer, D., Richter, R., &
 Feingersh, T. (2014). Operational BRDF effects correction for
 wide-field-of-view optical scanners (BREFCOR). IEEE Transactions on
 Geoscience and Remote Sensing, 53(4), 1855-1864. Thompson, D. R., Gao, B. C., Green, R. O.,
 Roberts, D. A., Dennison, P. E., & Lundeen, S. R. (2015).
 Atmospheric correction for global mapping spectroscopy: ATREM advances for
 the HyspIRI preparatory campaign. Remote Sensing of Environment, 167,
 64-77.
description: Surface reflectance images are stored in ENVI binary format and
 can be opened using common GIS and remote sensing software including QGIS,
 ENVI, ArcGIS and ERDAS Imagine.