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Dryad

Seasonal surface reflectance mosaics of Blackhawk Island, Wisconsin May-October 2018

Cite this dataset

Chlus, Adam; Townsend, Philip (2022). Seasonal surface reflectance mosaics of Blackhawk Island, Wisconsin May-October 2018 [Dataset]. Dryad. https://doi.org/10.5061/dryad.37pvmcvpw

Abstract

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.

Methods

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 (>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.

Usage notes

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.

Funding

United States Department of Agriculture, Award: McIntire-Stennis Award WIS01809

United States Department of Agriculture, Award: McIntire-Stennis Award WIS03008

National Science Foundation, Award: Macrosystems and Early NEON Science 1638720