10 October 2018 Snow and Water Imaging Spectrometer: mission and instrument concepts for earth-orbiting CubeSats
Holly A. Bender, Pantazis Mouroulis, Heidi M. Dierssen, Thomas H. Painter, David R. Thompson, Christopher D. Smith, Johannes Gross, Robert O. Green, Justin M. Haag, Byron E. Van Gorp, Ernesto Diaz
Author Affiliations +
Abstract
The Snow and Water Imaging Spectrometer (SWIS) is a science-grade imaging spectrometer designed for CubeSat integration, spanning a 350- to 1700-nm spectral range with 5.7-nm sampling, a 10-degree field-of-view, and 0.3-mrad spatial resolution. The system operates at F  /  1.8, providing the high throughput for low-reflectivity (<1  %  ) water surfaces, while avoiding saturation over bright snow or clouds. The SWIS design utilizes heritage from previously demonstrated instruments on airborne platforms while advancing the state of the art in compact sensors of this kind in terms of size and spectral coverage. Compared with airborne campaigns, the CubeSat platform allows for more frequent and regular sampling, while maintaining intermediate to high resolution relative to heritage global sensors. Through frequent repeat observations from space at a moderate spatial resolution, SWIS can address key science questions concerning aquatic and terrestrial ecosystem changes, cryosphere warming and melt behavior, cloud and atmospheric science, and potential impacts of climate change and human activities on the environment.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2018/$25.00 © 2018 SPIE
Holly A. Bender, Pantazis Mouroulis, Heidi M. Dierssen, Thomas H. Painter, David R. Thompson, Christopher D. Smith, Johannes Gross, Robert O. Green, Justin M. Haag, Byron E. Van Gorp, and Ernesto Diaz "Snow and Water Imaging Spectrometer: mission and instrument concepts for earth-orbiting CubeSats," Journal of Applied Remote Sensing 12(4), 044001 (10 October 2018). https://doi.org/10.1117/1.JRS.12.044001
Received: 10 February 2018; Accepted: 11 September 2018; Published: 10 October 2018
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CITATIONS
Cited by 14 scholarly publications.
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KEYWORDS
Spectroscopy

Sensors

Signal to noise ratio

Clouds

Spatial resolution

Satellites

Space operations

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