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Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements
  • +7
  • Niklas Bohn,
  • Thomas Painter,
  • David Thompson,
  • Nimrod Carmon,
  • Jouni Susiluoto,
  • Michael Turmon,
  • Mark Helmlinger,
  • Robert Green,
  • Joseph Cook,
  • Luis Guanter
Niklas Bohn
GFZ German Research Centre for Geosciences

Corresponding Author:nbohn@gfz-potsdam.de

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Thomas Painter
Joint Institute for Regional Earth System Science and Engineering, UCLA
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David Thompson
Jet Propulsion Laboratory, California Institute of Technology
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Nimrod Carmon
Jet Propulsion Laboratory, California Institute of Technology
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Jouni Susiluoto
Jet Propulsion Laboratory, California Institute of Technology
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Michael Turmon
Jet Propulsion Laboratory
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Mark Helmlinger
Jet Propulsion Laboratory, California Institute of Technology
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Robert Green
Jet Propulsion Laboratory, California Institute of Technology
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Joseph Cook
Department of Environmental Science - Environmental Microbiology and Circular Resource Flow, Aarhus Universitet
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Luis Guanter
Research Institute of Water and Environmental Engineering (IIAMA), Universitat Politècnica de València (UPV)
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Abstract

Snow and ice melt processes are a key in Earth’s energy-balance and hydrological cycle. Their quantification facilitates predictions of meltwater runoff as well as distribution and availability of fresh water. They control the balance of the Earth’s ice sheets and are acutely sensitive to climate change. These processes decrease the surface reflectance with unique spectral patterns due to the accumulation of liquid water and light absorbing particles (LAP), that require imaging spectroscopy to map and measure. Here we present a new method to retrieve snow grain size, liquid water fraction, and LAP mass mixing ratio from airborne and spaceborne imaging spectroscopy acquisitions. This methodology is based on a simultaneous retrieval of atmospheric and surface parameters using optimal estimation (OE), a retrieval technique which leverages prior knowledge and measurement noise in an inversion that also produces uncertainty estimates. We exploit statistical relationships between surface reflectance spectra and snow and ice properties to estimate their most probable quantities given the reflectance. To test this new algorithm we conducted a sensitivity analysis based on simulated top-of-atmosphere radiance spectra using the upcoming EnMAP orbital imaging spectroscopy mission, demonstrating an accurate estimation performance of snow and ice surface properties. A validation experiment using in-situ measurements of glacier algae mass mixing ratio and surface reflectance from the Greenland Ice Sheet gave uncertainties of ±16.4 μg/g_ice and less than 3%, respectively. Finally, we evaluated the retrieval capacity for all snow and ice properties with an AVIRIS-NG acquisition from the Greenland Ice Sheet demonstrating this approach’s potential and suitability for upcoming orbital imaging spectroscopy missions.