Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements

REMOTE SENSING OF ENVIRONMENT

卷:264

Article

Bohn, Niklas¹  Painter, Thomas H.²  Thompson, David R.³  Carmon, Nimrod³  Susiluoto, Jouni³  Turmon, Michael J.³  Helmlinger, Mark C.³  Green, Robert O.³  Cook, Joseph M.⁴  Guanter, Luis⁵ 

[1] GFZ German Res Ctr Geosci, D-14473 Potsdam, Germany

[2] Univ Calif Los Angeles, Joint Inst Reg Earth Syst Sci & Engn, Los Angeles, CA 90095 USA

[3] CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA

[4] Aarhus Univ, Dept Environm Sci Environm Microbiol & Circular R, DK-4000 Roskilde, Denmark

[5] Univ Politecn Valencia UPV, Res Inst Water & Environm Engn IIAMA, Valencia 46022, Spain

关键词: Imaging spectroscopy;    Optimal estimation;    Snow and ice;    Light-absorbing particles in snow and ice;    Greenland ice sheet;    Atmospheric correction;    EnMAP;   

DOI  :  10.1016/j.rse.2021.112613

来源: Elsevier

【 摘 要 】

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 mu g/gice 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.

【 授权许可】

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