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

Bohn, N., Painter, T. H., Thompson, D. R., Carmon, N., Susiluoto, J., Turmon, M. J., Helmlinger, M. C., Green, R. O., Cook, J. M., Guanter, L. (2021): Optimal estimation of snow and ice surface parameters from imaging spectroscopy measurements. - Remote Sensing of Environment, 264, 112613.
https://doi.org/10.1016/j.rse.2021.112613

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Bohn, Niklas1, Autor              
Painter, Thomas H.2, Autor
Thompson, David R.2, Autor
Carmon, Nimrod2, Autor
Susiluoto, Jouni2, Autor
Turmon, Michael J.2, Autor
Helmlinger, Mark C.2, Autor
Green, Robert O.2, Autor
Cook, Joseph M.2, Autor
Guanter, Luis2, Autor
Affiliations:
11.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146028              
2External Organizations, ou_persistent22              

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 Zusammenfassung: 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/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.

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Sprache(n): eng - Englisch
 Datum: 20212021
 Publikationsstatus: Final veröffentlicht
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 Identifikatoren: DOI: 10.1016/j.rse.2021.112613
GFZPOF: p4 T5 Future Landscapes
OATYPE: Green Open Access
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Titel: Remote Sensing of Environment
Genre der Quelle: Zeitschrift, SCI, Scopus
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Seiten: - Band / Heft: 264 Artikelnummer: 112613 Start- / Endseite: - Identifikator: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals427
Publisher: Elsevier