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Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument

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VITROCISET Belgium SPRL, Bratustrasse 7, D-64293 Darmstadt, Germany
2
Institut des Géosciences de l’Environnement (IGE), Université Grenoble Alpes, CNRS, UMR 5001, 38041 Grenoble, France
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Université Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, 38400 Grenoble, France
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Brockmann Consult, Max Planck Strasse 2, 21502 Geesthacht, Germany
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Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza, 1 20126 Milan, Italy
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Arctic Environment Research Center, National Institute of Polar Research, Tachikawa 190-8518, Japan
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Meteorological Research Institute, Japan Meteorological Agency, Tsukuba 305-0052, Japan
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Institute of Environmental Physics, University of Bremen, D-28359 Bremen, Germany
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USRA GESTAR, 7178 Columbia Gateway Drive, Columbia, MD 21046, USA
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Alfred-Wegener-Institute, 27570 Bremerhaven, Germany
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Institute of Earth and Environmental Sciences, University of Potsdam, 14476 Potsdam, Germany
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Center for Ice and Climate, Copenhagen University, 1165 Copenhagen, Denmark
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Geophysical Institute and Bjerknes Centre for Climate Research, University of Bergen, 5007 Bergen, Norway
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Geological Survey of Denmark and Greenland (GEUS), 1350 Copenhagen, Denmark
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ESTEC/ESA, Science, Applications and Climate Department (EOP-SME), 2201 Noordwijk, The Netherlands
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Institute of Physics, National Academy of Sciences of Belarus, 220072 Minsk, Belarus
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2280; https://doi.org/10.3390/rs11192280
Received: 19 July 2019 / Revised: 5 September 2019 / Accepted: 16 September 2019 / Published: 29 September 2019
(This article belongs to the Special Issue Remote Sensing: 10th Anniversary)
The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo. View Full-Text
Keywords: snow characteristics; optical remote sensing; sow grain size; specific surface area; albedo; Sentinel 3; OLCI snow characteristics; optical remote sensing; sow grain size; specific surface area; albedo; Sentinel 3; OLCI
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Kokhanovsky, A.; Lamare, M.; Danne, O.; Brockmann, C.; Dumont, M.; Picard, G.; Arnaud, L.; Favier, V.; Jourdain, B.; Le Meur, E.; Di Mauro, B.; Aoki, T.; Niwano, M.; Rozanov, V.; Korkin, S.; Kipfstuhl, S.; Freitag, J.; Hoerhold, M.; Zuhr, A.; Vladimirova, D.; Faber, A.-K.; Steen-Larsen, H.C.; Wahl, S.; Andersen, J.K.; Vandecrux, B.; van As, D.; Mankoff, K.D.; Kern, M.; Zege, E.; Box, J.E. Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument. Remote Sens. 2019, 11, 2280.

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