Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument
International audience 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 an...
Published in: | Remote Sensing |
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Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
Other Authors: | , , , , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2019
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Subjects: | |
Online Access: | https://meteofrance.hal.science/meteo-03657917 https://meteofrance.hal.science/meteo-03657917/document https://meteofrance.hal.science/meteo-03657917/file/remotesensing-11-02280-v3.pdf https://doi.org/10.3390/rs11192280 |
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ftunivsavoie:oai:HAL:meteo-03657917v1 |
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Open Polar |
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Université Savoie Mont Blanc: HAL |
op_collection_id |
ftunivsavoie |
language |
English |
topic |
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
spellingShingle |
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences Kokhanovsky, Alexander Lamare, Maxim Danne, Olaf Brockmann, Carsten Dumont, Marie Picard, Ghislain Arnaud, Laurent Favier, Vincent Jourdain, Bruno Le Meur, Emmanuel Di Mauro, Biagio Aoki, Teruo Niwano, Masashi Rozanov, Vladimir Korkin, Sergey Kipfstuhl, Sepp Freitag, Johannes Hoerhold, Maria Zuhr, Alexandra Vladimirova, Diana Faber, Anne-Katrine Steen-Larsen, Hans Wahl, Sonja Andersen, Jonas Vandecrux, Baptiste van As, Dirk Mankoff, Kenneth Kern, Michael Zege, Eleonora Box, Jason Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument |
topic_facet |
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
description |
International audience 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. |
author2 |
Centre d'Etudes de la Neige (CEN) Centre national de recherches météorologiques (CNRM) Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ) Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA) Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 ) ANR-16-CE01-0006,EBONI,Dépot, devenir et impact des impuretés absorbantes dans le manteau neigeux(2016) |
format |
Article in Journal/Newspaper |
author |
Kokhanovsky, Alexander Lamare, Maxim Danne, Olaf Brockmann, Carsten Dumont, Marie Picard, Ghislain Arnaud, Laurent Favier, Vincent Jourdain, Bruno Le Meur, Emmanuel Di Mauro, Biagio Aoki, Teruo Niwano, Masashi Rozanov, Vladimir Korkin, Sergey Kipfstuhl, Sepp Freitag, Johannes Hoerhold, Maria Zuhr, Alexandra Vladimirova, Diana Faber, Anne-Katrine Steen-Larsen, Hans Wahl, Sonja Andersen, Jonas Vandecrux, Baptiste van As, Dirk Mankoff, Kenneth Kern, Michael Zege, Eleonora Box, Jason |
author_facet |
Kokhanovsky, Alexander Lamare, Maxim Danne, Olaf Brockmann, Carsten Dumont, Marie Picard, Ghislain Arnaud, Laurent Favier, Vincent Jourdain, Bruno Le Meur, Emmanuel Di Mauro, Biagio Aoki, Teruo Niwano, Masashi Rozanov, Vladimir Korkin, Sergey Kipfstuhl, Sepp Freitag, Johannes Hoerhold, Maria Zuhr, Alexandra Vladimirova, Diana Faber, Anne-Katrine Steen-Larsen, Hans Wahl, Sonja Andersen, Jonas Vandecrux, Baptiste van As, Dirk Mankoff, Kenneth Kern, Michael Zege, Eleonora Box, Jason |
author_sort |
Kokhanovsky, Alexander |
title |
Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument |
title_short |
Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument |
title_full |
Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument |
title_fullStr |
Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument |
title_full_unstemmed |
Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument |
title_sort |
retrieval of snow properties from the sentinel-3 ocean and land colour instrument |
publisher |
HAL CCSD |
publishDate |
2019 |
url |
https://meteofrance.hal.science/meteo-03657917 https://meteofrance.hal.science/meteo-03657917/document https://meteofrance.hal.science/meteo-03657917/file/remotesensing-11-02280-v3.pdf https://doi.org/10.3390/rs11192280 |
genre |
albedo Antarc* Antarctica Arctic Greenland Ice Sheet |
genre_facet |
albedo Antarc* Antarctica Arctic Greenland Ice Sheet |
op_source |
ISSN: 2072-4292 Remote Sensing https://meteofrance.hal.science/meteo-03657917 Remote Sensing, 2019, 11 (19), pp.2280. ⟨10.3390/rs11192280⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs11192280 meteo-03657917 https://meteofrance.hal.science/meteo-03657917 https://meteofrance.hal.science/meteo-03657917/document https://meteofrance.hal.science/meteo-03657917/file/remotesensing-11-02280-v3.pdf doi:10.3390/rs11192280 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.3390/rs11192280 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
19 |
container_start_page |
2280 |
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1797572710883131392 |
spelling |
ftunivsavoie:oai:HAL:meteo-03657917v1 2024-04-28T07:53:37+00:00 Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument Kokhanovsky, Alexander Lamare, Maxim Danne, Olaf Brockmann, Carsten Dumont, Marie Picard, Ghislain Arnaud, Laurent Favier, Vincent Jourdain, Bruno Le Meur, Emmanuel Di Mauro, Biagio Aoki, Teruo Niwano, Masashi Rozanov, Vladimir Korkin, Sergey Kipfstuhl, Sepp Freitag, Johannes Hoerhold, Maria Zuhr, Alexandra Vladimirova, Diana Faber, Anne-Katrine Steen-Larsen, Hans Wahl, Sonja Andersen, Jonas Vandecrux, Baptiste van As, Dirk Mankoff, Kenneth Kern, Michael Zege, Eleonora Box, Jason Centre d'Etudes de la Neige (CEN) Centre national de recherches météorologiques (CNRM) Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ) Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA) Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes 2016-2019 (UGA 2016-2019 ) ANR-16-CE01-0006,EBONI,Dépot, devenir et impact des impuretés absorbantes dans le manteau neigeux(2016) 2019-10 https://meteofrance.hal.science/meteo-03657917 https://meteofrance.hal.science/meteo-03657917/document https://meteofrance.hal.science/meteo-03657917/file/remotesensing-11-02280-v3.pdf https://doi.org/10.3390/rs11192280 en eng HAL CCSD MDPI info:eu-repo/semantics/altIdentifier/doi/10.3390/rs11192280 meteo-03657917 https://meteofrance.hal.science/meteo-03657917 https://meteofrance.hal.science/meteo-03657917/document https://meteofrance.hal.science/meteo-03657917/file/remotesensing-11-02280-v3.pdf doi:10.3390/rs11192280 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess ISSN: 2072-4292 Remote Sensing https://meteofrance.hal.science/meteo-03657917 Remote Sensing, 2019, 11 (19), pp.2280. ⟨10.3390/rs11192280⟩ [SDU.STU]Sciences of the Universe [physics]/Earth Sciences info:eu-repo/semantics/article Journal articles 2019 ftunivsavoie https://doi.org/10.3390/rs11192280 2024-04-11T00:35:15Z International audience 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. Article in Journal/Newspaper albedo Antarc* Antarctica Arctic Greenland Ice Sheet Université Savoie Mont Blanc: HAL Remote Sensing 11 19 2280 |