Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei

In Arctic regions, a major concern is the release of carbon from melting permafrost that could greatly exceed current human carbon emissions. Arctic rivers drain these organic-rich watersheds (Ob, Lena, Yenisei, Mackenzie, Yukon) but field measurements at the outlets of these great Arctic rivers are...

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Published in:Remote Sensing
Main Authors: Pierre-Alexis Herrault, Laure Gandois, Simon Gascoin, Nikita Tananaev, Théo Le Dantec, Roman Teisserenc
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2016
Subjects:
DOC
Q
Ice
Online Access:https://doi.org/10.3390/rs8100803
https://doaj.org/article/85dcce72052645fe86c4bdf778f3b476
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spelling ftdoajarticles:oai:doaj.org/article:85dcce72052645fe86c4bdf778f3b476 2023-05-15T14:40:08+02:00 Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei Pierre-Alexis Herrault Laure Gandois Simon Gascoin Nikita Tananaev Théo Le Dantec Roman Teisserenc 2016-09-01T00:00:00Z https://doi.org/10.3390/rs8100803 https://doaj.org/article/85dcce72052645fe86c4bdf778f3b476 EN eng MDPI AG http://www.mdpi.com/2072-4292/8/10/803 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs8100803 https://doaj.org/article/85dcce72052645fe86c4bdf778f3b476 Remote Sensing, Vol 8, Iss 10, p 803 (2016) high spatio-temporal remote sensing DOC CDOM Arctic river Yenisei Take 5 Landsat 8 Science Q article 2016 ftdoajarticles https://doi.org/10.3390/rs8100803 2022-12-31T09:42:30Z In Arctic regions, a major concern is the release of carbon from melting permafrost that could greatly exceed current human carbon emissions. Arctic rivers drain these organic-rich watersheds (Ob, Lena, Yenisei, Mackenzie, Yukon) but field measurements at the outlets of these great Arctic rivers are constrained by limited accessibility of sampling sites. In particular, the highest dissolved organic carbon (DOC) fluxes are observed throughout the ice breakup period that occurs over a short two to three-week period in late May or early June during the snowmelt-generated peak flow. The colored fraction of dissolved organic carbon (DOC) which absorbs UV and visible light is designed as chromophoric dissolved organic matter (CDOM). It is highly correlated to DOC in large arctic rivers and streams, allowing for remote sensing to monitor DOC concentrations from satellite imagery. High temporal and spatial resolutions remote sensing tools are highly relevant for the study of DOC fluxes in a large Arctic river. The high temporal resolution allows for correctly assessing this highly dynamic process, especially the spring freshet event (a few weeks in May). The high spatial resolution allows for assessing the spatial variability within the stream and quantifying DOC transfer during the ice break period when the access to the river is almost impossible. In this study, we develop a CDOM retrieval algorithm at a high spatial and a high temporal resolution in the Yenisei River. We used extensive DOC and DOM spectral absorbance datasets from 2014 and 2015. Twelve SPOT5 (Take5) and Landsat 8 (OLI) images from 2014 and 2015 were examined for this investigation. Relationships between CDOM and spectral variables were explored using linear models (LM). Results demonstrated the capacity of a CDOM algorithm retrieval to monitor DOC fluxes in the Yenisei River during a whole open water season with a special focus on the peak flow period. Overall, future Sentinel2/Landsat8 synergies are promising to monitor DOC fluxes in Arctic rivers ... Article in Journal/Newspaper Arctic Ice permafrost Yukon Directory of Open Access Journals: DOAJ Articles Arctic Yukon Yenisei River ENVELOPE(84.738,84.738,69.718,69.718) Remote Sensing 8 10 803
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic high spatio-temporal remote sensing
DOC
CDOM
Arctic river Yenisei
Take 5
Landsat 8
Science
Q
spellingShingle high spatio-temporal remote sensing
DOC
CDOM
Arctic river Yenisei
Take 5
Landsat 8
Science
Q
Pierre-Alexis Herrault
Laure Gandois
Simon Gascoin
Nikita Tananaev
Théo Le Dantec
Roman Teisserenc
Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei
topic_facet high spatio-temporal remote sensing
DOC
CDOM
Arctic river Yenisei
Take 5
Landsat 8
Science
Q
description In Arctic regions, a major concern is the release of carbon from melting permafrost that could greatly exceed current human carbon emissions. Arctic rivers drain these organic-rich watersheds (Ob, Lena, Yenisei, Mackenzie, Yukon) but field measurements at the outlets of these great Arctic rivers are constrained by limited accessibility of sampling sites. In particular, the highest dissolved organic carbon (DOC) fluxes are observed throughout the ice breakup period that occurs over a short two to three-week period in late May or early June during the snowmelt-generated peak flow. The colored fraction of dissolved organic carbon (DOC) which absorbs UV and visible light is designed as chromophoric dissolved organic matter (CDOM). It is highly correlated to DOC in large arctic rivers and streams, allowing for remote sensing to monitor DOC concentrations from satellite imagery. High temporal and spatial resolutions remote sensing tools are highly relevant for the study of DOC fluxes in a large Arctic river. The high temporal resolution allows for correctly assessing this highly dynamic process, especially the spring freshet event (a few weeks in May). The high spatial resolution allows for assessing the spatial variability within the stream and quantifying DOC transfer during the ice break period when the access to the river is almost impossible. In this study, we develop a CDOM retrieval algorithm at a high spatial and a high temporal resolution in the Yenisei River. We used extensive DOC and DOM spectral absorbance datasets from 2014 and 2015. Twelve SPOT5 (Take5) and Landsat 8 (OLI) images from 2014 and 2015 were examined for this investigation. Relationships between CDOM and spectral variables were explored using linear models (LM). Results demonstrated the capacity of a CDOM algorithm retrieval to monitor DOC fluxes in the Yenisei River during a whole open water season with a special focus on the peak flow period. Overall, future Sentinel2/Landsat8 synergies are promising to monitor DOC fluxes in Arctic rivers ...
format Article in Journal/Newspaper
author Pierre-Alexis Herrault
Laure Gandois
Simon Gascoin
Nikita Tananaev
Théo Le Dantec
Roman Teisserenc
author_facet Pierre-Alexis Herrault
Laure Gandois
Simon Gascoin
Nikita Tananaev
Théo Le Dantec
Roman Teisserenc
author_sort Pierre-Alexis Herrault
title Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei
title_short Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei
title_full Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei
title_fullStr Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei
title_full_unstemmed Using High Spatio-Temporal Optical Remote Sensing to Monitor Dissolved Organic Carbon in the Arctic River Yenisei
title_sort using high spatio-temporal optical remote sensing to monitor dissolved organic carbon in the arctic river yenisei
publisher MDPI AG
publishDate 2016
url https://doi.org/10.3390/rs8100803
https://doaj.org/article/85dcce72052645fe86c4bdf778f3b476
long_lat ENVELOPE(84.738,84.738,69.718,69.718)
geographic Arctic
Yukon
Yenisei River
geographic_facet Arctic
Yukon
Yenisei River
genre Arctic
Ice
permafrost
Yukon
genre_facet Arctic
Ice
permafrost
Yukon
op_source Remote Sensing, Vol 8, Iss 10, p 803 (2016)
op_relation http://www.mdpi.com/2072-4292/8/10/803
https://doaj.org/toc/2072-4292
2072-4292
doi:10.3390/rs8100803
https://doaj.org/article/85dcce72052645fe86c4bdf778f3b476
op_doi https://doi.org/10.3390/rs8100803
container_title Remote Sensing
container_volume 8
container_issue 10
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