Variability of dissolved organic carbon (DOC) in the 6 largest Arctic rivers estimated using high resolution Sentinel-2 and Landsat-8 imageries over the 2013-2021 period.

• Main Authors: Jegou, Fabrice, Jallais, Gaëtane, Salmon, Elodie, Guenet, Bertrand, Herrault, Pierre-Alexis, Gogo, Sébastien, Gandois, Laure, Guimbaud, Christophe, Laggoun-Défarge, Fatima, Moulard, Nathalie, Teisserenc, Roman, Moquet, Jean-Sébastien
• Other Authors: Laboratoire de Physique et Chimie de l'Environnement et de l'Espace (LPC2E), Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Centre National d’Études Spatiales Paris (CNES), Université de Strasbourg (UNISTRA), Ecosystèmes, biodiversité, évolution Rennes (ECOBIO), Université de Rennes (UR)-Institut Ecologie et Environnement - CNRS Ecologie et Environnement (INEE-CNRS), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Institut des Sciences de la Terre d'Orléans - UMR7327 (ISTO), Bureau de Recherches Géologiques et Minières (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Biogéosystèmes Continentaux - UMR7327, Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2022
• Subjects: [SDU]Sciences of the Universe [physics]; [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces; environment; [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology; Arctic Ocean; Ice; permafrost; Yukon
• Online Access: https://insu.hal.science/insu-03810458; https://doi.org/10.5194/egusphere-egu22-9645

International audience Climate warming with permafrost thaw will modify lateral carbon export, from terrestrial to aquatic ecosystems with a potential huge impact on the Arctic rivers, draining organic-rich soils and in fine into the Arctic Ocean. The majority of annual DOC fluxes by Arctic rivers are transported during the snowmelt break-up period, which makes field measurements of DOC difficult. Passive spatial remote sensing is a very relevant tool to increase the spatial and temporal coverage of these observed values.In the framework of the French CNES DOC-Rivers project we proposed to apply the approach consisting in analyzing satellite imageries to evaluate DOC concentrations in the 6 great Arctic Rivers: Lena, Ob', Yenisey, Yukon, MacKenzie, Kolyma. The algorithm, first, establishes a multi-linear relationship between ground-based chromatic dissolved organic matter (CDOM) observations and specific satellite color bands to construct a complete satellite CDOM database. Another linear regression is used afterward with in-situ data from the Arctic Great Rivers Observatory (ArcticGRO) initiative to correlate CDOM and DOC observations. Using this second linear regression, we can predict the DOC content from the previous construct satellite CDOM database. River discharge measurements from the ArcticGRO database also enable to estimate the evolution of DOC export to the Arctic Ocean from satellite data.We applied this approach to high-resolution satellite data issued from Sentinel 2 (A 2015-2022, B 2017-2022) and Landsat 8 (2013-2022) to create a multi-instrumental synergy. This new database provides an unprecedented source of information for understanding DOC dynamics of in Arctic rivers and assessing its transfer from large catchments to the Arctic Ocean. This database provides information on the variability of DOC during the whole ice-free season and serve to locate areas with higher concentrations and fluxes during the 2013-2021 period. We plan to complement our database on future period with data from new ...