Geography

Document Type

Article

Abstract

The Kolyma River basin in northeastern Siberia, the sixth largest river basin draining to the Arctic Ocean, contains vast reserves of carbon in Pleistocene-aged permafrost soils. Permafrost degradation, as a result of climate change, may cause shifts in riverine biogeochemistry as this old source of organic matter is exposed. Satellite remote sensing offers an opportunity to complement and extrapolate field sampling of dissolved organic matter in this expansive and remote region. We develop empirically based algorithms that estimate chromophoric dissolved organic matter (CDOM) and dissolved organic carbon (DOC) in the Kolyma River and its major tributaries in the vicinity of Cherskiy, Russia. Field samples from July 2008 and 2009 were regressed against spectral data from the Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper-Plus. A combination of Landsat band 3 and bands 2:1 resulted in an R2 of 0.78 between measured CDOM and satellite-derived predictions. Owing to the strong correlation between CDOM and DOC, the resulting maps of the region show strong interannual variability of both CDOM and DOC, and important spatial patterns such as mixing zones at river confluences and downstream loading of DOC. Such variability was previously unobserved through field-based point observations and suggests that current calculations of DOC flux from the Kolyma River to the Arctic Ocean may be underestimates. In this era of rapid climate change, permafrost degradation, and shifts in river discharge, remote sensing of CDOM and DOC offers a powerful, reliable tool to enhance our understanding of carbon cycling in major arctic river systems. Copyright 2011 by the American Geophysical Union.

Publication Title

Journal of Geophysical Research: Biogeosciences

Publication Date

2011

Volume

116

Issue

3

ISSN

0148-0227

DOI

10.1029/2010JG001634

Keywords

algorithm, biogeochemistry, climate change, dissolved organic matter, Landsat thematic mapper, permafrost, Pleistocene, sampling, satellite data, satellite imagery, spatiotemporal analysis

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