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Biogeosciences An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 2
Biogeosciences, 9, 631-648, 2012
https://doi.org/10.5194/bg-9-631-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Earth observation for land-atmosphere interaction science

Biogeosciences, 9, 631-648, 2012
https://doi.org/10.5194/bg-9-631-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 03 Feb 2012

Research article | 03 Feb 2012

Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in senegal

A. M. Dieye1,2, D. P. Roy1, N. P. Hanan1, S. Liu3, M. Hansen4, and A. Touré2 A. M. Dieye et al.
  • 1Geographic Information Science Center of Excellence, South Dakota State University, Brookings, SD 57007-3510, USA
  • 2Centre de Suivi Ecologique, Fann Résidence, BP 15532, Dakar, Sénégal
  • 3U.S. Geological Survey Earth Resources Observation and Science, Sioux Falls, SD 57198, USA
  • 4Department of Geography, University of Maryland, College Park, MD 20742, USA

Abstract. Spatially explicit land cover land use (LCLU) change information is needed to drive biogeochemical models that simulate soil organic carbon (SOC) dynamics. Such information is increasingly being mapped using remotely sensed satellite data with classification schemes and uncertainties constrained by the sensing system, classification algorithms and land cover schemes. In this study, automated LCLU classification of multi-temporal Landsat satellite data were used to assess the sensitivity of SOC modeled by the Global Ensemble Biogeochemical Modeling System (GEMS). The GEMS was run for an area of 1560 km2 in Senegal under three climate change scenarios with LCLU maps generated using different Landsat classification approaches. This research provides a method to estimate the variability of SOC, specifically the SOC uncertainty due to satellite classification errors, which we show is dependent not only on the LCLU classification errors but also on where the LCLU classes occur relative to the other GEMS model inputs.

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