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
Received: 11 Jun 2011 – Discussion started: 08 Jul 2011
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.
Revised: 29 Dec 2011 – Accepted: 12 Jan 2012 – Published: 03 Feb 2012
Dieye, A. M., Roy, D. P., Hanan, N. P., Liu, S., Hansen, M., and Touré, A.: Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in senegal, Biogeosciences, 9, 631-648, doi:10.5194/bg-9-631-2012, 2012.