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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 1560km2 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.
DOI of Published Version
© 2012 Dieye, A. M., Roy, D.P., Hanan, N.P., Liu, S., Hansen, M., Toure, A.
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This work is licensed under a Creative Commons Attribution 3.0 License.
Dieye, Amadou M.; Roy, David P.; Hanan, N. P.; Lui, S.; Hansen, M.; and Toure, A., "Sensitivity Analysis of the GEMS Soil Organic Carbon Model to Land Cover Land Use Classification Uncertainties Under Different Climate Scenarios in Senegal" (2012). GSCE Faculty Publications. 53.