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Title:   Author  item
                                            Daughtry, Craigitem
                                            Hunt Jr, Earleitem
                                            Beeson, Peteritem
                                            LANG, MEGAN - Us Forest Service (FS)item
                                            SERBIN, G - Collaboratoritem
                                            Alfieri, Josephitem
                                            McCarty, Gregoryitem
                                            Sadeghi, Ali
  Submitted to: Book Chapter
                                    Publication Type: Book / Chapter
                                    Publication Acceptance Date: 10/1/2011
                                    Publication  Date: 6/1/2012
                                    Citation: Daughtry, C.S., Hunt, E.R., Beeson, P.C., Lang, M., Serbin, G., Alfieri, J.G., McCarty, G.W., Sadeghi, A.M. 2012. Remote sensing of soil carbon and greenhouse gas dynamics across agricultural landscapes. In: Liebig, M., Franzluebbers, A., Follett, R., editors. Managing Agricultural Greenhouse Gases. Amsterdam, The Netherlands: Elsevier. p. 385-408.Interpretive Summary:
                                Technical Abstract: Accurate assessments of the overall impact of the GRACEnet strategies for enhancing soil C sequestration and reducing greenhouse gases emissions requires extending results from small plot of field experiments to regional and national scales. This spatial scaling task is nontrivial because the mechanisms controlling the exchange of carbon,water, and energy are nonlinear and interact with each other. Remote sensing offers the only practical method to account for the spatial and temporal variability inherent across agricultural landscapes. In this chapter, we briefly reviewed the fundamental spectral properties of vegetation and soils and examined the potential synergies of in situ and remotely sensed measurements for providing frequent, spatially-explicit information about agricultural landscapes.Data
fusion and data assimilation techniques offer techniques for merging data acquired at differing spatial and temporal esolutions and creating synthetic datasets with high spatial and temporal resolutions. Process models coupled with these enhanced datasets should provide reliable descriptions of ecosystem functions at various scales.


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