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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #267924
Title: Remote sensing of soil carbon and greenhouse gas dynamics across agricultural landscapes

Author
item Daughtry, Craig
item Hunt Jr, Earle
item Beeson, Peter
item LANG, MEGAN - Us Forest Service (FS)
item SERBIN, G - Collaborator
item Alfieri, Joseph
item McCarty, Gregory
item 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.