An aircraft-based data analysis method for discerning individual fluxes in a heterogeneous agricultural landscape

Abstract The utility of aircraft-based flux data is hindered in heterogeneous landscapes by the averaging length required for proper flux calculations. In regions where the scale of heterogeneity is smaller than the traditional 3–5 km averaging length, it has been problematic to relate measured flux signals to an individual land use. This paper introduces the “Flux Fragment” method (FFM) which is based on traditional eddy-covariance flux techniques but uses a conditional sampling scheme to better segregate flux signals by surface type with aircraft-based data. Flux measurements from a low-flying aircraft and from towers were obtained as part of a campaign in the ‘patchy’ agricultural landscape of the Midwestern United States. The fluxes from maize and soybean were computed and compared to the tower-based flux signals from both land uses. The FFM-derived fluxes of CO 2 clearly discern the maize and soybean signals in all midday flights and display the same diurnal patterns as observed using the flux towers. The quantitative match between airborne and fixed measures of CO 2 exchange is good, but displays significant discrepancies. The discrepancies arise, we argue, from the natural variation in flux within the same land-use class over the landscape, a variation invisible to a single tower. Spatially averaged fluxes from the FFM complement towers’ temporal coverage to provide an improved basis for scaling local fluxes to regional estimates based on total areas of each land use.