Dis-aggregation of airborne flux measurements using footprint analysis

Aircraft measurements of turbulent fluxes are generally being made with the objective to obtain an estimate of regional exchanges between land surface and atmosphere, to investigate the spatial variability of these fluxes, but also to learn something about the fluxes from some or all of the land cover types that make up the landscape. In this study we develop a method addressing this last objective, an approach to disentangle blended fluxes from a landscape into the component fluxes emanating from the various land cover classes making up that landscape. The method relies on using a footprint model to determine which part of the landscape the airborne flux observation refers to, using a high resolution land cover map to determine the fractional covers of the various land cover classes within that footprint, and finally using multiple linear regression on many such flux/fractional cover data records to estimate the component fluxes. The method is developed in the context of three case studies of increasing complexity and the analysis covers three scalar fluxes: sensible and latent heat fluxes and carbon dioxide flux, as well as the momentum flux. A basic assumption under the dis-aggregation method is that the composite flux, i.e. the landscape flux, is a linear average of the component fluxes, i.e. the fluxes from the various land elements. We test and justify this assumption by comparing linear averages of component fluxes in simple ‘binary landscapes’, weighted by their relative area, with directly aircraft observed fluxes. In all case studies dis-aggregation of mixed values for fluxes from heterogeneous areas into component land cover class specific fluxes is feasible using robust least squares regression, both in simple binary ‘landscapes’ and in more complex cases. Both the differences between land cover classes and the differences between synoptic conditions can be resolved, for those land cover classes that make up sufficiently large fractions of the landscape. The regression F-statistic and the closely associated p-values are good indicators for this latter prerequisite and for other sources of uncertainty in the dis-aggregated flux estimates that render it meaningful or not. An analysis of the effect of various sources of errors in input data, footprint estimates and of skewed land cover class distributions is presented. A validation of flux estimates obtained through the dis-aggregation method against independent ground data proved satisfactorily. Recommendations for the use of the method are given as are suggestions for further developmen