Aircraft based measurement of surface exchange is now a widely used approach for determining fluxes. Since it requires 3-5km averaging length to generate meaningful fluxes, it is challenging to relate obtained flux signals to a single land cover type in heterogeneous or complex landscapes. The purpose of this research is to develop a new spatial flux calculation method (SFCM) for the quantification of terrestrial surface carbon exchange. The data used in method development, comparison and evaluation were obtained during summers of 2005 and 2006 in the agricultural ecosystem (corn and soybean) of the Midwestern United States. Four kilometers was used as the spatial averaging length to include all sizes of eddies. Then footprint analysis was interpolated to a resolution of 20m to provide estimates of fractional coverages for land surface components. Through application of the SFCM method, corn and soybean fluxes were calculated and displayed spatially along the flight transects. The associated uncertainties of fluxes were spatially located and plotted along the flight transects as a visual indicator of variability. These data complement fixed location and highly validated tower based measurements by providing surrounding spatial flux data and statistical confidences, which will aid in upscaling of fluxes from local to regional scale. Furthermore, with the additional knowledge of soil conditions, irrigation practices, crop planting times etc., these spatial fluxes can be used to help improve agricultural management. The dominant method proposed by Kirby et al. (2008) was reproduced in this research using the same datasets. By comparing fluxes calculated from the SFCM (developed herein) and the dominant method with tower data, it was found that they both can capture the diurnal patterns of fluxes, with some inevitable discrepancies in the values. Selection between these two methods should depend on desired use of such flux data. The dominant method can generate well separated component fluxes without explicitly providing spatial information while the SFCM is able to quantify both component fluxes and spatial information. Such capability of SFCM provides a new perspective (spatial component fluxes) in fluxes quantification and a deeper understanding in terrestrial carbon exchanges.