Inverse modeling methods have been used to estimate surface fluxes of atmospheric trace gases such as CFCs, CH4, and CO2 on the basis of atmospheric mass fraction measurements. A majority of recent studies use a classical Bayesian setup, in which prior flux estimates at regional or grid scales are specified in order to further constrain the flux estimates. This paper, on the other hand, explores the applicability of using a geostatistical approach to the inverse problem, a Bayesian method in which the prior probability density function is based on an assumed form for the spatial and/or temporal correlation of the surface fluxes, and no prior flux estimates are specified. The degree to which surface fluxes at two points are expected to be correlated is defined as a function of the separation distance in space or in time between the two points. Flux estimates obtained in this manner are not subject to some of the limitations associated with traditional Bayesian inversions, such as potential biases created by the choice of prior fluxes and aggregation error resulting from the use of large regions with prescribed flux patterns. In essence, they shed light on the information contained in the measurements themselves. The geostatistical algorithm is tested using CO2 pseudodata at 39 observation locations to recover surface fluxes on a 3.75° latitude by 5.0° longitude grid. Results show that CO2 surface flux variations can be recovered on a significantly smaller scale than that imposed by inversions that group surface fluxes into a small number of large regions.