Fluxes of greenhouse gases (GHG) are typically characterized by high spatial and temporal variability and large sample sizes (e.g. >30) are thus required to obtain a reliable estimate of the population mean and variance when using simple random sampling (SRS). Sample size, however, is often constrained by budget (time, labor) and therefore practical considerations induce significant (but unknown) measurement error and bias from sampling. In this paper we report a two-stage sampling strategy (2SS) by which the same level of sampling accuracy achievable by SRS can be achieved with significantly smaller sample sizes by optimizing sub-sample selection to retain the statistical characteristics of the sample population. Comparisons between 2SS and SRS were conducted using three datasets with low, medium and high coefficients of variance (CV). The size of the first (n′) and second (n) stage samples had significant effects on overall sample accuracy. Across all datasets, 2SS reduced RMSE of mean and variance by an average of 30%. The absolute reduction in RMSE of mean and variance was found to be nearly proportional to the value of CV, such that the dataset with the largest CV showed the largest benefit from 2SS. Logarithmic relationships were found between the difference in the RMSEs and the ratio, n′/n, serving as a guide to allocate sampling resources in practice. Employing 2SS will aid accurate quantification of soil GHG fluxes in all but the most homogeneous situations.