Key messagePost-stratification based on remotely sensed data is an efficient method in estimating regional-level results in the operational National Forest Inventory. It also enables calculating the results accurately for smaller areas than with the default method of using the field plots only.ContextThe utilization of auxiliary information in survey sampling through model-assisted estimation or post-stratification has gained popularity in forest inventory recently. However, post-stratification at a large scale involves practical concerns such as the availability of auxiliary data independent of the sample at hand, and a large number of variables for which the results are needed.AimsWe assessed the efficiency of two different types of post-stratification, either post-stratifying for each variable of interest separately or using one post-stratification for all variables, compared to the estimation based on the field sample plots only. In addition, we examined the precision of area and volume estimates, and the efficiency of post-stratification at different spatial scales.MethodsFor post-stratification, we used the volume maps based on Landsat satellite imagery, digital map data, and the sample plot data of the previous inventory. The efficiencies of post-stratifications based on the mean volume and the mean volumes by tree species were compared.ResultsIn estimating the total volume, the relative efficiency of post-stratification compared to field plot based estimation was 1.54–3.54 over the provinces in South Finland. In estimating the volumes by tree species groups, the relative efficiency was 0.93–2.39. The gain with a separate stratification compared to the stratification based on total mean volume for all variables was at largest 0.69. In the small test areas, the relative standard errors of the total volume estimates decreased on average by 33% by using post-stratification instead of sample plots only. The mean relative efficiency was 2.36.ConclusionThe utilization of an old forest resources map and post-stratification based on the mean volume is an operational approach for the National Forest Inventory. Post-stratification also enables calculating the results accurately for markedly smaller areas than with the field plots only. Post-stratification reduced the probability of very high sampling variances, making the results more robust.