This paper summarizes the output of an imputation model that simultaneously estimates multiple operational-scale forest inventory attributes in the Laurentian mixed forest type of the United States. The model was constrained by national forest inventory privacy protocols and temporal uncertainties in feature and reference data. Estimates were most accurate at the county level and more variable across smaller spatial extents. Model development and validation highlighted that performance and reliability were influenced by our approach of using publicly available remote sensing predictors and ground reference data in model building. Comprehensive validation included diagnostics of the chosen model and leveraged multiscale independent data for analysis of lack-of-fit spatially and by individual feature variables. Relatively poor performance in some forest types pointed to an impact of temporal mismatch in the estimation of forest stocking in typically even-aged stands dominated by fast-growing species.