Policy measures and management decisions aimed at enhancing the role of forests in mitigating climate change require reliable estimates of carbon (C)-stock dynamics in greenhouse gas inventories (GHGIs). The aim of this study was to assemble design-based estimators to provide estimates relevant for GHGIs using National Forest Inventory (NFI) data. We improve basic expansion (BE) estimators of living-biomass C-stock loss using only field data, by leveraging with remote sensing auxiliary data in model-assisted (MA) estimators. Our case studies from Norway, Sweden, Denmark, and Latvia covered an area of >70 Mha. Landsat-based forest cover loss (FCL) and one-time wall-to-wall airborne laser scanning (ALS) served as auxiliary data. ALS provided information on the C stock before a potential disturbance indicated by FCL. The use of FCL in MA estimators resulted in considerable efficiency gains, which in most cases were further increased by adding ALS. A doubling of efficiency was possible for national estimates and even larger efficiencies were observed at the subnational level. Average annual estimates were considerably more precise than pooled estimates of the NFI data from all years at once. The combination of remotely sensed and NFI field data yields reliable estimators, which is not necessarily the case when using remotely sensed data without reference observations.