In Norway, agroclimatic zones (ACZs) are a valuable tool for national analyses in subject areas concerning the optimized management of agricultural land resources. However, current Norwegian ACZs have been criticized for having an outdated standard climate normal (1931–1960), a limited representation of the local climatic variation, a lack of important model parameters, and weak methodological documentation. Therefore, this paper presents new ACZs for Norway that address these weaknesses. The most significant methodological updates are the use of the standard climate normal of 1991–2020, additional weather data variables, the downscaling of weather data to 250 m hexagons, and the incorporation of phenological crop models for spring wheat, spring barley, and forage grass. The grass model was calibrated with the number of grass harvests at research stations, while the grain models were calibrated with subsidy claim data. The modeled zones for the three crops were combined into the general ACZs. Example maps of the crop zones and new ACZs for the selected regions and the whole country are presented. The new ACZs are more robust, agronomically relevant, and better aligned with the current climatic conditions in Norway. The deliberate exclusion of factors other than climate ensures the new ACZs’ national comparability and their applicability in policy development, land-use planning, climate adaptation, and agronomic assessments at the national scale.

Land cover maps are frequently produced via the classification of satellite imagery. There is a need for a practicable and automated approach for the generalization of these land cover classification results into scalable, digital maps while minimizing information loss. We demonstrate a method where a land cover raster map produced using the classification of Sentinel 2 imagery was generalized to obtain a simpler, more readable land cover map. A replicable procedure following a formal generalization framework was applied. The result of the initial land cover classification was separated into binary layers representing each land cover class. Each binary layer was simplified via structural generalization. The resulting images were merged to create a new, simplified land cover map. This map was enriched by adding statistical information from the original land cover classification result, describing the internal land cover distribution inside each polygon. This enrichment preserved the original statistical information from the classified image and provided an environment for more complex cartography and analysis. The overall accuracy of the generalized map was compared to the accuracy of the original, classified land cover. The accuracy of the land cover classification in the two products was not significantly different, showing that the accuracy did not deteriorate because of the generalization.