Introduction: The knowledge about forest growth, influenced by factors such as tree species, tree age, and environmental conditions, is a key for future forest preservation. Height and age data can be combined to describe forest growth and used to infer known environmental effects. Methods: In this study, we built 14 height growth curves for stands composed of monospecific or mixed species using ground measurements and satellite data. We built a random forest height model from tree species, age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Using feature elimination and SHapley Additive exPlanations (SHAP) analysis, we identified six key features explaining the forest growth and investigated how they affect the height. Results: The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the main predictors of tree height (49% and 10%, respectively). The disturbed patch area, revealing the regeneration method, impacts post-disturbance growth at 19%. The soil pH, altitude, and climatic water budget in summer impact tree height differently depending on the age and tree species. Discussion: Methods integrating satellite and field data show promise for analyzing future forest evolution.