Atmospheric correction of satellite-based remote sensing data is an essential component of operational processing chains to ensure the highly accurate reconstruction of surface reflectance. The software for atmospheric correction of satellite data developed at the German Aerospace Center, "Python-based Atmospheric Correction" (PACO), and its Open‑Source follow-up project "CHIME L2 Open‑Source Library" are used in a wide range of operational and upcoming missions (e.g., DESIS, EnMAP, CHIME) and thus contribute extensively to the generation of the global satellite data base. The presentation aims to provide, for better understanding, an insight into the theoretical basis of current atmospheric correction algorithms and their significance for satellite-based remote sensing. It will also present current developments (with a focus on hyperspectral data, incorporation of external reference data, and machine learning methods) as well as access and usage for the scientific community.