This ATBD (Algorithm Theoretical Based Document) describes the proposed algorithm for the optical sensors, taking Level 2 products for SENTINEL-2 products for one example (the descriptions for Landsat8 and MODIS could be found in the Appendix). The level2 products are derived from SENTINEL-2 top of canopy normalized reflectance data and correspond to the following set of biophysical variables: GAI (Green Area Index) commonly referred as LAI due to misuse of language, fAPAR (fraction of absorbed photosynthetically active Radiation), and FVC that are essential climate variables (ECVs) as recognized by international organizations such as GCOS and GTOS. Two additional variables are also assessed: Canopy Chlorophyll Content and Vegetation Canopy Water. The proposed algorithm follows the same principal as the primary SENTINEL-2 biophysical processor available in the SNAP toolbox (Weiss and Baret 2020). They are based on methods that have already been proven to be efficient. They have been implemented to generate biophysical products from VEGETATION, MERIS, SPOT, and LANDSAT sensors. It mainly consists in generating a comprehensive data base of vegetation characteristics and the associated SENTINEL-2 top of canopy (TOC) reflectances. Neural networks are then trained to estimate the canopy characteristics from the TOC reflectances along with the corresponding angles defining the observational configuration.