Achieving the United Nations Sustainable Development Goal 2 that addresses food security and sustainable agriculture requires the promotion of readily transferable and scalable solutions for monitoring yields, carbon and water budgets. The combination of high-resolution remote sensing data, field information, and physical models was identified by the CIRCASA international initiative as a robust way of answering this requirement (Smith et al. 2020). Therefore, we present here the AgriCarbon-EO processing chain that provides the yield, biomass, water and carbon budget components of agricultural fields at a 10m resolution and at a regional scale. The tool has been optimized to assimilate high resolution optical remote sensing data (Sentinel-2 and Landsat-8) into a radiative transfer model and a crop model. First, the application of a spatial Bayesian retrieval approach to the PROSAIL radiative transfer model provides Leaf Area Index (LAI) with its associated uncertainty. Second, LAI is assimilated into the SAFYE-CO2 crop model using a temporal Bayesian retrieval that enables the calculation of the yield, biomass, carbon and water budgets components with their associated uncertainties. The model is applied over the South-West of France covering 3 Sentinel-2 tiles for major crops (wheat, maize, sunflower) and cover crops. The outputs are validated for the 2017-2018 and 2019-2020 cropping years against in-situ data for biomass (22 fields), yield and CO2/water fluxes measured at eddycovariance towers (FR-AUR and FR-LAM ICOS sites). We show the added value of assimilating high-resolution satellite data in driving the crop model to account for the impact of complex processes that are embedded in the LAI signal like vegetation water stress, disease, and agricultural practices on the yield, biomass and carbon/water budgets components estimates.