Incorporating observational data in carbon-cycle models provides a systematic framework for understanding complex ecosystem carbon dynamics, contributing essential insights for climate change mitigation and land ability to continue acting as a carbon sink. This study addresses the challenge of accurately quantifying carbon fluxes and pools, focusing on the information content of remote sensing observations. The research explores the impact of assimilating multiple observational datasets into the CARbon DAta MOdel fraMework (CARDAMOM). Satellite observations such as solar-induced fluorescence (SIF) and vegetation optical depth (VOD) are used as proxies for photosynthesis and aboveground biomass, respectively. The study aims to answer key questions about the reliability of remote sensing data in constraining the ecosystem respiration flux and sizes and dynamics of carbon pools and the relative usefulness of SIF and VOD across five FLUXNET sites. We conclude that assimilating remote SIF and VOD instead of site-based net ecosystem exchange did not deteriorate and even improved model predictions for all metrics except for interannual variability. Notably, the improved results correspond to a consistent shift in values for crucial model parameters across all five investigated sites.