Establishing an end-to-end uncertainty budget is essentially required for all ECVs of ESA’s Climate Change Initiative (CCI). The reference for expressing and propagating uncertainty consists of the GUM and its supplements, which describe multivariate analytic and Monte Carlo methods. But despite these guidelines, uncertainty propagation for ECVs remains challenging. Firstly, many retrieval algorithms do not incorporate the use of quantified uncertainty per datum. Analytic methods for propagating uncertainty require new algorithmic developments while Monte Carlo methods are straightforward to apply but lead to proliferation of computational and data curation resources. Secondly, operational radiometry data are usually not associated with a quantified uncertainty per datum, and error correlation structures between data are not quantified either. Deriving this information from original sensor telemetry and an according harmonisation with respect to an SI traceable satellite (SITSAT) reference is a future task. Nevertheless, it is feasible to explore and prepare ECV surface reflectance pre-processing for the use of uncertainty per Level 1 datum and error correlation structures among Level 1 data already now, based on instrument specifications and other simplifying assumptions.