Javier Gorroño , Luis Guanter, Lukas Valentin Graf, Ferran Gascon

The Copernicus Sentinel-2 (S2) satellite mission acquires high spatial resolution optical imagery over land and 
coastal areas. Delivering uncertainty estimates and spectral error correlation alongside S2 data products facilitates the constrain of retrieval algorithms, propagates further downstream the retrieval uncertainty, and fifinally makes informed decisions to end-users. This study presents a framework to produce uncertainty estimates and spectral error correlation associated to the S2 L2A data products (i.e. surface reflflectance). This framework has been implemented in a prototype code available at [1]. The uncertainty considers both the Level-1 (L1) uncertainty estimates for top?of-atmosphere (TOA) reflflectance factor and the atmospheric correction. The L2A error distribution cannot be systematically described as a normal distribution, the transformation can be non-linear and without an explicit mathematical model. Thus, a Multivariate MonteCarlo model (MCM) rather than the law of propagation of uncertainty (LPU) is selected for uncertainty propagation. We show results for surface reflflectance uncertainty over the Amazon forest and Libya4 desert site. It illustrates the large uncertainty and spectral error correlation variations depending on the scene. The comparison of an multivariate MCM against an LPU propagation methodology indicate the limitations of the latter for scenes dominated by the atmospheric 
path. Its implementation as an operational per-pixel processing and dissemination of both the uncertainty and spectral error correlation becomes challenging. Therefore, this methodology is not expected to run at an operational level but serve as the basis to defifine a strategy for an operational one.