The Sentinel-2 satellite mission, developed by the European Space Agency (ESA) for the Copernicus program of the European Union, provides repetitive multi-spectral observations of all Earth land surfaces at a high resolution. The Level 2A product is a basic product requested by many Sentinel-2 users: it provides surface reflectance after atmospheric correction, with a cloud and cloud shadow mask. The cloud/shadow mask is a key element to enable an automatic processing of Sentinel-2 data, and therefore, its performances must be accurately validated. To validate the Sentinel-2 operational Level 2A cloud mask, a software program named Active Learning Cloud Detection (ALCD) was developed, to produce reference cloud masks. Active learning methods allow reducing the number of necessary training samples by iteratively selecting them where the confidence of the classifier is low in the previous iterations. The ALCD method was designed to minimize human operator time thanks to a manually-supervised active learning method. The trained classifier uses a combination of spectral and multi-temporal information as input features and produces fully-classified images. The ALCD method was validated using visual criteria, consistency checks, and compared to another manually-generated cloud masks, with an overall accuracy above 98%. ALCD was used to create 32 reference cloud masks, on 10 different sites, with different seasons and cloud cover types. These masks were used to validate the cloud and shadow masks produced by three Sentinel-2 Level 2A processors: MAJA, used by the French Space Agency (CNES) to deliver Level 2A products, Sen2Cor, used by the European Space Agency (ESA), and FMask, used by the United States Geological Survey (USGS). The results show that MAJA and FMask perform similarly, with an overall accuracy around 90% (91% for MAJA, 90% for FMask), while Sen2Cor’s overall accuracy is 84%. The reference cloud masks, as well as the ALCD software used to generate them are made available to the Sentinel-2 user community.

The quantitative use of space-based optical imagery requires atmospheric correction to separate the contributions from the surface and the atmosphere. The MACCS (Multi-sensor Atmospheric Correction and Cloud Screening)-ATCOR (Atmospheric and Topographic Correction) Joint Algorithm, called MAJA, is a numerical tool designed to perform cloud detection and atmospheric correction. For the correction of aerosols effects, MAJA makes an estimate of the aerosol optical thickness (AOT) based on multi-temporal and multi-spectral criteria, but there is insufficient information to infer the aerosol type. The current operational version of MAJA uses an aerosol type which is constant with time, and this assumption impacts the quality of the atmospheric correction. In this study, we assess the potential of using an aerosol type derived from the Copernicus Atmosphere Monitoring Service (CAMS) operational analysis. The performances, with and without the CAMS information, are evaluated. Firstly, in terms of the aerosol optical thickness retrievals, a comparison against sunphotometer measurements over several sites indicates an improvement over arid sites, with a root-mean-square error (RMSE) reduced by 28% (from 0.095 to 0.068), although there is a slight degradation over vegetated sites (RMSE increased by 13%, from 0.054 to 0.061). Secondly, a direct validation of the retrieved surface reflectances at the La Crau station (France) indicates a reduction of the relative bias by 2.5% on average over the spectral bands. Thirdly, based on the assumption that surface reflectances vary slowly with time, a noise criterion was set up, exhibiting no improvement over the spectral bands and the validation sites when using CAMS data, partly explained by a slight increase in the surface reflectances themselves. Finally, the new method presented in this study provides a better way of using the MAJA processor in an operational environment because the aerosol type used for the correction is automatically inferred from CAMS data, and is no longer a parameter to be defined in advance.

The VEN$\mathsf{\mu }$S mission launched in 2017 provides multispectral optical images of the land surface with a 2-day revisit time at 5 m resolution for over 100 selected sites. A few sites are subject to seasonal snow accumulation, which gives the opportunity to monitor the variations of the snow cover area at unprecedented spatial and temporal resolution. However, the 12 spectral bands of VEN$\mathsf{\mu }$S only cover the visible and near-infrared region of the spectra while existing snow detection algorithms typically make use of a shortwave infrared band to determine the presence of snow. Here, we evaluate two alternative snow detection algorithms. The first one is based on a normalized difference index between the near-infrared and the visible bands, and the second one is based on a machine learning approach using the Theia Sentinel-2 snow products as training data. Both approaches are tested using Sentinel-2 data (as surrogate of VEN$\mathsf{\mu }$S data) as well as actual VEN$\mathsf{\mu }$S in the Pyrenees and the High Atlas. The results confirm the possibility of retrieving snow cover without SWIR with a slight loss in performance. As expected, the results confirm that the machine learning method provides better results than the index-based approach (e.g., an RMSE equal to the learning method 1.35% and for the index-based method 10.80% in the High Atlas.). The improvement is more evident in the Pyrenees probably due to the presence of vegetation which complicates the spectral signature of the snow cover area in VEN$\mathsf{\mu }$S images.

As part of the Copernicus program of the European Union (EU), the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) are currently operating the Sentinel-3 mission that consists of a constellation of two unites A and B (S3A, S3B). Each unit carries on board an Ocean and Land Colour Instrument (OLCI) that is acquiring moderate-spatial-resolution optical imagery. This article provides a description of the Level-1B radiometry product validation activities of the constellation Sentinel-3A and Sentinel-3B after six years in orbit. Several vicarious calibration methods have been applied independently by three expert groups and the results are compared over different surface target types. All methods agree on the good radiometric performance of both instruments. Although OLCI-A shows brighter Top-of-Atmosphere (TOA) radiance than OLCI-B by about 1–2%, both sensors exhibit very good stability and good image quality. The results are analyzed and discussed to propose a set of vicarious gain coefficients that could be used to align OLCI-A with OLCI-B radiometry time-series. Finally, recommendations for future missions are suggested.