As part of the Copernicus programme of the European Commission (EC), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This article provides a description of the calibration activities and the status of the mission products validation activities after one year in orbit. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. The presented results show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%.

Satellite Earth Observation (EO) sensors are becoming a vital source of information for land surface monitoring. The concept of the Virtual Constellation (VC) is gaining interest within the science community owing to the increasing number of satellites/sensors in operation with similar characteristics. The establishment of a VC out of individual missions offers new possibilities for many application domains, in particular in the fields of land surface monitoring and change detection. In this context, this paper describes the Copernicus Sen2Like algorithms and software, a solution for harmonizing and fusing Landsat 8/Landsat 9 data with Sentinel-2 data. Developed under the European Union Copernicus Program, the Sen2Like software processes a large collection of Level 1/Level 2A products and generates high quality Level 2 Analysis Ready Data (ARD) as part of harmonized (Level 2H) and/or fused (Level 2F) products providing high temporal resolutions. For this purpose, we have re-used and developed a broad spectrum of data processing and analysis methodologies, including geometric and spectral co-registration, atmospheric and Bi-Directional Reflectance Distribution Function (BRDF) corrections and upscaling to 10 m for relevant Landsat bands. The Sen2Like software and the algorithms have been developed within a VC establishment framework, and the tool can conveniently be used to compare processing algorithms in combinations. It also has the potential to integrate new missions from spaceborne and airborne platforms including unmanned aerial vehicles. The validation activities show that the proposed approach improves the temporal consistency of the multi temporal data stack, and output products are interoperable with the subsequent thematic analysis processes.

The Sentinel-2A and Sentinel-2B constellation is an Earth observation optical mission developed and operated by the European Space Agency in the frame of the Copernicus program of the European Commission. The novel observation capacity offered by the multispectral instruments (MSI) on-board Sentinel-2 mission provides a massive resource for the Earth observation community. The accurate radiometric calibration is a critical element of the success of the mission; therefore, in-orbit calibration and validation activities are conducted within the Sentinel-2 Mission Performance Centre. The database of the imaging multispectral instrument and tool for radiometric intercomparison and the ground measurements of the radiometric calibration network (RadCalNet) are used to perform the vicarious validation of the radiometry of the Level-1 products delivered to the users. The aims of this validation are to assess the quality of the data product, to monitor the evolution of the radiometry of both instruments, and to ensure that the products meet the mission requirement accuracy. This article outlines the vicarious methods—Rayleigh scattering, desert pseudoinvariant calibration sites, sensor-to-sensor intercalibration, and the ground reflectance-based approach—that are used for the Sentinel-2 radiometry validation. The validation results indicate good performance and stability of both sensors MSI-A and MSI-B and consistency up to ∼1% (∼2% for red-edge bands) between them. The results of the intercomparison with the in situ measurements from RadCalNet illustrate the relevance of such dataset for the vicarious validation activity.

Sen2Like is a processor designed to produce harmonized Level 2 surface reflectance dataset considering as reference Sentinel-2 data. Sen2Like fits with the on-going and growing expectations of the Earth Observation (EO) Community for the merging of an increasing number of input data streams from the various Multi-Spectral (MS) High Resolution (HR) EO mission instruments.The harmonization is an essential processing stage in order to fully exploit all the potential of physical data in the context of land use / land change classification / detection. However, because harmonization addresses several issues in the field of atmospheric / geometric / directional effects / spectral difference corrections, there is not a standard way to achieve this objective.To initiate this project, the Landsat 8 (LS8) mission has been considered as the most appropriate. The Sen2Like processor is now delivering S2/LS8 Analysis Ready Data (ARD) on S2 tiling system at a map resolution of 30 m and 10 m. After a description of the algorithms involved in Sen2Like, this paper discusses testing and validation results.

As part of the Copernicus programme of the European Union (EU), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This paper provides a description of the calibration activities and the current status of the mission products validation activities. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. Results presented in this paper show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.