As part of the Copernicus program, Sentinel-2 is the optical imaging mission designed for the operational monitoring of land and coastal areas. It offers a unique combination of global coverage with a wide field of view, a high revisit capability (5 days with two satellites), a high resolution and multi-spectral imagery. CNES, the French Space Agency, was involved in the commissioning of both Sentinel-2 satellites and is currently working in collaboration with ESA on their long-term monitoring. This paper reviews all the techniques used to ensure an absolute calibration of the 13 spectral bands to better than 5% (the target is 3%) at TOA level. After a brief description of the mission and its related radiometric calibration scheme, we show how standard vicarious calibration methods based on acquisitions over natural targets (oceans, deserts, and Antarctica during winter) are used to check and improve the accuracy of the absolute calibration coefficients. Finally, the verification scheme, exploiting photometer in-situ measurements over the La Crau plain in France and Gobabeb in Namibia, is described. The paper concludes with a summary that includes spectral coherence, agreement between the results obtained with various calibration methods and temporal evolution.

Blind source separation is a common processing tool to analyse the constitution of pixels of hyperspectral images. Such methods usually suppose that pure pixel spectra (endmembers) are the same in all the image for each class of materials. In the framework of remote sensing, such an assumption is no longer valid in the presence of intra-class variability due to illumination conditions, weathering, slight variations of the pure materials, etc. In this paper, we first describe the results of investigations highlighting intra-class variability measured in real images. Considering these results, a new formulation of the linear mixing model is presented leading to two new methods. Unconstrained pixel-by-pixel NMF (UP-NMF) is a new blind source separation method based on the assumption of a linear mixing model, which can deal with intra-class variability. To overcome the limitations of UP-NMF, an extended method is also proposed, named Inertia-constrained Pixel-by-pixel NMF (IP-NMF). For each sensed spectrum, these extended versions of NMF extract a corresponding set of source spectra. A constraint is set to limit the spreading of each source’s estimates in IP-NMF. The proposed methods are first tested on a semi-synthetic data set built with spectra extracted from a real hyperspectral image and then numerically mixed. We thus demonstrate the interest of our methods for realistic source variabilities. Finally, IP-NMF is tested on a real data set and it is shown to yield better performance than state of the art methods.