The Level-2A processing includes a scene classification and an atmospheric correction applied to Top-Of-Atmosphere (TOA) Level-1C orthoimage products. Level-2A main output is an orthoimage Bottom-Of-Atmosphere (BOA) corrected reflectance product.
Additional outputs are an Aerosol Optical Thickness (AOT) map, a Water Vapour (WV) map and a Scene Classification Map (SCM) together with Quality Indicators (QI) for cloud and snow probabilities at 60 m resolution. Level-2A output image products will be resampled and generated with an equal spatial resolution for all bands, based on the requested resolution (10 m, 20 m or 60 m). A 10 m resolution product contains the spectral bands 2, 3, 4 and 8 and an AOT map resampled from 20 m. A 20 m product contains bands 2 - 7, the bands 8A, 11 and 12 and an AOT and WV map. A 60 m product contains all components of the 20 m product and additionally the 60 m bands 1 and 9. The cirrus band 10 will be omitted, as it does not contain surface information.
The processor algorithm is a combination of state-of-the-art techniques for performing atmospheric corrections (including cirrus clouds correction [R1]), which have been tailored to the SENTINEL-2 environment together with a scene classification module described in [R2]. The scene classification algorithm allows detection of clouds, snow and cloud shadows and generation of a classification map, which consists of four different classes for clouds (including cirrus), together with six different classifications for shadows, cloud shadows, vegetation, soils/deserts, water and snow. The algorithm is based on a series of threshold tests that use as input TOA reflectance as input from the SENTINEL-2 spectral bands. In addition, thresholds are applied on band ratios and indexes like Normalised Difference Vegetation Index (NDVI) and Normalised Difference Snow and Ice Index (NDSI). For each of these threshold tests, a level of confidence is associated. It produces at the end of the processing chain a probabilistic cloud mask quality indicator and a snow mask quality indicator. The algorithm uses the reflective properties of scene features to establish the presence or absence of clouds in a scene. Cloud screening is applied to the data in order to retrieve accurate atmospheric and surface parameters, either as input for the further processing steps below or for being valuable input for processing steps of higher levels.
The aerosol type and visibility or optical thickness of the atmosphere is derived using the Dense Dark Vegetation (DDV) algorithm [R3]. This algorithm requires that the scene contains reference areas of known reflectance behaviour, preferably DDV and water bodies. The algorithm starts with a user-defined visibility (default: 20 km). If the scene contains no dark vegetation or soil pixels, the surface reflectance threshold in the 2 190 nm band is successively iterated in order to include medium brightness reference pixels. If the scene contains no reference and no water pixels the scene is processed with the start visibility instead.
Water vapour retrieval over land is performed with the Atmospheric Pre-corrected Differential Absorption (APDA) algorithm [R4] which is applied to the two SENTINEL-2 bands (B8a, and B9). Band 8a is the reference channel in an atmospheric window region. Band 9 is the measurement channel in the absorption region. The absorption depth is evaluated in the way that the radiance is calculated for an atmosphere with no water vapour assuming that the surface reflectance for the measurement channel is the same as for the reference channel. The absorption depth is then a measure of the water vapour column content.
Atmospheric correction is performed using a set of look-up tables generated via libRadtran. Baseline processing is the rural/continental aerosol type. Other look-up tables can also be used according to the scene geographic location and climatology.
Figure 1: The figure shows from left to right: (1) A simulated Level-1C TOA reflectance input Image, (2) the cirrus and atmospherically corrected Level-2A BOA reflectance image, (3) the cirrus band B10, (4) the scene classification of the Level-1C input image (AVIRIS data supplied by NASA).
[R1]: Richter, R., Wang, X., Bachmann, M., and Schlaepfer, D., "Correction of cirrus effects in Sentinel-2 type of imagery", Int. J. Remote Sensing, Vol.32, 2931-2941 (2011).
[R2]: J. Louis, A. Charantonis & B. Berthelot, "Cloud Detection for Sentinel-2", Proceedings of ESA Living Planet Symposium (2010).
[R3]: Kaufman, Y., Sendra, C. Algorithm for automatic atmospheric corrections to visible and near-IR satellite imagery, International Journal of Remote Sensing, Volume 9, Issue 8, 1357-1381 (1988).
[R4]: Schläpfer, D. et al., "Atmospheric precorrected differential absorption technique to retrieve columnar water vapour", Remote Sens. Environ., Vol. 65, 353366 (1998).