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The Copernicus programme is a European initiative for the implementation of information services dealing with environment and security, mainly based on observation data received from Earth Observation (EO) satellites. In the frame of this programme, ESA launched the Copernicus Sentinel- 2A and Sentinel-2B optical imaging satellites. These satellites deliver a new generation of optical data products designed to directly feed downstream services mainly related to land monitoring, emergency management and security. To ensure the highest quality of service for the Copernicus Sentinel-2 mission, ESA set up the Sentinel-2 Mission Performance Centre (MPC) supported by several Expert Support Laboratories (ESL). This presentation reports results of performance analysis of the Sentinel-2 Level-2A processor Sen2Cor performed by the ESL-L2A. Sen2Cor Level 2A processing is applied to Top-Of-Atmosphere (TOA) Level 1C ortho-image reflectance products. Sen2Cor L2A outputs are a scene classification image, aerosol and water vapour maps and the ortho-image surface reflectance (SR) product. SR is a Bottom-Of-Atmosphere (BOA) reflectance product corrected for the influence of the atmosphere. Scene classification is generated together with Quality Indicators for cloud and snow probabilities. Many earth remote sensing applications rely on time series of satellite data. However at many locations on the globe it is almost impossible to build time series relying only on cloudless data due the cloudiness of the region.. Generally time series get denser if they can include data containing some clouds. However, how accurate and reliable are L2A satellite products of cloudy images? To answer this question, the presentation is focused on providing Sen2Cor performance in dependence of cloud cover of the images. Sen2Cor performance of AOT and WV retrieval is analysed by direct comparison of Sen2Cor outputs with reference data provided by AERONET sunphotometers. Quality assessment of SR outputof Sen2Cor is based on AERONET corrected SR as reference. AERONET corrected SR are obtained by performing the atmospheric correction with in input the aerosol and WV information given by AERONET instruments. The analysis is performed within 9 km x 9 km area around the location of sunphotometer for more than 20 AERONET sites around the globe, covering different surface types, latitudes, climate zones and atmospheric conditions.

Sen2Cor is a Level-2A processor designed to correct Sentinel-2 Level-1C products from the effects of the atmosphere in order to deliver a Level-2A surface reflectance product. A key-parameter for accurate atmospheric correction is the knowledge of the spatially and temporally very variable aerosol content of the atmosphere The multi-spectral instrument on board Sentinel-2 has the appropriate spectral bands to derive the aerosol optical thickness (AOT) of the atmosphere, provided the image contains reference areas of known reflectance behavior, preferably dense dark vegetation (DDV). If the granule contains no DDV pixels, a fallback solution is required. The former solution was to generate an AOT map based on the start visibility set in the configuration file. In this updated Sen2Cor version, meteorological AOT estimates are retrieved from ECMWF ftp server and pre-processed. The integration and assimilation of ECMWF aerosol estimates into Sen2Cor atmospheric correction is described and recent validation results are presented.

The Sentinel‐2 mission is fully operating since June 2017 with a constellation of two polar orbiting satellite units. Both Sentinel‐2A and Sentinel‐2B are equipped with an optical imaging sensor MSI (Multi‐Spectral Instrument) which acquires high spatial resolution optical data products. The Sentinel‐2 mission serves for observation of land‐cover change and deriving biophysical variables related to agriculture and forestry, monitors coastal and inland waters and is useful for risk and disaster mapping. Atmospheric correction processor Sen2Cor was developed to remove the effect of the atmosphere from Sentinel‐2 data. Sen2Cor is designed for mono‐temporal processing of Sentinel‐2 L1C data products providing Level‐2A Bottom‐of‐Atmosphere (BOA) reflectance product together with Aerosol Optical Thickness (AOT),Water Vapor (WV) and Scene Classification (SCL) maps. Sen2Cor can be downloaded from ESA website for individual processing by the users. In parallel, Sen2Cor is used by ESA for systematic L2A‐processing of Sentinel‐2 acquisitions over Europe since June 2017. ESA is preparing an operational global L2A‐processing with the integration of Sen2Cor in Sentinel‐2 ground segment (PDGS). The presentation provides recent validation results for the BOA reflectance product focusing on vegetated areas. Accuracy represents the mean difference of BOA‐reflectance retrieval to a reference value and uncertainty gives the rms around the reference. BOA‐reflectance reference is computed from Sentinel‐2 L1C Top‐of‐Atmosphere products using a radiative transport model with AERONET aerosol data as an input.

Sen2Cor is a Level-2A processor with the main purpose to correct single-date Sentinel-2 Level1C products from the effects of the atmosphere in order to deliver a Level-2A surface reflectance(SR)product. Side products are Cloud Screening and Scene Classification, Aerosol Optical Thickness (AOT) and Water Vapour(WV) maps. The accuracy of the retrieved surface reflectance product is strongly related to the accuracy of the atmospheric Parameters used during the atmospheric correction process, mainly the Aerosol Optical Thickness and the Water Vapour content. The Validation results of Sen2Cor have shown that the retrieval of water vapour is very accurate, whereas the method used to estimate the Aerosol Optical Thickness (AOT)at 550nm, the Dark Dense Vegetation (DDV)Pixel method, faces some limitations. The AOT estimation is not possible when there are no DDV Pixels in the image. The current fallback solution for that case is to perform the atmospheric correction starting with a default AOT value. A novel fallback solution for Sen2Cor is presented. The idea is to use AOT estimates at 550nm from the Copernicus Atmosphere Monitoring Service (CAMS)retrieved from ECMWF. A first Quality assessment of These meteorological AOT data overselected AERONET testsites is presented. Then the method of pre-processing and ingestion of this meteorological data in the Sen2Cor atmospheric correction module is described. Preliminary results are illustrated with an example of L2A surface reflectance product over a specific testsite.

Sen2Cor is a Level-2A (L2A) processor which main purpose is to correct single-date Sentinel-2 Level-1C products from the effects of the atmosphere in order to deliver a Level-2A surface reflectance product. This paper provides a description of L2A products contents and format. It presents also the different ways to run and configure the Sen2Cor processor and provides up-to-date information about the Sen2Cor release status and early validation results at the time of the Living Planet Symposium 2016.

The Copernicus programme is a European initiative for the implementation of information Services dealing with environment and security, mainly based on observation data received from Earth Observation (EO) satellites. In the frame of this programme, ESA launched the Sentinel-2A optical imaging mission on 23th June 2015. This satellite delivers a new generation of optical data products designed to directly feed downstream services mainly related to land monitoring, emergency management and security. To ensure the highest quality of service for the Sentinel-2 mission, ESA set up the Sentinel-2 Mission Performance Centre (MPC) supported by several Expert Support Laboratories (ESL). This presentation reports early results of validation of the Sentinel-2 L2A processor Sen2Cor performed by the ESL-L2A. Sen2Cor Level 2A processing is applied to Top-Of-Atmosphere (TOA) Level 1C ortho-image reflectance products. Sen2Cor L2A outputs are a scene classification image, aerosol and water vapour maps and the ortho-image Bottom-Of-Atmosphere (BOA) corrected reflectance product. Scene classification is generated together with Quality Indicators for cloud and snow probabilities at 60 m resolution. In this poster we present the calibration and validation datasets used for our on-going L2A cal/val activities, covering different land cover types, different atmospheric conditions and including different latitudes in order to cover various solar angles and seasons. The already performed and on-going insitu campaigns for collecting atmospheric and ground reference data to support the L2A products validation are also described. The methodologies of calibration and validation are presented under the form of a workflow chart, showing the two steps validation approach for atmospheric correction (validation of aerosol optical thickness and water vapour estimates and the validation of BOA reflectance product), the validation approach for cloud screening and scene classification (stratified random sampling), and the interactions with the L2A processor calibration activities. Details are given about the specifics of each method. Finally, the results of Sen2Cor L2A products validation activities (at the time of the LPS 2016 conference) will be provided for the cloud screening and scene classification, as well as for the

ESA has developed and launched the Sentinel-2A optical imaging mission that delivers optical data products designed to feed downstream services mainly related to land monitoring, emergency management and security. Many of these applications require accurate correction of satellite images for atmospheric effects to ensure the highest quality of scientific exploitation of Sentinel-2 data. Therefore the atmospheric correction processor Sen2Cor was developed by TPZ V on behalf of ESA. TPZ F and DLR have teamed up in order to provide the calibration and validation of the Level-2A processor Sen2Cor. Level-2A processing is applied to Top-Of-Atmosphere (TOA) Level-1C ortho-image reflectance products. Level-2A main output is the 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 (SC) map with Quality Indicators for cloud and snow probabilities. The poster will present some processing examples of Sen2Cor applied to Sentinel-2A data together with first performance investigations. Different situations will be covered like processing with and without DEM (Digital Elevation Model). Sen2Cor processing is controlled by several configuration parameters. Some examples will be presented demonstrating the influence of different settings of some parameters.