Sentinel-2 Level-2 processing: Sen2Cor status and outlook for 2021

The Copernicus 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 is dedicated to land monitoring, emergency management and security. It serves for monitoring of land-cover change and biophysical variables related to agriculture and forestry, monitors coastal and inland waters and is useful for risk and disaster mapping. Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high-quality applications. Therefore, the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map including cloud / cloud shadow classes for further processing. Sen2Cor processor can be downloaded from ESA website as a standalone tool for individual Level-2A processing by the users. It can be run either from command line or as a plugin of the Sentinel-2 Toolbox (SNAP-S2TBX). In parallel, ESA started in June 2017 to use Sen2Cor for systematic Level-2A processing of Sentinel-2 acquisitions over Europe. Since March 2018, Level-2A products are generated by the official Sentinel-2 ground segment (PDGS) and are available on the Copernicus Open Access Hub. The objective of this presentation is to provide users with an overview of the Level-2A product contents and up-to-date information about the data quality of the Level-2A products (processing baseline >= PB.02.12) generated by Sentinel-2 PDGS since May 2019, in terms of Cloud Screening and Atmospheric Correction. In addition, the presentation will give an outlook on the upcoming updates of Sen2Cor which will improve L2A Data Quality: updated L2A metadata, updated scene classification, updated fall-back method using meteorological information from the Copernicus Atmosphere Monitoring Service, updated Copernicus DEM

Topography processing in Sen2Cor - Impact of horizontal resolution of Digital Surface Model

The Copernicus 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. Accurate atmospheric correction of satellite observations is a precondition for the development and delivery of high-quality applications. Therefore the atmospheric correction processor Sen2Cor was developed with the objective of delivering land surface reflectance products. Sen2Cor is designed to process single tile Level-1C products, providing Level-2A surface (Bottom-of-Atmosphere) reflectance product together with Aerosol Optical Thickness (AOT), Water Vapour (WV) estimation maps and a Scene Classification (SCL) map including cloud / cloud shadow classes for further processing. Sen2Cor processor can be downloaded from ESA website as a standalone tool for individual Level-2A processing by the users. It can be run either from command line or as a plugin of the Sentinel-2 Toolbox (SNAP-S2TBX). In parallel, ESA started in June 2017 to use Sen2Cor for systematic Level-2A processing of Sentinel-2 acquisitions over Europe. Since March 2018, Level-2A products are generated by the official Sentinel-2 ground segment (PDGS) and are available on the Copernicus Open Access Hub. Since the beginning of the Sentinel-2 mission, the digital surface model “PlanetDEM 90” from Planet Observer is used as source of Earth topography information, within the Sentinel-2 PDGS. It is at 90- meter resolution, based on SRTM data filled and corrected for 40% of Earth surface. However, until now most users had only access to original SRTM data to run with Sen2Cor or had to provide their own DEM following SRTM or DTED formats. The objective of this presentation is to provide users with an overview of how Sen2Cor makes use of the topography information to improve the quality of the cloud screening and scene classification as well as in the atmospheric correction and terrain correction. In addition, the presentation gives an outlook on Sen2Cor working with the upcoming Copernicus DEM, a new Digital Surface Model (DSM), which represents the surface of the Earth, including buildings, infrastructure and vegetation. This DEM is derived from an edited DSM named WorldDEM. The presentation shows the different L2A surface reflectance obtained with PlanetDEM 90, global Copernicus DEM at 30 m and at 90 m horizontal resolution, and some of these differences are discussed

Comparison of the Copernicus Sentinel-2 L2A Core Product distributed by ESA and the Sen2Cor Toolbox ‘user-generated’ product

Sen2Cor is a Level-2A (L2A) processor whose main purpose is to correct mono-temporal Copernicus Sentinel-2 (S2) mission Level-1C (L1C) products from the effects of the atmosphere in order to deliver radiometrically corrected Bottom-of-Atmosphere (BOA) data. Byproducts are Aerosol Optical Thickness (AOT), Water Vapour (WV) and Scene Classification (SCL) maps. The Sen2Cor Toolbox can be downloaded from the ESA website for autonomous processing of S2 L1C data by the users, thus generating BOA products here referred as ‘user’ products. In parallel, Sen2Cor is used for systematic processing of Sentinel-2 L1C data thus generating the S2 L2A products systematically distributed to users. Operational global L2A processing with the integration of Sen2Cor in the Sentinel-2 Ground Segment started in December 2018. These S2 L2A core products can be downloaded from the Copernicus SciHub. The S2 L2A core products agree with ‘user’ products as long as both are generated with the same Sen2Cor version and configuration. However, sometimes new versions of Sen2Cor Toolbox are released to public at a different time than their implementation in the S2 Ground Segment processing chain. This may result in a disagreement between ‘user’ and core products for some time. Additionally, differences between the outcome of the two processing lines can arise due to the DEM used and different minor patches included. Patches are usually included faster in the ESA-L2A core products. Whereas ESA-L2A core products have been using Planet-DEM for a while, the current DEM available to the users is SRTM-DEM. However, the new Copernicus DEM is now available also to users and is going to be used also to generate the S2 L2A core products, this limiting those differences. Both production lines are compared for what concerns AOT and WV maps, and BOA outputs per band. Both AOT and WV retrievals are validated by comparison with reference data provided by AERONET sunphotometers. Comparison of BOA outputs is performed by statistical metrics for pixelby-pixel differences between both processing lines over 9 km x 9 km areas. The influence of the different DEMs on resulting BOA will be discussed. ESA-L2A core products are generated with default configuration values, which are suitable for most situations of this operational mission. ‘User’ production gives the opportunity to apply individual configuration settings, which may prove favourable in some cases. This will be demonstrated on a few examples

Sen2Cor version 2.10: Last evolutions and Focus on the update of Cloud Screening and Scene Classification algorithm

Sen2Cor is a Level-2A processor with the main purpose 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. Side products are Cloud Screening and Scene Classification (SCL), Aerosol Optical Thickness (AOT) and Water Vapour (WV) maps. The Sen2Cor version 2.10 has been developed with the aim to improve the quality of both the surface reflectance products and the Cloud Screening and Scene Classification (SCL) maps in order to facilitate their use in downstream applications like the Sentinel-2 Global Mosaic (S2GM) service. This version is planned to be used operationally within Sentinel-2 Ground Segment and for the Sentinel-2 Collection 1 reprocessing. The Cloud Screening and Scene Classification module is performed prior to the atmospheric correction and provides a Scene Classification map divided into 11 classes. This map does not constitute a land cover classification map in a strict sense. Its main purpose is to be used internally in Sen2Cor’s atmospheric correction module to distinguish between cloudy -, clear - and water pixels. Two quality indicators are also provided: a Cloud - and a Snow confidence map with values ranging from 0 to 100 (%). The presentation provides an overview of the last evolutions of Sen2Cor including the support of new L1C products with processing baseline >= 04.00 and the provision of additional L2A quality indicators. The different steps of the Cloud Screening and Scene Classification algorithm are recalled: cloud/snow -, cirrus -, cloud shadow detection, pixel recovery and post-processing with DEM information. It will also detail the latest updates of version 2.10 that makes use of the parallax properties of the Sentinel-2 MSI instrument to limit the false detection of clouds above urban and bright targets. Finally, SCL validation results with Sen2Cor 2.10 are included in the presentation. The recent improvements as well as the current limitations of the SCL-algorithm are presented. Some advices are given on the configuration choices and on the use of external auxiliary data files

Evaluation of SEN2COR surface reflectance products over land surface with reference measurements on ground

Sen2Cor is the atmospheric correction processor selected by ESA for operational, systematic processing of Copernicus Sentinel-2 mission data. It is used for generating the Level 2A products distributed to users by the Copernicus SciHub. Accurate atmospheric correction of Sentinel-2 data and knowledge of its uncertainties are preconditions for high quality downstream applications. In this work we present the comparison of Sentinel-2 Bottom-of-Atmosphere products with measurements of surface reflectance on ground. Source of reference measurements are both surface reflectance data from RadCalNet and from dedicated field campaigns. The analysis shows, that the uncertainty of SR-retrieval with Sen2Cor is better than about 7% for bright surfaces and about 17% for darker. In addition to this performance evaluation, the data are also applied to compare the use of reference data coming from permanent operating bright RadCalNet sites and from ad-hoc field campaigns at darker sites

Uncertainty of Sentinel-2 AOT, WV and SR retrieval with Sen2Cor

Sen2Cor is a Level-2A (L2A) processor whose main purpose is to correct mono-temporal Copernicus Sentinel-2 (S2) mission Level-1C (L1C) products from the effects of the atmosphere in order to deliver radiometrically corrected Bottom-of-Atmosphere (BOA) data. Byproducts are Aerosol Optical Thickness (AOT), Water Vapor (WV) and Scene Classification (SCL) maps. Sen2Cor is used for systematic processing of Sentinel-2 L1C data thus generating the S2 L2A products distributed to users by the Copernicus SciHub. In parallel, a Sen2Cor Toolbox can be downloaded from the ESA website for autonomous processing of S2 L1C data by the users. Several important evolutions had been realized from Sen2Cor version 2.8 to 2.10. Version 2.9 runs with Copernicus DEM if correctly formatted. Significant improvements were realized on scene classification module from Sen2Cor 2.8 to 2.10. Whereas atmospheric correction core modules and the AOT estimation based on dense dark vegetation (DDV)-pixels remain unchanged, a new AOT estimation fallback solution was implemented in the recent version. This new fallback solution takes AOT from Copernicus Atmospheric Monitoring Service (CAMS) data in snowy and arid landscapes in case the Sentinel-2 granule does not contain enough DDV-pixel required for AOT estimation. Sen2Cor 2.10 is designed to work with next generation product format which includes this external AOT information in the metadata. Sentinel-2 products processed with Sen2Cor are almost compliant with the Analysis Ready Data (ARD) specifications. Knowledge of uncertainty of products is one major key to foster interoperability both through time and with other datasets. This presentation will provide a status update on the Sentinel-2 product uncertainties. Both AOT and WV retrievals are validated by comparing with reference data provided by AERONET sun photometers at 80 locations distributed over the globe, all continents and climate zones. Spatial average of retrieval from Sentinel-2 over 9x9 km2 region around AERONET station is compared to ±15 min time average of AERONET data around satellite overpass time. Quality of SR retrieval is assessed by comparison with pseudo reference data. These are generated by running Sen2Cor with fixed aerosol optical thickness as input which is set equal to the value provided by the AERONET. The presentation will compare uncertainty of AOT, WV and SR per band for different geographical regions and climate zones. The presentation will also analyze the sensitivity of Sen2Cor processing to parameters which can be configured by the user with running Sen2Cor Toolbox. The difference between processing with rural or maritime aerosol type and between summer or winter atmospheric profile will be discussed

Sentinel-2 Level-2 processing: Regional distribution of Sen2Cor version 2.8 performance for AOT and SR retrieval over Europe

Sen2Cor is the atmospheric correction processor selected by ESA for operational, systematic processing of Copernicus Sentinel-2 mission data. It is used for generating the Level-2A products distributed to users by the Copernicus SciHub. In addition, it can be downloaded from ESA website as standalone tool for individual processing by the users. The main purpose of Sen2Cor is to correct mono-temporal Sentinel-2 (S2) Level-1C (L1C) products from the effects of the atmosphere in order to deliver atmospherically corrected Bottom-of-Atmosphere (BOA) respectively surface reflectance (SR) data. Part of the atmospheric correction process is estimation of aerosol optical thickness and integrated water vapor of the atmosphere. Provided SR data are used in a wide field of downstream applications on land surface related to agriculture, forestry and land-cover change and are also used to monitor coastal and inland water. Downstream applications need information on quality of atmospheric correction products. This presentation will show performance of AOT and WV retrievals estimated by comparing with reference data provided by AERONET sun photometers at different locations distributed over Europe. Spatial average of retrievals from Sentinel-2 over 9x9 km2 region around AERONET station is compared to ±15 min time average of AERONET data around satellite overpass time. Performance of SR retrieval is assessed by comparison with pseudo reference data. These are generated by running Sen2Cor with fixed aerosol optical thickness as input which is set equal to the value provided by the AERONET

Sen2Cor - Sentinel-2 Level-2 Optical Processor Applied to Landsat-8 Data

Sen2Cor is the official ESA Sentinel-2 ground segment processor for the generation of the Sentinel-2 Level 2A core products from the Level 1C top of atmosphere reflectance in fixed cartographic geometry. Sen2Cor can also be downloaded from the ESA website as a standalone tool for individual Level 2A processing by the users. It can be run either via command line or as a plugin of the Sentinel-2 Toolbox (SNAP-S2TBX). The Sentinel-2 A/B Level 2A products are bottom of atmosphere reflectance in cartographic geometry, which are widely distributed to the users since March 2018 over Copernicus Open Access Hub and Sentinel Hub. In this study, we test the capability of the Sen2cor algorithm for scene classification and atmospheric correction to be able to perform Landsat-8 Level 1 input data processing. Both instruments have eight overlapping spectral bands and the measurements are often used complimentarily for studies of vegetation and land parameters. However, there are also distinct differences between the two sensors, such as spectral bands response, calibration and viewing geometries, which are reflected in the differences between the L1 products. In the bands arrangements of Landsat-8, the water vapor band is missing. These have to be taken into account in the modification and upgrade of the Sen2Cor algorithm. The ability of Sen2Cor to process Landsat-8 scenes in the same manner as the Sentinel-2 ones is of interest for the Sen2Like framework, which is detailed by Telespazio France on a parallel presentation at this workshop. This will allow Sentinel-2 and Landsat-8 TOA reflectance to be converted to surface reflectance using the same atmospheric correction algorithm and a radiative transfer model adapted to the Landsat conditions. We address the necessary algorithmic modifications and the uncertainty due to the Level 1 to Level 2 processing methodology. A qualitative comparison of both Sen2Cor generated products and comparison to the Landsat-8 LaSrc products from USGS is also presented

Sen2Cor Version 3.0 Processor Applied to Landsat-8 Data: Implementation and Preliminary Results

Sen2Cor (latest version 2.10) is the official ESA Sentinel-2 processor for the generation of the Level-2A Bottom-Of-Atmosphere reflectance products starting from Level-1C Top-Of-Atmosphere reflectance. In this work, we introduce Sen2Cor 3.0, an evolution of Sen2Cor 2.10 able to perform the processing of Landsat-8 Level-1 products in addition to Sentinel-2 Level-1C products. In this study, we test the resulting capability of the Sen2Cor 3.0 algorithms (also updated to work in a Python 3 environment) such as the scene classification and the atmospheric correction, to process Landsat-8 Level-1 input data. This work is part of the Sen2Like framework that aims to support Landsat-8-9 observations and to prepare the basis for future processing of large set of data from other satellites and missions. Testing and measuring the capacity of Sen2Cor 3.0 to adapt to different input and reliably produce the expected results is, thus, crucial. Sentinel-2 and Landsat-8 have seven overlapping spectral bands and their measurements are often complimentary used for studying and monitoring, for example, the status and variability of the Earth’s vegetation and land conditions. However, there are also important differences between these two sensors, such as the spectral-band response, spatial resolution, viewing geometries and calibrations. These differences and quantities are all reflected in their resulting L1 products. A dedicated handling process for those differences is, thus, needed. Moreover, contrary to Sentinel-2, Landsat-8 does not have the water-vapour band that is used by Sen2Cor to perform the atmospheric correction of Sentinel-2 products. Therefore, important information is missing and further implementation is required in order to retrieve the necessary data from external sources to prepare the scene for the Landsat-8 processing. Moreover, new set of Look-Up Tables had to be prepared. In this work, we address the modifications applied to Sen2Cor and the uncertainty due to the Level 1 to Level 2 processing methodology. Further, we present a qualitative comparison between Sen2Cor 3.0 generated Sentinel-2 and Landsat-8 L2 products and Sen2Cor 2.10 generated Sentinel-2 L2A products. Finally, we list foreseen optimizations for future development

Modélisation des propriétés statistiques de la luminance infrarouge du fond de ciel observée au limbe depuis la stratosphère

Airborne IR surveillance systems are used to detect ballistic missiles but their performances are limited by the atmospheric background heterogeneities. This thesis offers a new code, FACLUM-2D, which computes the 2D atmospheric radiance fluctuations autocorrelation function (ACF) for airborne configurations and adapted to the thermal IR. In these conditions, the clear-sky atmospheric structures result mainly from local fluctuations in temperature and in water vapor mass fraction. FACLUM-2D is then the first model which takes into account the water vapor contribution. In addition, the radiance ACF calculation depends on the absorption coefficient and its derivative with respect to the temperature and to the water vapor mass fraction. Therefore, I have developed a line by line code, RPR-IRT, dedicated to the thermal IR, which tabulate these derivatives, with a spectral resolution of 10-4 cm-1. Lack of experimental measurement available in the litterature, FACLUM-2D has been partially validated against analytical solutions for a homogeneous and gray medium and compared, as far as possible, to standard radiative transfer codes. First results show, for the first time, that water vapor mass fraction fluctuations impact strongly on radiance fluctuations. In the future, it could be interesting to extend FACLUM-2D to a larger spectral domain, so as to cover the whole IR region.Les performances des systèmes de veille IR aéroportés pour la detection de missiles balistiques sont limitées par les fluctuations spatiales de la luminance du fond de ciel. Cette thèse propose un nouveau code, FACLUM-2D, capable de calculer la fonction d'autocorrélation (FAC) 2D des fluctuations de la luminance atmosphérique, observées en visée quasi-horizontale depuis la stratosphere et spécifique à l'IR thermique. Les fluctuations du fond atmosphérique sont, dans ce cas, essentiellement dues aux fluctuations spatiales de la température et de la fraction massique de la vapeur d'eau. La prise en compte de ces dernières dans FACLUM-2D constitue la contribution majeure de ce travail de thèse. De plus, la détermination de la FAC de la luminance requiert la connaissance du coefficient d'absorption et de sa dérivée par rapport à la température et de la fraction massique de la vapeur d'eau. Pour cela, j'ai développé un code raie par raie pour l'IR thermique (RPR-IRT), permettant de tabuler la dérivée du coefficient d'absorption par rapport à la température et par rapport à la fraction massique de la vapeur d'eau avec une résolution spectrale de 10-4 cm-1. Faute de mesures expérimentales, le code FACLUM-2D a été validé avec des solutions analytiques pour un milieu homogène gris et comparé, dans la mesure du possible, à des codes de transfert radiatif standards. Les premiers résultats montrent, pour la première fois, l'impact significatif des fluctuations de la fraction massique de vapeur d'eau sur les fluctuations de la luminance. Une des perspectives majeures est d'étendre FACLUM-2D à une bande spectrale plus large, afin de couvrir tout l'IR