A Note on the Temporary Misregistration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery

The Landsat-8 and Sentinel-2 sensors provide multi-spectral image data with similar spectral and spatial characteristics that together provide improved temporal coverage globally. Both systems are designed to register Level 1 products to a reference image framework, however, the Landsat-8 framework, based upon the Global Land Survey images, contains residual geolocation errors leading to an expected sensor-to-sensor misregistration of 38 m (2σ). These misalignments vary geographically but should be stable for a given area. The Landsat framework will be readjusted for consistency with the Sentinel-2 Global Reference Image, with completion expected in 2018. In the interim, users can measure Landsat-to-Sentinel tie points to quantify the misalignment in their area of interest and if appropriate to reproject the data to better alignment

Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable

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

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

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

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