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

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

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

Sen2Like: Paving the Way towards Harmonization and Fusion of Optical Data

Satellite Earth Observation (EO) sensors are becoming a vital source of information for land surface monitoring. The concept of the Virtual Constellation (VC) is gaining interest within the science community owing to the increasing number of satellites/sensors in operation with similar characteristics. The establishment of a VC out of individual missions offers new possibilities for many application domains, in particular in the fields of land surface monitoring and change detection. In this context, this paper describes the Copernicus Sen2Like algorithms and software, a solution for harmonizing and fusing Landsat 8/Landsat 9 data with Sentinel-2 data. Developed under the European Union Copernicus Program, the Sen2Like software processes a large collection of Level 1/Level 2A products and generates high quality Level 2 Analysis Ready Data (ARD) as part of harmonized (Level 2H) and/or fused (Level 2F) products providing high temporal resolutions. For this purpose, we have re-used and developed a broad spectrum of data processing and analysis methodologies, including geometric and spectral co-registration, atmospheric and Bi-Directional Reflectance Distribution Function (BRDF) corrections and upscaling to 10 m for relevant Landsat bands. The Sen2Like software and the algorithms have been developed within a VC establishment framework, and the tool can conveniently be used to compare processing algorithms in combinations. It also has the potential to integrate new missions from spaceborne and airborne platforms including unmanned aerial vehicles. The validation activities show that the proposed approach improves the temporal consistency of the multi temporal data stack, and output products are interoperable with the subsequent thematic analysis processes

First Application of high resolution BRDF Algorithm (HABA) for reflectance normalization on a Fusion dataset from the Sen2Like Processor

Normalized Bidirectional Adjusted Reflectance (NBAR) is a key parameter for a consistent time series monitoring over non-lambertian surfaces. The Sen2like is a Virtual Constellation (VC) which harmonizes and fuses Landsat 8 / Landsat 9 & Sentinel 2 dataset giving out a higher spatial and temporal resolution surface reflectance. However, for adequate monitoring of land surface is necessary the correction of sun and sensor angle view across the VC acquisitions. In this context, the High resolution Adjusted BRDF Algorithm (HABA) provides up to 10m NBAR product retrieved from the disaggregation of the Bidirectional Reflectance Distribution Function (BRDF) parameters based on the VJB method applied to MODIS M{O,Y} D09 Climate Model Grid (CMG) at 1km resolution. HABA downscales this product to Sen2Like resolution inverting BRDF parameters (V & R) using the k-means unsupervised classification for each dataset. In order to compensate for the impact on images that do not present sufficient data representativeness due to cloud coverage, the disaggregated parameters are stabilized computing linear trends of time series of Normalized Difference Vegetation Index (NDVI) versus V & R. The model was evaluated on stable sites, such as Sahara Desert (Libya) and Amazonian Forest (Brazil) by comparing the impact of View Zenith Angle (VZA) and Solar Zenith Angle (SZA) of directional reflectance, a static NBAR model and HABA for Near InfraRed (NIR) and red spectrum. Also, the Sen2Like performance was assessed on dynamic sites with a mosaic of land covers across the Belgium tiles, calculating the absolute difference per tile in a 5-day window. The results of stable sites show a decline of linear dependency on the Amazon VZA from R² 0.57 (directional) to 0.37 (HABA) in NIR and R² 0.04 (directional) to 0.0 (HABA) in red. The Sahara Desert showed a correction of 4% of linear dependency of SZA versus reflectance. Finally, in Belgium, HABA corrected up to 12,74 % the directional effect on the time series. This work contributes to develop a dynamic and operationalization of NBAR correction method based on pixel scale for high resolution datasets

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

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