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

Comparison of DDV-algorithm for AOT estimation in Sen2Cor and use of AOT from CAMS data

The Copernicus Sentinel-2 mission provides data since the launch of the Sentinel-2A unit in 2015. The launch of the Sentinel-2B in 2017 created 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 decametric 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) surface reflectance product together with Aerosol Optical Thickness (AOT), Integrated Water Vapour (WV) and Scene Classification (SCL) maps. The processor relies on Dense Dark Vegetation (DDV) pixels for estimation of AOT and uses AOT from Copernicus Atmospheric Monitoring Service (CAMS) as fall-back option in case there are not enough DDV-pixels in the image. The data set for validation was split into two subsets so far investigating the performance of the DDValgorithm and the CAMS-fall-back option. CAMS fall-back option performs better than the DDV-algorithm in some cases and worse in others. However, this is no comparison of DDV-algorithm and CAMS fall-back option because it is based on different images in each subset. The presentation will compare both AOT-options of Sen2Cor on the same images. The analysis will start as before reporting results for the two subsets. Then, Sen2Cor will be forced using CAMS data for reprocessing the subset of images with enough DDV-pixels in the image allowing a direct comparison of DDV-algorithm and CAMS data use. The comparison is done with reference data from AERONET on a dataset of more than 1000 Sentinel-2 images distributed around the globe

Initial Validation of Sentinel-2 Collection-1 L2A-Products

The Copernicus Sentinel-2 mission provides data since the launch of the Sentinle-2A unit in 2015. With the launch of the Sentinel-2B in 2017 it is 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) surface reflectance product together with Aerosol Optical Thickness (AOT), Integrated Water Vapour (WV) and Scene Classification (SCL) maps. Since June 2017, ESA uses Sen2Cor for systematic, operational Level-2A processing of Sentinel-2 acquisitions. Products are available on the Copernicus Open Access Hub. However, several evolutions of Sen2Cor and L2A-products since 2017 resulted in a quite inhomogeneous time series. Therefore, ESA started a reprocessing campaign of the complete Sentinel-2 data archive. The resulting Collection-1 of Sentinel data archive provides a real homogeneous time series based on the recent Sen2Cor processor version. The presentation provides initial validation results for AOT, WV and (BOA) surface reflectance retrieval together with quality assessment of cloud screening. Accuracy and uncertainty of AOT and WV retrieval is assessed with reference measurements from AERONET stations. BOA reflectance retrieval can be estimated on a limited number of reference data only from RadCalNet-sites and in-situ campaigns. Reference data for cloud screening are generated by manual labelling of test images

Copernicus Sentinel-2 Collection-1: A Consistent Dataset of Multispectral Imagery with enhanced Quality

The Copernicus Sentinel-2 satellite mission, with its Sentinel-2A and Sentinel-2B units, offers since several years now a massive quantitative and qualitative resource for the Earth Observation community. Since the launch of Sentinel-2A in 2015, and Sentinel-2B in 2017, many lessons have been learnt leading to continuous improvements of the radiometric and the geometric performances. However, the current archive is composed of heterogenous processing baselines with inconsistent product formats and uneven data quality, which limits its use for multi-temporal monitoring applications. To overcome this limitation, the Copernicus program has undertaken a complete reprocessing with the latest processing baseline (05.00). It concerns the L1C (Top-OfAtmosphere reflectance) and L2A (Surface Reflectance) products. This paper recalls the features of Collection-1 products and gives an overview of the first validation results

A new method for the validation of the GOMOS high resolution temperature profiles products

This article proposes a new validation method for GOMOS HRTP atmospheric temperature and density profiles, with the aim of detecting and removing 0.2 to 5 km scale vertical structures in order to minimise the impact of atmospheric artefacts in the comparison exercises. The proposed approach is based on the use of the “Morlet” Continuous Wavelet Transformation (CWT), for the characterisation and removal of non-stationary and localised vertical structures, in order to produce wave-free profiles of atmospheric temperature and density. Comparison of wave-free temperature/density profiles and wavy structures profiles with those estimated from a limited number of collocated SHADOZ soundings for the years of 2003, 2004 and 2008, is discussed in detail. First results suggest that the proposed approach could lead to a significantly improved HRTP validation scheme, in terms of reduced uncertainties in the estimated biases. Furthermore, this method may be adopted for the study of the vertical component of gravity waves from high spatial/temporal resolution data

Validation of Sentinel-2 Auxiliary Data: Focus on Aerosol Optical Depth at 550 nm and Total Column of Water

The Copernicus Sentinel-2 mission comprises a constellation of two polar-orbiting satellites placed in the same sun-synchronous orbit, phased at 180° to each other. Since 2015 it has been monitoring the variability in land surface conditions thanks to its wide swath width (290 km) and high revisit time (10 days at the equator with one satellite, and 5 days with 2 satellites under cloud-free conditions which results in 2-3 days at mid-latitudes). Since the beginning of the mission, in complement of sentinel-2 imagery files, meteorological auxiliary data from European Centre for Medium-Range Weather Forecasts (ECMWF) are provided to the users within L1C and L2A products. The format (GRIB V1) and contents of this file are identical in L1C and L2A products. This auxiliary data is provided in a gridded format which results from a temporal (linear) and spatial interpolation (bilinear with a Ground Sample Distance of 12.5km) of the raw ECMWF global forecast dataset into a geographical area covering the Level-1C tiles footprint. Grid points are provided in latitude/longitude using WGS84 reference system. This meteorological auxiliary data file initially provided three parameters: - Total column ozone (TCO3) [Kg/m2]; - Total column water vapour (TCWV) [Kg/m2]; - Mean sea level pressure (MSL) [hPa]. Since the 25th of January 2022, the number of ECMWF meteorological parameters has been extended with three other parameters: - 10-meter V wind component (10v) - 10-meter U wind component (10u) - Relative Humidity @ Isobaric Surface , together with a new set of 10 atmospheric parameters from the Copernicus Atmosphere Monitoring Service (CAMS) that generates every day, five-day forecasts of aerosols, atmospheric pollutants, greenhouse gases, stratospheric ozone, and the UV-Index. This contribution reports on the monitoring of the two following parameters: - Aerosol Optical Depth at 550 nm (extracted from AUX_CAMS_FO file), - Total Column of Water (extracted from AUX_ECMWFT file). The Aerosol Optical Depth at 550 nm can be used as atmospheric information fallback in the L2A processor when performing the atmospheric correction for certain Sentinel-2 tiles when not enough dark dense vegetation pixels are present to perform an independent aerosol retrieval . The Total Column of Water is not used in the L2A processor when performing the atmospheric correction. However, it is interesting to check how its performance compares with the L2A processor outputs. The monitoring is performed on 24 different locations distributed over all continents and all climate zones using 24 AERONET stations. The data period covers more than 18 months of data, between 25/01/2022 to 25/08/2023, starting with Sentinel-2 PB 04.00, since the AUX_CAMS_FO files are embedded in the L1C and L2A products. The values of AUX_CAMS_FO and AUX_ECMWFT are extracted at the AERONET site location using its geographic coordinates. The Aerosol Optical Depth at 550 nm AERONET values are spectrally and temporally interpolated to the Sentinel-2 acquisition time. The Precipitable Water AERONET values are temporally interpolated to the Sentinel-2 acquisition time. We present the results of the monitoring in the form of scatter plots of all the concomitant Sentinel-2 auxiliary CAMS data with respect to the AERONET data for all the 24 AERONET test sites. These consolidated statistics on all test sites show that 53% of Aerosol Optical Depth at 550 nm CAMS data is within the uncertainty goal with an overall uncertainty of 0.17 without significant bias. It should be noted however that depending on the test site this value can range from 23% for the lowest agreement (Ilorin) up to 84% for the best agreement (Kangerlussuaq). In general, the agreement is better for test sites with lower aerosol load. For water vapour, these consolidated statistics on all test sites show that 88% of Total Column of Water ECMWF data is within the uncertainty goal with an overall uncertainty of 0.27 g.cm-2 with a slight positive bias of 0.15 g.cm-2 . It should be noted however that depending on the test site, this value can range from 75% for the lowest agreement (OHP_OBSERVATOIRE) up to 98% for the best agreement (Kangerlussuaq). In general, the agreement is better for test sites with dryer atmosphere

Improved H2O GOMOS profiles using a new algorithm based on a Levenberg-Marquardt method

International audienceH2O plays a very important role in the upper troposphere and stratosphere. It has a strong radiative effect and it plays a key role in the ozone chemistry, being a source of HOx species involved in the catalytic destruction of ozone. The evolution of H2O in the lower stratosphere during the last decades is still not well determined. Contradictory results are obtained depending of the source of data (balloons, satellites).H2O measurements by GOMOS (Global Ozone Monitoring by Occultation of Stars) on board Envisat can play a significant role in this area. The two advantages of the stellar occultations method are the self-calibration nature and the well-defined geometry. The IPF 6 algorithm provides greatly improved H2O profiles compared to IPF5 due to a correction of the intra-pixel PRNU. However there is still some room for improvement. A new algorithm has been developed in which the wavelength assignment is improved, the new HITRAN 2012 database is used for H2O absorption and a Levenberg-Marquartdt method is applied for spectrum fitting instead of using look-up tables for the estimation of H2O slant columns. This new algorithm provides improved H2O profiles to be used for studies on H2O variability and trends in the UTLS.These studies have been performed in the framework of ESA-funded ALGOM (GOMOS Level 2 algorithm evolution studies) project