Ferran Gascon

Copernicus Sentinel-2A Calibration and Products Validation Status

As part of the Copernicus programme of the European Commission (EC), the European Space Agency (ESA) has developed and is currently operating the Sentinel-2 mission that is acquiring high spatial resolution optical imagery. This article provides a description of the calibration activities and the status of the mission products validation activities after one year in orbit. Measured performances, from the validation activities, cover both Top-Of-Atmosphere (TOA) and Bottom-Of-Atmosphere (BOA) products. The presented results show the good quality of the mission products both in terms of radiometry and geometry and provide an overview on next mission steps related to data quality aspects.

S2eteS: An End-to-End Modeling Tool for the Simulation of Sentinel-2 Image Products

In the upcoming years, many new remote sensing sensors will start operating in space. Sentinel-2 is certainly one of the most outstanding systems that will deliver a flood of detailed and continuous data from the Earths surface during the next years. However, the heterogeneity of remote sensing data recorded using different sensors demands prelaunch activities to develop the synergies for efficient multisensor data analysis. In this context, accurate sensor simulations are a valuable tool that enables a meaningful intersensor comparison. This paper addresses the simulation of the future Sentinel-2 data and products. The presented Sentinel-2 end-to-end simulation (S2eteS) software models Sentinel-2 data acquisition, sensor calibration, and data preprocessing, which are strongly oriented on the real system. Several tests were performed to prove the software capability to generate accurate Sentinel-2 products, with regard to the quality of the radiance and reflectance products. As an example for a large variety of possible applications, the effects of unknown spectral band shifts, sensor noise, and radiometric accuracy on the accuracy of different Sentinel-2 vegetation indexes (VIs) were investigated. The software also holds the possibility to simulate other similar multispectral sensors because of its generic design.

Using multi-directional high-resolution imagery from POLDER sensor to retrieve leaf area index

Multi‐directional satellite optical imagery collected at high spatial resolution potentially allows improving the accuracy of biophysical variable retrieval. The improvements result from the inclusion of the directional anisotropy of the target, which provides additional information related to vegetation structural properties. The research presented here analyses airborne imagery and ground reference data in order to quantify the accuracy of the retrieval methods for LAI (leaf area index). Both variables are estimated through processing of airborne POLDER (POLarization and Directionality of Earth Reflectances) sensor images from an agricultural area. In a first step, the BRDF (Bi‐directional Reflectance Distribution Function) of the surface is estimated using a simple parametric model, whose parameters where derived from fitting POLDER BRF (Bi‐directional Reflectance Factor) measurements. LAI estimation was performed using two different approaches, both based on an artificial neural network designed to invert a 1D soil‐vegetation radiative transfer model. The difference between the two methods is that one of them uses only the isotropic component of the BRDF parametric model and the other the full BRDF information, i.e. adding the anisotropic components. The algorithm using isotropic information shows a clear improvement when compared to semi‐empirical approaches. Root mean square error between estimated and ground measured LAI values is 0.87. However, the method using the full BRDF information yielded poorer estimates, pointing out the difficulty of fully exploiting the multi-directional information. The performance decrease is partially explained by the incoherence between real and modelled BRDF measurements.

Atmospheric Correction Inter-Comparison Exercise

The Atmospheric Correction Inter-comparison eXercise (ACIX) is an international initiative with the aim to analyse the Surface Reflectance (SR) products of various state-of-the-art atmospheric correction (AC) processors. The Aerosol Optical Thickness (AOT) and Water Vapour (WV) are also examined in ACIX as additional outputs of an AC processing. In this paper, the general ACIX framework is discussed; special mention is made of the motivation to initiate this challenge, the inter-comparison protocol and the principal results. ACIX is free and open and every developer was welcome to participate. Eventually, 12 participants applied their approaches to various Landsat-8 and Sentinel-2 image datasets acquired over sites around the world. The current results diverge depending on the sensors, products and sites, indicating their strengths and weaknesses. Indeed, this first implementation of processor inter-comparison was proven to be a good lesson for the developers to learn the advantages and limitations of their approaches. Various algorithm improvements are expected, if not already implemented, and the enhanced performances are yet to be investigated in future ACIX experiments.

A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data

The Sentinel missions have been designed to support the operational services of the Copernicus program, ensuring long-term availability of data for a wide range of spectral, spatial and temporal resolutions. In particular, Sentinel-2 (S-2) data with improved high spatial resolution and higher revisit frequency (five days with the pair of satellites in operation) will play a fundamental role in recording land cover types and monitoring land cover changes at regular intervals. Nevertheless, cloud coverage usually hinders the time series availability and consequently the continuous land surface monitoring. In an attempt to alleviate this limitation, the synergistic use of instruments with different features is investigated, aiming at the future synergy of the S-2 MultiSpectral Instrument (MSI) and Sentinel-3 (S-3) Ocean and Land Colour Instrument (OLCI). To that end, an unmixing model is proposed with the intention of integrating the benefits of the two Sentinel missions, when both in orbit, in one composite image. The main goal is to fill the data gaps in the S-2 record, based on the more frequent information of the S-3 time series. The proposed fusion model has been applied on MODIS (MOD09GA L2G) and SPOT4 (Take 5) data and the experimental results have demonstrated that the approach has high potential. However, the different acquisition characteristics of the sensors, i.e. illumination and viewing geometry, should be taken into consideration and bidirectional effects correction has to be performed in order to reduce noise in the reflectance time series.

Copernicus Sentinel-2 mission: products, algorithms and Cal/Val

The Copernicus programme is a European initiative for the implementation of information services dealing with environment and security, based on observation data received from Earth Observation (EO) satellites and ground based information. Within this context, ESA is responsible in particular, for the implementation of the Copernicus Sentinel missions, feeding the Copernicus services with operational EO data. The Sentinel-2 optical high-resolution imaging mission will be devoted to the operational and systematic monitoring of land and coastal areas. To maximize the products suitability and readiness to downstream usage for the majority of applications, the Sentinel-2 Payload Data Ground Segment (PDGS) will systematically generate, archive and distribute Level-1C products, which will provide Top-of-Atmosphere (TOA) reflectance images, orthorectified using a global Digital Elevation Model (DEM) and projected on Universal Transverse Mercator (UTM) coordinate system. A Level-1B product will also be available for expert users, providing radiance images in sensor geometry together with an appended geometric model. Additionally, a complementary atmospheric correction and enhanced cloud screening algorithm is being prototyped. This processor will allow converting the Level-1C TOA reflectance image into Bottom-of-Atmosphere (BOA) reflectance. The processor will be provided as plug-in software of the Sentinel-2 Toolbox that will run on user side. During the operational phase, the Sentinel-2 Mission Performance Centre (MPC), as integrating part of the mission ground segment, will be in charge of ensuring that mission performances are met in terms of data quality through the calibration and validation activities.

A Radiometric Uncertainty Tool for the Sentinel 2 Mission

In the framework of the European Copernicus programme, the European Space Agency (ESA) has launched the Sentinel-2 (S2) Earth Observation (EO) mission which provides optical high spatial resolution imagery over land and coastal areas. As part of this mission, a tool (named S2-RUT, from Sentinel-2 Radiometric Uncertainty Tool) has been developed. The tool estimates the radiometric uncertainty associated with each pixel in the top-of-atmosphere (TOA) reflectance factor images provided by ESA. This paper describes the design and development process of the initial version of the S2-RUT tool. The initial design step describes the S2 radiometric model where a set of uncertainty contributors are identified. Each of the uncertainty contributors is specified by reviewing the pre- and post-launch characterisation. The identified uncertainty contributors are combined following the guidelines in the ‘Guide to Expression of Uncertainty in Measurement’ (GUM) model and this combination model is further validated by comparing the results to a multivariate Monte Carlo Method (MCM). In addition, the correlation between the different uncertainty contributions and the impact of simplifications in the combination model have been studied. The software design of the tool prioritises an efficient strategy to read the TOA reflectance factor images, extract the auxiliary information from the metadata in the satellite products and the codification of the resulting uncertainty image. This initial version of the tool has been implemented and integrated as part of the Sentinels Application Platform (SNAP).

Novel techniques for the analysis of the TOA radiometric uncertainty

In the framework of the European Copernicus programme, the European Space Agency (ESA) has launched the Sentinel-2 (S2) Earth Observation (EO) mission which provides optical high spatial -resolution imagery over land and coastal areas. As part of this mission, a tool (named S2-RUT, from Sentinel-2 Radiometric Uncertainty Tool) estimates the radiometric uncertainties associated to each pixel using as input the top-of-atmosphere (TOA) reflectance factor images provided by ESA. The initial version of the tool has been implemented — code and user guide available1 — and integrated as part of the Sentinel Toolbox. The tool required the study of several radiometric uncertainty sources as well as the calculation and validation of the combined standard uncertainty in order to estimate the TOA reflectance factor uncertainty per pixel. Here we describe the recent research in order to accommodate novel uncertainty contributions to the TOA reflectance uncertainty estimates in future versions of the tool. The two contributions that we explore are the radiometric impact of the spectral knowledge and the uncertainty propagation of the resampling associated to the orthorectification process. The former is produced by the uncertainty associated to the spectral calibration as well as the spectral variations across the instrument focal plane and the instrument degradation. The latter results of the focal plane image propagation into the provided orthoimage. The uncertainty propagation depends on the radiance levels on the pixel neighbourhood and the pixel correlation in the temporal and spatial dimensions. Special effort has been made studying non-stable scenarios and the comparison with different interpolation methods.

Bulk processing of the Landsat MSS/TM/ETM+ archive of the European Space Agency

Landsat is a joint USGS and NASA space program for Earth Observation (EO), which represents the world’s longest running system of satellites for moderate-resolution. The European Space Agency (ESA) has acquired Landsat data over Europe, Northern Africa and the Middle East during the last 40 years. A new ESA Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) processor has been developed. This enhanced processor aligns historical Landsat products to the highest quality standards that can be achieved with the current knowledge of the instruments. The updated processor is mainly based on the USGS algorithm; however it has some different features that are detailed in this paper. Current achievements include the processing and availability of approximately 860,000 new TM/ETM+ high-quality products between 1983 and 2011 from the Kiruna (S), Maspalomas (E) and Matera (I) archives; Matera includes data from the Fucino (I), Neustrelitz (D), O’Higgins (Antarctica), Malindi (Kenya), Libreville (Gabon) and Bishkek (Kyrgyzstan) ground stations. The products are freely available for immediate download to the users through a very fast and simple dissemination service (at: https://landsat-ds.eo.esa.int/app/) and through ESA’s browsing system, EOLI. The remaining MSS data, dating back more than 40 years, will gradually become available during 2015 and 2016. The ESA Landsat processor algorithm enhancement, together with the results of the ESA archive bulk-processing data regarding production, quality control and data validation are herein presented.

Sentinel-2 L1C Radiometric Validation Using Deep Convective Clouds Observations

ABSTRACT In the frame of the European Space Agency (ESA) Scientific Exploitation of Operational Missions (SEOM) program new algorithms are developed to validate the Sentinel-2 level 1C (L1C) product radiometry, beyond the baseline algorithms used operationally in the frame of the Sentinel-2 Mission Performance Centre (MPC). In this context this paper presents the implementation of a Sentinel-2 radiometric validation approach based on deep convective cloud (DCC) observations. Due to their physical properties DCCs can be used to monitor the radiometric response degradation of the reflective solar bands of optical sensors. Their observation allows interband radiometry validation in the visible-near infrared (VIS-NIR) domain relatively to an a priori well calibrated reference band. We first present the selection of Sentinel-2 data acquired over DCC targets, as well as the tools and assumptions used for the modeling of the theoretical DCC radiometric response. The validation methodology is then thoroughly described and justified. It is based on the comparisons between the observed and the simulated top-of-atmosphere reflectance spectrum. Interband radiometric validation is performed through the statistical analysis of a large collection of individual observations. Results show the very good radiometric performance of Sentinel-2 with interband gains lower than 2%.