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Recent availability of low-cost small satellites and innovation of constellations have resulted in an increasing number of commercial companies having established information services fed by their own satellite systems. ESA aims to foster cooperation and collaboration with not only other national agencies, but also these New Space players, inviting some missions as potential candidates for ESA's long standing Earthnet Programme. The Earthnet Data Assessment Pilot (EDAP), headed by Telespazio UK, provides a consortium of expertise to assess data quality and suitability of individual missions for this purpose. The service framework has developed a common quality assessment methodology adapted for instrument-specific domains covering Optical, Synthetic Aperture Radar (SAR) and Atmospheric missions, with emphasis on multi-mission studies. This paper describes the EDAP framework and provides a summary of specific tasks performed by the service to assist the ESA Earthnet programme in supporting various commercial mission providers in preparation for future EO missions.

Presented is a new quality assurance (QA) framework for Earth Observation missions that has been developed as a joint initiative between ESA and NASA. It aims ensure the rigorous assessment of all aspects of relevant aspects of mission quality, verifying claimed mission performance and, where applicable, reviewing the extent to which the mission follows community best practice in a manner that is “fit for purpose”. The QA framework has potential for more general use in both institutional and commercial Earth Observation – helping mission providers to understand the information their users' needs and empowering users to make informed decisions about which data is fit for their purpose.

During its commissioning phase, the Copernicus Sentinel-3B satellite has been placed in a tandem formation with Sentinel-3A for a period of 6 months. This configuration allowed a direct comparison of measurements obtained by the two satellites. The purpose of this paper was to present the range of analyses that can be performed from this dataset, highlighting methodology aspects and the main outcomes for each instrument. We examined, in turn, the benefit of the tandem in understanding instrument operational modes differences, in assessing inter-satellite differences, and in validating measurement uncertainties. The results highlighted the very good consistency of the Sentinel-3A and B instruments, ensuring the complete inter-operability of the constellation. Tandem comparisons also pave the way for further improvements through harmonization of the sensors (OLCI), correction of internal stray-light sources (SLSTR), or high-frequency processing of SRAL SARM data. This paper provided a comprehensive overview of the main results obtained, as well as insights into some of the results. Finally, we drew the main lessons learned from the Sentinel-3 tandem phase and provided recommendations for future missions.

In recent years, the increasing range of applications of Earth Observation data products and availability of low-cost satellites has resulted in an increasing number of commercial satellite systems. These services may provide complementary capabilities to those of Space Agencies. Adoption of these data products for many applications requires that they meet an assured level of quality that is fit for the given purpose. For the most efficient exploitation of EO data, therefore, assessment of data quality, calibration and validation are indispensable tasks, forming the basis for reliable scientific conclusions. In this context, the European Space Agency has established the Earthnet Data Assessment Pilot (EDAP) project, which aims to enable maximum exploitation of growing data availability by performing early data assessment for various missions that fall into one of the following instrument domains number of missions, in the Optical, SAR and atmospheric domains. These assessments are intended to evaluate and report the quality of a satellite mission with respect to what is “fit for purpose” within the context of the its stated performance and application. This activity compliments similar activities from other international partners, including NASA. Such quality information is often communicated to users in an ill-defined or incomplete manner. We show the development of a generic satellite mission quality assessment framework, developed within EDAP, which is designed provide a thorough review of all important aspects of mission quality. The assessment results are conveye d ata top level to the user as a quality assessment matrix diagram. The framework itself is based on the principles of CEOS QA4EO (Quality Assurance for Earth Observation) and builds on the experience of several European projects that worked towards practically implementing them. In a wider context, such a framework has potential for more general use in both institutional and commercial Earth Observation – helping mission providers to understand the information their users need and empowering users to make informed decisions about which data is fit for their purpose. As such, there is potential for international collaboration, between space agencies, to synergise quality assessment approaches and to work towards the development of a common standard.

Providing uncertainties in satellite datasets used for Earth observation can be a daunting prospect because of the many processing stages and input data required to convert raw detector counts to calibrated radiances. The Sea and Land Surface Temperature Radiometer (SLSTR) was designed to provide measurements of the Earth’s surface for operational and climate applications. In this paper the authors describe the traceability chain and derivation of uncertainty estimates for the thermal infrared channel radiometry. Starting from the instrument model, the contributing input quantities are identified to build up an uncertainty effects tree. The characterisation of each input effect is described, and uncertainty estimates provided which are used to derive the combined uncertainties as a function of scene temperature. The SLSTR Level-1 data products provide uncertainty estimates for fully random effects (noise) and systematic effects that can be mapped for each image pixel, examples of which are shown.

As part of the Sentinel-2 mission, a Radiometric Uncertainty Tool (RUT) has been recently released to the community. This tool estimates the Sentinel-2 radiometric uncertainty associated with each pixel in the top-of-atmosphere (TOA) reflectance factor images provided by the European Space Agency (ESA). The use of such information enables users to assess the “fitness for purpose” of the data to their specific application. The work described here summarises the efforts and results of integrating the RUT for radiometric validation activities for the Sentinel-2 mission. Starting from the results provided by the RUT, the focus will be on providing a methodology to calculate the uncertainty associated with the mean TOA reflectance factor in a Region of Interest (ROI). Two different methods – one simple method directly using the RUT and a more rigorous one based on Monte Carlo method (MCM) propagation – are proposed and compared. These two methods focus on the effect of the spectral, spatial and temporal correlation of the errors in different ROI pixels and the impact of correlation on the uncertainty associated with the mean TOA reflectance factor. The study has also considered the impact of uncertainty contributions not included in the first version of the RUT.

For over 40 years ESA’s Earthnet Programme has played a significant role as part of ESA’s mandatory activities, being a major contributor to the Global Earth Observation System of Systems (GEOSS). This role involved providing the framework for integrating non-ESA missions, i.e. Third Party Missions (TPM), into the overall ESA Earth Observation (EO) strategy. Complementary to ESA-owned EO missions, the programme allows European users access to a large portfolio of TPM and is particularly important for promoting the international use of EO data. In line with the Earthnet Programme objectives first established in 1977, ESA aims to foster cooperation and collaboration with not only other national space agencies, but also commercial mission providers. In recent years the availability of low cost small satellites and the innovation of constellations resulted in an increased number of commercial companies who have established business models to provide information services fed by their own satellite systems. These New Space players are now portraying an important role in the EO international strategy. Some of these new missions are potential candidates for Earthnet TPMs, and ESA have therefore set up a project to assess the quality and the suitability of these missions and also to establish dialogues with the various mission providers in order to improve the overall coherence of the EO system. This project is known as the Earthnet Data Assessment Pilot (EDAP). The EDAP consortium, headed by Telespazio VEGA UK is aimed at the provision of various clusters of expertise to perform an early data quality assessment of existing or future EO missions from national or commercial providers, which may potentially become TPMs within ESA’s Earthnet Programme. Complementary to this support is a focus on the generation of methodologies and guidelines for training and capacity building with each mission provider in regards to performing efficient data quality assessments in preparation for future missions. This work presents how the EDAP activities are organized and executed, and will also provide details of the various missions included within each of the instrument-specific domains covering Optical Sensors, Synthetic Aperture Radar (SAR) and Atmospheric missions. Important multi-mission aspects will also be presented for studies that will require inputs from several missions, possibly spanning multiple instrument domains; such studies contribute to interoperability across existing and future missions and help foster synergies between these missions.

Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. Since any satellite operates over a limited period of time only, creating a climate data record requires the combination of space-borne measurements from a series of several (often similar) satellite sensors. A simple combination of calibrated measurements from several sensors, however, can produce an inconsistent climate data record. This is particularly true of older, historic sensors whose behavior in space was often different from their behavior during pre-launch calibration in the laboratory. More scientific value can be derived from considering the series of historical and present satellites as a whole. Here we consider harmonisation as a process that obtains new calibration coefficients for revised sensor calibration models by comparing calibrated measurements over appropriate satellite-to-satellite match-ups, such as simultaneous nadir overpasses. When we perform a comparison of two sensors, however, we must consider that those sensors are not observing exactly the same Earth radiance. This is in part due to differences in exact location and time tolerated by the match-up process itself, but also due to differences in the spectral response functions of the two instruments, even when nominally observing the same spectral band. To derive a harmonised data set we do not aim to correct for spectral response function differences, but to reconcile the calibration of different sensors given their estimated spectral response function differences. Here we present the concept of a framework that establishes calibration coefficients and their uncertainty and error covariance for an arbitrary number of sensors in a metrologically-rigorous manner. We describe harmonisation and its mathematical formulation as an inverse problem. Solving this problem is challenging when some hundreds of millions of match-ups are involved and the errors of fundamental sensor measurements are correlated. We solve the harmonisation problem as marginalised errors in variables regression. The algorithm involves computation of first and second order partial derivatives, for which the corresponding computer source code is generated by Automatic Differentiation. Finally we present re-calibrated AVHRR radiances from a series of 10 sensors. It is shown that the new time series have much less match-up differences while being consistent with uncertainty statistics.

Meaningful study and quantification of climatic trends requires decades of observational data. Within satellite remote sensing this motivates the generation fundamental climate data records (FCDRs) - datasets containing the combined observations from a series of missions of a given sensor, to span the required duration for study. Ensuring the radiometric stability of such series is vital for such applications. To this end a consistent in-flight retrospective recalibration of all the sensors in such a series is required, called harmonisation. Presented here is a methodology for achieving this by analysing match-ups between sensors in the series and applicable sensors with modern, well-calibrated reference sensors, fitting new calibration parameters for each sensor in the series. Such a problem is not tractable in the most optimal, metrologically rigorous manner by existing solvers, due to the possibly complex error and geophysical correlation structures and high data volume ( $\mathrm{potentially} > 10^{8}$ match-ups). Discussed are a palette of novel optimisation algorithms developed to overcome this, which investigate alternative approaches to handling the full problem. Preliminary results are shown for one these approaches, Fast EIV (Errors in Variables), for the recalibration of the AVHRR sensor series.

The Radiometric Calibration Network (RadCalNet, www.radcalnet.org) routinely provides top-of-atmosphere (TOA) reflectance data from instrumented ground sites. The data represents the nadir view of the ground for different sites that cover areas ranging from 50 m × 50 m to 1 km x 1 km. The smaller sites can only be used with high resolution sensors (≤ 30 m), but the larger sites, such as Railroad Valley (RRV) in Nevada can also be used for the validation or vicarious calibration of medium resolution sensors (> 250 m spatial resolution). Prior to utilising RadCalNet data in this manner, this paper describes the application of a high and a medium resolution sensor to assess potential biases between the RadCalNet data and satellite data at two different spatial resolutions. Results are shown for initial comparisons over RRV for the high resolution Sentinel-2 MultiSpectral Instrument (S2-MSI) and the medium resolution Sentinel-3 Ocean and Land Colour Instrument (S3-OLCI), and indicate the potential for RadCalNet to validate and vicariously calibrate sensors with differing spatial resolutions. The comparison analysis includes taking into account the temporal differences between the Sentinel-2 and Sentinel-3 overpasses and the time of RadCalNet data collection, as well as the spectral response functions (SRF) of the bands for both instruments. The comparison against the RRV site has shown there are significant biases between the RadCalNet data and S2-MSI and S3-OLCI for non-nadir viewing geometries that may be due to directional viewing and illumination effects and the non-Lambertian character of the RadCalNet RRV site.

Quantifying long-term environmental variability, including climatic trends, requires decadal-scale time series of observations. The reliability of such trend analysis depends on the long-term stability of the data record, and understanding the sources of uncertainty in historic, current and future sensors. We give a brief overview on how metrological techniques can be applied to historical satellite data sets. In particular we discuss the implications of error correlation at different spatial and temporal scales and the forms of such correlation and consider how uncertainty is propagated with partial correlation. We give a form of the Law of Propagation of Uncertainties that considers the propagation of uncertainties associated with common errors to give the covariance associated with Earth observations in different spectral channels.