A new generation of satellites designed for low-uncertainty, SI-traceable measurements - termed ”SITSats” - marks a major advancement in Earth Observation (EO) capability. These missions aim to enhance the performance and interoperability of the EO “system of systems”. Among them, the ESA Earth Watch TRUTHS mission is designed to serve as a ”gold-standard” radiometric reference for cross-calibrating EO sensors in the solar reflective domain. In this work, uncertainties in cross-calibration comparisons arising from sensor characterisation and design are investigated. A processing chain to prepare collocated data for uncertainty-quantified comparison is presented. This includes steps to perform spectral band adjustment and spatial resampling. Using the Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS) Hyperspectral Imaging Spectrometer (HIS) as the reference and Sentinel-2 MultiSpectral Imager (MSI) as the target, a simulation study based on high-resolution imagery assesses achievable comparison performance. A subset of uncertainty effects driven by sensor characterisation is propagated through the spectral and spatial processing using a Monte Carlo approach. Sentinel-2 data are assumed at 10 m resolution, which is most sensitive to the errors considered. The results highlight the importance of sensor characterisation, particularly inherent in-flight wavelength knowledge for target sensors, in such comparisons. Results from the simulation analysis give uncertainty estimates (k=1) of 0.31 % (blue), 0.50 % (green), and 0.23 % (red) for the combined error effects arising from sensor characterisation and geolocation uncertainty for comparisons over the Libya-4 desert Pseudo Invariant Calibration Sites (PICS) using an instantaneous 205 m square comparison region. Results for more heterogeneous scenes, such as rainforest, still achieve uncertainties of 0.6-1.2 % for the red-green-blue (RGB) bands over a 200 m×200 m area. The uncertainty is driven largely by the spectral component-up to 1 % due to the inherent Sentinel-2 wavelength knowledge of 1 nm across various representative scenes outside of the atmospheric absorption bands. While the impact of these uncertainties may decrease when considering a diverse range of scene types, they introduce systematic errors when scenes share similar spectral characteristics. The impact of some uncertainty contributions, e.g., geolocation uncertainty, is shown to substantially reduce by aggregating samples over larger regions or over longer time periods. This analysis supports the development of low-uncertainty, ideally SITSat-enabled intercalibration approaches needed to ensure radiometric consistency across missions for generating long-term climate data records.

Atmospheric correction (AC) algorithms for ocean color (OC) data processing usually rely on ancillary data documenting the atmosphere and the sea state to help the calculation of the remote sensing reflectance$R_{\text {RS}}$from the radiance measured by a space sensor. This study aims at assessing the impact that the uncertainties associated with these ancillary data have on the AC outputs. For this objective, a full year of global Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery is processed with the standard AC algorithml2genof the National Aeronautics and Space Administration with different sets of ancillary data, the reference case with National Centers for Environmental Prediction (NCEP) Reanalysis-2 meteorological data and satellite ozone products, as well as with ten ensemble members from the European Centre for Medium-Range Weather Forecast (ECMWF) CERA-20C data. The spread within the ensemble data and the differences with respect to the reference case are taken as a measure of the uncertainties associated with ancillary data. The impact on$R_{\text {RS}}$of perturbations in ancillary variables vary in space, the variables having the largest effects being wind speed and relative humidity, and ozone at bands where ozone absorption is largest, while sea-level pressure and precipitable water have the smallest effect. Sensitivity coefficients quantifying the relationship between perturbations in ancillary variables and effects on$R_{\text {RS}}$change with variable and wavelength. At the global scale, the variations found on$R_{\text {RS}}$when ancillary data are perturbed are usually small but not negligible and should be considered in the ocean color (OC) data uncertainty budget.

The Sentinel-3 mission, part of the European Union's Copernicus program, provides wide swath data for marine and land applications. One of its key payloads is OLCI (Ocean and Land Colour Instrument), a push broom spectrometer with a 1270 km swath width, producing multichannel images in the visible to near infrared. In this work, analysis of the OLCI L1b product (top-of-atmosphere radiance) is carried out to aid the evaluation of the radiometric uncertainty, following the framework outlined in the FIDUCEO project. The measurement function used to determine the radiance is defined, and a corresponding uncertainty tree diagram produced. From these, error effects are identified and described using effects tables, which document the associated uncertainty, sensitivity coefficients and correlation structure when that information is known. Recommendations are made to aid the development and provision of the L1b product uncertainties.

The HYPERNETS land and water networks are a set of autonomous field sites for the measurement of fiducial reference measurements of hyperspectral surface reflectance for a wide range of surface types for use in satellite Earth observation validation. In order to generate the reflectance products a software ground processor, called the hypernets_processor, is required for automated processing of the acquisitions through data transmission and conversion, application of calibration, evaluation of reflectance and other variables and archiving for web distribution. Furthermore, to achieve fiducial reference measurement quality, measurement uncertainty is propagated through the full processing chain, including treatment of temporal and wavelength error-covariance, a level of detail unique for any such satellite validation network. The processor is now operationally running for a series of test network sites.

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.

Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper we have for the first time analysed the uncertainties in the atmospheric correction for OC arising from the uncertainties in the ancillary data (such as wind speed and column densities of gases and aerosols). The spread in the ERA5 ensemble is used as estimate for the uncertainty in the ancillary parameters, which is then propagated to uncertainties in remote sensing reflectances using the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where first we illustrate the complete uncertainty budget using an uncertainty tree diagram. The uncertainties from the ancillary data are then propagated using a Monte Carlo approach. Wind speed and relative humidity are found to be the main contributors (among the ancillary parameters) to the remote sensing reflectance uncertainties. In total, the ancillary data add about 1 % uncertainty at 412nm, increasing to 2% for 555nm with significant variability as a function of observation conditions.

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 (potentially >108match-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.