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.

Continuous validation of the accuracy of surface reflectance products derived from satellite sensors is vital to understanding their performance over time and detecting any variation which could influence the quality of downstream products reliant on the data.In this study, we present an update on the validation of surface reflectance products derived from the Sentinel 2 and Landsat 8/9 missions over vegetated sites using LANDHYPERNET surface reflectance data. We show that there is a good agreement between the datasets, with it being possible to achieve less than 2% bias in certain bands. Additionally, we outline a method for enabling conformity testing of the satellite products using the satellite mission requirements using the uncertainties propagated from the LANDHYPERNET and satellite data products. The presented results show the potential of these sites for the validation of surface reflectance products over dynamic sites.

Traceable surface-based radiometric data for use in vicarious calibration and validation of satellites is necessary to continually check the performance of sensors through their mission lifetimes. RadCalNet is an established network providing traceable bottom-of-atmosphere (BOA) and top-of-atmosphere (TOA) reflectance data for the land calibration and validation community. A new prototype hyperspectral sensor, the HYperspectral Pointable System for Terrestrial and Aquatic Radiometry-Extended Range (HYPSTAR®-XR), has recently been installed in the vicinity of one of the established RadCalNet sites in Gobabeb, Namibia (GONA), providing hyperspectral surface reflectance data, alongside the incumbent band sensors, and allowing comparison of the instrument products. BOA data from both instruments has been compared over a six-month period and displays initial difference to within 5% in the visible and near infrared (VNIR), as well as 1%-2.5% agreement in the short-wave infrared (SWIR). These results are promising and prove that the measurements from GONA and the new instrument agree within their associated combined uncertainties.

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 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.

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.