To evaluate the radiometric performance of top-of-atmosphere reflectance images, Deep Convective Clouds (DCCs) can be used as temporally, spatially and spectrally stable targets. The DCCs method has been developed more than 20 years ago and applied recently to Sentinel-2 and Sentinel-3 sensors. In this paper, among other developments, we built a new methodology upon those existing by using the bootstrap method and spectral band adjustment factors computed with the Hyper-Spectral Imager (HSI) from the Environmental Mapping and Analysis Program (EnMAP). This methodology is applied to the two Multi-Spectral Imager (MSI) instruments onboard Sentinel-2A and 2B, but also the two Operational Land Imager (OLI) instruments onboard Landsat 8 and 9, from visible wavelength at 442 nm to shortwave-infrared at 2200 nm, using images with a ground resolution spanning from 10 m to 60 m. The results demonstrate the good inter-calibration of MSI units A and B, which are within one percent of relative difference on average between January 2022 and June 2024 for all visible, near-infrared and shortwave-infrared bands, except for the band at 1375 nm for which saturation prevents the use of the method. Similarly, OLI and OLI-2 are found to have a relative difference on the same period lower than one percent for all 30 m resolution bands. Evaluation of the relative difference between the MSI sensors and the OLI sensors with the DCCs method gives values lower than three percent. Finally, these validation results are compared to those obtained with Pseudo-Invariant Calibration Sites (PICSs) over Libya-4: an agreement better than two percent is found between the DCCs and PICSs methods.

Earth Observation (EO) systems are generating an ever-increasing amount of data to be handled on board yet with limited resources, which sometimes hinders a full exploitation of the information content. In this paper, we present a demonstrator of a super-resolved compressive imager operating in whiskbroom mode in the Visible-Near Infrared (VISNIR) and Medium Infrared (MIR) spectral ranges. The demonstrator, which is under development in the frame of the EU H2020 funded SURPRISE project, is based on the use of a Digital MicroMirror Device (DMD) as a core element of its architecture and it is inspired by a single-pixel camera in order to avoid the use of large focal plane arrays. The demonstrator has 10 channels in the VNIR and two channels in the MIR and it can reach a super-resolution factor from 4 x 4 to 32 x 32, that is the ratio between the number of pixels of the image reconstructed at the end of the process and the number of pixels of the detector. Besides, on the grounds of the results obtained by image reconstruction tests on simulated datasets by using Deep Learning based algorithms, data are expected to be natively compressed with a Compression Ratio up to 50%. The study is expected to provide valuable insight for the future development of a novel class of EO instruments with improved performances in terms of ground sampling distance, native compression and on-board processing capabilities. Additional presentation content can be accessed on the supplemental content page.

Earth observation (EO) payload performances in the infrared spectral region from geostationary platforms are often limited by spatial resolution. In this paper, we investigate an instrumental concept leveraging a compressive sensing paradigm and super-resolution architecture to implement an EO payload from a geostationary platform aimed at the monitoring of wildfires with a nominal spatial sampling distance of 500 m. The core device of the instrument is a European-technology-based micromirror array under study for space applications. Besides payload specifications and working principles, the main critical aspects and the expected impact on EO applications are discussed.

The need of high-resolution Earth Observation (EO) images for scientific and commercial exploitation has led to the generation of an increasing amount of data with a material impact on the resources needed to handle data on board of satellites. In this respect, Compressive Sensing (CS) can offer interesting features in terms of native compression, onboard processing and instrumental architecture. In CS instruments the data are acquired natively compressed by leveraging on the concept of sparsity, while on-board processing is offered at low computational cost by information extraction directly from CS data. In addition, instrument's architecture can enjoy super-resolution capabilities that ensure a higher number of pixels in the reconstructed image with respect to that natively provided by the detector. In this paper, we present the working principle and main features of a CS demonstrator of a super-resolved instrument for EO applications with ten channels in the visible and two channels in the medium infrared. Besides the feature of merging in a single step the acquisition and compression phases of the image generation, its architecture allows to reach a super-resolution factor of at least 4x4 in the images reconstructed at the end of process. The outcome of the research can open the way to the development of a novel class of EO instruments with improved Ground Sampling Distance (GSD) - with respect to that one provided natively by the number of sensing elements of the detector - and impact EO applications thanks to native compression, on-board processing capabilities and increased GSD.

The anisotropy of land surfaces reflectance of greatly impacts the acquisition and analysis of satellite images. Normalization methods using semi empirical kernel-driven models commonly remove these directional effects. Here, we present a physically-based approach to assess the accuracy of 2 normalization methods of Sentinel-2 (S2) and 3 (S3) images. A ReBeLS-based process fits the parameters of a kernel-driven model with S3 and S2 images simulated by the DART RT model in agricultural and urban areas. Here, normalization methods (c-factor, NDVI-disaggregation) hardly validate their assumptions: surface type and orientation have a great influence. We show the potential of RT models to assess and improve normalization methods.

Due to a growing interest for imagery with high spatial and spectral resolution, Earth Observation sensors are producing increasing amounts of data. This poses a severe challenge in terms of computational, memory and transmission requirements. In order to overcome these limitations, a fascinating approach is the implementation of a compressive sensing architecture. In this paper, we present an instrumental concept based on the use of a spatial light modulator to implement a super-resolved, compressive demonstrator of an instrument aimed at Earth Observation in the visible and medium infrared spectral regions from geostationary platform.

Increased spatial resolution and revisit time of payloads operating in the infrared spectral region can offer unprecedented advantages to Earth Observation. This, however, poses several technological challenges, such as large array detector availability and data bandwidth. In this paper, we present a super-resolved demonstrator—based on a compressive sensing architecture—which is being developed to address enhanced performance in terms of at-ground spatial resolution, on-board data processing and encryption functionalities for Earth Observation payloads. The demonstrator’s architecture is here presented, together with its working principle, main features and the approach used for image reconstruction.

This study is a follow-up of a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instrument (OLCI) payloads based on the OLCI-A/OLCI-B tandem phase analysis. This analysis provided cross-calibration factors between the two instruments with a very high precision, providing a ‘truth’ from the direct comparison of simultaneous and collocated acquisitions. The long-term monitoring of such cross-calibration is a prerequisite for an operational application of sensors harmonisation along the mission lifetime, no other tandem phase between OLCI-A and OLCI-B being foreseen due to the cost of such operation. This article presents a novel approach for the monitoring of the OLCI radiometry based on statistics of Deep Convective Clouds (DCC) observations, especially dedicated to accurately monitor the full across-track dependency of the cross-calibration of OLCI-A and OLCI-B. Specifically, the inflexion point of DCC reflectance distributions is used as an indicator of the absolute calibration for each subdivision of the OLCI Field-of-View. This inflexion point is shown to provide better precision than the mode of the distributions which is commonly used in the community. Excess of saturation in OLCI-A high radiances is handled through the analysis of interband relationships between impacted channels and reference channels that are not impacted by saturation. Such analysis also provides efficient insights on the variability of the target’s response as well as on the evolution of the interband calibration of each payload. First, cross-calibration factors obtained over the tandem period allows to develop and validate the approach, notably for the handling of the saturated pixels, based on the comparison with the ‘truth’ obtained from the tandem analysis. Factors obtained out of (and far from) the tandem period then provides evidence that the cross-calibration reported over the tandem period (1–2% bias between the instruments) as well as inter-camera calibration residuals persist with very similar proportions, to the exception of the 400 nm channel and with slightly less precision for the 1020 nm channel. For all OLCI channels, relative differences between the cross-calibration factors obtained from the tandem analysis and the factors obtained over the other period are below 1% from a monthly analysis, even below 0.5% from a multi-monthly analysis). This opens the way not only to an accurate long-term monitoring of the OLCI radiometry but also, and precisely targeted for this study, to the monitoring of the cross-calibration of the two sensors over the mission lifetime. It also provides complementary information to the tandem analysis as the calibration indicators are traced individually for each sensor across-track, confirming and quantifying inter-camera radiometric biases, independently for both sensors. Assumptions used in this study are discussed and validated, also providing a framework for the adaptation of the presented methodology to other optical sensors.

The Sentinel-3 tandem project represents the first time that two ocean colour satellites have been flown in the same orbit with minimal temporal separation (~30 s), thus allowing them to have virtually identical views of the ocean. This offers an opportunity for understanding how differences in individual sensor uncertainty can affect conclusions drawn from the data. Here, we specifically focus on trend estimation. Observational chlorophyll-a uncertainty is assessed from the Sentinel-3A Ocean and Land Colour Imager (OLCI-A) and Sentinel-3B OLCI (OLCI-B) sensors using a bootstrapping approach. Realistic trends are then imposed on a synthetic chlorophyll-a time series to understand how sensor uncertainty could affect potential long-term trends in Sentinel-3 OLCI data. We find that OLCI-A and OLCI-B both show very similar trends, with the OLCI-B trend estimates tending to have a slightly wider distribution, although not statistically different from the OLCI-A distribution. The spatial pattern of trend estimates is also assessed, showing that the probability distributions of trend estimates in OLCI-A and OLCI-B are most similar in open ocean regions, and least similar in coastal regions and at high northern latitudes. This analysis shows that the two sensors should provide consistent trends between the two satellites, provided future ageing is well quantified and mitigated. The Sentinel-3 programme offers a strong baseline for estimating long-term chlorophyll-a trends by offering a series of satellites (starting with Sentinel-3A and Sentinel-3B) that use the same sensor design, reducing potential issues with cross-calibration between sensors. This analysis contributes an important understanding of the reliability of the two current Sentinel-3 OLCI sensors for future studies of climate change driven chlorophyll-a trends.

This study is a follow-up of a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instrument (OLCI) payloads based on the tandem phase analysis. Sentinel-3B was manoeuvred into a tandem configuration with its operational twin Sentinel-3A already in orbit few weeks after its launch, which was followed by a short drift phase during which Sentinel-3B was progressively moved to a specific orbit phasing of 140° separation from the sentinel-3A. Harmonisation is performed at Level 1 for the radiometric alignment of the OLCI-A TOA radiances to the ones of OLCI-B, considering the slight spectral differences between the two instruments. The benefits of this harmonisation for the main Level 2 products are assessed in the present manuscript for both land and water products. The results validate such benefits showing accuracy between the two sensors after harmonisation better than the products requirements specifications. For the water processing branch, this accuracy opens a path toward an ensemble Sentinel-3 system vicarious calibration with ground-truth measurements. For land products, the tandem phase analysis is also an opportunity to demonstrate that the terrestrial chlorophyll index product requires improvements of the preliminary spectral adjustment of the red-edge channel at 709 nm. As comparisons from the measurements acquired over the tandem phase provides confidence in the alignment of the OLCI-A and OLCI-B series of products, preliminary analysis of the measurements acquired over the drift phase provides the first insights into the sensitivity of the processing algorithms to the geometry of acquisition as well as to calibration residuals of the OLCI field-of-view. As the harmonisation currently performs a radiometric alignment of OLCI-A to OLCI-B, the question of the reference sensor for operational implementation of the harmonisation raises concerns on the individual quality of the calibration of each sensor, notably their across-track consistency. Following the investigations performed at Level 1, where relatively strong calibration residuals are shown between the OLCI cameras and very similarly for both instruments; we discuss the impact of these residuals at L2 using an empirical correction and further conclude with the need to address these problematics with more attention in the future. We conclude with the extreme usefulness of the tandem phase analysis, presently for Level 2 products, and the need to further monitor the temporal stability of the cross-calibration of the OLCI payloads with a view to implementing their harmonisation at operational level.

Copernicus is a European system for monitoring the Earth in support of European policy. It includes the Sentinel-3 satellite mission which provides reliable and up-to-date measurements of the ocean, atmosphere, cryosphere, and land. To fulfil mission requirements, two Sentinel-3 satellites are required on-orbit at the same time to meet revisit and coverage requirements in support of Copernicus Services. The inter-unit consistency is critical for the mission as more S3 platforms are planned in the future. A few weeks after its launch in April 2018, the Sentinel-3B satellite was manoeuvred into a tandem configuration with its operational twin Sentinel-3A already in orbit. Both satellites were flown only thirty seconds apart on the same orbit ground track to optimise cross-comparisons. This tandem phase lasted from early June to mid October 2018 and was followed by a short drift phase during which the Sentinel-3B satellite was progressively moved to a specific orbit phasing of 140° separation from the sentinel-3A satellite. In this paper, an output of the European Space Agency (ESA) Sentinel-3 Tandem for Climate study (S3TC), we provide a full methodology for the homogenisation and harmonisation of the two Ocean and Land Colour Instruments (OLCI) based on the tandem phase. Homogenisation adjusts for unavoidable slight spatial and spectral differences between the two sensors and provide a basis for the comparison of the radiometry. Persistent radiometric biases of 1–2% across the OLCI spectrum are found with very high confidence. Harmonisation then consists of adjusting one instrument on the other based on these findings. Validation of the approach shows that such harmonisation then procures an excellent radiometric alignment. Performed on L1 calibrated radiances, the benefits of harmonisation are fully appreciated on Level 2 products as reported in a companion paper. Whereas our methodology aligns one sensor to behave radiometrically as the other, discussions consider the choice of the reference to be used within the operational framework. Further exploitation of the measurements indeed provides evidence of the need to perform flat-fielding on both payloads, prior to any harmonisation. Such flat-fielding notably removes inter-camera differences in the harmonisation coefficients. We conclude on the extreme usefulness of performing a tandem phase for the OLCI mission continuity as well as for any optical mission to which the methodology presented in this paper applies (e.g., Sentinel-2). To maintain the climate record, it is highly recommended that the future Sentinel-3C and Sentinel-3D satellites perform tandem flights when injected into the Sentinel-3 time series.

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.

Copernicus is the European Union's Earth Observation and Monitoring programme, delivering free access to operational and historical environmental data to support applications in a wide range of societal benefit areas. To allow meaningful long-term environmental monitoring and robust decision-making, it is essential to ensure that satellite-retrieved products are of high quality and consistency. This paper describes the outputs of an international workshop on the radiometric calibration validation of the Copernicus Sentinel-2A and Sentinel-2B Multi-Spectral Instrument. A wide range of vicarious methodologies have been applied independently and then compared per type of target. All methods agree on the good radiometric performance of both Sentinel-2A and Sentinel-2B with respect to the mission requirements as well as on evidence of a slight bias between the two instruments. Comparisons of all these results are discussed to highlight the benefits and advantages of the methods as well as to propose potential improvements either for the methods themselves and/or for the comparison exercise.

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.

The Sentinel-3 SYNERGY (SYN) processing branch has been designed as a combination of the acquisition of the two optical instruments, the Ocean and Land Color Instrument (OLCI) and the Sea and Land Surface Temperature Radiometer (SLSTR), with the main objective to provide surface vegetation products similar to those obtained from the VEGETATION instrument on SPOT. Synergistic and collocated measurements from both OLCI and SLSTR are ingested in an aerosol retrieval module. Then for each 300m pixel, Aerosol optical thickness data and Surface directional reflectances are retrieved and outputted in S3 SYNERGY L2 products. A description of the whole processing chain from OLCI/SLSTR L1 products to SYN L2 ones will be provided followed by a detailed description of the SYN L2 products. Quality assessment of these products, performed by the S3 MPC consortium and using in situ comparisons with AERONET data and with PROBA-V data will be discussed. Finally, a third section will be dedicated to the development of a future global aerosol products and the possible evolutions of this SYNERGY processing branch. More details about the whole S3 algorithms and products can be found in the Sentinel 3 Handbook [1]

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%.