Global observations at 30-m ground sampling distance (GSD) are now possible at a cadence of one-three days by combining Landsat 8 and 9 with Sentinel-2A and -2B satellites. Previous studies characterizing pixel-level Landsat-class measurement frequency used data from different sources but offered little information on observation availability after rigorous quality screening. This study examined the coverage frequency of Harmonized Landsat and Sentinel-2 (HLS) V2.0 data for 2022, the first year all four satellites data were available. These data have had quality control filtering and harmonization, and therefore reflect the spatial-temporal distribution of usable observations. On average, HLS data provide observations every 1.6 days at the global scale, and 2.2 days in the data-scarce tropical regions, regardless of cloud cover. The global mean and median cloud-free observations were 69 and 64, respectively. The frequency of good-quality observations varies geographically and seasonally due to changes in satellite swath overlap, cloud frequency, and solar illumination. High latitudes ($\gt \sim 75^{\circ }$N) exhibit the highest number of cloud-free observations between March and September. However, data are unavailable during winter months due to low solar elevation angles and boreal regions have a lower number of clear observations in the summer months. The tropical regions have the lowest number of clear observations. More frequent HLS observations could improve terrestrial monitoring. We mapped the monthly and weekly number of clear observations globally to show where HLS data could support monthly or subweekly time series applications.

The Copernicus Sentinel-2 (S2) satellite mission acquires high spatial resolution optical imagery over land and coastal areas. Delivering uncertainty estimates and spectral error correlation alongside S2 data products facilitates the constrain of retrieval algorithms, propagates further downstream the retrieval uncertainty, and, finally, makes informed decisions to end-users. This study presents a framework to produce uncertainty estimates and spectral error correlation associated with the S2 L2A data products (i.e., surface reflectance). This framework has been implemented in a prototype code available athttps://doi.org/10.5281/zenodo.11971517. The uncertainty considers both the Level-1 (L1) uncertainty estimates for the top-of-atmosphere (TOA) reflectance factor and the atmospheric correction. The L2A error distribution cannot be systematically described as a normal distribution; the transformation can be nonlinear and without an explicit mathematical model. Thus, a multivariate Monte Carlo model (MCM) rather than the law of propagation of uncertainty (LPU) is selected for uncertainty propagation. We show results for surface reflectance uncertainty over the Amazon forest and Libya4 desert site. It illustrates the large uncertainty and spectral error correlation variations depending on the scene. The comparison of a multivariate MCM against an LPU propagation methodology indicates the limitations of the latter for scenes dominated by the atmospheric path. Its implementation as an operational per-pixel processing and dissemination of both the uncertainty and spectral error correlation becomes challenging. Therefore, this methodology is not expected to run at an operational level but serves as the basis to define a strategy for an operational one.

The Copernicus Sentinel-2 satellite mission, with its Sentinel-2A and Sentinel-2B units, offers since several years now a massive quantitative and qualitative resource for the Earth Observation community. Since the launch of Sentinel-2A in 2015, and Sentinel-2B in 2017, many lessons have been learnt leading to continuous improvements of the radiometric and the geometric performances. However, the current archive is composed of heterogenous processing baselines with inconsistent product formats and uneven data quality, which limits its use for multi-temporal monitoring applications.To overcome this limitation, the Copernicus program has undertaken a complete reprocessing with the latest processing baseline (05.00). It concerns the L1C (Top-Of-Atmosphere reflectance) and L2A (Surface Reflectance) products. This paper recalls the features of Collection-1 products and gives an overview of the first validation results.

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) will provide routine hyperspectral observations over the land and coastal zone through the Copernicus Program in support of EU- and related policies for the management of natural resources, assets and benefits [1] , [2] . CHIME is an operational mission covering land and coastal areas which already uses of well-established ground processing routines in place that is independent of the AI unit we are discussing here. Areas outside the nominal observation scenario call for new methodologies such as on-board processing which will bring advantages including: fast processing of data, reducing the amount of data to be down-linked especially over ocean that CHIME sensor will be on but there is no regular acquiring plan, capability of on demand request handling. However there are some drawbacks i.e. complex validation and setting up update routines. Therefore, there has been an assessment going on to have an artificial intelligence unit on board to process acquired data and select only part of the data that contains desired targets. Based on the importance of detecting floating plastic debris for applications such as fishing farms preservation, navigation, tourism, etc., in marine environment, this application has been selected to serve as one of the test cases to evaluate Artificial Intelligence (AI) on board [3] .

The amount of remote sensing data available from different satellite platforms is increasing freely and openly every day. Thus, raising multiple issues which require innovative approaches to organize, manage, and analyze remote-sensing imagery. Discrete Global Grid Systems (DGGS) currently under study by the OGC DGGS SWG, can in principle act as a unified framework for EO data integration, multisource data fusion and cloud computing on a global scale. This study assesses different DGGS candidates: H3, rHEALpix, ISEA7H, ISEA4T/D, etc., analyzing their main features with the perspective of potentially using them for Copernicus Sentinel-2 mission. We then discuss the first outcomes for the different use cases considered and their applicability regarding the benefits and limitations of the DGGS, with a view to setting up a dedicated demonstrator.

Detecting methane in satellite hyperspectral images (HSIs) can play a key role in environmental monitoring, as taking timely actions to reduce its emission and handle (unexpected) super emitters is of paramount importance. We tackle this issue and propose a machine learning pipeline for this task, with the ultimate goal of deploying it on board a satellite. Such solutions can offer global scalability, and they can act as a smart data prioritization step, as only those HSIs which contain methane can be downlinked for further analysis. However, the on-board deployment induces additional practical challenges—such algorithms should be resource-frugal, and should effectively operate on the target image data which may not be available during their development, since the satellite is not in orbit yet. Our experimental study revealed that the data-driven approaches can effectively detect methane in original airborne HSIs, as well as in HSIs emulating the target sensor and generated through data-level simulations.

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) will provide high-quality, global, operational hyperspectral observations in support of European Union and related policies for the management of natural resources, assets and benefits. In this contribution, the main outcomes of the activities carried out in Phase A/B1 and B2, as well as the planned activities for Phase C/D/E will be presented, covering the scientific support studies, the technical developments and the user community preparatory activities. The ongoing international collaboration towards increasing synergies of current and future Imaging Spectroscopy missions in space will be reported as well.

The more high-quality Earth Observation (EO) data are available to users, the more their potential applications increase in number. This is boosting the request for EO data already processed to a minimum set of requirements and needing limited pre-processing steps in order to be used, also when distributed by different EO data providers. The Analysis Ready Data (ARD) concept has been conceived, and a large effort is being made at international levels in order to converge towards a common definition. In this context, the Committee on Earth Observation Satellites (CEOS), with its member Agencies and institution, has worked to define the CEOS ARD for Land (CARD4L) specifications. The Copernicus Sentinel-2 Level-2A product has undergone a self-assessment process performed by the European Space Agency (ESA) aimed at preliminary assessing its compliance status with respect to the CARD4L specifications for optical surface reflectance.

Reliable cloud screening remains a critical issue for the analysis of optical imagery. In this work we present the recent evolutions of the Cloud Screening and Scene Classification module of Sen2Cor processor for the Copernicus Sentinel-2 mission. In addition to a Level-2A surface reflectance product, Sen2Cor provides a Scene Classification (SCL) map divided into 11 classes. The information provided by this map is of great interest for automated processing chains. It can be used to mask out clouds, cloud shadow, water, snow/ice from the Sentinel-2 imagery, so that downstream processing can be performed on clear land pixels, suitable for time-series analysis and quantitative remote sensing. The performance and limitations of the current algorithm are recalled. The updates aimed at improving the overall accuracy of the cloud screening are described (efficient topographic shadows computation, mitigation of false snow detection in clouds and mitigation of false clouds detection on bright targets). Preliminary results based on a Sen2Cor prototype are discussed.

Evolution in the Copernicus Space Component (CSC) is foreseen in the second half of 2020s to meet priority user needs not addressed by the existing infrastructure, and/or to reinforce services by monitoring capability in the thematic domains of CO2, polar, and agriculture/forestry. This evolution will be synergetic with the enhanced continuity of services for the next generation of CSC.

There are growing and evolving expectations from the Earth Observation (EO) Community for merging an increasing number of input data streams recorded with the various space based Multi-Spectral (MS) High Resolution (HR) EO mission instruments. The various agencies and industries are now delivering Analysis Ready Data (ARD) aiming at stacking together multi source data. One critical objective of ARD is to enable the monitoring of rapid and subtle changes. This category of applications sets some strong requirements regarding the quality of the surface reflectance harmonization in the time series. In the context of Sentinel-2 / Landsat 8, this paper proposes a comprehensive analysis of the Sen2Like processing. It shows that it is possible to increase significantly the geometric coregistration accuracy and to ensure an appropriate cross calibration. However, some issues persist and are due to directional effects. These are partially corrected with the current approach but remain a major source of noise in time series.

Sen2Cor is a Level-2A processor designed to correct Sentinel-2 Level-1C products from the effects of the atmosphere in order to deliver a Level-2A surface reflectance product. ESA has been using Sen2Cor for systematic Level-2A processing of Sentinel-2 acquisitions over Europe since June 2017. It has since then been successfully integrated into Sentinel-2 ground segment (PDGS) with a global production over the World started in December 2018. In this manuscript, the Level-2A product and algorithm are presented. The performance of this operational Level-2A product is described in terms of cloud screening accuracy and atmospheric correction accuracy. Finally, the on-going parallel developments aimed at improving the product quality at global scale in terms of cloud screening and atmospheric correction are discussed.

Sen2Like is a processor designed to produce harmonized Level 2 surface reflectance dataset considering as reference Sentinel-2 data. Sen2Like fits with the on-going and growing expectations of the Earth Observation (EO) Community for the merging of an increasing number of input data streams from the various Multi-Spectral (MS) High Resolution (HR) EO mission instruments. The harmonization is an essential processing stage in order to fully exploit all the potential of physical data in the context of land use /land change classification/detection. However, because harmonization addresses several issues in the field of atmospheric/geometric/directional effects/spectral difference corrections, there is not a standard way to achieve this objective. To initiate this project, the Landsat 8 (LS8) mission has been considered as the most appropriate. The Sen2Like processor is now delivering S2/LS8 Analysis Ready Data (ARD) on S2 tiling system at a map resolution of 30 m and 10 m. After a description of the algorithms involved in Sen2Like, this paper discusses testing and validation results.

ACIX is international collaborative initiatives to inter-compare a set of atmospheric correction (AC) algorithms for high-spatial resolution optical sensors. The exercises focus on Sentinel-2 and Landsat-8 data over a set of test areas. The inter-comparison contributes to a better understanding of strengths and weaknesses of different algorithms and ultimately to contribute to a reduction of a major error source for atmospheric correction and surface parameter retrievals.

Sentinel-2 is an Earth Observation optical mission developed and operated by the European Space Agency (ESA) in the frame of the Copernicus program of the European Commission. The mission consists on a MultiSpectral Instruments (MSI) on board a constellation of two satellites flying on the same orbit but phased at 180°: Sentinel-2A launched in June 2015 and Sentinel-2B launched in March 2017. It covers the Earth's land surfaces and inland and coastal waters every five days at the equator under the same viewing conditions with high spatial resolution and wide field of view. Accurate radiometric calibration is key to the success of the mission; therefore, in-orbit calibration and validation activities are conducted within the Sentinel-2 Mission Performance Centre (MPC), including a consortium of Expert Support Laboratories (ESL). The Database of Imaging Multispectral Instrument and Tool for Radiometric Intercomparison (DIMITRI) is used to perform the vicarious validation of Level-l products delivered to users. The aims of this validation are 1) to assess the quality of the data product at Level-1, 2) to monitor the evolution of the radiometry of both instruments and 3) to ensure that the products meet the mission requirement accuracy. Three vicarious methods are used, such as Rayleigh scattering, Desert Pseudo- Invariant Calibration Sites (PICS) and Sensor-to-Sensor intercalibration methods. Although the results indicate good stability and performance of both sensors MSI-A/B, a slight discrepancy between them of ~1 % over desert sites could be observed. Slight decrease of band B8A (865 nm) signal over Algeria-3 site has been detected since April 2017. This effect seems to be related to the impact of human and industry activities near the area. Radiometric performances and image quality of Sentinel-2 MSI-A/B level-1C products appear stable and meet the mission requirements.

As part of the Copernicus programme of the European Commission (EC), the European Space Agency (ESA) operates the Sentinel-2 mission that acquires high spatial resolution optical imagery. The consistency of the image time series is ensured by some specific performance requirements such as multi-temporal spatial registration and radiometric stability, routinely monitored by the Sentinel-2 Mission Performance Centre (S2MPC). The products also provide a rich set of metadata and auxiliary data to support high-level processing activities. This presentation provides a description of the main level-1 products calibration and validation activities, performed by the S2MPC including several Expert Support Laboratories (ESL). Measured performances and analysed results show the good quality of the mission products both in terms of radiometry and geometry. The current performances for Sentinel-2A and Sentinel-2B are compared to mission product quality requirements.

Sentinel-2 is an Earth Observation mission developed by the European Space Agency (ESA) in the frame of the Copernicus program of the European Commission. The mission consists on a MultiSpectral Instruments (MSI) on board a constellation of two satellites: Sentinel-2A launched in June 2015 and Sentinel-2B launched in March 2017. It covers the Earth's land surfaces and coastal waters every five days at the equator and every two days at mid-latitudes under the same viewing conditions with high spatial resolution and a wide field of view. Several projects have been launched by ESA, for instance as contribution to the R&D component of the GEOGLAM initiative and to the JECAM network activities. This paper gives an overview of Sentinel-2 mission and some examples of applications for agriculture monitoring.

The Radiometric Uncertainty Tool (RUT) is a software tool integrated as part of the Sentinel Application Platform (SNAP). The tool estimates the radiometric uncertainty associated with each pixel in the top-of-atmosphere reflectance factor images of the Sentinel-2 (S2) Level-1C products provided by the European Space Agency. The tool and the uncertainty analysis are under continuous evolution. This paper summarises the main features and improvements considered for a second version of the RUT. This second version has under-gone a set of software improvements such as: metadata associated to the uncertainty image, integration of the S2 L1C masks, and the feasibility of parallel processing. Furthermore, it includes upgrades in the uncertainty analysis as distinguishing between S2A and S2B satellites or the estimate of the uncertainty of a mean of pixels in a region-of-interest. Finally, the second version also includes an improved user guide and documentation.

Sen2Cor is a Level-2A processor designed to correct Sentinel-2 Level-1C products from the effects of the atmosphere in order to deliver a Level-2A surface reflectance product. A key-parameter for accurate atmospheric correction is the knowledge of the spatially and temporally very variable aerosol content of the atmosphere The multi-spectral instrument on board Sentinel-2 has the appropriate spectral bands to derive the aerosol optical thickness (AOT) of the atmosphere, provided the image contains reference areas of known reflectance behavior, preferably dense dark vegetation (DDV). If the granule contains no DDV pixels, a fallback solution is required. The former solution was to generate an AOT map based on the start visibility set in the configuration file. In this updated Sen2Cor version, meteorological AOT estimates are retrieved from ECMWF ftp server and pre-processed. The integration and assimilation of ECMWF aerosol estimates into Sen2Cor atmospheric correction is described and recent validation results are presented.

The Sentinel-3 mission forms part of the Copernicus Space Component. Its main objectives are to measure sea-surface topography, sea- and land-surface temperature and ocean- and land-surface colour in support of ocean forecasting systems, and for environmental and climate monitoring. The series of Sentinel-3 satellites will ensure global, frequent and near-real time ocean, ice and land monitoring, with the provision of observation data in routine, long term (up to 20 years of operations) and continuous fashion, with a consistent quality and a high level of reliability and availability. The Sentinel-3 mission addresses these requirements by implementing and operating the following instruments, building on experience and heritage from the ERS and ENVISAT missions: •A dual frequency, delay-Doppler Synthetic Aperture Radar Altimeter (SRAL) instrument supported by a dual frequency passive microwave radiometer (MWR) for wet-tropospheric correction, and a Precise Orbit Determination package. This combined package provides measurements of sea-surface height and topography measurements over sea ice, ice sheets, rivers and lakes. •A highly sensitive Ocean and Land Colour Imager (OLCI) delivering multichannel wide-swath optical measurements for ocean and land surfaces. With 21 bands, compared to the 15 on Envisat's MERIS, a design optimised to minimise sun-glint and, a resolution of 300 m over all surfaces, OLCI marks a new generation of measurements over the ocean and land. The swath of OCLI and nadir SLSTR fully overlap. •A dual-view Sea and Land Surface Temperature Radiometer (SLSTR) delivering accurate surface ocean, land, and ice temperature, with an accuracy better than 0.3 K. SLSTR measures in 9 spectral channels and two additional bands optimised for fire monitoring. SLSTR has a spatial resolution in the visible and shortwave infrared channels of 500 m and 1 km in the thermal infrared channels.

The Copernicus Programme, being Europe's Earth Observation and Environment Monitoring Programme led by the European Union, aims to provide, on a sustainable basis, reliable and timely services related to environmental and security issues. The Copernicus Programme uses multiple source data and comprises a service component, a space infrastructure component and an in-situ component. The objective of the Copernicus Space Component (CSC) Programme is to fulfil the space-based observation requirements in response to European policy priorities with a particular emphasis on the Copernicus core services as identified by the European Commission. It aims at developing a fully operational capability in view of feeding Copernicus services with satellite data. The CSC Programme also aims at the operational provision of satellite data for other European and national services. The main data source for the CSC Programme are the Sentinels, dedicated missions providing continuity to past or present data sets. In addition, the CSC Programme covers the development and operations of a Data Access Layer, the Coordinated Data access System (CDS), aiming at providing Copernicus Services also with satellite data from other missions (by ESA, National, EUMETSAT and other Third Party Missions) that are of relevance to the overall space component of Copernicus.

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 Earth's 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.

FLEX (FLuorescence EXperiment) is a candidate mission for the European Space Agency (ESA) Earth Explorer program. The main objective of the mission is the measurement the chlorophyll fluorescence signal emitted by vegetation at the red and far-red spectral regions (roughly 630-770 nm). The current FLEX mission design includes different instruments intended to provide the appropriate characterization of those atmospheric and surface parameters necessary for the retrieval and interpretation of the fluorescence signal. The complete processing chain for the derivation of fluorescence and reflectance products from the radiance data acquired by the different instruments included in the FLEX pay-load is described in this paper. Six processing modules have been implemented: cloud screening, aerosol optical thickness (AOT) retrieval, automatic spectral characterisation, columnar water vapor (CWV) retrieval, fluorescence retrieval and reflectance retrieval. The processing chain has been tested against a scene-based simulated data set which reproduces FLEX instruments and realistic atmospheric conditions.

In the frame of the Global Monitoring for Environment and Security programme (GMES) jointly implemented by ESA and EC, ESA is developing the Sentinel-2 system, providing globally with systematic acquisition high resolution (10-20 m) optical observations with a high revisit tailored towards the needs of operational land services. This system will ensure data continuity of SPOT and Landsat satellite series and further enhancement to account of future service evolution.

DART (Discrete Anisotropic Radiative Transfer) was developed in 1996 for simulating radiative transfer in 3D scenes. Since then, it was greatly improved to make it more accurate, comprehensive and operational (e.g., simulation of thermal infrared and atmospheric radiative transfer). Presently, a single DART simulation gives 2 major products. (1) 3-D radiation budget of the Earth-Atmosphere system. (2) Optical remote sensing images at any altitude from bottom up to top of the atmosphere, for many view directions, simultaneously in several spectral bands, from the visible up to thermal infrared. DART works with natural landscapes (i.e., forests, field mosaics, etc.) made of trees, grass, rivers, etc. and urban landscapes made of buildings, roads, etc. Topography is simulated with digital elevation models. Atmosphere (vertical profiles, etc.) and Earth surface (spectral reflectance, etc.) databases can be used, sensor characteristics can be accounted for, etc. Moreover, a Graphic User Interface (GUI) is used to input scene parameters and to display scene and DART simulations. Recent improvements of DART (patent (PCT/FR 02/01181)) are presented here.

A simulator of high spatial resolution satellite images is introduced. It is based on the coupling of two radiative transfer models: discrete anisotropic radiative transfer (DART) for terrestrial landscapes and second simulation of the satellite signal in the solar spectrum (6S) for the atmosphere. It works in the visible, near infrared, and midinfrared domains. The simulation procedure involves four steps: 1) assessment of the optical properties of the atmosphere, 2) simulation of high-resolution reflectance images of the Earth surface, 3) transformation of the bottom of the atmosphere (BOA) images to top of the atmosphere (TOA) images, and 4) convolution of the TOA spectral images with the sensor spectral response. Two applications of the simulator are presented: the detection of targets and the analysis of the spatial information. This simulator is being used as a radiative transfer reference model at the Centre National d'Etudes Spatiales (CNES), Toulouse, France.