Evolution in the Copernicus Space Component is foreseen in the mid-2020s to meet priority user needs not addressed by the existing infrastructure, and/or to reinforce existing services. In this context, the European Commission is intending to evaluate the overall potential utility of a complementary Copernicus hyperspectral mission to be added to the Copernicus Sentinels fleet. Hyperspectral imaging is a powerful remote sensing technology that, allowing the characterization and quantification of Earth surface materials, has the potential to deliver significant enhancements in quantitative value-added products. This study aims to illustrate the interaction methodology that was set up to collect and assess user-driven requirements in different thematic areas to demonstrate the potential benefit of a future Copernicus hyperspectral mission. Therefore, an ad hoc interaction matrix was circulated among several user communities to gather preferences about hyperspectral-based products and services. The results show how the involvement of several user communities strengthens the identification of these user requirements. Moreover, the requirement evaluation is used to identify potential opportunities of hyperspectral imaging in addressing operational needs associated with policy obligations at European, national, and local levels. The frequency distribution of spectral range classes and spatial and temporal resolutions are also derived from the preference expressed by the user communities in each thematic area investigated.

This study explores performance requirements for the retrieval of the atmospheric aerosol optical depth (AOD) by airborne optical remote sensing instruments. Independent of any retrieval techniques, the calculated AOD retrieval requirements are compared with the expected performance parameters of the upcoming hyperspectral sensor APEX at the reference wavelength of 550nm. The AOD accuracy requirements are defined to be capable of resolving transmittance differences of 0.01 to 0.04 according to the demands of atmospheric corrections for remote sensing applications. For the purposes of this analysis, the signal at the sensor level is simulated by radiation transfer equations. The resulting radiances are translated into the AOD retrieval sensitivity (Δτ_λ^aer ) and compared to the available measuring sensitivity of the sensor (NE ΔL_λ^sensor). This is done for multiple signal-to-noise ratios (SNR) and surface reflectance values. It is shown that an SNR of 100 is adequate for AOD retrieval at 550nm under typical remote sensing conditions and a surface reflectance of 10% or less. Such dark surfaces require the lowest SNR values and therefore offer the best sensitivity for measuring AOD. Brighter surfaces with up to 30% reflectance require an SNR of around 300. It is shown that AOD retrieval for targets above 50% surface reflectance is more problematic with the current sensor performance as it may require an SNR larger than 1000. In general, feasibility is proven for the analyzed cases under simulated conditions.

During the commissioning of the Sentinel-3B satellite, a single lunar observation was performed to assess the possible use of the moon for characterization and validation of onboard instruments. The observation was carried out in stable orientation after a roll maneuver, allowing the moon to be imaged by the Earth view of instruments. Data acquired by the Ocean Land Color Instrument (OLCI) allowed inflight verification of stray-light correction (SLC) performed by the Mission Performance Centre (MPC), and assessment of radiometric behavior of instrument in comparison with lunar irradiance models performed in cooperation between European Space Research and Technology Centre (ESTEC) and European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). This paper describes the results of those activities along with the proposed update of stray-light correction developed with the use of lunar data.

AI on spaceborne platforms optimizes operations and increases automation, crucial for satellites with limited downlink capacity. It can ensure that only valuable information is transmitted, minimizing resources spent on unnecessary data, which is especially important in hyperspectral Earth Observation missions, producing large data volumes. Our previous work showed that the 1D-CNN, 1D-Justo-LiuNet, outperformed 2D-CNNs and Vision Transformers for hyperspectral segmentation with an accuracy of 0.93 and 4563 parameters, making our model the best choice for in-orbit deployment. While the state of the art has deployed 1D-CNNs on low-power platforms, such as Unmanned Aerial Vehicles, they have still not been deployed in space before. In this work, we mark the first deployment and testing of a 1D-CNN in a satellite. We implement a C version of the 1D-Justo-LiuNet and, after ground validation, we deploy it on board the HYPSO-1 satellite. We demonstrate in-flight segmentation of hyperspectral images via the 1D-CNN to classify pixels into sea, land, and cloud categories. We show how in-orbit segmentation improves satellite operations, increases automation, and optimizes downlink. We give examples of how in-orbit segmentation addresses mission challenges in HYPSO-1, such as incomplete data reception, incorrect satellite pointing, and cloud cover, helping to decide whether to transmit or discard data on board. An additional CNN autonomously interprets the segmented images, enabling on-board decisions on data downlink.

In order to meet Earth observation needs of the European Union-ESA Global Monitoring for Environment and Security (GMES) programme, ESA decided to develop the Sentinels as first series of operational satellites. The series of Sentinel-3 satellites will provide global, frequent and near-realtime ocean, ice and land monitoring. It continues Envisat's altimetry, the multispectral, medium-resolution visible and infrared ocean and land-surface observations of ERS, Envisat and Spot, and includes enhancements to meet the operational revisit requirements and to facilitate new products and evolution of services. The first launch is expected in 2012. In this paper the design of the major instruments and their basic performance parameters will be introduced as well as the expected accuracies of the main data products.

The facilities to support the ESA's airborne APEX hyperspectral mission simulator are described. These facilities include calibration tools, such as specific processing in a dedicated Processing and Archiving Facility (PAF), operational calibration and characterization using the Calibration Home Base (CHB), the In-Flight Characterization facility (IFC) and the Calibration Test Master (CTM). Further on, a preview on major applications and the corresponding development efforts to provide scientific data products up to level 2/3 to the user are outlined. Products dedicated for the retrieval of limnology, vegetation, atmospheric parameters, as well as general classification routines and rapid mapping tasks are currently under development and prepared for dissemination by the APEX Science Center (ASC) and the APEX Operations Center (AOC).