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] .

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.

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.

The Sentinel-3 (S3) mission is developed as part of the Copernicus Space Component (CSC) Programme 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 Commission.

Evolution in the Copernicus Space Component (CSC) is foreseen in the mid-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.

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 Sentinel-3 (S3) mission is developed as part of the Copernicus Space Component (CSC) Programme 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 Commission. The Sentinel-3 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. A 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. Sentinel-3 carries an Ocean and Land Colour Instrument (OLCI), a Sea and Land Surface Temperature Radiometer (SLSTR), a SAR Radar altimeter (SRAL) supported by a Microwave Radiometer (MWR) and a suite of orbit determination instruments. A full description of Sentinel-3 can be found in [Donlon et al (2012)]. The paper will focus on S3 optical payloads described in Nieke et al (2015) and Coppo et al (2015).

Evolution in the Copernicus Space Component (CSC) is foreseen in the mid-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.

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.

The Copernicus Programme, being Europe's Earth Observation and 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 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 System (CDS), aiming at providing Copernicus Services with satellite data from other than Sentinel missions (ESA, National, EUMETSAT and other Third Party Missions) of relevance to the overall space component of Copernicus.

The series of Global Monitoring for Environment and Security (GMES) Sentinel satellites will continue and extend the European heritage of ENVISAT to provide data to numerous user communities. Sentinel-3 is being developed to support GMES Ocean and global Land monitoring services. Two Sentinel-3 satellites are in development with a first launch in 2014 and the second satellite expected approximately 18 months after the first. This paper provides an overview of the Sentinel-3 Mission.

In the frame of the Global Monitoring for Environment and Security (GMES) program ESA is currently implementing the Sentinel-3 mission [3], [4]. It is designed as a constellation of two identical polar orbiting satellites, separated by 180 deg, for the provision of long-term operational marine and land monitoring services. These services include the generation of land and ocean colour products, sea and land surface temperature, vegetation products and sea, ice and land surface topography. With a planned launch in 2013 of the first satellite, the Sentinel-3 mission will provide valuable information for scientists and policy makers in the European Union and its Member States for the next decades. The operational character of this mission implies a high level of availability of the data products and fast delivery time, which have been important design drivers for the mission.

The applicability of MERIS data for the retrieval of water constituent concentrations in oligo- to mesotrophic perialpine lakes is demonstrated by means of two different algorithms. The C2R algorithm is an easily applicable neural network processor which is bound to MERIS data. The MIP algorithm is a complex, coupled inversion program, which can be used with a variety of remote sensing sensors, but requires extensive parameterization. Both algorithms were applied with the ICOL adjacency effect correction. C2R's potential for automatic processing of large data quantities is validated in a time series study with water quality monitoring data. Individual C2R image products are then compared to MIP, which will allow for an extended comparison with new APEX imaging spectrometry data in the near future.

The product generation from hyperspectral sensor data has high requirements on the processing infrastructure, both hardware and software. The Airborne Prism Experiment (APEX) processing and archiving facility has been set up to provide for the automated generation of level-1 calibrated data and user-configurable on-demand product generation for higher processing levels. The system offers full reproducibility of user orders and processing parameters by employing a relational database. The flexible workflow software allows for the quick integration of novel algorithms or the definition of new processing sequences. Reprocessing of data is supported by the archiving approach. Configuration management based on the database enables the control over different versions of processing modules to be applied. The system is described with a focus on the APEX instrument; however, its generic design allows adaptation to other sensor systems.

The Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) on-orbit radiometric performance has been improved through the comparison of cross-calibration and onboard lamp calibration. We proposed a new cross-calibration scheme which uses top-of-atmosphere reflectance functions of satellite zenith angle at temporally and spatially stable ground sites. Each function is made from Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution data over 16 days, which includes AVNIR-2 observation dates. The results showed that the radiances of AVNIR-2 bands 1 (463 nm), 2 (560 nm), and 3 (652 nm) agreed with the radiances of Aqua and Terra MODIS within 3% accuracy (standard deviation of 2%). AVNIR-2 band 4 (821 nm) had a difference of about 7% (AVNIR-2< MODIS) due to water-vapor absorption which could explain more than half of the 7%. Using many samples from this scheme, we found dependences of radiometric calibration errors within the field of view (FOV) and for different gain modes. The lamp calibration system onboard AVNIR-2 unveiled these dependences over the FOV and time. Furthermore, for the gain mode, consistent results could be retrieved using the cross-calibration scheme. The retrieved radiometric correction factors (over the FOV and gain modes) have been applied to the Japan Aerospace Exploration Agency AVNIR-2 processing scheme. The subsequent validation of the correction showed, for polar snow areas, an improved radiometric performance over the entire FOV.

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).

A physically based water constituent retrieval algorithm is used for the automatic processing of MERIS level 1B full resolution data. The algorithm requires several input variables for individual optimization with different sensors (i. e. channel calibration and weighting), aquatic regions (i. e. specific inherent optical properties) or atmospheric conditions (i. e. Aerosol models). The optical properties are derived from optical in situ measurements during concurrent MERIS data acquisition on 20 April 2007. Remaining parameters are iteratively optimized for best performance with 21 MERIS datasets of Lake Constance in the years 2003-2005, and validated with 11 datasets in 2006. Operational water quality sampling measurements acquired by local authorities serve as reference.

The increasing quantity and sophistication of imaging spectroscopy applications have led to a higher demand on the quality of Earth observation data products. In particular, it is desired that data products be as consistent as possible (i.e., ideally uniform) in both spectral and spatial dimensions. Yet, data acquired from real (e.g., pushbroom) imaging spectrometers are adversely affected by various categories of artifacts and aberrations including as follows: singular and linear (e.g., bad pixels and missing lines), area (e.g., optical aberrations), and stability and degradation defects. Typically, the consumer of such data products is not aware of the magnitude of such inherent data uncertainties even as more uncertainty is introduced during higher level processing for any particular application. In this paper, it is shown that the impact of imaging spectrometry data product imperfections in currently available data products has an inherent uncertainty of 10%, even though worst case scenarios were excluded, state-of-the-art corrections were applied, and radiometric calibration uncertainties were excluded. Thereafter, it is demonstrated how this error can be reduced (<5%) with appropriate available technology (onboard, scene, and laboratory calibration) and assimilation procedures during the preprocessing of the data. As a result, more accurate, i.e., uniform, imaging spectrometry data can be delivered to the user community. Hence, the term uniformity of imaging spectrometry data products is defined for enabling the quantitative means to assess the quality of imaging spectrometry data. It is argued that such rigor is necessary for calculating the error propagation of respective higher level processing results and products.

Abstract—This paper addresses the main goals and objec-tives of the Hyperspectral Imaging Network (HYPER-I-NET), a recently started Marie Curie Research Training Network. The project is designed to build an interdisciplinary research community focusing on hyperspectral imaging activities. The core strategy of the network is to create a powerful interdisciplinary synergy between different domains of expertise closely related to hyperspectral imaging activities in Europe, ranging from sensor design and flight operation to data collection, processing, interpretation, and dissemination. Our main goals in this paper are to present the project to the Geoscience and Remote Sensing community and to provide an overview of the planned activities in each sub-activity covered by the network.

Efficient and accurate imaging spectroscopy data processing asks for perfectly consistent (i.e., ideally uniform) data in both the spectral and spatial dimensions. However, real pushbroom-type imaging spectrometers are affected by various point spread function (PSF) nonuniformity artifacts. First, individual pixels or lines may be missing in the raw data due to bad pixels originating from the detector, readout errors, or even electronic failures. Second, so-called smile and keystone optical aberrations are inherent to imaging spectrometers. Appropriate resampling strategies are required for the preprocessing of such data if emphasis is put on spatial PSF uniformity. So far, nearest neighbor interpolations have been often recommended and used for resampling. This paper shall analyze the radiometric effects if linear interpolation is used to optimize the spatial PSF uniformity. For modeling interpolation effects, an extensive library of measured surface reflectance spectra as well as real imaging spectroscopy data over various land cover types are used. The real measurements are systematically replaced by interpolated values, and the deviation between original and resampled spectra is taken as a quality measure. The effects of nearest neighbor resampling and linear interpolation methods are compared. It is found that linear interpolation methods lead to average radiometric errors below 2% for the correction of spatial PSF nonuniformity in the subpixel domain, whereas the replacement of missing pixels leads to average errors in the range of 10%-20%

An aerosol retrieval algorithm is currently under development in scope of the upcoming APEX hyperspectral imager. It will be able to close the gap between global remote sensing and one-dimensional in situ measurements of atmospheric particles. This paper presents a feasibility study of the proposed APEX aerosol retrieval approach for hyperspectral imagery with high spatial resolution. The extraction of a sample aerosol optical thickness is done by fitting radiation transfer model results to measured at-sensor radiances at two near-UV/blue bands (394 and 404 nm) from the PHILLS imager. The PHILLS hyperspectral data are used to simulate the APEX Visible Near- Infrared detector and comprehend a dark surface reference target to avoid most uncertainties from the surface reflection contribution.

Recently, the Advanced Earth Observing Satellite 2 (ADEOS-2) was launched (December 14, 2002) successfully, and the Global Imager (GLI) onboard the ADEOS-2 satellite became operational in April 2003. In a first calibration checkup, the radiometric performance of GLI was compared relatively to that of other sensors on different satellites with different calibration backgrounds. As a calibration site, a large snowfield near Barrow, Alaska, was used, where space sensors in polar orbits view the same ground target on the same day with small differences in the local crossing times. This is why GLI, the Moderate Resolution Imaging Spectroradiometer (Terra, Aqua), the Sea-viewing Wide Field-of-view Sensor, the Advanced Very High Resolution Radiometer (N16, N17), the Medium Resolution Imaging Spectrometer, and the Advanced Along Track Scanning Radiometer datasets were selected for the following clear-sky condition days: April 14 and 26, 2003. At the same time, ground-truth experiments (e.g., measurements of ground reflectance, bidirectional reflectance distribution function, aerosol optical thickness) were carried out. Thereinafter, top-of-atmosphere (TOA) radiance/reflectance was forward calculated by means of radiative transfer code for each sensor, each band, and each day. Finally, the vicariously retrieved TOA signal was compared to TOA sensor Level 1B data. As a result, GLI's performance is encouraging at that time of the mission. GLI and the other seven sensors deliver similar sensor output in the range of about 5% to 7% around the expected vicariously calculated TOA signal.