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.

Upcoming SI-traceable satellite (SITSat) missions such as traceable radiometry underpinning terrestrial and helio studies (TRUTHS) aim to achieve unprecedented accuracy for SI-traceable measurements of the Earth-reflected radiation. These measurements will support the generation of low-uncertainty climate records and significantly improve the calibration of other sensors. In such a context, the calibration transfer rather than the reference sensor dominates the uncertainty budget. This study presents an end-to-end global intercalibration simulator capable of assessing the potential uncertainty for multiple scenarios that consider the interrelation of different error sources and match-ups. We first define the sensor-to-sensor match-ups through an orbital analysis that is followed by a top-of-atmosphere (TOA) radiance modeling of each match-up. Finally, we calculate the radiometric uncertainty based on different error sources combined globally. In this first implementation, we have calculated the match-ups of TRUTHS against observations by the Copernicus Sentinel-2A satellite over land areas throughout the year. We calculate the angular mismatch for both viewing differences and solar changes from different overpass times. We define multiple intercalibration scenarios based on temporal, angular, or cloud constraints. These first results show that considering overpasses up to 15-min difference, low cloud probability, and matching field-of-view (FoV), within 5°, we sample most land areas with a mean error <0.1% and bias regression <0.5%. We have also restricted the sun zenith angle (SZA) to 60° to minimize solar angle and view azimuthal dispersion over the poles. This also results in data gaps of several months that might be complemented with dedicated maneuvers or dedicated processing of these polar-region match-ups.

Methane mitigation from anthropogenic sources such as in the production and transport of fossil fuels has been found as one of the most promising strategies to curb global warming in the near future. Satellite-based imaging spectrometers have demonstrated to be well-suited to detect and quantify these emissions at high spatial resolution, which allows the attribution of plumes to sources. The PRISMA satellite mission (ASI, Italy) has been successfully used for this application and the recently-launched EnMAP mission (DLR/GFZ, Germany) presents similar spatial and spectral characteristics (30 m spatial resolution, 30 km swath, about 8 nm spectral sampling at 2300 nm). In this work, we investigate the potential and limitations of EnMAP for methane remote sensing, using PRISMA as a benchmark to deduce its added-value. We analyze the spectral and radiometric performance of EnMAP in the 2300 nm region used for methane retrievals acquired using the matched-filter method. Our results show that in arid areas, EnMAP spectral resolution is about 2.7 nm finer and the signal-to-noise-ratio values are approximately twice as large, which leads to an improvement in retrieval performance. Several EnMAP examples of plumes from different sources around the world with flux rate values ranging from 1 to 20 t/h are illustrated. We show plumes from sectors such as onshore oil and gas and coal mining, but also from more challenging sectors such as landfills and offshore oil and gas. We detect two plumes in a close-to-sunglint configuration dataset with unprecedented flux rates of about 1 t/h, which suggests that the detection limit in offshore areas can be considerably lower under favorable conditions.

Spaceborne imaging spectroscopy is undergoing a rapid expansion with a new generation of missions in recent years. Following the Hyperion (2000) and HICO (2009) missions, new spaceborne imaging spectroscopy missions have recently started operating: DESIS (2018), PRISMA (2019), HISUI (2019) and more recently EnMAP (2022) and EMIT (2022). These missions face the common challenge of providing accurate spectral and radiometric results over a wide spectral range. This requires accurate instrument calibration and the validation of the results obtained. In this contribution, we provide an overview of the calibration and validation (CalVal) activities in the EnMAP mission, and we present the CalVal results that were obtained as part of the commissioning phase (April - October 2022).

The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth's environment on a global scale. The mission is now ready to start with the sensor being by end of 2021 in Flight Acceptance Review, ready to be shipped to the launch pad in early 2022. This paper presents first an update of the mission status with recent activities and developments from the space and the ground segment. Then, an update of selected highlights of the science segment activities at launch phase are presented including preparation and if possible early results for the validation of EnMAP products, updates on EnMAP science algorithms (EnMAP-Box) developed at GFZ, online education initiative (HYPERedu), and further mission support activities such as background mission.

The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth's environment on a global scale. EnMAP core themes are environmental changes, ecosystem responses to human activities, and management of natural resources. After several years delay, the instrument is finished and in the final stage of environmental characterization and assembly for a launch early 2022. This paper presents an update of the mission status and activities in the frame of the science preparation and mission support project led by the German Research Center for Geosciences (GFZ) Potsdam. Further, this paper presents a specific focus on the planning for the independent EnMAP data product validation.

The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth's environment on a global scale. After several years delay due to a major design issue to meet the mission requirements, the mission is now back on track and planned for launch in 2021. This paper presents an update of the mission status with recent activities and developments from the space and the ground segment. Furthermore, a draft plan for the independent validation of EnMAP radiance and reflectance products was developed and will be introduced, along with highlights of the science preparatory activities in 2019 including airborne campaigns, algorithm consolidations, and HYPERedu education initiative.

EnMAP (Environmental Mapping and Analysis Program, www.enmap.org) is a German, Earth observing, imaging spectroscopy, spaceborne mission planned for launch in 2020. The data products will cover the spectral range from 420 nm to 2450 nm with a spectral sampling distance between 5 and 12 nm with an expected signal-to-noise-ratio of 400:1 in the visible near-infrared and 180:1 in the shortwave infrared parts of the electro-magnetic spectrum. The resulting images will cover an area of 30 km in the across- track direction with a ground sampling distance of 30 m. The across-track tilt-capability of 30° enables revisit times of less than four days. The resulting data products will be freely available to the scientific user community for measuring, deriving, and analyzing diagnostic parameters, which describe vital processes on the Earth's surface comprising agriculture, forestry, soil and geological environments, as well as coastal zones and inland waters. This work concentrates on the description of activities performed and facilities involved for the preparation of these products. It starts out by the description of the User Portals for observation requests and acquisition planning, touches the aspects of creating the time-lines, the commanding and controlling of the satellite, the downlink of the telemetry and payload data, the design of the processing chain and the archiving of data plus a set of activities flanking the above for the provision of high-quality data products.

Combining airborne LiDAR (ALS) and hyperspectral data refers to utilize the LiDAR based Digital Elevation Model (DEM) and the spectral information of the hyperspectral imaging (HSI) sensor. The separation of both discretized data entities leads to a substantial loss of information and does not exhaust the full capabilities of the contrasting sensors. A physically based in-flight fusion of HSI and ALS sensor characteristics is presented. Based on their respective intensity information overlaps, ray tracing and radiative transfer procedures utilize geometric and radiometric synergies. In a first step a rigorous parametric co-alignment procedure is realized using an automated and adjustable tie point detection algorithm. It ensures sub-pixel co-alignment of the contrasting sensors. In a second step we present a rigorous illumination correction of HSI data based on the radiometric cross-calibrated return intensity information of ALS data. This radiometric fusion corrects cloud and cast shadowing effects, across track illumination, partly anisotropy effects and illumination changes over time for the entire HSI wavelength domain. The presented fundamental fusion of the passive and active sensor characteristics is aimed at improving and developing the complete, sensor inherent data density to ensure highest spectral and geometric information content for a variety of applications.

The Environmental Mapping and Analysis Program (En-MAP) is an spaceborne imaging spectroscopy mission under development by a consortium of German Earth Observation research institutions. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to 4 days at Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide. In this talk we will provide an overview of the mission status including both technical developments and preparatory activities for the implementation of the EnMAP scientific program.

The German Environmental Mapping and Analysis Program (EnMAP) will provide hyperspectral spaceborne data to the global geoscientific community. Here we present EnGeoMAP the EnMAP Geological Mapper and EnSoMAP the EnMAP Soil Mapper which are two geomapping tool provided in the EnMAP Box. They offer geologists and soil scientists worldwide a unique toolset for the analysis of future EnMAP data. The structure, workflow and results of EnGeoMAP and EnSoMAP are shown and discussed together with a brief overview on further developments. Synergies between EnMAP and other sensors are furthermore demonstrated.

The SCOPE model embeds the state of art for coupling soil vegetation atmosphere transfer (SVAT) and radiative transfer models (RTM). For that reason the FLuorescence EXplorer (FLEX) mission selected this model to derive vegetation properties through inversion. However inverse problem is often ill-posed, providing equally likely solutions and hence inflating the uncertainty of the retrieved parameters. In this work we test the use of different priors based on ancillary measurements and literature to support multiple-constrain inversion of SCOPE. Results show that prior information on the relationships between variables such as leaf chlorophyll content (Cab), leaf carotenoids content (Cca), leaf water content (Cw) and/or maximum carboxylation rate (Vcmax) reduce inversion uncertainties and overfitting, and should be sampled/estimated together with optical data.

EnMAP (Environmental Mapping and Analysis Program) is a German spaceborne imaging spectrometer Earth observing mission planned for launch in 2019. This paper reflects the status of the mission with an focus to changes of the Ground Segment based on a major review conducted in 2016 and the EnMAP Data Exploitation and Application Development Program and recent activities.

The fusion of hyperspectral imaging (HSI) sensor and airborne lidar scanner (ALS) data provides promising potential for applications in environmental sciences. Standard fusion approaches use reflectance information from the HSI and distance measurements from the ALS to increase data dimensionality and geometric accuracy. However, the potential for data fusion based on the respective intensity information of the complementary active and passive sensor systems is high and not yet fully exploited. Here, an approach for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data, is presented. The cross calibration utilizes a ray tracing-based fusion of both sensor measurements by intersecting their particular beam shapes. The developed method is capable of compensating for the drawbacks of passive HSI systems, such as cast and cloud shadowing effects, illumination changes over time, across track illumination, and partly anisotropy effects. During processing, spatial and temporal differences in illumination patterns are detected and corrected over the entire HSI wavelength domain. The improvement in the classification accuracy of urban and vegetation surfaces demonstrates the benefit and potential of the proposed HSI illumination correction. The presented approach is the first step toward the rigorous in-flight fusion of passive and active system characteristics, enabling new capabilities for a variety of applications.

Hyperspectral rare earth elements detection in space borne and near-field acquired images becomes more and more important for global exploration. In comparison to classic exploration methods, the benefit of hyperspectral surveys is the fast and in-situ generation of spatial information. Current hyperspectral investigations do more and more include rare earth element mappings - one new tool for hyperspectral rare earth mapping is the REEMAP algorithm. So far it is trained for five rare earth elements (erbium, dysprosium, holmium, neodymium and thulium). Previous versions of REEMAP did not map samarium. The here presented study focusses on the extension of REEMAP to identify samarium and presents a detailed mapping of the samarium and dysprosium occurrences of a two-carbonatite units containing outcrop (rauhaugites - dolomitic carbonatites and rødbergites - hematitic carbonatites) at Fen Complex, Norway. Four absorption bands of samarium were scrutinized for their shape characteristics in order to extend REEMAP for the detection of samarium. REEMAP was extended with these newly defined filter parameters. The mapping result for the investigated outcrop show that two absorption bands proved to be robust enough to be used in the REEMAP algorithm. The two remaining absorption bands are superimposed by H2O absorptions and are therefore not recommended for space borne or near-field hyperspectral analyses. However, the resulting samarium map shows the two-rock units represented by different samarium concentration levels and revealed a gradual increase of samarium towards the top of the rauhaugites rock unit. This study shows that REEMAP can be trained for the detection of samarium, especially for two of the investigated absorption bands (1250 and 1567 nm), and that REEMAP helps for in-situ interpretations of REE ore distributions.

Sun-induced fluorescence (SIF) is an electromagnetic signal emitted by the chlorophyll of green leaves which has been shown to be a good proxy for plant photosynthetic activity. Recent developments in spaceborne spectrosopy have led to the derivation of the first global maps of SIF from a number of space-based spectrometers. This contribution will provide an overview on the state-of-the-art of terrestrial SIF monitoring in terms of existing and upcoming satellite missions and application fields.

The Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill the current gap in space-based imaging spectroscopy data. An overview of the main characteristics and current status of the mission will be provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to 4 days at Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

Remote sensing of sun-induced chlorophyll fluorescence (SIF) is a novel optical tool for assessment of terrestrial photosynthesis or gross primary production (GPP). Along with the breakthroughs of global retrievals of SIF from space-borne sensors, exploitation of SIF in improving the representation of photosynthesis and its role in Earth System models became a very relevant and active field. Recent space-borne measurements of SIF can offer an observational constraint on photosynthesis simulations. Tailored to this special session, this presentation gives a discussion on recent advances in the retrievals of leaf biological traits from SIF, e.g., the maximum carboxylation rate (Vcmax), regarding the applications and problems.

Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration.

This study reports the calibration and validation activities for the Environmental Mapping and Analysis Program (EnMAP; www.enmap.org). EnMAP is a German imaging spectroscopy satellite mission with the declared goal to investigate the Earth's surface with a so far surpassing quality. The key scientific questions to which EnMAP will contribute are related to climate change impacts, land cover changes and processes, natural resources, biodiversity and ecosystems, water availability and quality, geohazards and risk management. The satellite operates in a sun synchronous orbit in 650 km height with a local time of the descending node set to 11:00 and an across tilt opportunity to improve the local revisit time. Two pushbroom spectrometers with 242 channels in total cover the spectral range from 420 nm to 2450 nm with a mean resolution of 6.5 nm in the visible and 10 nm in the shortwave-infrared. The ground nadir pixel size is 30 m and 1000 spatial pixels generate a swath with of 30 km. For the CalVal activities, the routine calibration is conducted within the ground segment of DLR, while the independent validation activities are lead by GFZ. Data is operationally processed on-ground to standardized calibrated products and delivered to the international user community [1]. Standardized data products will comprise radiance and reflectance products that make use of calibration information gained pre- and inflight. To ensure high quality standards, additional independent product validation activities are planned.

First global retrievals of sun-induced chlorophyll fluorescence (F8) have been achieved in the last years by using data from the Fourier transform spectrometer (FTS) onboard the Japanese Greenhouse Gases Observing Satellite (GOSAT). The high spectral resolution (approximately 0.025 nm) of the FTS enables measurements of F8by the evaluation of the in-filling of solar Fraunhofer lines around 755 nm. This study presents a new F8retrieval algorithm (GARLiC, for GOSAT retrieval of chlorophyll fluorescence) and compares its results to F8from two previously implemented approaches. GARLiC is intended to simplify some of the assumptions of existing retrieval approaches without a loss in retrieval accuracy. We show that GARLiC F8retrievals are comparable to the previously used methods. We also assess the effect of clouds on F8retrieval from GOSAT data through the analysis of the effect of different cloud filter thresholds on F8time series. Our results show a low sensitivity of Fraunhofer-line-based F8retrievals to cloud contamination.

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.

This paper describes the algorithm underlying the ESA DUE globAlbedo product. The purpose of the project is to produce a global 8-day land surface albedo product with associated uncertainty on a 1 km grid with continuous spatial coverage using data from European sensors. The product covers the period 1999-2011.

This paper evaluates nonlinear retrieval methods to derive atmospheric properties from hyperspectral infrared sounding spectra, with emphasis on the retrieval of temperature, humidity, and ozone atmospheric profiles. We concentrate on the Infrared Atmospheric Sounding Interferometer (IASI) onboard the MetOp-A satellite data for the future Meteosat Third Generation Infrared Sounder (MTG-IRS). The methods proposed in this work are compared in terms of both accuracy and speed with the current MTG-IRS L2 processing concept, which processes MetOp-IASI and proxy MTG-IRS data. The official chain consists of a principal component extraction, typically referred to as empirical orthogonal functions (EOF) and a subsequent canonical linear regression. This research proposes the evaluation of some other methodological advances considering: 1) other linear feature extraction methods instead of EOF, such as partial least squares; and 2) the linear combination of nonlinear regression models in the form of committee of experts. The nonlinear regression models considered in this work are artificial neural networks and kernel ridge regression as nonparametric multioutput powerful regression tools. Results show that, in general, nonlinear models yield better results than linear retrieval for both MetOp-IASI and MTG-IRS synthetic and real data. Averaged gains throughout the column of +1.8 K and +2.2 K are obtained for temperature profile estimation from MetOp-IASI and IRS data, respectively. Similar gains are obtained for the estimation of dew point temperatures. In both variables, these improvements are more noticeable in lower atmospheric layers. The combination of models makes the retrieval more robust, improves the accuracy, and decreases the estimated bias. The nonlinear statistical approach is successfully compared to optimal estimation (OE) in terms of accuracy, bias and computational cost. These results confirm the potential of statistical nonlinear inversion techniques for the retrieval of atmospheric profiles.

Earth observation satellites currently provide a large volume of images at different scales. Most of these satellites provide global coverage with a revisit time that usually depends on the instrument characteristics and performance. Typically, medium-spatial-resolution instruments provide better spectral and temporal resolutions than mapping-oriented high-spatial-resolution multispectral sensors. However, in order to monitor a given area of interest, users demand images with the best resolution available, which cannot be reached using a single sensor. In this context, image fusion may be effective to merge information from different data sources. In this letter, an image fusion approach based on multiresolution and multisource spatial unmixing is used to obtain a composite image with the spectral and temporal characteristics of medium-spatial-resolution instrument along with the spatial resolution of high-spatial-resolution image. A time series of Landsat/TM and ENVISAT/MERIS Full Resolution images acquired in the 2004 European Space Agency (ESA) Spectra Barrax Campaign illustrates the method's capabilities. The qualitative and quantitative assessments of the product images are given. The proposed methodology is general enough to be applied to similar sensors, such as the multispectral instruments which will fly on board the ESA GMES Sentinel-2 and Sentinel-3 upcoming satellite series.

Earth observation satellites provide a valuable source of data which when conveniently processed can be used to better understand the Earth system dynamics. In this regard, one of the prerequisites for the analysis of satellite image time series is that the images are spatially coregistered so that the resulting multitemporal pixel entities offer a true temporal view of the area under study. This implies that all the observations must be mapped to a common system of grid cells. This process is known as gridding and, in practice, two common grids can be used as a reference: 1) a grid defined by some kind of external data set (e.g., an existing land-cover map) or 2) a grid defined by one of the images of the time series. The aim of this paper is to study the impact that gridding has on the quality of satellite time series. More precisely, the impact of the so-called gridding artifacts is quantified using a time series of 12 images acquired over The Netherlands by the Medium Resolution Imaging Spectrometer (MERIS). First, the impact of selecting a reference grid is evaluated in terms of geolocation errors and pixel overlap. Then, the effect of observation geometry is studied as nongeostationary satellites, like MERIS, can acquire images from the same area from a number of orbits. Finally, a high-resolution land-cover data set is used to account for temporal information consistency (pixel homogeneity in terms of land-cover composition). Results have shown an average pixel overlap with the nearest pixel between 20% and 41% depending on the selected reference grid and on the differences in observation geometry. These results indicate that inappropriate gridding might result in collocated time series that are not adequate for temporal studies at pixel level (particularly over nonhomogeneous areas) and that, in any case, it is interesting to identify areas with low pixel overlap in order to further analyze the reliability of the products derived over these areas.

Data from current medium-spatial-resolution imaging spectroradiometers are used for land-cover mapping and land-cover change detection at regional to global scales. However, few landscapes are homogeneous at these scales, and this creates the so-called mixed-pixel problem. In this context, this study explores the use of the linear spectral mixture model to extract subpixel land-cover composition from medium-spatial-resolution data. In particular, a time series of MEdium Resolution Imaging Spectrometer (MERIS) full-resolution (FR; pixel size of 300 m) images acquired over The Netherlands is used to illustrate this study. The Netherlands was selected because of the following: 1) the fragmentation of its landscapes and 2) the availability of a high-spatial-resolution land-cover data set (LGN5) which can be used as a reference. The question then is to what extent a multitemporal unmixing of MERIS FR data delivers land-cover information comparable with the one provided by the LGN5. To this end, fully constrained linear spectral unmixing is applied to each individual MERIS image and to the multitemporal composite. The unmixing results are validated at both subpixel and per-pixel scales and at two thematic aggregation levels (12 and 4 land-cover classes). The obtained results indicate that the described unmixing approach yields moderate results for the 12-class case and good results for the 4-class case. These results might be explained by MERIS preprocessing steps, gridding effects, vegetation phenophases, and spectral class separability.

Nowadays, the increasing quantity of applications using images from Earth Observation satellites makes demanding better spatial, spectral and temporal resolutions. Nevertheless, due to the technical constraint of a trade off between spatial and spectral resolutions, and between spatial resolution and coverage, high spatial resolution is related with low spectral and temporal resolutions and vice versa. Data fusion methods are a good solution to combine information from multiple sensors in order to obtain image products with better characteristics. In this paper, we propose an image fusion approach based on a multi-resolution and multi-source unmixing. The proposed methodology yields a composite image with the spatial resolution of the higher resolution image (downscaling) while retaining the spectral and temporal characteristics of the medium spatial resolution image. The approach is tested in the specific cases of ENVISAT/MERIS and Landsat/TM instruments, but is general enough to be applied to other sensor combination.

The simulation of remote sensing images is a valuable tool for defining future Earth observation systems, optimizing instrument parameters, and developing and validating data-processing algorithms. A scene simulator for optical Earth observation data has been developed within the Environmental Mapping and Analysis Program (EnMAP) hyperspectral mission. It produces EnMAP-like data following a sequential processing approach consisting of five independent modules referred to as reflectance, atmospheric, spatial, spectral, and radiometric modules. From a modeling viewpoint, the spatial module is the most complex. The spatial simulation process considers the satellite-target geometry, which is adapted to the EnMAP orbit and operating characteristics, the instrument spatial response, and the sources of spatial nonuniformity (keystone, telescope distortion and smile, and detector coregistration). The spatial module of the EnMAP scene simulator is presented in this paper. The EnMAP spatial and geometric characteristics will be described, the simulation methodology will be presented in detail, and the capability of the EnMAP simulator will be shown by illustrative examples.

The project CHRIS/Proba Toolbox for BEAM (CHRIS-Box) has been developed in order to support users of data from the CHRIS sensor onboard of ESA's Proba platform. BEAM and the CHRIS-Box are user tools which ESA/ESRTN are providing free of charge to the Earth Observation Community. The CHRIS-Box software provides extensions for BEAM that allows accomplishing the following tasks: a) Noise reduction to remove the vertical striping and other noise present in CHRIS response-corrected images; b) Cloud screening to mark cloudy pixels in CHRIS noise-corrected images; the cloud screening algorithm provides cloud probability and abundances for each pixel; c) Atmospheric correction that provides surface reflectance without external information; and d) Geometric correction that provides geographic coordinates for each pixel of a CHRIS image.

The simulation of remote-sensing images is a useful tool for a variety of tasks, such as the definition of future Earth Observation systems, the optimization of instrument specifications, and the development and validation of data processing algorithms. A scene simulator for optical hyperspectral and multispectral data has been implemented in the frame of the Environmental Mapping and Analysis Program (EnMAP) mission. EnMAP is a German-built hyperspectral space sensor scheduled for launch in 2012. EnMAP will measure in the 420-2450-nm spectral range at a varying spectral sampling of 6.5-10 nm. Images will cover 30 times 30 km areas at an approximate ground sampling distance of 30 m. The EnMAP scene simulator presented in this paper is able to generate realistic EnMAP-like data in an automatic way under a set of user-driven instrumental and scene parameters. Radiance and digital numbers data are generated by five sequential processing modules which are able to produce data over a range of natural environments, acquisition and illumination geometries, cloud covers, and instrument configurations. The latter include the simulation of data nonuniformity in the spatial and spectral domains, spatially coherent and noncoherent instrumental noise, and instrument's modulation transfer function. Realistic surface patterns for the simulated data are provided by existing remote-sensing data in different environments, from dry geological sites to green vegetation areas. A flexible radiative transfer simulation scheme enables the generation of different illumination, observation, and atmospheric conditions. The methodology applied to the complete scene simulation and some sample results are presented and analyzed in this paper.

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.

The Environmental Mapping and Analysis Program (EnMAP) is a German built hyperspectral space sensor scheduled for launch in 2012. EnMAP will measure over the 420-2450 nm spectral range at a varying spectral sampling of 5-10 nm. Images will covered 30 kmtimes30 km areas at approximate pixel sizes of 30 m. The primary goal of EnMAP is the exploitation of hyperspectral data for the derivation of high-spectral resolution observations of biophysical, biochemical and geochemical variables from a range of surface covers, such as vegetation canopies, rock and soil targets and coastal waters, on a global scale. General descriptions of the EnMAP instrument, the satellite operation concept, the data processing and archiving structures and current project development activities are provided in this paper.

This letter presents a modification to the established Fraunhofer line discrimination (FLD) method for improving the accuracy of the solar-induced chlorophyll fluorescence (ChF) retrieval over terrestrial vegetation. The FLD method relies on the decoupling of reflected and ChF emitted radiation by the evaluation of measurements inside and outside the absorption bands. The improved FLD method introduces two correction coefficients that relate the values of the fluorescence and the reflectance inside and outside the absorption band. The new method uses the full spectral information around the absorption band to derive these coefficients. A sensitivity analysis has been performed to evaluate the impact of the correction coefficients on the accuracy of the ChF estimation. The new formulation has been tested for the$\hbox{O}_{2}$A-band on synthetic data obtaining lower errors in comparison to the standard FLD and has been successfully applied to real measurements at canopy level.

This paper discusses the application and adaptation of two existing operational algorithms for land surface emissivity ($\varepsilon$) retrieval from different operational satellite/airborne sensors with bands in the visible and near-infrared (VNIR) and thermal IR (TIR) regions: 1) the temperature and emissivity separation algorithm, which retrieves$\varepsilon$only from TIR data and 2) the normalized-difference vegetation index thresholds method, in which$\varepsilon$is retrieved from VNIR data.

The Fraunhofer Line Discrimination (FLD) principle is established as a good method for remote sensing of solar induced chlorophyll fluorescence. Some improvements to the method are analysed in order to determine and reduce the sources of error in the estimation of the fluorescence emission. A sensitivity analysis has been performed over simulated data generated from real diurnal cycle measurements.

Vegetation monitoring is a key issue in Earth Observation due to its relation with the global CO2cycle. Chlorophyll fluorescence (ChF) emitted by the vegetation is an accurate indicator of the plant status and their photosynthetic activity. This work analyses the diurnal evolution of the ChF emission spectrum and the fluorescence yield in order to determine the best conditions for remote sensing of ChF from a satellite platform. The ChF evolution is studied at leaf level during several diurnal cycles, in simulated conditions, for two species under different stress conditions. The analysis of the signal levels gives an estimation of the values of ChF emission which could be observed from a remote sensing platform, and determines the best overpass time for this observation.

This paper presents a methodology for cloud screening of multispectral images acquired with the Medium Resolution Imaging Spectrometer (MERIS) instrument on-board the Environmental Satellite (ENVISAT). The method yields both a discrete cloud mask and a cloud-abundance product from MERIS level-1b data on a per-pixel basis. The cloud-screening method relies on the extraction of meaningful physical features (e.g., brightness and whiteness), which are combined with atmospheric-absorption features at specific MERIS-band locations (oxygen and water-vapor absorptions) to increase the cloud-detection accuracy. All these features are inputs to an unsupervised classification algorithm; the cloud-probability output is then combined with a spectral unmixing procedure to provide a cloud-abundance product instead of binary flags. The method is conceived to be robust and applicable to a broad range of actual situations with high variability of cloud types, presence of ground covers with bright and white spectra, and changing illumination conditions or observation geometry. The presented method has been shown to outperform the MERIS level-2 cloud flag in critical cloud-screening situations, such as over ice/snow covers and around cloud borders. The proposed modular methodology constitutes a general framework that can be applied to multispectral images acquired by spaceborne sensors working in the visible and near-infrared spectral range with proper spectral information to characterize atmospheric-oxygen and water-vapor absorptions.

The surface processes and ecosystem changes through response analysis (SPECTRA) Barrax campaigns were validation campaigns developed in the framework of the SPECTRA mission in order to verify that the geophysical data products provided by satellite imagery are consistent with the measurements made by independent means. Two campaigns took place in Barrax, Spain, during the summers of 2003 and 2004. This paper presents the results of the characterization of the atmospheric composition from solar radiation, radiosoundings, and lidar measurements. Several potentially interesting situations involving atmospheric layers with different types of aerosols and water content are discussed. The presence of a residual layer capping the mixing layer during some days of the 2003 campaign and the arrival of a dust-rich air mass from the Sahara on the last two days of the 2004 campaign provide some relevant aerosol vertical profiles to test atmospheric correction algorithms. The study of the effects of these atmospheric situations on radiative transfer calculations is required in the development and validation of advanced atmospheric correction codes for the new generation of Earth observation systems.

The Project for On-Board Autonomy (PROBA) platform developed by the European Space Agency was launched on October 22, 2001. The instrument payload includes the Compact High Resolution Imaging Spectrometer (CHRIS). The coupled system provides high spatial resolution hyperspectral/multiangular data, which represents a new-generation source of information for Earth observation purposes. The first results obtained from the preprocessing (noise removal and geometric/atmospheric correction) of two different datasets, collected over agricultural crops and inland waters, are presented in this letter. In situ measurements are used to assess the quality of the data and to validate the processing algorithms. The capabilities of this new kind of information for an improved analysis of the surface properties are shown, focusing on the advantages that the coupling between the spectral and the angular domains may have in future Earth observation systems.

The Compact High Resolution Imaging Spectrometer (CHRIS) onboard the Project for On-Board Autonomy (PROBA) platform system provides the first high spatial resolution hyperspectral/multiangular remote sensing data from a satellite system, what represents a new source of information for Earth Observation purposes. A fully consistent radiative transfer approach is always preferred when dealing with the retrieval of surface reflectance from hyperspectral/multiangular data. However, due to the reported calibration anomalies for CHRIS data, a direct atmospheric correction based on physical radiative transfer modeling is not possible, and the method must somehow compensate for such calibration problems in specific wavelength ranges. A dedicated atmospheric correction algorithm for PROBA/CHRIS data over land is presented in this work. It consists in the combination of radiative transfer and empirical line approaches to atmospheric correction, in order to retrieve surface reflectance images free from both the atmospheric distortion and artifacts due to miscalibration. The atmospheric optical parameters and the updated set of calibration coefficients are obtained jointly in an autonomous process, without the need for any ancillary data. Results from the application of the algorithm to PROBA/CHRIS data from the two European Space Agency SPectra bARrax Campaign (SPARC) held at the Barrax study site (La Mancha, Spain) in 2003 and 2004 are presented in this work, focusing on the validation of the final surface reflectance using in situ measurements acquired simultaneously to PROBA overpasses.