The European Space Agency's FLuorescence EXplorer-Sentinel 3 (ESA FLEX-S3) mission, scheduled for launch in 2026, aims to remotely detect vegetation fluorescence at 300x300-meter pixel resolution. The ESA requires a national Calibration and Validation (Cal/Val) plan for FLEX-S3 products that addresses the selection of test sites, measurement protocols, and uncertainty budgets. Despite Spain's significant Cal/Val test sites, it lacks a permanent instrumented site in international networks. The SpaFLEXImp initiative aims to implement a FLEX Cal/Val plan, standardizing protocols, and establishing a coordinated network of sites. Led by the National Institute of Aerospace Technology (INTA), the project involves the University of Valencia and the Doñana Biological Station-CSIC. With a long experience in in situ, airborne, and spaceborne measurements, the teams will conduct specific Cal/Val campaigns in 3 sites, making Spain a European and international reference for terrestrial Cal/Val activities.

The Fluorescence Explorer (FLEX) mission was selected in 2015, by the European Space Agency (ESA), as an Earth Explorer mission, with a launch planned for 2025. The main scientific objective is the quantitative global mapping of actual photosynthetic activity of terrestrial ecosystems, and its spatial/temporal variability with vegetation health status and environmental stress conditions. The very high spectral resolution measurements (0.1 nm sampling) provided by the FLORIS instrument, with a spatial resolution of 300 m, are designed to derive, as the main mission product, the spectrally resolved fluorescence emission spectrum in the range 600–780 nm. The mission is designed to fly in tandem with Copernicus Sentinel-3. The OLCI and SLSTR instruments on Sentinel-3 provide the necessary information to retrieve the emitted fluorescence, and to allow proper interpretation of the spatial and temporal dynamics of vegetation photosynthesis from such global measurements. As the launch date approaches, the scientific community is getting ready to exploit the data, developing not only the Level-1 and Level-2 data processing but also the high level data analysis strategies, involving exploitation of FLEX data, in combination with other data sources, in data assimilation schemes into global dynamical vegetation models and terrestrial carbon cycle models. This paper addresses recent developments in mission preparation, with particular focus on mission data exploitation plans.

Atmospheric radiative transfer models (RTMs) simulate the light propagation in the Earth's atmosphere. With the evolution of RTMs, their increase in complexity makes them impractical in routine processing such as atmospheric correction. To overcome their computational burden, standard practice is to interpolate a multidimensional lookup table (LUT) of prestored simulations. However, accurate interpolation relies on large LUTs, which still implies large computation times for their generation and interpolation. In recent years, emulation has been proposed as an alternative to LUT interpolation. Emulation approximates the RTM outputs by a statistical regression model trained with a low number of RTM runs. However, a concern is whether the emulator reaches sufficient accuracy for atmospheric correction. Therefore, we have performed a systematic assessment of key aspects that impact the precision of emulating MODTRAN: 1) regression algorithm; 2) training database size; 3) dimensionality reduction (DR) method and a number of components; and 4) spectral resolution. The Gaussian processes regression (GPR) was found the most accurate emulator. The principal component analysis remains a robust DR method and nearly 20 components reach sufficient precision. Based on a database of 1000 samples covering a broad range of atmospheric conditions, GPR emulators can reconstruct the simulated spectral data with relative errors below 1% for the 95th percentile. These emulators reduce the processing time from days to minutes, preserving sufficient accuracy for atmospheric correction and providing model uncertainties and derivatives. We provide a set of guidelines and tools to design and generate accurate emulators for satellite data processing applications.

End-To-End mission performance simulators (E2Es) are software tools developed to support satellite mission preparatory activities. For passive remote sensing missions, E2Es generate synthetic scenes simulating the interaction of the solar radiation between the atmosphere and the surface; therefore allowing the estimation of the mission performance before its launch. In this paper, we present the CHIME Scene Generator Module (SGM) as part of CHIME E2Es, with state-of-the-art parallelization and optimization that give a performance allowing to obtain a whole year of daily worldwide Top-Of-Atmosphere radiance images in a matter of hours. The CHIME SGM generates 100x200km hyperspectral scenes with elevation effects, shadow projecting clouds, and detailed surface definition in less than an hour. This high performance is due to the producer-consumer design and clever use of the Intel Threading Building Blocks library. The design paves the way to integrate a sensor definition as a library in the part of the convolution of the SGM algorithm, giving an even better performance in the processing chain of the CHIME E2Es.

The Fluorescence Explorer (FLEX) mission was selected in 2015, by the European Space Agency (ESA), as an Earth Explorer mission, with a launch planned for 2024. The key scientific objective of the mission is the quantitative global mapping of actual photosynthetic activity of terrestrial ecosystems, as a function of variable vegetation health status and environmental stress conditions. The measurements will have a spatial resolution of 300 m, adequate to resolve land surface processes associated to vegetation dynamics at a global scale. To be able to accomplish such objective, the FLEX mission carries the FLORIS spectrometer, specially optimized to map vegetation fluorescence with a spectral sampling of 0.1 nm, and is designed to fly in tandem with Copernicus Sentinel-3. Together with FLORIS, the OLCI and SLSTR instruments on Sentinel-3 provide all the necessary information to retrieve the emitted fluorescence, and to allow proper interpretation of the spatial and temporal dynamics of vegetation photosynthesis from such global measurements.

In the context of the development and implementation of the Fluorescence Explorer (FLEX) mission, selected in 2015 by the European Space Agency (ESA) as the 8th Earth Explorer, many recent developments in the field of vegetation chlorophyll fluorescence have taken place in the last years. These advances include new retrieval approaches, more accurate and robust, and a much better understanding of the variability of the signal, facilitating the interpretation of the dynamical changes and the usage of the fluorescence signal in Earth models and applications. Although chlorophyll fluorescence is related to the actual photosynthetic activity of the plants, the link between fluorescence and photosynthesis is not straightforward, and only advanced retrieval methods and usage of proper physical models allow a quantitative exploitation of the spatial, temporal and spectral variability of the fluorescence signal. This paper reviews such recent developments and describes achievements and perspectives from current ongoing research activities.

Physically based radiative transfer models (RTMs) are widely used in Earth observation to understand the radiation processes occurring on the Earth's surface and their interactions with water, vegetation, and atmosphere. Through continuous improvements, RTMs have increased in accuracy and representativity of complex scenes at expenses of an increase in complexity and computation time, making them impractical in various remote sensing applications. To overcome this limitation, the common practice is to precompute large lookup tables (LUTs) for their later interpolation. To further reduce the RTM computation burden and the error in LUT interpolation, we have developed a method to automatically select the minimum and optimal set of input-output points (nodes) to be included in an LUT. We present the gradient-based automatic LUT generator algorithm (GALGA), which relies on the notion of an acquisition function that incorporates: 1) the Jacobian evaluation of an RTM and 2) the information about the multivariate distribution of the current nodes. We illustrate the capabilities of GALGA in the automatic construction and optimization of MODTRAN-based LUTs of different dimensions of the input variables space. Our results indicate that when compared with a pseudorandom homogeneous distribution of the LUT nodes, GALGA reduces:1) the LUT size by >24%; 2) the computation time by 27%; and 3) the maximum interpolation relative errors by at least 10%. It is concluded that an automatic LUT design might benefit from the methodology proposed in GALGA to reduce interpolation errors and computation time in computationally expensive RTMs.

Accurately disentangling the tiny Solar–Induced Chlorophyll Fluorescence (SIF) from canopy reflected solar irradiance by using passive remote sensing techniques is always challenging. Regardless the scale at which SIF is measured, i.e., proximal sensing, airborne or satellite level; instrumental and atmospheric effects must be accounted for and compensated as part of the SIF retrieval strategy. Regarding the instrumental effects, the use of very high spectral resolution spectrometers makes mandatory an accurate characterization of the Instrument Spectral Response Function (ISRF); and – in the case of imager spectrometers – an accurate characterization of the full instrument response in the spectral and in the spatial domain. Otherwise, spectral distortions derived by an inaccurate instrument's response characterization would rapidly derive into errors on SIF estimations. With regards to the atmospheric effects, when exploiting the oxygen absorption features to estimate SIF, aerosol characterization generally becomes the bottleneck of the atmospheric correction strategy due to its strong impact on these spectral regions. In this work, instrumental and atmospheric effects are analyzed twofold: (1) focusing on their detection by inspecting the at–sensor radiance and surface apparent reflectance, and (2) proposing an atmospheric and instrument compensation strategy to mitigate these effects.

While the mapping of LAI green (LAIG) is well established, current operational products are not calibrated for LAI brown (LAIB), i.e. LAI estimation over senescent vegetation. With Sentinel-2 (S2) new opportunities are opened to estimate LAI brown. An explicit distinction between LAIGand LAIBcan be achieved thanks to the S2 bands in the red edge (B5: 705 nm and B6: 740 nm) and in the shortwave infrared (B11: 1610 nm). By using LAI ground measurements data from multiple campaigns together with available S2 data, independent LAIGand LAIBmodels were optimized using Gaussian processes regression (LAIG: R2= 0.89, NRMSE= 7.1%; LAIB: R2= 0.75, NRMSE= 13.7%). These models can then be combined into LAIGBcomposite maps. The uncertainty estimates were used to map only those LAI estimated values that fall within a 50% uncertainty threshold. As only the vegetated areas fall within that threshold there is no need to apply additional masks. For multiple European core test sites, LAIGBcomposite maps were generated from S2 images, enabling to quantify when crops start senescing across the European regions.

A diverse range of vegetation indices have earlier been developed for the remote estimation of canopy water content (CWC), but most of them are not universally applicable. The aim of this study is to define new indices valid for a wide variety of crop types, that allow to obtain CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain), which consists of field data including water content and other biophysical parameters collected for 6 different crops (lucerne, corn, potato, sugar beet, garlic and onion) and associated TOC reflectance spectra acquired by the HyMap airborne sensor. Specifically, Water Absorption Area Index (WAAI) has been defined as the area between the spectrum with null water content, i.e. a straight line whose slope depends only on the reflectance at 800 nm, and the spectrum between 911 and 1271 nm. On the other hand, it is proposed the Depth Water Index (DWI), which is a simple index, applicable to those sensors with lower spectral resolution, based on the spectral depths estimation produced by the water absorption at 970 and 1200 nm. These algorithms outperform commonly used indices in predicting CWC, being applicable to heterogeneous zones, with a R2of 0.8 and 0.7, respectively, using an exponential fit.

The Ocean Color 2 (OC2), Ocean Color 3 (OC3) and Dall'Olmo three-band (TBDO) algorithms used for the estimation of the chlorophyll-a concentration [Chl-a] have been calibrated and validated for Sentinel-2 spectral bands. Measurements of in situ chlorophyll-a, radiometry and simulations with HydroLight have been used for this purpose within the Ecological Status of AQuatic Systems Satellites (ESAQS) Project to estimate water quality in aquatic systems in Spain. The results show that Sentinel-2 spectral bands are suitable for studying the chlorophyll-a concentration in inland waters. The TBDO algorithm have been applied to Sentinel-2 images and satisfactory results have been obtained in Albufera of Valencia region (Spain).

In this paper, an algorithm using Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify changes of tillage over agricultural fields at approximately ~100m resolution is presented. The methodology implements a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements. An initial assessment at farm scale using in situ and S-1 and SPOT5-Take5 data, acquired over the Apulian Tavoliere in southern Italy in 2015, is illustrated. A full validation of the approach is in progress over three European agricultural areas located in Italy, Spain and France. Results will be further reported in the paper.

An End-to-end simulator (E2ES) is a tool to evaluate the performance of a satellite mission. Once a mission is approved for operation, E2ES evolves during Phase C/D to become a supporting tool for the development and validation of the ground data processor, as well as for simulating data sets to test the Prototype and Operational Processors. FLEX-E is the E2ES of the FLEX/Sentinel-3 tandem mission, which was selected in 2015 as ESA's eighth Earth Explorer. The FLEX-E evolution implies the consolidation of all the retrieval algorithms (e.g. fluorescence, reflectance, biophysical variables), the implementation of new scientific developments, as well the improvement of the co-registration process, the atmospheric correction, and the retrieval of the Level-2 products. The high-level modular design of the improved Level-2 retrieval module will permit a detailed analysis of the errors propagating through the processing chain.

Physical radiative transfer models (RTMs) of leaf and canopies with sufficient realism enable the retrieval of biophysical variables from imaging spectroscopy through numerical inversion. However, advanced RTMs are computationally intensive, which hampers practical applicability of inversion schemes against remote sensing images. To bypass the computational load such RTMs, it has been proposed to approximate these models by means of statistical learning, i.e. emulation. Here we tested three machine learning regression algorithms, i.e. neural networks, kernel ridge regression and Gaussian processes regression, on their ability to emulate the advanced RTM SCOPE (Soil-Canopy-Observation of Photosynthesis and the Energy balance) for limited set of input variables. The best performing emulator was implemented into a numerical inversion scheme to process a subset of an hyperspectral image into a multitude of vegetation properties. Obtained maps are not only consistent, but also processing time was in the order of minutes - in comparison, by using SCOPE the processing would have taken days.

The collection of field data (biophysical variables with associated spectral data) are an essential part of the development and validation of imaging spectroscopy vegetation products. Yet, their quality can only be assessed in the subsequent analysis, and often it appears that there is a wish for extra data to fill up gaps. In an attempt to generate such additional data, we propose to exploit emulation, i.e. variables-based reconstruction of spectral data through statistical learning. We evaluated emulation against classical interpolation techniques using an experimental field dataset with associated airborne hyperspectral HyMap reflectance spectra to produce HyMap-like spectra for any combination of input variables. Results indicate that: (1) emulation produces reflectance spectra more accurately than interpolation when validating against a split part of the field dataset (8% vs 12% errors), (2) emulation produces spectral data multiple times (tens to hundreds) faster than interpolation, and (3) emulation enables meaningful extrapolation outputs. Consequently, this technique opens various new analysis opportunities, e.g., emulators not only allow to produce large experimental-like datasets in a fraction of a second, but they also can be implemented into computationally intensive processing routines to speed up processing, such as global sensitivity analysis or inversion schemes.

ESA's FLEX/Sentinel-3 tandem mission aims at mapping Sun-induced fluorescence (SIF) as a proxy to quantify photosynthetic activity of terrestrial vegetation. Due to the complexity of the mission concept and stringent requirements for the data processing algorithms, ESA developed a Phase A/B1 End-to-End Mission Performance Simulator (E2ES) tool to reproduce the expected mission performance and check the mission and instrument concepts. In the current Phase C/D, the E2ES concept must evolve to consolidate the whole data processing chain, providing an accurate figures of the whole mission error budget and serving as a roadmap for the future development of FLEX Ground Segment. This paper gives an overview of the activities involving the FLEX E2ES, from the conceptual development phase (Phase A/B1) to the implementation of the Ground Segment and operations (Phase C/D). Particularly, we will focus on the high-level description of the Level-2 data processing chain from an architecture and software design point of view.

End-to-end mission performance simulators (E2ES) are suitable tools to accelerate satellite mission development from concet to deployment. One core element of these E2ES is the generation of synthetic scenes that are observed by the various instruments of an Earth Observation mission. The generation of these scenes rely on Radiative Transfer Models (RTM) for the simulation of light interaction with the Earth surface and atmosphere. However, the execution of advanced RTMs is impractical due to their large computation burden. Classical interpolation and statistical emulation methods of pre-computed Look-Up Tables (LUT) are therefore common practice to generate synthetic scenes in a reasonable time. This work evaluates the accuracy and computation cost of interpolation and emulation methods to sample the input LUT variable space. The results on MONDTRAN-based top-of-atmosphere radiance data show that Gaussian Process emulators produced more accurate output spectra than linear interpolation at a fraction of its time. It is concluded that emulation can function as a fast and more accurate alternative to interpolation for LUT parameter space sampling.

Vegetation dynamically reacts to the available photosynthetically active radiation (PAR) by adjusting the photosynthetic apparatus to either a light harvesting or a photoprotective modus. When activating the photoprotection mechanism, either minor or major pigment-protein interactions may occur at the leaf level, resulting in different light absorption and consequently reflectance intensities. The reflectance changes were measured during sudden illumination transients designed to provoke fast adaptation to high irradiance. Different spectral reflectance change features were observed during different stages of photoprotection activation, extending over part of the visible spectral range (i.e. 490–650 nm). Due to this multiple wavelength reflectance modification, which affects also the reference band at 570 nm, the commonly used Photochemical Reflectance Index (PRI) is unable to trace and quantify such strong photoprotection mechanism. To quantify the entire photoprotection with a required accuracy, the spectral changes in the full visible range must be characterized.

During the design phase of a satellite mission, end-to-end mission performance simulator (E2ES) tools allow scientists and engineers evaluating the mission concept, consolidating system technical requirements and analyzing the suitability of the implemented technical solutions and data processing algorithms. The generation of synthetic scenes is one of the core parts of an E2ES, providing scenes (ground truth) as would be observed by satellite instruments and used as reference against simulated retrieved mission products. An appropriate generation of the scene also allows assessing the performance of the ground data processing chain replacing real instrument data before the mission is in orbit, for which the fidelity of the scene generation is critical. This paper describes the design of a generic scene generator (GSG) with capabilities to generate complex 3-D synthetic scenes that combine the effects of surface, heterogeneity, topography and atmosphere, and viewing/illumination geometry. The proposed design allows generating consistent high spatial and spectral resolution top-of-atmosphere radiance scenes for multiple instruments based on the use of thematic maps, radiative transfer models, and reflectance databases. The described GSG was implemented within the FLuorescence EXplorer (FLEX) E2ES software tool and showed its capabilities to generate compatible scenes for the fluorescence imaging spectrometer, ocean and land color instrument, and sea and land surface temperature radiometer instruments of ESA’s FLEX/Sentinel-3 tandem mission and to validate the fulfillment of the FLEX mission requirements.

Although traditional remote sensing systems based on spectral reflectance can already provide estimates of the “potential” photosynthetic activity of terrestrial vegetation through the quantification of total canopy chlorophyll content or absorbed photosynthetic radiation, the determination of the “actual” photosynthetic activity of terrestrial vegetation requires information about how the absorbed light is used by plants, such as vegetation fluorescence, using very high spectral resolution spectroscopy in the range 650-800 nm. The Fluorescence Explorer (FLEX) mission, selected in November 2015 as the 8th Earth Explorer by the European Space Agency (ESA), carries the FLORIS spectrometer, with a spectral resolution of 0.3 nm and a spatial resolution of 300 m, with a swath of 150 km. The FLEX mission is designed to fly in tandem with the Copernicus Sentinel-3 satellite, in order to provide all the necessary information to disentangle emitted fluorescence from the background reflected radiance, and to allow proper interpretation of the fluorescence spatial and temporal changes in relation to photosynthesis dynamics, accounting for non-photochemical energy dissipation and canopy temperature effects.

Since oxygen (O2) absorption of light becomes more pronounced at higher pressure levels, even a few meters distance between the target and the sensor can strongly affect canopy-leaving Solar-Induced chlorophyll Fluorescence (SIF) retrievals. This study was conducted to quantify the consequent error propagation and the impact of ignoring oxygen absorption effects on proximal sensing SIF measurements based on the O2-A absorption band with field-acquired and simulated data. It was demonstrated that the uncorrected oxygen transmittance between target and sensor distance of 10 m can lead to SIF relative errors ranging from 66% to higher than 100% when using a Spectral Fitting (SF) technique or the 3FLD retrieval method, respectively. A proposed strategy to include oxygen transmittance effects on the well-known 3FLD and SF techniques is presented here and applied to the NASA-GSFC multi-angular spectral system known as FUSION over a field of corn plants (Zea mays L.) during the second half of the 2014 growing season. Daily averages of oxygen-corrected SIF measurements from FUSION were related to daily averages of heat and energy fluxes obtained from a nearby Eddy-Covariance (EC) flux tower, and showed a consistent behaviour with similar experiments performed at leaf level.

In November 2015, the FLuorescence EXplorer (FLEX) was selected as the eighth Earth Explorer mission of the European Space Agency. The tandem mission concept will provide measurements at a spectral and spatial resolution enabling the retrieval and interpretation of the full chlorophyll fluorescence spectrum emitted by the terrestrial vegetation. This paper provides a mission concept overview of the scientific goals, the key objectives related to fluorescence, and the requirements guaranteeing the fitness for purpose of the resulting scientific data set. We present the mission design at the time of selection, i.e., at the end of project phase Phase A/B1, as developed by two independent industrial consortia. The mission concepts both rely on a single payload Fluorescence Imaging Spectrometer, covering the spectral range from 500 to 780 nm. In the oxygen absorption bands, its spectral resolution will be 0.3 nm with a spectral sampling interval of 0.1 nm. The swath width of the spectrometer is 150 km and the spatial resolution will be 300 × 300 m-2. The satellite will fly in tandem with Sentinel-3 providing different and complementary measurements with a temporal collocation of 6 to 15 s. The FLEX launch is scheduled for 2022.

The Fluorescence Explorer (FLEX) mission has been recently selected as the 8thEarth Explorer by the European Space Agency (ESA). It will be the first mission specifically designed to measure from space vegetation fluorescence emission, by making use of very high spectral resolution imaging spectroscopy techniques. Vegetation fluorescence is the best proxy to actual vegetation photosynthesis which can be measurable from space, allowing an improved quantification of vegetation carbon assimilation and vegetation stress conditions, thus having key relevance for global mapping of ecosystems dynamics and aspects related with agricultural production and food security. The FLEX mission carries the FLORIS spectrometer, with a spectral resolution in the range of 0.3 nm, and is designed to fly in tandem with Copernicus Sentinel-3, in order to provide all the necessary spectral / angular information to disentangle emitted fluorescence from reflected radiance, and to allow proper interpretation of the observed fluorescence spatial and temporal dynamics.

Chlorophyll-a concentration ([Chl-a]) and Lake Surface Temperature (LST) were retrieved in Lake Titicaca (Peru-Bolivia) using MODIS and Landsat-8 images. The lake was chosen as a case-study for evaluating the feasibility of Landsat-8 images for [Chl-a] and LST monitoring in oligotrophic and mesotrophic water bodies. The big size of the lake and its spatial and temporal variability, allowed the comparison of MODIS and Landsat-8 products for a wide range of [Chl-a] and LST. The atmospheric correction of the images was facilitated by the very high altitude of the lake. MODIS images were processed with standard ocean color algorithms whereas for Landsat-8, specific algorithms were tested and validated The results show that Landsat-8 is capable of retrieving [Chl-a] and LST with an accuracy comparable to that of MODIS and with a finer spatial resolution, revealing surface patterns in greater detail. The combined use of both sensors allows monitoring the eutrophication and temperature trends of Lake Titicaca, which is a water body of the highest ecological interest, increasingly affected by human activities in its watershed and very sensitive to climate changes.

Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing data made available for analysis and the possibility of using a large amount of simulated data from radiative transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques will need to be implemented. Active learning (AL) methods enable to select the most informative samples in a data set. This letter introduces six AL methods for achieving optimized biophysical variable estimation with a manageable training data set, and their implementation into a Matlab-based MLRA toolbox for semiautomatic use. The AL methods were analyzed on their efficiency of improving the estimation accuracy of the leaf area index and chlorophyll content based on PROSAIL simulations. Each of the implemented methods outperformed random sampling, improving retrieval accuracy with lower sampling rates. Practically, AL methods open opportunities to feed advanced MLRAs with RTM-generated training data for the development of operational retrieval models.

The FLuorescence EXplorer (FLEX) mission, selected as the European Space Agency's eighth Earth Explorer, aims to globally measure the sun-induced-chlorophyll-fluorescence spectral emission from terrestrial vegetation. In the frame of the FLEX mission, several industrial and scientific studies have analyzed the instrument design, image processing algorithms, or modeling aspects. At the same time, a common tool is needed to address the overall FLEX mission performance by combining all these features. For this reason, an end-to-end mission performance simulator has been developed for the FLEX mission (FLEX-E). This paper describes the FLEX-E software design, which combines the generation of complex synthetic scenes with an advanced modeling of the instrument behavior and the full processing scheme up to the final fluorescence product. The results derived from FLEX-E simulations indicate that the instrument and developed image processing algorithms are able to retrieve the sun-induced fluorescence with an accuracy below the 0.2$\text{mW}\cdot\text{m}^{-2}\cdot\text{sr}^{-1}\cdot \text{nm}^{-1}$mission requirement. It is expected that FLEX-E will not only optimize the FLEX retrieval algorithms and technical requirements, but also serve as the baseline for the ground processing implementation and testing of calibration/validation procedures.

The main objective of this study is to assess the use of Sentinel-1 (S-1) data for surface soil moisture (SSM) retrieval and wheat mapping (WM) at high spatial resolution (e.g. 100–500m), which constitute valuable information for improving crop yield forecast at large scale. A knowledge based classification method and a SSM retrieval algorithm, developed in view of the European Space Agency Sentinel-1 mission, have been applied to a time series of S-1A data collected from October 2014 to April 2015 over a well-documented agricultural site in southern Italy. In particular, observations of SSM content recorded by a network of ground stations deployed in an experimental farm have been used to test the accuracy of the retrieved SSM values. First results indicate an rms error between 5% and 6%. However, the range of observed SSM values is still quite limited and, therefore, longer time series are needed to investigate the retrieval performance over the full range of SSM values.

A new fluorescence retrieval method is proposed to support ESA's 8th Earth Explorer FLuorescence EXplorer/Sentinel-3 (FLEX-S3) candidate tandem mission. FLEX is the first mission specially dedicated to measure the Sun-Induced vegetation chlorophyll fluorescence (SIF) strongly related with the vegetation photosynthetic activity. Most hyperspectral fluorescence retrieval algorithms available in the literature are very sensitive to true reflectance modelization and/or they assume the atmospheric status as known. The proposed algorithm delivers the retrieval of full fluorescence spectrum at canopy level by using only Top Of Atmosphere (TOA) radiances from S3 and FLEX as input. Once the spatial co-registration and cross-calibration of S3 and FLEX images have been performed, the proposed method starts with (1) the atmospheric correction of TOA radiances, characterizing the state of the atmosphere, (2) performing a first estimation of fluorescence values in main oxygen absorption bands without any approximation of true reflectance spectrum, and using this fluorescence estimation to initialize a Spectral Fitting Method (SFM) to finally retrieving a full fluorescence spectrum. This proposed fluorescence retrieval method is currently being implemented at the Level-2 Retrieval Module (L2RM) of the FLEX/End-To-End Simulator (E2ES).

Physically-based radiative transfer models (RTMs) help in understanding the processes occurring on the Earth's surface and their interactions with vegetation and atmosphere. However, advanced RTMs can take a long computational time, which makes them unfeasible in many real applications. To overcome this problem, it has been proposed to substitute RTMs through so-called emulators. Emulators are statistical models that approximate the functioning of RTMs. They are advantageous in real practice because of the computational efficiency and excellent accuracy and flexibility for extrapolation. We here present an `Emulator toolbox' that enables analyzing three multi-output machine learning regression algorithms (MO-MLRAs) on their ability to approximate an RTM. As a proof of concept, a case study on emulating sun-induced fluorescence (SIF) is presented. The toolbox is foreseen to open new opportunities in the use of advanced RTMs, in which both consistent physical assumptions and data-driven machine learning algorithms live together.

The Hyperspectral Imager for the Coastal Ocean (HICO) is an imaging spectrometer designed with a very high signal-to-noise ratio to monitor coastal ocean and inland waters. The processing of Top-Of-Atmosphere radiance data down to surface reflectance is fundamental for the retrieval of water quality products. However, the current HICO processing chain does not provide atmospheric corrected data nor higher-level water quality products. This paper describes the algorithms implemented within an HICO data processing chain that includes image pre-processing, atmospheric correction and the retrieval of water quality parameters. The implemented algorithms have been validated over a set of HICO images showing a good match with in-situ surface reflectance data and correlation (R2= 0.95) between in-situ measured and retrieved Chl-a over a water body. It is expected that the presented algorithms will ease the processing of HICO data down to surface reflectance allowing to derive water quality parameters.

Scattering from a leaf responds differently at different wavelengths to changes in leaf properties such as pigment concentrations, chemical constituents, internal structure, and leaf-surface properties. Radiation scattered by leaves and exiting the vegetation canopy toward the sensor is affected by canopy structure. The concept of canopy spectral invariants is used to decompose multiangular hyperspectral Compact High Resolution Imaging Spectroradiometer–PROBA surface reflectances over agricultural crops during peak growth season into structural and optical components. The former, called the directional area scattering factor, is determined by the canopy geometrical properties and varies with crop type. The latter is a function of the leaf scattering properties and more directly related to the leaf interior. For dense crops, the decomposition technique does not require the use of canopy radiation models, prior knowledge, or ancillary information regarding the leaf scattering properties and thus provides a powerful means to remove canopy structural influences in hyperspectral remote sensing of leaf biochemical constituents. Our results also suggest that leaf-surface characteristics can increase canopy scattering spectra. This may decrease the ability to remotely sense leaf biochemistry.

Inversion of radiative transfer models (RTM) using a lookup-table (LUT) approach against satellite reflectance data can lead to concurrent retrievals of biophysical parameters such as leaf chlorophyll content$(Chl)$and leaf area index (LAI), but optimization strategies are not consolidated yet. ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity of old generation satellite sensors by providing superspectral images of high spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust, accurate, and operational retrieval methods. For three simulated Sentinel settings (S2-10 m: 4 bands, S2-20 m: 8 bands and S3-OLCI: 19 bands) various optimization strategies in LUT-based RTM inversion have been evaluated, being the role of i) added noise, ii) multiple best solutions, iii) combined parameters$(Chl \times \hbox{LAI})$, and iv) applied cost functions. By inverting the PROSAIL model and using data from the ESA-led field campaign SPARC (Barrax, Spain), it was demonstrated that introducing noise and opting for multiple best solutions in the inversion considerably improved retrievals. However, the widely used RMSE was not the best performing cost function. Three families of alternative cost functions were applied here: information measures, minimum contrast, and M-estimates. We found that so-called “Power divergence measure”, “Trigonometric”, and spectral measure with “Contrast function$K(x) = -\log(x) + x$”, yielded more accurate results, although this also depended on the biophysical parameter. Particularly, when simultaneous retrieval of multiple biophysical parameters is the objective then “Contrast function$K(x) = -\log(x) + x$” provided most consistent optimized estimates of leaf$Chl$, LAI and canopy$Chl$across the different Sentinel configurations (relative RMSE: 24–29$\%$).

In this paper we present the scientific objectives of the FLEX mission and the underlying rationale. It sketches the basic ideas of the new measurement concept, which is making use of the tandem configuration of FLEX with GMES/Sentinel-3, and outlines the most important instrument and system requirements. We will describe the envisaged instrument configuration that is in line with the measurement objectives, and which is supported by the latest results of the scientific investigations.

The main objective of FLEX is the measurement of vegetation chlorophyll fluorescence (Fs) from space and the exploitation of this signal to better understand the carbon cycle. FLuORescence Imaging Spectrometer (FLORIS) is the main instrument of the FLEX mission concept. ESA's Earth Science Advisory Committee recommended the investigation of the FLEX concept as an in-orbit demonstrator to be flown as a tandem mission with Sentinel-3 (S-3). S-3 is amongst others equipped with the Ocean Land Colour Instrument (OLCI). When flown in tandem these instruments are expected to provide an accurate characterization of key atmospheric and surface parameters to facilitate Fs retrieval for FLORIS. In this work the performance of FLORIS and S3-OLCI sensors and their synergy was evaluated on their capability of retrieving relevant biophysical parameters using simulated top-of-atmosphere radiance data (LTOA). For both sensors, LTOAdata were simulated across a wide range of vegetation, atmospheric and geometry parameters by coupling leaf, canopy and atmospheric radiative transfer models. The pursued analysis was to train for each retrievable parameter (here: Chl, LAI, soil type and Ftotal) a regression model using the simulated datasets and then evaluate its performance. Two regression types were chosen, a conventional linear regressor and a more advanced nonlinear regressor, and two types of training/validation strategies were followed: a local strategy (at least 2 parameters fixed) and a generic strategy (uniform random subset of the complete dataset). The simulation study led to the following conclusions: 1) FLORIS is well equipped for accurate retrieval of biophysical parameters; 2) however, advanced nonlinear regressors may be needed to achieve robust results, and 3) the large number of bands can lead to redundancy in the nonlinear regressors which can be overcomed by band optimization strategies. Finally, 4) it was demonstrated that a synergy of both FLORIS and S3-OLCI datasets leads to improved biophysical parameter retrieval.

Inversion of radiative transfer models using a lookup-table (LUT) approach against hyperspectral data streams leads to retrievals of biophysical parameters such as chlorophyll content (Chl), but necessary optimization strategies are not consolidated yet. Here, various regularization options have been evaluated to the benefit of improved Chl retrieval from hyperspectral CHRIS data, being: i) the role of added noise, ii) the role of multiple best solutions, and iii) the role of applied cost functions in LUT-based inversion. By using data from the ESA-led field campaign SPARC (Barrax, Spain), it was found that introducing noise and opting for multiple best solutions in the inversion considerably improved retrievals. However, the widely used RMSE was not the best performing cost function. Three families of alternative cost functions were applied here: information measures, minimum contrast and M-estimates. We found that so-called ‘Power divergence measure’, ‘Trigonometric’ and spectral measure with ‘Contrast function K(x)=−log(x)+x’ outperformed RMSE. The whole inversion approach, including more than 60 different cost functions, has been implemented in the ARTMO (Automated Radiative Transfer Models Operator) GUI toolbox and can easily be applied to other kinds of multispectral or hyperspectral images.

An analysis of the sensitivity of synthetic aperture radar (SAR) backscatter$(\sigma^{o})$to crop and soil conditions was conducted using 57 RADARSAT-2 C-band quad-polarized SAR images acquired from April to September 2009 for large fields of wheat, barley, oat, corn, onion, and alfalfa in Barrax, Spain. Preliminary results showed that the cross-polarized$\sigma_{\rm HV}^{o}$was particularly useful for monitoring both crop and soil conditions and was the least sensitive to differences in beam incidence angle. The greatest separability of barley, corn, and onion occurred in spring after the barley had been harvested or in the narrow time window associated with grain crop heading when corn and onion were still immature. The time series of$\sigma^{o}$offered reliable information about crop growth stage, such as jointing and heading in grain crops and leaf growth and reproduction in corn and onion. There was a positive correlation between$\sigma^{o}$and the Normalized Difference Vegetation Index for onion and corn but not for all crops, and the impact of view direction and incidence angle on the time series was minimal compared to the signal response to crop and soil conditions. Related to planning for future C-band SAR missions, we found that quad-polarization with image acquisition frequency from 3–6 days was best suited for distinguishing crop types and for monitoring crop phenology, single- or dual-polarization with an acquisition frequency of 3–6 days was sufficient for mapping crop green biomass, and single- or dual-polarization with daily image acquisition was necessary to capture rapid changes in soil moisture condition.

This paper evaluates state-of-the-art parametric and nonparametric approaches for the estimation of leaf chlorophyll content$(Chl)$, leaf area index, and fractional vegetation cover from space. The parametric approach involves comparison of established and generic narrowband vegetation indices (VIs) and the Normalized Area Over reflectance Curve method, which calculates the continuum spectral region sensitive to$Chl$. However, as not all available bands take part in these spectral algorithms, it remains unclear whether optimal estimations are achieved. Alternatively, the nonparametric approach is based on Gaussian process (GP) techniques and allows inclusion of all bands. GP builds a nonlinear regression as a linear combination of spectra mapped to a high-dimensional space. Moreover, GP provides an indication of the most contributing bands for each parameter, a weight for the most relevant spectra contained in the training data set, and a confidence estimate of the retrieval. GP has previously demonstrated to be competitive in accuracy with support vector regression and neural networks. Results from hyperspectral Compact High Resolution Imaging Spectrometer data over the Spanish Barrax test site show that GP outperformed the VIs in assessing the vegetation properties when using at least four out of the 62 bands. GP identified most contributing bands in the red and red edge and, to a lower extent, in the blue and NIR parts of the spectrum. Since the proposed GP method is able to build robust relationships between the parameter of interest and only a few bands, it is a promising approach for multispectral data as well.

This paper presents a family of metrics for assessing image similarity. The methods use the Hilbert-Schmidt Independence Criterion (HSIC) to estimate nonlinear statistical dependence between multidimensional images. The proposed methods have very good theoretical and practical properties. We illustrate the performance in evaluating the quality of natural photographic images, hyperspectral images under different noise levels, in synthetic multiresolution problems, and real pansharpening products.

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.

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 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.

A problem when working with optical satellite or airborne images is the need to compensate for changes in the illumination conditions at the time of acquisition. This is particularly critical when working with time series of data. Atmospheric correction strategies based on radiative transfer codes may provide a rigorous solution but it may not be the best solution for situations where a huge amount of hyperspectral images may need to be processed and computational time is a critical factor. The GMES (¿Global Monitoring for Environment and Security¿) initiative has promoted the creation of a new generation of satellites (the SENTINEL series) with ¿ultra-high resolution¿ and ¿superspectral imaging¿ capabilities. Therefore, there is an urgent need to quickly and reliably compensate for changes in the atmospheric transmittance and varying solar illumination conditions. In this paper three different forms of affine transformation models (general, particular and diagonal) are considered as candidates for rapid compensation of illumination variations. They are tested on a series of simulated multispectral images of Top-Of-Atmosphere (TOA) radiance, where the surface is a synthetic scene of a test site in Spain called Barrax, where reference data for validation is available. The results indicate that in 2 of the more moderate Sun positions, for all the Visibilities tested, the particular affine method is better than the other 2. The results also indicate that the proposed methodology is satisfactory for practical normalization of varying illumination and atmospheric conditions in remotely sensed images required for operational or time critical applications.

This paper presents a cloud screening algorithm based on ensemble methods that exploits the combined information from both MERIS and AATSR instruments on board ENVISAT in order to improve current cloud masking products for both sensors. The first step is to analyze the synergistic use of MERIS and AATSR images in order to extract some physically-based features increasing the separability of clouds and surface. Then, several artificial neural networks are trained using different sets of input features and different sets of training samples depending on acquisition and surface conditions. Finally, outputs of the trained neural networks are combined at the decision level to construct a more accurate and robust ensemble of classifiers. The proposed classifier is tested on more than 80 coregistered MERIS/AATSR images providing better classification accuracy than the official cloud flags and available operational cloud screening algorithms for MERIS and AATSR. Moreover, thanks to the synergy of both sensors, it correctly classifies critical cloud-screening problems such as snow and ice covers over land and sun-glint over ocean.

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.

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.

It has been demonstrated that the spectrum of fluorescence emission is dependent on leaf temperature, thus there is a need for thermal information in order to interpret fluorescence signals. Temperature is also related to transpiration and stomata closure, which affects CO2 uptake and fluorescence. Therefore temperature measurements help to confirm the trends observed in fluorescence measurements. While fluorescence is immediately and uniquely related to photosynthesis, temperature provides additional information about plant status and instantaneous energy/water fluxes between plants and the atmosphere. The objective of this paper is to demonstrate the role of surface temperature in the context of FLuorescence EXplorer (FLEX) mission. To this end a database of land surface emissivity and temperature obtained from thermal radiometric measurements carried out in the framework of the Sen2FLEX (SENtinel-2 and FLuorescence Experiment) campaign has been used. These data were acquired in the agricultural site of Barrax (Spain) in June and July of 2005 simultaneously with airborne imagery acquired with Airborne Hyperspectral Scanner (AHS) and Compact Airborne Spectrographic Imager (CASI) sensors and data from the airborne fluorescence measuring instrument (AIRFLEX). As a result of these studies we have identified the optimal band configuration for the FLEX mission, that allows the estimation of land surface temperature with an accuracy lower than 1.5 K. To this end single-channel, split-window, and Temperature Emissivity Separation algorithms have been compared using a database of simulated brightness temperatures.

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 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.

We propose the use of support vector machines (SVMs) for automatic hyperspectral data classification and knowledge discovery. In the first stage of the study, we use SVMs for crop classification and analyze their performance in terms of efficiency and robustness, as compared to extensively used neural and fuzzy methods. Efficiency is assessed by evaluating accuracy and statistical differences in several scenes. Robustness is analyzed in terms of: (1) suitability to working conditions when a feature selection stage is not possible and (2) performance when different levels of Gaussian noise are introduced at their inputs. In the second stage of this work, we analyze the distribution of the support vectors (SVs) and perform sensitivity analysis on the best classifier in order to analyze the significance of the input spectral bands. For classification purposes, six hyperspectral images acquired with the 128-band HyMAP spectrometer during the DAISEX-1999 campaign are used. Six crop classes were labeled for each image. A reduced set of labeled samples is used to train the models, and the entire images are used to assess their performance. Several conclusions are drawn: (1) SVMs yield better outcomes than neural networks regarding accuracy, simplicity, and robustness; (2) training neural and neurofuzzy models is unfeasible when working with high-dimensional input spaces and great amounts of training data; (3) SVMs perform similarly for different training subsets with varying input dimension, which indicates that noisy bands are successfully detected; and (4) a valuable ranking of bands through sensitivity analysis is achieved.

In this paper, we propose a procedure to reduce data dimensionality while preserving relevant information for posterior crop cover classification. The huge amount of data involved in hyperspectral image processing is one of the main problems in order to apply pattern recognition techniques. We propose a dimensionality reduction strategy that eliminates redundant information and a subsequent selection of the most discriminative features based on classification and regression trees (CART). CART allow feature selection based on the classification success, it is a non-linear method and specially allows knowledge discovery. The main advantage of our proposal relies on model interpretability, since we can get qualitative information by analyzing the surrogate and main splits of the tree. This method is tested with a crop cover recognition application of six hyperspectral images from the same area acquired with the 128-bands HyMap spectrometer. Even though CART do not provide the best results in classification it is useful for a previous pre-processing step of feature selection. Finally, we analyze the selected bands of the input space in order to gain knowledge on the problem and to give a physical interpretation of results.

Measurement of vegetation photosynthetic activity from space has been an objective for the development of new techniques. Among the different existing techniques, passive fluorescence measurements, specific reflectance indices such as the PRI (Photochemical Reflectance Index), and the derivative of high spectral resolution reflectance in the red-edge (680 - 750 nm) are the three methods that can be used as remote sensing techniques from airborne and space sensors and that have proven to give consistent and reliable information about the canopy photosynthetic activity.

In this paper, we propose a procedure to reduce dimensionality of hyperspectral data while preserving relevant information for posterior crop cover classification. One of the main problems with hyperspectral image processing is the huge amount of data involved. In addition, pattern recognition methods are sensitive to problems associated to high dimensionality feature spaces (referred to as Hughes phenomenon of curse of dimensionality). We propose a dimensionality reduction strategy that eliminates redundant information by means of local correlation criterion between contiguous spectral bands; and a subsequent selection of the most discriminative features based on a Sequential Float Feature Selection algorithm. This method is tested with a crop cover recognition application of six hyperspectral images from the same area acquired with the 128-bands HyMap spectrometer during the DAISEX99 campaign. In the experiments, we analyze the dependence on the dimension and employed metrics. The results obtained using the Gaussian Maximum Likelihood improve the classification accuracy and confirm the validity of the proposed approach. Finally, we analyze the selected bands of the input space on order to gain knowledge on the problem and to give a physical interpretation of the results.

A new approach to the classification of hyperspectral images is proposed. The main problem with supervised methods is that the learning process heavily depends on the quality of the training data set. In remote sensing, the training set is useful only for simultaneous images or for images with the same classes taken under the same conditions; and, even worse, the training set is frequently not available. On the other hand, unsupervised methods are not sensitive to the number of labelled samples since they work on the whole image. Nevertheless, relationship between clusters and classes is not ensured. In this context, we propose a combined strategy of supervised and unsupervised learning methods that avoids these drawbacks and automates the classification process. The method is based on the general formulation of the expectation-maximization (EM) algorithm. This method is applied to crop cover recognition of six hyperspectral images from the same area acquired with the HyMap spectrometer during the DAISEX-99 campaign. For classification purposes, six different classes are considered. Classification accuracy results are compared to common methods: ISODATA, Learning Vector Quantization, Gaussian Maximum Likelihood, Expectation-Maximization, and Neural Networks. The good performance confirms the validity of the proposed approach in terms of accuracy and robustness.

As the land degradation is a complex process, influenced also by climatic and human-induced factors, its understanding and mapping requires a methodology based on Earth Observation integrated with ancillary data such as socio- economic and, optionally, meteorological data. For this reason, the service, which has been defined, implemented, and validated in close cooperation with End Users, is based both on scaleable indexes - derived from Earth Observation data - and on indicators relevant to the influence of human and animal pressure on natural resources. The ultimate goal is the generation of vulnerability maps to provide to decision makers for prevention purposes.

Typical environment plant stress factors are excess of light, deficiencies of water and nutrients, temperature extremes, diseases, pests and pollutants. An early indicator for vegetation status and vitality by means of remote sensing would therefore serve a range of applications such as renewable resource management and precision farming. Vegetation fluorescence is a direct indicator for plant physiology, and could therefore be used as an early indicator for vegetation health status and vitality. Vegetation chlorophyll fluorescence is a function of photochemical processes and efficiency, which are directly linked to primary productivity and CO/sub 2/ flux from the atmosphere, and could therefore also provide a means to assess the terrestrial carbon cycle. A study was launched in October 2002 by the European Space Agency to advance the underlying science of a possible future vegetation fluorescence space mission by addressing the need for an integrated canopy fluorescence model. The objective of this study is to review and advance existing fluorescence models at the leaf level and to integrate these into canopy models in order to simulate the combined spectral reflected radiance and passive fluorescence emission signals. This model is to be validated with new and existing field campaign measurements. This paper reports on the status of this project, the input radiometric and photosynthetic variables have been selected to define the vegetation fluorescence signal consisting of far-red and red-chlorophyll fluorescence as spectral emission features, normalized to the canopy illumination levels, when linked to the leaf-level fluorescence reflectance-transmittance model defined in this study. Measurement protocols to validate fluorescence-leaf models will be defined.

Quantitative vegetation monitoring by means of remote sensing methods, beyond simple spectral indices, is an objective for the next generation of satellite systems, such as the ESA Earth Explorer Core Mission candidate SPECTRA (Surface Processes and Ecosystem Changes Through Response Analysis). In order to derive and validate methods for retrieval of biophysical information, and in particular vegetation properties from hyperspectral / multiangular measurements, a series of campaigns genetically called DAISEX (Digital Airborne Imaging Spectrometer Experiment) were carried out in La Mancha, Spain, from 1998-2000. A combination of aircrafts carrying different sensors and flying in different configurations allowed to get a complete dataset of hyperspectral and multiangular data acquired simultaneously, together with the necessary atmospheric information and ground data needed for validation of biophysical retrievals. A two-years study funded by ESA has exploited in depth the DAISEX dataset to evaluate the accuracy achieved in the retrieval of each one of the different vegetation properties, by using different methods applied over the same dataset. The results have shown potentials and limitations, but have allowed to set accuracy limits realistically achievable and to set more precise requirements for future developments.

The main scientific objective of DAISEX (Digital Airborne Spectrometer Experiment) was to demonstrate the retrieval of geo/biophysical variables from imaging spectrometer data. Target variables included surface temperature, Leaf Area Index (LAI), canopy biomass, leaf water content, canopy height, canopy structure and soil properties. The imaging spectrometers used for DAISEX were the DAIS-7915, HyMap and POLDER. The campaign took place during the summers of 1998, 1999 and 2000 in Barrax (Spain) and Colmar (France). A new algorithm is under development for the atmospheric correction of the hyperspectral and multiangular data acquired during this campaign. This algorithm is intended to improve the current atmospheric correction by taking into account the coupling between atmosphere and surface (including a non-Lambertian treatment of the latter). Moreover, the hyperspectral data allow to characterise in detail the absorption and the multiangular characteristics of the data allow to describe accurately the aerosol scattering. The method consists in identifying some pixels on an image with an a priori information about its BRDF and assume that the atmosphere is the same over the whole image. Applying a radiative transfer code we can reproduce the reflectance measured by the sensor by modifying the parameters describing the surface and the aerosols through an iterative process. Once the atmosphere is known the system atmosphere-surface is uncoupled and the reflectance for the whole image can be obtained.

Detecting vegetation fluorescence from space would provide new insight on terrestrial biosphere response to climate variability, and help quantifying atmospheric carbon sequestration. This paper outlines technical and scientific studies undertaken for the preparation of the FLEX-Fluorescence Explorer space mission proposed to ESA in the framework of the Earth Explorer Opportunity Missions program.

Two main problems must be solved in the geometric processing of satellite data: geometric registration and resampling. When the data must be geometrically registered over a reference map, and particularly when the output pixel size is not the same as the original pixel size, the quality of the resampling can determine the quality of the output, not only in the visual appearance of the image, but also in the numerically interpolated values when used in multitemporal or multisensor studies. The "optimum" interpolation algorithm for AVHRR data is defined over a 6/spl times/6 window in order to: consider overlapping effects among adjacent pixels. The response for each new pixel R(x, y) is determined as a linear combination of the response R/sub i/(x/sub i/y/sub i/) of the surrounding pixels in the window (i=1,36). The weighting coefficients /spl mu//sub i/ are calculated from the ground projection of the effective spatial response function for each AVHRR pixel, taking into account the particular viewing angle and geometry of the pixels on the ground. This method is intended to give an optimal interpolation of AVHRR scenes along all the scanline, in order to compensate for off-nadir radiometric alterations associated to the varying spatial resolution and the blurring introduced by the pixel overlaps. The optimum method, as mathematically defined, is highly expensive in CPU time. Then, a big effort is necessary to implement the algorithms so that they could be operationally applied. Two approaches are considered: a general numerical method and a pseudo-analytical approximation. A Landsat TM image corresponding to the same date of the AVHRR image is used to test the quality of the radiometric interpolation procedure.>

A method for the geometric correction of NOAA Advanced Very High Resolution Radiometer (AVHRR) high-resolution picture transmission (HRPT) data is presented. After precise determination of nominal attitude angles for each time instant, geometric correction is done for ground control points (GCPs), and residual errors are interpreted as attitude angle variation effects and in this way corrected. All attitude angle deviations (in pitch, roll, and yaw) are simultaneously corrected by applying to two reference vectors (the vector normal to the scanning plane and the vector that defines the instantaneous viewing direction of the first pixel of each line) a three-axis rotation. A separate program performs the geometric correction, applying the orbital model to each point of the desired output geographical area. An application of this method is presented, in which AVHRR data are registered over a 1:25000 topographic map with subpixel accuracy, allowing the use of AVHRR data in a nearly local scale in combination with other high-resolution data, such as Landsat TM or SPOT.>

Information available from ephemeris data, combined with a few ground control points, is used to carry out the geometric combination of NOAA, AVHRR, and SPOT images by means of orbital models for each satellite. The observation conditions are reconstructed and the areas observed in common determined in the SPOT image pixels and each low-resolution AVHRR pixel. The determination of effective parameters, at low spatial resolution, from high-spatial-resolution data takes into account overlapping effects between adjacent pixels, and pixel size and shape variations with viewing angle for AVHRR data. The method is applied to surface temperature mapping of a selected heterogeneous area by incorporating NOAA AVHRR thermal data in a high-spatial-resolution emissivity map derived from SPOT data.>