Developments for vegetation fluorescence retrieval from spaceborne high resolution spectrometry in the O 2 A and O 2 B absorption bands

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115,, doi: /2009jd013716, 2010 Developments for vegetation fluorescence retrieval from spaceborne high resolution spectrometry in the O 2 A and O 2 B absorption bands L. Guanter, 1,2 L. Alonso, 2 L. Gómez Chova, 2 M. Meroni, 3 R. Preusker, 1 J. Fischer, 1 and J. Moreno 2 Received 16 December 2009; revised 27 May 2010; accepted 7 July 2010; published 6 October [1] Solar induced chlorophyll fluorescence is a weak electromagnetic signal emitted in the red and far red spectral regions by vegetation chlorophyll under excitation by solar radiation. Chlorophyll fluorescence has been demonstrated to be a close proxy to vegetation physiological functioning. The basis for fluorescence retrieval from passive space measurements is the exploitation of the O 2 A and O 2 B atmospheric absorption features to isolate the fluorescence signal from the solar radiation reflected by the surface and the atmosphere. High spectral resolution measurements and a precise modeling of the atmospheric radiative transfer in the visible and near infrared regions are mandatory. Recent developments for fluorescence retrieval from passive high spectral resolution spaceborne measurements are presented in this work, which has been performed in preparation of the FLuorescence EXplorer (FLEX) mission, which is currently under development by the European Space Agency. A large data set of FLEX like measurements has been simulated for the purpose of methodology development and testing. Issues related to vegetation chlorophyll fluorescence retrieval from space, a description of the proposed methodology, initial results from simulated test cases, and general guidelines for the specification of fluorescence retrieval instruments are presented and discussed in this work. Citation: Guanter, L., L. Alonso, L. Gómez Chova, M. Meroni, R. Preusker, J. Fischer, and J. Moreno (2010), Developments for vegetation fluorescence retrieval from spaceborne high resolution spectrometry in the O 2 A and O 2 B absorption bands, J. Geophys. Res., 115,, doi: /2009jd Introduction [2] Solar induced chlorophyll fluorescence (F s ) is emitted by vegetation chlorophyll a under excitation by solar radiation. The fluorescence emission occurs in two broad peaks in the red (685 nm) and far red (740 nm) regions of the spectrum. A number of laboratory and field experiments have demonstrated that chlorophyll fluorescence is directly linked to the instantaneous plant photosynthesis [e.g., Papageorgiou and Govindjee, 2004; Baker, 2008], opposite to traditional reflectance based vegetation parameters which are only indicators of the potential photosynthetic activity of the plant. [3] The low intensity of the fluorescence signal with respect to the solar radiation reflected by vegetation in the same spectral region makes the estimation of fluorescence 1 Institute for Space Sciences, Free University of Berlin, Berlin, Germany. 2 Image Processing Laboratory, University of Valencia, Paterna Valencia, Spain. 3 Remote Sensing of Environmental Dynamics Laboratory, DISAT, University of Milan Bicocca, Milan, Italy. Copyright 2010 by the American Geophysical Union /10/2009JD from remote measurements a challenging problem. Entcheva Campbell et al. [2008] estimated steady state F s to be between 1.5 and 3.4 mw m 2 sr 1 nm 1 at 685 nm and between 2.4 and 5.4 mw m 2 sr 1 nm 1 at 740 nm for different plant species. Those numbers represent a fraction of the radiation reflected by vegetation between 8.7% and 21.9% at 685 nm and between 2.0% and 5.2 % at 740 nm. Similar numbers were reported by other studies [Corp et al., 2006; Amorós López et al., 2008; Zarco Tejada et al., 2009]. Therefore, the main challenge in fluorescence retrieval from remote measurements is the isolation of the fluorescence signal from the radiance arriving at the sensor, which in the red and far red regions is mainly due to the solar radiation reflected by the surface under clear atmosphere conditions. [4] Measurements of backscattered sunlight in atmospheric absorption features overlapping the fluorescence emission can serve to this purpose. The different atmospheric optical paths crossed by the fluorescence and reflected signals provide the information required to decouple these two components from the radiance measured at the top of atmosphere (TOA) level. The atmospheric O 2 A and O 2 B absorption features in nm and nm, respectively, can be used for fluorescence retrieval, as they provide a good sampling of 1of16

2 Figure 1. Real top of canopy (TOC) chlorophyll fluorescence spectrum superposed to a top of atmosphere (TOA) radiance spectrum simulated from a green vegetation target. The O 2 AandO 2 B absorption features are marked with rectangles. Illumination and observation zenith angles in the simulation were 30 and 0, respectively, and the spectral resolution 1 cm 1. the two peaks of the fluorescence emission, and O 2 is a wellmixed gas in the atmosphere. Fraunhofer lines in the solar spectrum were also proposed for fluorescence retrieval in the past [Sioris et al., 2003], but are now discarded because of their low spectral overlap with the most intense emission in the chlorophyll a fluorescence spectrum; water vapor features are also discarded because of the high variability of water vapor in both the temporal and the spatial dimension. The suitability of the O 2 absorption features for fluorescence retrieval is illustrated in Figure 1. A real top of canopy fluorescence spectrum is superposed to a TOA radiance spectrum simulated for a green vegetation target. The location of the O 2 A and O 2 B absorption features is depicted. [5] As reported in Meroni et al. [2009], most of the methods in the literature for F s detection from either ground based, airborne, or spaceborne instruments are based on the simplistic Fraunhofer Line Depth (FLD) principle [Plascyk, 1975] applied to the O 2 A absorption feature. The FLD method is a suite of 2 or 3 band differential absorption technique in which the in filling of the absorption feature by fluorescence is used to decouple the fluorescence and reflectance signals. A nonfluorescent target, either a reference panel or a bare soil surface, is used for the normalization of the nonmodeled atmospheric effects [Moya et al., 2004; Pérez Priego et al., 2005; Alonso et al., 2008]. However, FLD like approaches cannot be applied for F s estimation in the O 2 B feature, which does not present deep and broad absorption lines to set the measuring and reference channels required by the FLD technique, and can hardly be applied to spaceborne fluorescence retrieval, as reference targets (bare soil targets surrounding green vegetation surfaces) are seldom available from the satellite scale [Guanter et al., 2007]. [6] Spectral fitting methods have been proposed as an alternative to FLD like methods for the retrieval of F s from O 2 AandO 2 B measurements [Meroni and Colombo, 2006; Meroni et al., 2010; Mazzoni et al., 2008]. These methods exploit high spectral resolution measurements (considered to be of the order of 0.1 nm in this work) for the decoupling of reflectance and fluorescence by means of multichannel regressions. The higher amount of information contained in high resolution data enables the better normalization of atmospheric effects, the characterization of the instrument spectral calibration, and to account for nonlinear spectral variations of the background vegetation reflectance, which are especially important in O 2 B. [7] Vegetation chlorophyll fluorescence retrieval from high resolution spectrometry in the O 2 A and O 2 B atmospheric absorption features is the main objective of the FLuorescence EXplorer (FLEX) mission concept under development by the European Space Agency (ESA) [Drusch and FLEX Team, 2008]. FLEX, whose core instrument is the Fluorescence Imaging Spectrometer (FIMAS), is presently under evaluation for implementation as an in orbit technology demonstrator which would fly in tandem with the Sentinel 3 system [Drinkwater and Rebhan, 2007] of the Global Monitoring for Environment and Security (GMES) program. The exploitation of Sentinel 3 Ocean Land Color Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) data for the support of FIMAS measurements in cloud screening and the characterization of atmospheric conditions is considered as baseline for the latest FLEX concept. Sentinel 3 OLCI and SLSTR will continue ENVISAT s MEdium Resolution Imaging Spectrometer (MERIS) and Advanced Along Track Scanning Radiometer (AATSR) multispectral and multiangular measurements. If finally selected for implementation and according to the most recent instrument concept, FIMAS would measure with a high signal to noise ratio (SNR) in two spectral windows of width between 20 and 30 nm centered at the O 2 A and O 2 B absorption features, with spectral sampling interval (SSI) and full width at half maximum (FWHM) between 0.1 and 0.3 nm and ground sampling distance (GSD) between 300 m and 1 km. FIMAS observations would be in quasi nadir mode and would cover an at ground area of about 100 km. A summary of some of the most important FIMAS requirements is presented in Table 1. [8] Developments for operational F s retrieval from highresolution measurements in O 2 A and O 2 B performed during the FLEX preparatory activities are presented hereinafter in this paper. In particular, an overview of a tentative F s retrieval methodology implemented for FIMAS like type Table 1. Key Specifications of the FLEX/FIMAS Instrument as of December 2009 a O2 A O2 B Spectral window [745, 775] nm (O) [672, 702] nm (O) [750, 770] nm (T) [677, 697] nm (T) SSI ( FWHM) 0.1 nm (O) 0.3 nm (T) 0.1 nm (O) 0.3 nm (T) SNR [300:1 1600:1] (O) [600:1 900:1] (O) [150:1 800:1] (T) [200:1 400:1] (T) Pol. Sensit. 1% (O) 2% (T) 1% (O) 2% (T) GSD 300 m (O) 1000 m (T) 300 m (O) 1000 m (T) Swath 100 km 100 km a O and T labels refer to optimum and threshold values, respectively. SNR is given at the mission reference radiance level. Pol. Sensit. stands for sensitivity to polarization. 2of16

3 of data is described, as well as the simulated data set used for algorithm development and validation purposes. First estimates of potential F s retrieval error figures and guidelines for the definition of specifications for future spaceborne spectrometers for fluorescence retrieval are also presented and discussed. 2. FLEX/FIMAS Simulated Data Set 2.1. Atmospheric Forward Model [9] Under the assumption that both surface reflectance and fluorescence emission are isotropic, the spectral TOA radiance signal L TOA can be formulated by a simple expression accounting for the interaction with the atmosphere of the radiation reflected and emitted by the surface, ð L TOA ¼ L 0 þ E dir il þ E dif Þ s þ F s T" ð1þ 1 S s where r s is the surface reflectance; L 0 is the atmospheric path radiance; m il is the cosine of the illumination zenith angle, measured between the solar ray and the surface normal; E dir m il and E dif are the direct and diffuse fluxes, respectively, arriving at the surface; S is the atmospheric spherical albedo, reflectance of the atmosphere for isotropic light entering it from the surface, and T is the total atmospheric transmittance (for diffuse plus direct radiation) in the observation direction. It must be remarked that directional effects in the vegetation reflectance and their potential interaction with the F s signal and the atmospheric radiative transfer are neglected by this formulation. The atmospheric parameters required for the conversion from r s and F s to L TOA were generated in this work by the Matrix Operator MOdel (MOMO) [Fell and Fischer, 2001] and the MODTRAN4 [Berk et al., 2003] radiative transfer codes. [10] As shown in this work, aerosol scattering and surface pressure are the two atmospheric parameters which may introduce the highest uncertainty in F s retrieval under clearsky conditions. Atmospheric aerosols can be characterized in passive remote sensing studies by the aerosol optical depth (AOD) at a given wavelength, the aerosol model, and the aerosol vertical profile. AOD is the column integral of the aerosol extinction coefficient, the aerosol model defines the spectral scattering and absorption properties of the aerosol mass and the aerosol vertical profile describes the vertical distribution of the aerosol extinction. [11] Concerning surface pressure (SPR), it can be formulated as a major component given by the surface elevation plus a minor component dependent on the atmospheric fronts. The major component explains about 90% of the surface pressure at a given location and time, and can be derived from an accurate digital elevation model. The variable component, in turn, must be estimated according to the instantaneous atmospheric conditions [Lindstrot et al., 2009]. [12] AOD at 550 nm (AOD550), aerosol model, and SPR are considered free variables in the atmospheric forward model developed in this work for the simulation of FIMASlike data. A look up table containing sets of atmospheric optical parameters (atmospheric path radiance, at ground direct and diffuse irradiance, spherical albedo, and atmospheric transmittance) as a function of AOD550, aerosol model and SPR has been compiled with MOMO and MODTRAN4 calculations. Breakpoints of this look up table are AOD550 of {0.05, 0.2, 0.4}, combined with rural, maritime and urban models and SPR of {750, 940, 1030} hpa. It is acknowledged that such a look up table structure is too sparse to enable accurate interpolation in the parameter space, but it is considered sufficient for the analysis presented in this work. [13] The impact of AOD, aerosol model, aerosol height, and SPR on TOA radiance at O 2 A and O 2 B regions is compared with that of F s in Figures 2 and 3, respectively. The normalized difference between the L TOA simulated for a given input configuration and the L TOA for the reference configuration (specified in the figure caption) is plotted as a function of wavelength. The aerosol, surface pressure and F s input parameters are varied at a time with respect to the reference configuration. [14] A very different sensitivity of L TOA to the different parameters is observed in O 2 A and O 2 B. Overall, the impact of F s on O 2 A is relatively small with respect to the other parameters tested. It also presents a high spectral correlation with the other parameters, especially with surface pressure. The main spectral difference is on the left hand side of the O 2 A feature, where variations in F s transmit to L TOA and in the smaller spectral dependence of the sensitivity to F s than to the other parameters. On the other hand, a significant correlation can be observed between surface pressure and the height of the aerosol layer and between the AOD and the aerosol model. In the second case, it can be argued that different combinations of AOD and aerosol model could lead to a similar impact on L TOA. Regarding the aerosol models, there seems to be a larger difference between the rural and the urban models than between the rural and the maritime models, despite only a small proportion of soot like aerosol is added in the urban model to the water soluble and dust like components of the rural aerosol model. [15] Concerning the O 2 B window, the impact of F s is spectrally uncorrelated to the other parameters due to particular spectral shape of the fluorescence emission in O 2 B. Moreover, F s has a stronger impact in O 2 B than in O 2 A due the much lower contribution of the reflected solar radiation to L TOA, which can be observed in Figure 1. The impact of surface pressure and the height of the aerosol layer is much smaller than in O 2 A, whereas the variation of the AOD for the three aerosol models leads to a similar spectral pattern with a different intensity depending on the model. In view of these results, the better performance of F s retrieval from O 2 B than from O 2 A can a priori be expected Atmospheric Parameters Not Considered in the Forward Model [16] There are other potential error sources for F s retrieval related to the atmosphere surface radiative transfer formulation. These are not considered explicitly by the atmospheric forward model in this work, but either they are assumed to be provided by external sources or they have to be added to the F s retrieval error budget Thin and Subpixel Clouds [17] Nondetected thin and subpixel clouds would introduce large errors in the modeling of the O 2 A and O 2 B absorption features under the assumption of clear sky con- 3of16

4 Figure 2. Sensitivity of the TOA radiance in the FIMAS O 2 A spectral window to different atmospheric parameters. The reference configuration is for nadir observation, illumination zenith angle = 30, midlatitude summer atmosphere, green vegetation reflectance spectrum, F s = 0 mw m 2 sr 1 nm 1, surface pressure = 991 hpa, AOD550 = 0.2, rural aerosol model, aerosol layer between 2 9 km, spectral sampling interval = 0.1 nm. ditions. However, given the spatial resolution of FLEX/ FIMAS data ( m), it can be assumed that pure cloud free pixels can be screened out from cloudy pixels even in the case of partially cloudy skies [Miller et al., 2007]. It is also assumed that the high spectral resolution of FIMAS data in O 2 A and O 2 B, alone and in synergy with Sentinel 3 OLCI and SLSTR measurements (which include channels in the visible, shortwave infrared and thermal infrared regions, and in particular two channels in 1375 and 1610 nm suited for cirrus cloud detection), are sufficient for accurate cloud screening including the optically thinnest cirrus clouds. No F s retrieval would be attempted for pixels suspicious of being contaminated by clouds Coupling of Atmospheric Radiative Transfer and Directional Reflectance Effects [18] Directional effects in vegetation reflectance may lead to the vegetation reflectance response to be different for the 4of16

5 Figure 3. Same as Figure 2 but for the O 2 B spectral window. incoming diffuse and direct solar radiation fluxes. This effect is not considered by the Lambertian formulation in equation (1), and directional reflectance effects coupled to the incoming atmospheric fluxes would appear as perturbations in the O 2 A and O 2 B absorption features to be modeled. Such directional effects are expected in vertically heterogeneous vegetation covers and for canopies with a preferential leave orientation. No systematic analysis of the impact of directional reflectance on F s retrieval is yet available in the literature, but the topic is considered for near future research. It must be remarked that such formulation of directional reflectance effects is not considered in other retrieval approaches requiring a rigorous modeling of the O 2 A absorption feature [e.g., Bösch et al., 2006] Aerosol Vertical Profile [19] The vertical location of the aerosol layer affects the balance between atmospheric scattering and absorption, as the incoming solar radiation being reflected by aerosols back to the TOA crosses a shorter atmospheric path when the aerosols are located at a higher altitude [Dubuisson et al., 2009]. The simulations presented in Figure 2 show 5of16

6 Figure 4. Set of (top) reflectance and (bottom) fluorescence spectra used for FIMAS TOA radiance simulations. The O 2 A and O 2 B spectral windows covered by FIMAS are marked with rectangles. that the uncertainty of the aerosol vertical profile may have a very large impact on F s retrieval in O 2 A. Since no information about the aerosol profile is going to be provided by Sentinel 3 instruments, this error source is so far to be included in the F s retrieval error budget. This error will be quantified later in this work. The retrieval from FIMAS measurements of an effective surface pressure product to be used for subsequent F s retrieval is considered a topic for further research. Such a pressure product would be biased by variations in the aerosol profile according to Figure 2, and could thus serve to partially compensate this uncertainty Temperature Vertical Profile [20] The oxygen absorption bands are composed of individual absorption lines that are subject to pressure and temperature dependent broadening processes. Even though the dominant process in the lower atmosphere is pressure broadening, an increase of temperature at constant pressure results in narrower, more intense absorption lines, and vice versa. A sensitivity analysis presented by Lindstrot et al. [2009] shows that shifting the temperature profile by 1K can result in a maximum error of 5 hpa in surface pressure retrieval from MERIS data in O 2 A. This uncertainty would be smaller due to the compensation of errors if the sign of the uncertainty in the temperature profile changed with height, as expected in the real case. Time and latitude resolved climatology or near real time ancillary information of temperature profiles can be added to the F s retrieval scheme to improve the retrieval accuracy Polarization of Radiation [21] The spectral dependence of the degree of polarization of the solar radiation reflected by the atmosphere could lead to in filling effects of the O 2 A and O 2 B absorption features [Natraj et al., 2007; Boesche et al., 2009], which might in turn cause errors in F s retrieval. However, the demanding sensitivity to polarization requirement (1% 2%) in FIMAS is expected to minimize errors in FIMAS measurements caused by polarization Water Vapor [22] Nonneglectable water vapor absorption lines overlap part of the O 2 B absorption feature, roughly between nm. However, the continuity in Sentinel 3 OLCI of the accurate columnar water vapor product provided by MERIS [Bennartz and Fischer, 2001] is assumed to be enough for the compensation of water vapor absorption within the O 2 B feature Ring Effect and Dayglow Emission [23] The filling in of the O 2 A and O 2 B features by the Ring effect [Grainger and Ring, 1962] associated to rotational Raman scattering is expected to be small for nadirlooking observations with respect to the fluorescence signal in the red and far red spectral regions [Sioris et al., 2003; Sioris and Evans, 2000]. The same is true for O 2 A dayglow emissions in the upper atmosphere [Wallace and Hunten, 1968] Input Vegetation Reflectance and F s Emission Spectra [24] The spectral shape and magnitude of vegetation reflectance in the O 2 A and O 2 B spectral regions may have a strong impact on F s retrieval. In order to recreate the widest range of vegetation reflectance patterns, a number of top ofcanopy vegetation reflectance spectra has been used in the generation of the FIMAS like database developed for this study. For this purpose, the FluorSAIL and FluorMODleaf codes [Pedrós et al., 2010], which simulate top of canopy and leaf radiative transfer, respectively, have been run under 20 combinations of chlorophyll content and leaf area index. This has led to a range of red edge positions, reflectance levels and spectral shapes at O 2 A and O 2 B. In particular, values of chlorophyll a + b content of {25, 50, 75, 95} mg cm 2 and of leaf area index of {2, 3, 4, 5, 6} were combined in order to generate a spectral library of green vegetation reflectance spectra. The rest of the parameters driving the leaf and canopy models, such as the leaf internal structure parameter, the water equivalent thickness, the dry matter content or the leaf inclination distribution function were set to FluorSAIL and FluorMODleaf default values. Concerning fluorescence, a real sunflower F s emission spectrum has been scaled to 5 different intensity levels (including 0 fluorescence) which cover the range between 0 and 4 mw m 2 sr 1 nm 1 at 760 nm. This intensity range is selected according to supporting laboratory and field based 6of16

7 Figure 5. True color composites of the MERIS full resolution subsets used to simulate FIMAS like scenes. Each subset covers an approximate area of 120 km. studies referenced in the Introduction. Resulting reflectance and F s spectra are displayed in Figure FIMAS Simulations in the Spatial Domain [25] A consistent performance analysis and error budget estimation can be performed when realistic spatial distributions of vegetation covers, atmospheric parameters and surface elevation are included in the assessment. According to the latest developments in the definition of the FLEX mission, a GSD of 300 m and a spatial swath of about 100 km can be assumed for FIMAS. For the simulation set up, this spatial configuration can be reproduced from spatial subsets of ENVISAT MERIS full resolution images [Rast et al., 1999]. MERIS full resolution images present a swath of about 1150 km and a GSD of 300 m. In the normal operation mode, MERIS provides measurements in the nm spectral range in 15 spectral channels with varying bandwidths ranging from 3.75 nm (at O 2 A) to 20 nm, 10 nm being a typical value in most of the bands. Realistic spatial patterns and texture for FIMAS scenes simulation are retrieved from pixel subsets extracted from 6 MERIS full resolution images. MERIS data were atmospherically corrected with the Self Contained Atmospheric Parameters Estimation (SCAPE M) atmospheric processor [Guanter et al., 2008], which generates reflectance and AOD550 maps from MERIS TOA radiance data. The six subsets used in this work are displayed in Figure 5. They were selected so that the maximum variability of spatial patterns and vegetation types was obtained. [26] The conversion from the MERIS multispectral type of data to the FIMAS high spectral resolution in the O 2 A and O 2 B spectral bands is achieved by means of spectral 7of16

8 Figure 6. Example of a priori aerosol optical depth at 550 nm and surface pressure error maps used with the subset 1 for F s retrieval in the end to end simulation process. unmixing. A nonnegative least squares (NNLS) unmixing algorithm [Lawson and Hanson, 1974] was used together with the FluorSAIL spectral library in Figure 4 to calculate end member abundances from the MERIS reflectance subsets. The resulting abundances were then combined with the spectral library resampled to the FIMAS spectral response to convert from the original MERIS data to FIMAS like reflectance spectra. [27] Real spatial patterns of AOD550 and SPR were also used in the simulation of the FIMAS like TOA radiance scenes. AOD subsets were extracted from the AOD550 maps generated by SCAPE M. SPR was calculated from the Global Earth Topography And Sea Surface Elevation at 30 arc sec resolution (GETASSE30) digital elevation model which was co registered to each of the MERIS images. In addition, horizontal distributions of AOD550 and SPR uncertainties were simulated for the F s retrieval step. These error maps were not correlated to the input maps, but were generated from independent AOD550 and SPR spatial patterns. An example of AOD550 and SPR error maps for F s retrieval is displayed in Figure 6. These error maps are intended to recreate uncertainties in AOD and SPR as provided by Sentinel 3 measurements. Pairs of input and error maps for AOD550 and SPR were generated for each of the test sites simulated from MERIS data. Concerning the aerosol model, constant abundances of aerosol types were used for the entire simulated area. Input relative abundances of the rural, urban and maritime models in the simulations were, respectively, 50%, 20% and 30%, while the corresponding values assumed in the retrieval step were 70%, 0% and 30%. [28] Input F s maps to be used in the simulation were derived by means of an empirical linear relationship between F s and the Normalized Difference Vegetation Index (NDVI) [Tucker, 1979] calculated from the MERIS reflectance subsets. Maximum F s was 4 mw m 2 sr 1 nm 1 in both O 2 A and O 2 B. [29] FIMAS like TOA radiance scenes were finally produced by applying equation (1) to each pixel, the per pixel atmospheric parameters being generated through interpolation from the atmospheric look up table FIMAS Instrument Model [30] The spectral convolution of the resulting TOA radiance data to the FIMAS spectral response and the simulation of instrumental noise are the last step in the forward simulation process. Only a very basic FIMAS instrument model could be specified at this point. In order to cover all the possible FIMAS spectral configurations under consideration, the FIMAS SSI was varied between 0.1 and 0.3 nm, and the spectral windows between 20 and 30 nm (Table 1). A Gaussian spectral response function and a fixed ratio between FWHM and SSI of 1.2 was selected for all the simulations. Uncertainties in the knowledge of the instrument spectral response were also simulated as spectral shift and channel broadening. Errors in spectral wavelength position and width up to ±0.2 and ±0.1 spectral pixels, respectively, were simulated in the spectral convolution step. [31] Instrumental noise, in turn, was simulated on TOA radiance as Gaussian noise in accordance with the FIMAS SNR specifications presented in Table 1. The dependence of signal to noise ratio with spectral bandwidth was also simulated by means of an empirical relationship derived from a preliminary instrument design. 3. F s Retrieval Algorithm 3.1. F s Retrieval From FIMAS Like High Resolution Measurements [32] Approaches for F s retrieval from spaceborne FIMAS type of data can be classified in terms of how they deal with the uncertainty in the atmospheric conditions. On the one hand, the atmospheric state can be characterized prior to F s retrieval by means of either ancillary data sources or FIMAS measurements concurrent to F s retrieval. On the other hand, a second type of approach would assume that FIMAS highresolution measurements contain sufficient information for the consistent retrieval of aerosol parameters, surface pressure, surface reflectance, and F s by means of a multiparameter retrieval scheme. The optimal estimation technique [Rodgers, 2000] developed for the retrieval of atmospheric profiles and trace gases could be adapted to this purpose. [33] However, given the fact that FIMAS would fly in tandem with the Sentinel 3 OLCI and SLSTR instruments capable of providing information for cloud screening and for 8of16

9 the accurate retrieval of aerosol parameters and surface pressure, only approaches of the first type focusing on fluorescence and reflectance decoupling have been considered in this work. Uncertainties in AOD and SPR retrieval and modeling errors associated to those parameters are considered additional error sources to be propagated along F s retrieval Initial Data Processing for F s Retrieval [34] Assuming Sentinel 3 OLCI and SLSTR flying in tandem with FLEX/FIMAS, a series of processing steps must precede the actual F s retrieval. [35] 1. First is the cloud screening step. Pixels suspicious of being affected by any degree of cloud contamination will not be considered for F s retrieval. The synergy between Sentinel 3 and FIMAS data would provide visible and infrared multispectral measurements (near, shortwave and thermal) and high resolution in O 2 A and O 2 B, which are considered sufficient for accurate cloud screening [Gómez Chova et al., 2009]. The detection of semitransparent high altitude cirrus clouds would be possible by means of collocated Sentinel 3 SLSTR measurements in the so called cirrus bands at 1375 and 1610 nm water vapor bands. [36] 2. A second step is FIMAS spectral characterization. The accurate knowledge of the instrument spectral response is known to be essential for the reliable F s retrieval. For this reason, a processing step devoted to the scene based spectral characterization of FIMAS must follow cloud screening. The effects of spectral shift, spectral broadening, and array compression or stretching are decorrelated from those of the environmental parameters under consideration. This enables to perform spectral characterization as an independent processing step. An algorithm for the spectral characterization of FIMAS has already been developed. It estimates spectral channel position, bandwidth and a parameter accounting for spectral stretching or compression from O 2 A and O 2 B spectra. [37] 3. A third step is aerosol retrieval. The synergy between Sentinel 3 OLCI s visible and near infrared spectral coverage and SLSTR s dual view is expected to provide sufficient information for accurate aerosol retrieval. OLCI presents two channels in the shortest visible wavelengths (410 nm and 440 nm), whereas SLSTR will measure in nadir and backward (55 views. Good performance of existing AOD retrieval methods from OLCI and SLSTR predecessors MERIS and AATSR are in the literature [e.g., Grey et al., 2006; Vidot et al., 2008; Guanter et al., 2008; Kokhanovsky et al., 2007]. The exploitation of measurements in the blue wavelengths, where the aerosol contribution is normally highest, the dual view capability to discriminate between surface and atmospheric signals, and the fact that fluorescent green vegetation targets have a very dark reflectance response in the visible wavelength range are expected to enable AOD retrieval to the accuracy levels required by F s retrieval. [38] 4. In the surface pressure retrieval step, existing algorithms for surface pressure retrieval from MERIS data will be adapted to Sentinel 3 OLCIdata[Lindstrot et al., 2009]. These algorithms, complemented with a high precision digital elevation model and collocated near real time pressure measurements (e.g., from the European Centre for Medium Range Weather Forecasts) can provide a very accurate SPR product. The high spectral resolution of FI- MAS measurements in O 2 A could also be applied independently for surface pressure retrieval. In this case, surface pressure retrieval could be performed before cloud screening so that the pressure product could be used for cloud detection purposes. As discussed previously, the retrieval of surface pressure from FIMAS measurements would compensate the error introduced by the uncertainty of the aerosol vertical profile. [39] Once these preprocessing steps have been applied, the at sensor atmospheric parameters in equation (1) can be calculated for clear sky pixels. F s retrieval becomes then a problem of decoupling reflectance and fluorescence from TOA measurements Decoupling of Fluorescence and Reflectance From TOA Radiance Spectra [40] The baseline method proposed for F s and r s decoupling is based on a spectral fitting method (SFM), and is applicable to both O 2 A and O 2 B absorption features. However, an evolution of the FLD method [Plascyk, 1975] designed for the particular properties of the O 2 A absorption feature (deep and broad absorption and spectrally linear vegetation reflectance response) and adapted to the satellite case for its use with FLEX data (FLD S) [Guanter et al., 2007] has also been tested F s Retrieval Based on Spectral Fitting (SFM) [41] The decoupling of F s and r s terms from high resolution TOA radiance spectra can be performed by means of spectral fitting techniques based on linear regression. equation (1) can be linearized in terms of r s and F s as L j TOA Lj 0 1 S j j s;ap ¼ E t j s j þ T j " F s j j where r s,ap is the apparent reflectance (reflectance including the fluorescence contribution) at the FIMAS jth channel, and ð2þ E t j ¼< ðe dir il þ E dif ÞT " > j ; ð3þ where hi j refers to the spectral convolution operation at channel j. [42] In the proposed approach, surface reflectance within the FIMAS spectral windows is modeled as a linear combination of end members, j s ¼ XNemb i¼1 a i j em ; where a i are the end member abundance coefficients and j r em the reflectance end members. Four end members, three green vegetation and one bare soil reflectance spectra generated with FluorMODleaf and FluorSAIL, have been found to be sufficient for the reproduction of most of the spectral shapes and red edge positions in the reflectance library in Figure 4 (top), and also of the mixed vegetation pixels generated in the scene based simulation data set. The selected end members are plotted in Figure 7. Previous developments represented r s by a n order polynomial, but this approach showed a limited capability to account for real vegetation patterns, especially in the O 2 B band, and a much ð4þ 9of16

10 where the subscripts i, o refer to in and out of band finite spectral intervals, i defined to be at the bottom of the O 2 A absorption feature (defined at nm) and o at the continuum region (757.0 nm). Macrochannels of ±0.5 nm in the bottom of the O 2 A feature and of ±1 nm in the borders are generated by channel binning in order to reduce retrieval errors from instrumental noise and spectral calibration issues. [46] To constrain the system, correction coefficients are defined in order to relate r s and F s within the two spectral windows, 9 A ¼ i s;ap o >= s;ap ð7þ B ¼ Fi s F o s >; Figure 7. Reflectance end members used in the decoupling of F s from r s. higher sensitivity to instrumental noise. The extension of the set of end members to include regionally representative vegetation reflectance patterns can be considered for future work. [43] The fluorescence contribution is modeled as a fixed spectral pattern f(l) derived from models modulated by an intensity coefficient F 0, Fs j ¼ F 0f j ð5þ This simple formulation of F s is considered sufficient for fluorescence retrieval according to the low spectral variability of the fluorescence spectrum within the O 2 A and O 2 B spectral windows. [44] Using equations (4) and (5) in equation (2), and assuming that the atmospheric variables are provided by external data sources, reflectance and fluorescence contributions to TOA radiance ({a i, F 0 }) can be estimated by least squares optimization of the FIMAS spectral channels F s Retrieval Based on the FLD Method for Space Measurements in O 2 A (FLD S) [45] As an alternative to the SFM, a modified version of the FLD method has also been implemented for fluorescence retrieval from FIMAS measurements in the O 2 A band. This FLD like approach takes advantage of FIMAS high spectral sampling measurements to compensate for nonlinear spectral variations of reflectance and fluorescence. Fluorescence in the O 2 A spectral region is retrieved by the solution of the system of two equations expressing the TOA radiance inside and outside the O 2 A band: L o TOA ¼ Lo 0 þ Eo o s t þ Fo s T 9 " o 1 S o o >= s L i TOA ¼ Lo i þ Ei i s t þ Fi s T ð6þ " i >; 1 S i i s The A coefficient in equation (7) is derived from apparent reflectance r s,ap, assuming that r s and r s,ap have the same spectral derivative around 760 nm. d s d d s;ap d Apparent reflectance is calculated from the inversion of equation (1) for F s = 0. The assumption in equation (8) is one of the weakest points in FLD like methods, as the spectral derivative of intrinsic and apparent reflectance can be very different for large F s values [Alonso et al., 2008]. This is refined by an iterative procedure which updates A once a first estimation of F s is performed. The apparent reflectance inside the absorption band, which is necessary for the calculation of the A coefficient, is calculated by means of polynomial interpolation from the continuum region in order to account for nonlinear trends in the reflectance spectral pattern. On the other hand, a fixed value of has been selected for B from models, which is justified by the low spectral variation of the fluorescence emission in this region of the spectrum [Alonso et al., 2008]. [47] From equations (6) and (7), F s in the O 2 A band is given by " Fs i ¼ B X i Et o þ X o S o AX o Et i þ X # i S i T" i BEo t þ X o S o T o " AEt i þ X ð9þ i S i where ð8þ X j ¼ L j TOA Lj 0 ; j ¼ i; o: ð10þ 3.4. Use of Reference Targets to Constrain F s Retrieval [48] Previous analysis have shown that F s retrieval in both O 2 A and O 2 B regions can be biased by a number of environmental and instrumental factors. In principle, errors in AOD retrieval, aerosol model and vertical profile, surface pressure, polarization, temperature profiles or radiometric or spectral errors would affect F s retrieval. However, it can be a priori expected that the resulting errors in F s may appear systematically in all the pixels with similar environmental and instrumental conditions. [49] Nonfluorescent surfaces can be used to improve F s retrieval by the normalization of those systematic errors. These surfaces could be used for the estimation of the zero error in F s retrieval over potentially fluorescent targets, or to constrain the estimation of atmospheric para- 10 of 16

11 Figure 8. Root mean square error (RMSE) in F s retrieval associated to instrumental noise and to the uncertainty in AOD550, surface pressure, and aerosol model for the SFM and FLD S retrieval methods and spectral windows of 20 and 30 nm. The label ALL refers to the combination of the four error sources. meters by forcing reference targets to give F s = 0. Suitable reference targets must have a reflectance response comparable to that of vegetation, and should be located close enough to vegetation pixels to validate the assumption that at least the same values of surface pressure and aerosol conditions apply to both reference targets and green vegetation pixels. [50] In order to ensure that errors in F s are systematic to both vegetation and bare soil pixels, the initial F s retrieval over vegetation and soil surfaces must be performed using a retrieval approach with the minimum sensitivity to the background reflectance. This would be the FLD S method from the approaches discussed previously, as end member based spectral fitting approaches are optimized for application to green vegetation targets that can be represented by the end members in the spectral library. [51] It must be remarked that the need for such reference targets in F s retrieval would handicap FLEX spatial coverage, as F s retrieval could only be performed over areas where a sufficient number of these surfaces is available. This would discard most of the dense green forest covers in the planet. The best GSD would be required in FIMAS in order to identify the purest vegetation free pixels. the simulations according to the variation ranges given in Table 1. For each spectral configuration, which is associated to a given SNR figure, normally distributed random errors in AOD550, SPR and abundances of the rural, urban and maritime aerosol models have been simulated. Up to 1000 independent cases have been generated for each set of parameters, varying each at a time or all at the same time. Mean and maximum absolute errors in AOD550 were and 0.075, respectively, and 2.5 hpa and 5 hpa in surface pressure. F s is calculated from each of the 1000 cases by means of the SFM (O 2 A and O 2 B) and the FLD S (only in O 2 A) retrieval schemes presented in section 3. [53] Root mean square error (RMSE) in F s retrieval associated to instrumental noise and to the uncertainty in AOD550, surface pressure and aerosol model for the SFM and FLD S retrieval methods and spectral windows of 20 nm and 30 nm is presented in Figure 8. It can be observed that F s the estimated total RMSE is larger in O 2 A than in O 2 B. This is explained by the relatively low contribution of F s to TOA radiance with respect to reflectance and atmospheric distortion in O 2 A, as shown in Figures 2 3. Errors in O 2 A F s retrievals with the SFM method are driven by instrumental noise and surface pressure, while less sensitivity to noise is found in the FLD S method due to the spectral binning performed to generate the i,o macrochannels. The total error is higher for FLD S than for SFM, which confirms the results reported by Meroni et al. [2010] about the better performance of SFMs for fluorescence and reflectance decoupling. Concerning F s retrieval in O 2 B, the impact of both instrumental noise and atmospheric parameters remains very small due to the relatively high contribution of F s to TOA radiance. It is also observed that the error decreases with the increase of the spectral window both in O 2 A and in O 2 B due to the better discrimination between reflectance and fluorescence with the wider spectral range. [54] In order to complete the sensitivity analysis presented before, the error in F s retrieval caused by the uncertainty in 4. Results 4.1. Spectrum Based Sensitivity Analysis [52] The sensitivity of F s retrieval to different spectral configurations in FIMAS and to the uncertainty in aerosol parameters and surface pressure has been tested in the first place by means of a spectrum based simulated database. FIMAS like TOA radiance data in O 2 A and O 2 B were generated for varying aerosol and surface pressure values, the rest of the input parameters being those described as the reference simulation setup in Figure 2. The width of the spectral window and the SSI have been varied in Figure 9. Bias in F s retrieval from the uncertainty of the height of the aerosol layer for the SFM and FLD S retrieval methods and spectral windows of 20 and 30 nm. 11 of 16

12 O 2 B must be noted. It is observed that the impact of the aerosol vertical profile on O 2 B retrievals is negligible, while it is very important in the case of O 2 A for the two retrieval methods and spectral windows. Errors in O 2 A due to the uncertainty of the aerosol vertical profile are of up to 150% for the simulated cases. [55] It must be remarked that the absolute errors in Figure 8 and Figure 9 are not fully representative of the real case, as they are referred to a pure green vegetation reflectance pattern. It is expected that those errors increase for mixed vegetation and soil pixels, as corresponds to the real case. With this consideration, RMSE in O 2 A from both the SFM and FLD S methods is reckoned as too large for the subsequent exploitation of the fluorescence signal if no F s retrieval normalization is performed. This conclusion reinforces the need to use of reference targets to constrain F s retrieval in O 2 A. Figure 10. Input and retrieved F s maps in O 2 Awiththe FLD S approach before and after the normalization by reference soil targets. the aerosol vertical profile has been assessed. FIMAS like TOA radiance data have been simulated for a range of aerosol vertical distributions, each shifted in height by steps of 1 km. F s retrieval is performed under the assumption of the aerosol layer being located in the range 2 9 km. The systematic absolute error estimated from this simulations is shown in Figure 9. The different vertical axes for O 2 A and 4.2. Scene Based Sensitivity Analysis [56] The scene based simulated data set can be used to provide more information about the feasibility of using reference targets for F s retrieval and about the impact of different vegetation types and distributions. [57] An example of scene based F s retrieval in O 2 A after normalization with reference surfaces for the Northern Spain site #6 in Figure 5f is displayed in Figure 10. Maps of input F s, retrieved F s and retrieved F s after normalization by reference targets are displayed. F s is retrieved with the FLD S approach, as the end member based SFM might lead to biases in F s retrieval due to the wrong representation of soil spectral reflectance by the selected vegetation end members. The normalization by reference surfaces was performed by subtraction of a F s correction map derived from 2 D interpolation from all the bare soil areas in the image. These were defined as those with 0.0 < NDVI < 0.2. An overall overestimation of fluorescence of about 0.8 mw m 2 sr 1 nm 1 when no normalization by reference targets is performed is observed in Figure 10b. This error is mostly due to the uncertainty in the input atmospheric parameters, which in this simulation are those depicted in Figure 6, plus a constant deviation in the aerosol model from the 50%, 20% and 30% abundances of the rural, urban and maritime models, respectively, to the 70%, 0% and 30% proportions used in the retrieval step. The improvement from normalization by the nonzero F s retrieved over bare soils can be noticed in Figure 10c. It can be observed that input and retrieved F s levels are very similar within the entire area. It must be remarked that the area in Figure 5f can be considered the best case scenario for the application of this normalization method, as a number of bare soil surfaces are distributed uniformly over the image. [58] Results of F s retrieval from three of the simulated test sites in Figure 5(#1, #2, #3) and for the 30 nm spectral window configuration are presented in Figure 11. F s retrieval in O 2 A is performed by the FLD S technique with normalization by reference targets, while the SFM is used for O 2 B. Different distributions of green vegetation patterns, from very green and homogeneous vegetation bodies to very sparse vegetation pixels, and different AOD and SPR input error maps are present in the three sites. RMSE and the Pearson s correlation coefficient R 2 12 of 16

13 Figure 11. Results from F s retrieval in O 2 A and O 2 B for a spectral window of 30 nm ( and nm, respectively) over three of the test sites in Figure 5. Colors from blue to red depict an increasing density of points for a given interval of F s values. are calculated between the input and the retrieved F s maps. Mean relative errors in AOD550 and SPR for each site are included in the plots. Only green vegetation pixels, defined as those with NDVI > 0.7, are considered to generate these plots. The very different performance in O 2 A and O 2 B stated in Figures 8 9 can also be observed in the results in Figure 11. Very high linear correlations between the input and retrieved F s values are derived for O 2 B, for which a systematic overestimation of the retrieved values is also detected. Since the absolute errors in the input AOD550 and SPR can be indistinctly positive or negative for each site, such bias is assumed to be due to a bad reconstruction of the vegetation reflectance pattern by the SFM end member technique applied to O 2 B. It appears in all the 6 sites tested. The decoupling between F s and r s in O 2 B improves as the F s values increase. In the case of the O 2 A band, the correlation between input and output F s is smaller, although the retrieval bias is smaller than in O 2 B due to the normalization by nonfluorescent targets. [59] A summary of the results obtained from all 6 simulated sites in Figure 5 is depicted in Figure 12. RMSE and R 2 calculated between the input and the retrieved F s maps are plotted for the O 2 A and O 2 B regions and 20 nm and 30 nm spectral windows. From the analysis of the results obtained from all the sites for the 20 nm and 30 nm 13 of 16