Influence of aerosol and surface reflectance variability on hyperspectral observed radiance

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Atmos. Meas. Tech., 5, 1193–1203, 2012 doi:10.5194/amt-5-1193-2012 © Author(s) 2012. CC Attribution 3.0 License. Atmospheric Measurement Techniques Influence of aerosol and surface reflectance variability on hyperspectral observed radiance C. Bassani1 , R. M. Cavalli1 , and P. Antonelli2 1 Institute for Atmospheric Pollution Research (IIA) – Italian National Research Council (CNR) – Via Salaria Km. 29, 300 – 00015 Monterotondo, Rome, Italy 2 Space Science Engineering Center, University of Wisconsin, Madison, USA Correspondence to: C. Bassani ( Received: 1 October 2011 – Published in Atmos. Meas. Tech. Discuss.: 5 December 2011 Revised: 14 May 2012 – Accepted: 15 May 2012 – Published: 1 June 2012 Abstract. Current aerosol retrievals based on visible and near infrared remote-sensing, are prone to loss of accuracy, where the assumptions of the applied algorithm are violated. This happens mostly over land and it is related to misrepresentation of specific aerosol conditions or surface properties. New satellite missions, based on high spectral resolution instruments, such as PRISMA (Hyperspectral Precursor of the Application Mission), represent a valuable opa retrievable from portunity to improve the accuracy of τ550 a remote-sensing system developing new atmospheric measurement techniques. This paper aims to address the potential of these new observing systems in more accurate rea , specifically over land in heterogeneous and/or trieving τ550 homogeneous areas composed by dark and bright targets. The study shows how the variation of the hyperspectral observed radiance can be addressed to recognise a variation of a = 0.02. The goal has been achieved by using simu1τ550 lated radiances by combining two aerosol models (urban and continental) and two reflecting surfaces: dark (represented by water) and bright (represented by sand) for the PRISMA instrument, considering the environmental contribution of the observed radiance, i.e., the adjacency effect. Results showed that, in the continental regime, the expected instrument sensitivity would allow for retrieval accuracy of the aerosol optical thickness at 550 nm of 0.02 or better, with a dark surface surrounded by dark areas. The study also showed that for the urban regime, the surface plays a more significant role, with a bright surface surrounded by dark areas providing favourable conditions for the aerosol load retrievals, and dark surfaces representing less suitable situations for inversion independently of the surroundings. However, over all, the results obtained provide evidence that high resolution observations of Earth spectrum between 400 and 1000 nm would allow for a significant improvement of the accuracy of a for anthropogenic/natural aerosols over land. the τ550 1 Introduction Aerosols play a significant role in atmospheric radiative forcing by scattering and absorbing radiation and by modifying physical and radiative properties of clouds, IPCC (2007). Despite the improvements in knowledge about aerosol forcing, a great deal remains uncertain. Significant effort is being made to infer the properties of aerosol at a regional and global scale by the use of data from passive airborne and space-borne observing systems. An overview of the aerosol retrieval algorithms developed for passive space-borne sensors is presented in King et al. (1999). However, the implementation of these strategies led to operational algorithms which provide a variety of results. Recent inter-comparisons of aerosol retrievals obtained over land by different algorithms, presented in Kokhanovsky et al. (2007), have shown relatively large discrepancies between different satellite observations. Furthermore, it was shown that the methodologies used for aerosol retrieval from multispectral data, are heavily dependent on the characterisation of the a priori knowledge on aerosol models and ground surface properties. For example, the algorithms used to determine aerosol properties over land and over ocean based on EOS-MODIS (both Terra and Aqua satellites) observed radiances, assumes that surface reflectances in the visible and near infrared are Published by Copernicus Publications on behalf of the European Geosciences Union. 1194 C. Bassani et al.: Influence of aerosol and surface reflectance variability correlated (Kaufman et al., 1997b, 2002). Cases for which the assumptions are violated lead to lower accuracy of the retrieval products. The challenge of imaging spectroscopy, hereafter referred to as hyperspectral remote-sensing, is to mitigate the dependency of current aerosol retrieval algorithms from these kind of assumptions, by providing observations at higher spectral and spatial resolution (Guanter et al., 2009; Gao et al., 2009; Goetz et al., 1985). However, having a single instrument in orbit represents a limitation in data availability which translates to a lack of opportunities for hyperspectral-based algorithm development and validation. Currently, high spectral resolution data are acquired by the Compact High-Resolution Imaging Spectrometer (CHRIS) as part of the Project for On-Board Autonomy (PROBA) platform system Barnsley et al. (2004). In addition, two new hyperspectral missions namely: EnMAP (Environmental Mapping and Analysis Program), Kaufmann et al. (2008), and PRISMA (Hyperspectral Precursor and Application Mission), Galeazzi et al. (2008, 2009), have started. Both missions are intended to provide new observations at approximately 30 m resolution to test and improve the algorithms currently used in atmospheric studies. There is extensive literature on the results obtained from simulated hyperspectral data (Kaufman et al., 1997a; Vermote et al., 1997; Kotchenova et al., 2008; Kokhanovsky et al., 2010). In particular, Guanter et al. (2007); Gao et al. (2009); Bassani et al. (2010) showed that with hyperspectral data, minimization algorithms can be used to solve the inverse problem to infer the aerosol optical thickness for a given aerosol model. For this reason, the aim of this study is to investigate the influence of the aerosol model, the surface properties and the adjacency effect on the accuracy of the aerosol optical thickness at 500 nm retrieval from hyperspectral observations. The whole study was done on simulated data and was divided in two parts. The first part was based on an ideal instrument with 2.5 nm spectral resolution, and the second part on PRISMA-like data obtained convolving the ideal instrument data with an instrument Gaussian response function with Full Width at Half Maximum (FWHM ≤ 10 nm) and spectral coverage between 400–1000 nm. Aerosol properties considered in this study were: aerosol a , loading at optical thickness of 550 nm, referred to as τ550 and aerosol model (urban or continental). The analysis was performed on a dark (clear-water) and a bright (sand) surface to evaluate the radiative impact of the reflective characteristics of the target on the simulated radiances, which also accounted for the surrounding environment contribution. Synthetic radiances were simulated using the 6SV1.1 version of the forward model 6SV (Second Simulation of a Satellite Signal in the Solar Spectrum – Vector) (Vermote et al., 2006). The first specific goal was to provide qualitative analysis of how observed radiance depend on aerosol optical thicka at 500 nm, on aerosol models over land (continental ness τ550 Atmos. Meas. Tech., 5, 1193–1203, 2012 and urban) and on target surface reflective properties (including target surroundings). Analysis was based on simulated radiances at 2.5 nm resolution for the spectral region of 400– 2500 nm. The second specific goal was to identify optimal conditions at high spatial resolution, in terms of target and surrounding surface reflective characteristics, for anthropogenic/natural aerosol property retrievals. For this part, simulated data were tailored to PRISMA instrument specifications. The goal was achieved by comparing the sensitivity of the simulated radiances to incremental changes in aerosol optical thickness of 0.02, with the signal-to-noise ratio specified for the PRISMA instrument, for different target areas. The methodology followed in the study is described in detail in Sect. 2, while Sect. 3 contains the general results obtained for an idealized hyperspectral instrument, and Sect. 4 focuses on the specific results obtained for a PRISMA-like instrument, with the conclusions dedicated to the closing section. 2 Methodology This section aims to introduce the scientific methodology followed in the study. In both cases, simulated radiances accounted explicitly for the adjacency effect, i.e., the impact of the surroundings of the target area on the observed radiances. The analysis of the environmental contribution was taken into account to address whether the environment contributions play a significant role in the accuracy of the retrieved aerosol optical thickness or not. This section is divided into three subsections; the first is dedicated to the theoretical aspects of the simulation of observed radiances for an ideal instrument, the second is focused on the contribution of the aerosol loading and models on the simulated data, and the last one is dedicated to the surface contribution to the simulated radiances. 2.1 Observation simulation Synthetic radiances used for this study, were generated within the spectral domain of 400–2500 nm sample at 2.5 nm, for different values of aerosol optical thickness at a and for urban and continental models. The 550 nm, τ550 model used in the simulation is based on the equation for the top of the atmosphere radiance presented in Vermote et al. (1997), which considers the anisotropy negligible of the reflecting surface, (assumption of Lambertian surface): " # T ↑ (λ)T ↓ (λ)ρgnd (λ) µs Es (λ) g atm Lv (λ) = t (λ) ρ (λ) + π 1 − S(λ)ρgnd (λ) (1) where Lv (λ) is the radiance by the ideal sensor within the considered Field Of View (FOV); Es (λ) is the solar irradiance at the Top Of Atmosphere (TOA); t g (λ) is the transmittance due to gaseous absorption; ρ atm (λ) is the intrinsic C. Bassani et al.: Influence of aerosol and surface reflectance variability Table 1. The volumetric percentage of the four basic components (oceanic, water-soluble, soot and dust-like) describing the urban and continental aerosol model, d’Almeida et al. (1991). 1195 Oceanic 61 % 29 % 22 % 1% 17 % 70 % 0% 0% L(λ) = " µs Es (λ) g t (λ) π ρ atm (λ) + T ↓ (λ)e−τ/µv ρgnd (λ) + T ↓ (λ)td (µv ) < ρgnd (λ) > 1 − S(λ) < ρgnd (λ) > # (2) where L(λ) is the total observed radiance coming from the considered FOV and its surroundings and < ρgnd (λ) > represents the mean of the environmental reflectance around the viewed target. When the neighbouring targets are equal to the viewed target (< ρgnd (λ) >= ρgnd (λ)), Eqs. (2) and (1) become identical. 2.2 Aerosol contribution Aerosol contribution to observed radiances is mostly determined by the radiation extinction due to scattering and absorption. Aerosol loading is the aerosol primary quantity in driving radiation extinction within the atmospheric window in the visible spectral domain, and it is generally parametera . ized by its optical thickness at 550 nm, referred to as τ550 The sensitivity study presented in this paper is based on radiances simulated for: 0.2 atmospheric reflectance; T ↑ (λ) = e−τ (λ)/µv + td (µv , λ) and T ↓ (λ) = e−τ (λ)/µs + td (µs , λ) are the total upwelling and downwelling transmittance, both with direct, e−τ (λ)/µs,v , and diffuse, td (µs,v , λ), components; µs,v = cos(θs,v ) where θs,v are the solar, “s”, and view, “v”, zenith angle; τ (λ) is the atmospheric optical thickness; S(λ) is the atmospheric spherical albedo, defined in Kokhanovsky (2008), and ρgnd (λ) is the at-ground surface reflectance. Simulated radiances were obtained using the last version (v. 6SV1.1, Kotchenova et al., 2008) of the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer code Vermote et al. (1997). The 6SV1.1 is an opensource code which implements Eq. (1). It shows significant improvements with respect to previous versions, as described in Vermote and Kotchenova (2009). Simulation of the radiance observed by the ideal instrument were obtained accounting not only for the radiative contribution of the viewed target, but also for the contribution of areas surrounding the target FOV (Field Of View), due to scattering processes. This second contribution is generally referred to as the adjacency effect. The equation used to calculate the observed radiance which accounts for the environmental contribution was presented by Vermote et al. (1997) 0.4 Dust-like Single Scattering Albedo − ω0 Soot 0.0 Urban Continental Water-soluble 0.6 0.8 1.0 Single Scattering Albedo in DRY condition CONTINENTAL aerosol model URBAN aerosol model 500 1000 1500 2000 2500 Wavelength (nm) Fig. 1. Single scattering albedo (ω0 ) of continental and urban aerosol model, available in dry condition from the 6SV1.1 code. a ∈ {0.00 − 2.00} at intervals of 1τ a = 0.02. The – τ550 550 a was set to 0.02 to allow for direct calincrement 1τ550 1L culation of the radiance gradient 1τ with the same aca 550 curacy level of in situ observation provided by sun skyradiometer, as CIMEL Holben et al. (1998), generally used for the validation of the remote aerosol retrievals. It is worth mentioning that also aerosol retrievals provided a ) by MODIS have an expected error of ±(0.05+0.15τ550 over land, as described in Levy et al. (2010). Range of aerosol optical thicknesses used in the simulation was the widest allowed by 6SV1.1. Furthermore, it is the same range used to generate the main-group ela in the retrieval algorithm applied to hyements for τ550 perspectral remotely data, Guanter et al. (2007); Bassani a outside of this domain must et al. (2010). Values of τ550 be treated with different atmospheric radiative transfer codes, as in the L-POM model presented in Alakian et al. (2008), which enables the simulation of the radiative field in very high aerosol loading conditions. – Both urban and continental aerosol models to verify the role of the optical and microphysical properties of the aerosol in the direct and diffuse components of the solar beam during its propagation through the atmosphere in a homogeneous and heterogeneous environment composed by dark and/or bright target. The present study focuses on the two models which are mostly used in remote data acquired over land. Both models combine the four basic components: oceanic, water-soluble, soot and dust-like, d’Almeida et al. (1991). Table 1 shows the volumetric percentage of the basic components for the urban and continental aerosol regimes, as contained in the source code 6SV1.1. Figure 1 shows the single scattering albedo in dry condition for both the aerosol models, like available from the 6SV1.1 code. Atmos. Meas. Tech., 5, 1193–1203, 2012 1196 C. Bassani et al.: Influence of aerosol and surface reflectance variability Table 2. The observed radiance simulated using sand (representative for bright) and water (representative for dark) for viewed and adjacent targets. 0.6 Observed radiance Viewed target Adjacent targets 0.4 sand clear−water Lss (λ) Lsw (λ) Lws (λ) Lww (λ) sand sand clear-water clear-water sand clear-water clear-water sand 0.0 0.2 At−ground Reflectance 0.8 SAMPLES surface reflectance 500 1000 1500 2000 2500 Wavelength (nm) Fig. 2. Surface reflectance, ρgnd (λ) contained in the 6SV1.1 source code: sand for bright and water for dark in the spectral domain: 400– 2500 nm sampled at 2.5 nm. 2.3 Surface contribution To properly simulate observed radiances for the presented study, the influence of the surface contributions was explicitly taken into consideration. The interaction between the radiation and the surface affects both the direct and diffuse components coming off the viewed target and the diffuse component from adjacent targets which is, successively, scattered from the atmospheric aerosols. Simulation of observed radiances, generated for this study, satisfies the Lambertian condition, required by Eqs. (1) and (2). Therefore, the ρgnd (λ), in order to satisfy the previous requirement, were selected for sand (representative of a bright surface) and water (representative of a dark surface). Figure 2 shows the two reflectance spectra used by 6SV1.1 for the analysis. The two spectral signatures were used for the viewed and adjacent target. Table 2 provides the naming convention for the radiance generated by combining the two spectral signatures for the viewed and adjacent targets. 3 General results for idealized instrument This section shows the results obtained by investigating the dependency of the observation simulated according to Sects. 2.1, 2.2 and 2.3 for different combinations of aerosol and surface properties. The simulation was performed in dry condition. In this way, it was possible to neglect the hygroscopic properties of the aerosol models that can change the radiative effects of both the aerosol models on the observed radiance. The geometrical conditions used in the simulation were chosen to maximise the upwelling solar irradiance reflected by the surface. The FOV was located in Rome (Latitude: 41 58′ N, Longitude: 12 40′ E), Italy. The acquisition time was assumed midday in July with the soAtmos. Meas. Tech., 5, 1193–1203, 2012 lar zenith angle of θs = 33.97 and the azimuth solar angle of φs = 238.41. The nadir viewing angle was chosen to verify the symmetry in azimuth of the environment contribution (adjacency effect) on the observed radiance, as shown in Fig. 5 of Vermote et al. (1997). 3.1 The impact of aerosol loading on the observed radiance As a first step, the analysis was conducted, not taking into equilibrium value of MeCpG steps (,+14 deg.) [31,44]. In comparison, methylation has a significantly lower stability cost when happening at major groove positions, such as 211 and 21 base pair from dyad (mutations 9 and 12), where the roll of the nucleosome bound conformation (+10 deg.) is more compatible with the equilibrium geometry of MeCpG steps. The nucleosome destabilizing effect of cytosine methylation increases with the number of methylated cytosines, following the same position dependence as the single methylations. The multiple-methylation case reveals that each major groove meth- PLOS Computational Biology | 3 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning ylation destabilizes the nucleosome by around 1 kJ/mol (close to the average estimate of 2 kJ/mol obtained for from individual methylation studies), while each minor groove methylation destabilizes it by up to 5 kJ/mol (average free energy as single mutation is around 6 kJ/mol). This energetic position-dependence is the reverse of what was observed in a recent FRET/SAXS study [30]. The differences can be attributed to the use of different ionic conditions and different sequences: a modified Widom-601 sequence of 157 bp, which already contains multiple CpG steps in mixed orientations, and which could assume different positioning due to the introduction of new CpG steps and by effect of the methylation. The analysis of our trajectories reveals a larger root mean square deviation (RMSD) and fluctuation (RMSF; see Figures S2– S3 in Text S1) for the methylated nucleosomes, but failed to detect any systematic change in DNA geometry or in intermolecular DNA-histone energy related to methylation (Fig. S1B, S1C, S4–S6 in Text S1). The hydrophobic effect should favor orientation of the methyl group out from the solvent but this effect alone is not likely to justify the positional dependent stability changes in Figure 2, as the differential solvation of the methyl groups in the bound and unbound states is only in the order of a fraction of a water molecule (Figure S5 in Text S1). We find however, a reasonable correlation between methylation-induced changes in hydrogen bond and stacking interactions of the bases and the change in nucleosome stability (see Figure S6 in Text S1). This finding suggests that methylation-induced nucleosome destabilization is related to the poorer ability of methylated DNA to fit into the required conformation for DNA in a nucleosome. Changes in the elastic deformation energy between methylated and un-methylated DNA correlate with nucleosomal differential binding free energies To further analyze the idea that methylation-induced nucleosome destabilization is connected to a worse fit of methylated DNA into the required nucleosome-bound conformation, we computed the elastic energy of the nucleosomal DNA using a harmonic deformation method [36,37,44]. This method provides a rough estimate of the energy required to deform a DNA fiber to adopt the super helical conformation in the nucleosome (full details in Suppl. Information Text S1). As shown in Figure 2, there is an evident correlation between the increase that methylation produces in the elastic deformation energy (DDE def.) and the free energy variation (DDG bind.) computed from MD/TI calculations. Clearly, methylation increases the stiffness of the CpG step [31], raising the energy cost required to wrap DNA around the histone octamers. This extra energy cost will be smaller in regions of high positive roll (naked DNA MeCpG steps have a higher roll than CpG steps [31]) than in regions of high negative roll. Thus, simple elastic considerations explain why methylation is better tolerated when the DNA faces the histones through the major groove (where positive roll is required) that when it faces histones through the minor groove (where negative roll is required). Nucleosome methylation can give rise to nucleosome repositioning We have established that methylation affects the wrapping of DNA in nucleosomes, but how does this translate into chromatin structure? As noted above, accumulation of minor groove methylations strongly destabilizes the nucleosome, and could trigger nucleosome unfolding, or notable changes in positioning or phasing of DNA around the histone core. While accumulation of methylations might be well tolerated if placed in favorable positions, accumulation in unfavorable positions would destabilize the nucleosome, which might trigger changes in chromatin structure. Chromatin could in fact react in two different ways in response to significant levels of methylation in unfavorable positions: i) the DNA could either detach from the histone core, leading to nucleosome eviction or nucleosome repositioning, or ii) the DNA could rotate around the histone core, changing its phase to place MeCpG steps in favorable positions. Both effects are anticipated to alter DNA accessibility and impact gene expression regulation. The sub-microsecond time scale of our MD trajectories of methylated DNAs bound to nucleosomes is not large enough to capture these effects, but clear trends are visible in cases of multiple mutations occurring in unfavorable positions, where unmethylated and methylated DNA sequences are out of phase by around 28 degrees (Figure S7 in Text S1). Due to this repositioning, large or small, DNA could move and the nucleosome structure could assume a more compact and distorted conformation, as detected by Lee and Lee [29], or a slightly open conformation as found in Jimenez-Useche et al. [30]. Using the harmonic deformation method, we additionally predicted the change in stability induced by cytosine methylation for millions of different nucleosomal DNA sequences. Consistently with our calculations, we used two extreme scenarios to prepare our DNA sequences (see Fig. 3): i) all positions where the minor grooves contact the histone core are occupied by CpG steps, and ii) all positions where the major grooves contact the histone core are occupied by CpG steps. We then computed the elastic energy required to wrap the DNA around the histone proteins in unmethylated and methylated states, and, as expected, observed that methylation disfavors DNA wrapping (Figure 3A). We have rescaled the elastic energy differences with a factor of 0.23 to match the DDG prediction in figure 2B. In agreement with the rest of our results, our analysis confirms that the effect of methylation is position-dependent. In fact, the overall difference between the two extreme methylation scenarios (all-in-minor vs all-in-major) is larger than 60 kJ/mol, the average difference being around 15 kJ/ mol. We have also computed the elastic energy differences for a million sequences with CpG/MeCpG steps positioned at all possible intermediate locations with respect to the position (figure 3B). The large differences between the extreme cases can induce rotations of DNA around the histone core, shifting its phase to allow the placement of the methylated CpG steps facing the histones through the major groove. It is illustrative to compare the magnitude of CpG methylation penalty with sequence dependent differences. Since there are roughly 1.5e88 possible 147 base pairs long sequence combinations (i.e., (4n+4(n/2))/2, n = 147), it is unfeasible to calculate all the possible sequence effects. However, using our elastic model we can provide a range of values based on a reasonably large number of samples. If we consider all possible nucleosomal sequences in the yeast genome (around 12 Mbp), the energy difference between the best and the worst sequence that could form a nucleosome is 0.7 kj/mol per base (a minimum of 1 kJ/mol and maximum of around 1.7 kJ/mol per base, the first best and the last worst sequences are displayed in Table S3 in Text S1). We repeated the same calculation for one million random sequences and we obtained equivalent results. Placing one CpG step every helical turn gives an average energetic difference between minor groove and major groove methylation of 15 kJ/ mol, which translates into ,0.5 kJ/mol per methyl group, 2 kJ/ mol per base for the largest effects. Considering that not all nucleosome base pair steps are likely to be CpG steps, we can conclude that the balance between the destabilization due to CpG methylation and sequence repositioning will depend on the PLOS Computational Biology | 4 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning Figure 3. Methylated and non-methylated DNA elastic deformation energies. (A) Distribution of deformation energies for 147 bplong random DNA sequences with CpG steps positioned every 10 base steps (one helical turn) in minor (red and dark red) and major (light and dark blue) grooves respectively. The energy values were rescaled by the slope of a best-fit straight line of figure 2, which is 0.23, to source of circulating FGF-21. The lack of association between circulating and muscle-expressed FGF-21 also suggests that muscle FGF-21 primarily works in a local manner regulating glucose metabolism in the muscle and/or signals to the adipose tissue in close contact to the muscle. Our study has some limitations. The number of subjects is small and some correlations could have been significant with greater statistical power. Another aspect is that protein levels of FGF-21 were not determined in the muscles extracts, consequently we cannot be sure the increase in FGF-21 mRNA is followed by increased protein expression. In conclusion, we show that FGF-21 mRNA is increased in skeletal muscle in HIV patients and that FGF-21 mRNA in muscle correlates to whole-body (primarily reflecting muscle) insulin resistance. These findings add to the evidence that FGF-21 is a myokine and that muscle FGF-21 might primarily work in an autocrine manner. Acknowledgments We thank the subjects for their participation in this study. 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