Aerosol retrieval over land using a multiband polarimeter and comparison with path radiance method

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 112,, doi: /2006jd008029, 2007 Aerosol retrieval over land using a multiband polarimeter and comparison with path radiance method F. Waquet, 1 P. Goloub, 2 J.-L. Deuzé, 2 J.-F. Léon, 2 F. Auriol, 2 C. Verwaerde, 2 J.-Y. Balois, 2 and P. François 2 Received 13 September 2006; revised 1 February 2007; accepted 28 February 2007; published 13 June [1] This study focuses on the development of a new approach to retrieve aerosol properties over land, based on the use of multispectral polarized measurements ( mm). We use the measurements of the airborne MICROPOL polarimeter during regional aircraft field experiments located in France and dedicated to the study of aerosol (pollutant and mineral dust particles) and surface properties. We have developed a multiband polarization algorithm (MBP) and compared the retrievals with both a path radiance algorithm and Sun photometer data. It is shown that surface polarized reflectance exhibits only a small spectral variation for forward scattering geometries (3% on average, 15 20% for a single view and scattering angle < 110 ). The atmospheric contribution at 2.2 mm is small for aerosol optical thicknesses (AOTs) up to 0.15 at 0.67 mm and can be accounted for in the retrieval of surface properties. The 2.2 mm channel therefore enables us to accurately derive the surface polarization in the shorter MICROPOL bands. For the observations we have made, the surface model developed for the analysis of the Polarization and Directionality of the Earth Reflectance (POLDER) measurements overestimates surface polarization from a few to fifty percents. This leads to AOT underestimation by a factor two. For the pollutant aerosol cases (0.065 < AOT < 0.20), the MBP approach retrieves AOT with an accuracy of 0.03, over both natural and urban surfaces. However, this method remains only weakly sensitive to coarse mode particles and fails when dust particles associated with large AOTs (0.5) are considered. Citation: Waquet, F., P. Goloub, J.-L. Deuzé, J.-F. Léon, F. Auriol, C. Verwaerde, J.-Y. Balois, and P. François (2007), Aerosol retrieval over land using a multiband polarimeter and comparison with path radiance method, J. Geophys. Res., 112,, doi: /2006jd Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York, USA. 2 Laboratoire d Optique Atmosphérique, Université de Lille, Villeneuve d Ascq, France. Copyright 2007 by the American Geophysical Union /07/2006JD Introduction [2] Aerosols affect the atmospheric radiation budget both directly by scattering and absorbing the solar radiation and indirectly by changing the cloud albedo and lifetime. The total radiative forcing of aerosol particles, due to direct and indirect effects, is estimated to be of the same order of magnitude as man-made greenhouse gases, but with opposite sign [Charlson et al., 1992]. However, large uncertainties are associated with the effects of aerosols in global climate modeling. Global aerosol monitoring from satellite remains a challenge over land since the aerosol contribution is generally smaller than the surface one. Many methods have been developed but remain only partially successful. Each of them is efficient either for only one kind of aerosol or for one surface type and requires some assumptions to either eliminate or estimate the surface contribution. Aerosol optical thickness was initially retrieved over dense dark vegetation where the surface contribution is minimized in some visible channels [Kaufman and Sendra, 1988]. This approach has been adapted more recently to the Moderateresolution Imaging Spectrometer MODIS [Kaufman et al., 1997a], the Multiangle Imaging Spectroradiometer MISR [Martonchik et al., 2002] and the Medium Resolution Imaging Spectrometer MERIS [Vidot, 2005], with a few modifications. Other methods have also been considered in order to extend aerosol retrievals to brighter surfaces in the visible. For example the contrast technique over heterogeneous surface used with MISR [Martonchik et al., 2002, 2004], the use of atmospheric corrections with MERIS and the use of a preestimated surface reflectance database derived from satellite measurements [Hsu et al., 2004]. In the thermal infrared, methods have also been developed to study mineral dust particles using METEOSAT [Legrand et al., 1988, 1989, 2001] the Advanced Very High Resolution Radiometer [Ackerman, 1989, 1997] and the Advanced Infrared Sounder [Pierangelo et al., 2004, 2005]. Techniques based on the use of ultraviolet measurements from the Total Ozone Mapping Spectrometer (TOMS) were also developed to study absorbing aerosols [Torres et al., 1of13

2 1998, 2002] and used to investigate the radiative effect of mineral dust particles [Hsu et al., 2000] and identify the main sources of mineral dust [Prospero et al., 2002]. [3] Aerosol remote sensing over land surfaces is also possible using the polarization of light scattered by the atmosphere. There are several advantages to using polarized radiances rather than total radiances. First, the relative contribution of the surface is lower, second the surface polarized contribution does not exhibit spectral dependence and third there is only weak spatial contrast, except in the case of farm land or urban/rural transitions. Moreover, polarization measurements exhibit high sensitivity to aerosol properties as shown in different experimental and theoretical studies [Mishchenko and Travis, 1997; Cairns et al., 1997; Chowdhary et al., 2005]. Taking advantage of these features of polarization Deuzé et al. [2001] have developed a method based on polarized radiance measurements provided by the Polarization and Directionality of the Earth Reflectance instruments (POLDER I and II) between 0.44 and 0.87 mm to retrieve aerosol optical thickness over lands. Following on from the POLDER I, II and the Polarization and Anisotropy of Reflectances at the top of the Atmosphere (PARASOL) spaceborne missions in France, an advanced Aerosol Polarimetry Sensor (APS) is expected to be launched in 2008, on board the NASA Glory satellite. This instrument will be able to collect polarimetric measurements within the solar reflective spectral region (0.4 to 2.25 mm). However, before getting these new satellite observations airborne measurements are essential to develop and improve the retrieval algorithms that will be applied to these new data sets. Airborne simulators such as the Research Scanning Polarimeter (RSP) [Cairns et al., 1997] and the MICROPOL instrument [Waquet et al., 2005] have already been developed. MICROPOL is a single viewing angle multiwavelength prototype polarimeter that provides accurate polarized measurements in five spectral bands centered on 0.49, 0.67, 0.87, 1.6 and 2.2 mm. A detailed description of the design of this instrument has been presented by Waquet et al. [2005]. This instrument was specifically developed to overcome two limitations in the aerosol retrieval over land using POLDER which are the use of a semiempirical model for ground surface correction and a spectral range that is restricted to 0.67 and 0.87 mm. The POLDER-1 experiment showed that the surface contribution in polarization is spectrally flat within the range mm [Nadal, 1999]. Recent experimental studies [Cairns et al., 2001; Elias et al., 2004] show that the surface polarized reflectances remain, in many cases, spectrally neutral up to 2.25 mm. At this wavelength, the atmospheric contribution is minimized and the polarized surface contribution can then be directly measured and used to accurately estimate the atmospheric contribution in the shorter wavelengths. [4] In this study, we present and evaluate the capacity of a multiband polarized algorithm (MBP), based on the use of polarized radiance measurements from 0.67 to 2.2 mm, to retrieve aerosol properties over land. We also investigate the two main assumptions which constitute the basis of the MBP method: (1) small spectral dependence of the surface in polarization and (2) small effect of the atmosphere at 2.2 mm. Since 2001, MICROPOL has participated in several airborne campaigns with various surface types, aerosol loads and types and viewing geometries. In addition, measurements at low altitude were also performed in order to provide the best possible characterization of surface polarization. The results of this new algorithm are compared with both a path radiance (PR) method, similar to that used for MODIS, and regional Aerosol Robotic Network Photométrie pour le Traitement Opérationnel de Normalisation Satellitaire (AERONET/PHOTONS) data. [5] The next section describes the MBP method and also briefly recaps the PR method. In the third section, we compare the surface polarization estimation approach developed for MICROPOL with the one used for the analysis of the POLDER data. The fourth section focuses on aerosol retrieval results, validation and comparison when applied to various surface types and atmospheric conditions. Finally, we summarize our results and briefly discuss the advantages and limitations of each method. 2. Algorithm Descriptions [6] The MBP and PR methods are similar in that they are based on using the 2.2 mm channel to derive the surface contribution (to polarization in the case of MBP and reflectance in the case of PR) and remove it from the top of the atmosphere (TOA) or airborne level signal. We used the normalized total and polarized radiances, L and L p, defined in term of Stokes parameters as they are derived from the MICROPOL measurements [Waquet et al., 2005]. L is given by L ¼ pi=e 0 ; where I is the first Stokes parameter (W m 2 sr 1 ) and E 0 is the spectral solar extraterrestrial irradiance at the time of observation (W m 2 ). Lp is given by ð1þ Lp ¼ p Q 2 þ U 2 1=2=E0 ; ð2þ where Q and U are the second and third Stokes parameters (W m 2 sr 1 ). Throughout the rest of this paper when we discuss radiances we will be referring to these normalized radiances. Because of the calibration, the relative uncertainties on these radiances are 3% in the visible channels, 4% at 1.6 mm and 6% at 2.2 mm. The instrumental noise is estimated to be in total radiance and 10 4 in polarized radiance [Waquet et al., 2005]. [7] The total and polarized reflectances are computed by dividing the total and polarized radiances by the cosine of the solar zenith angle q s, respectively. We also introduce the viewing zenith angles q v, the scattering angle Q and a relative azimuth angle 8 r (varying from 0 in the specular direction to 180 in the opposite direction). In our data processing, the polarized radiance is signed positive when the angle between the direction of polarization and the normal to the plane of scattering (the plane which contains the solar and view directions) is less than 45 and signed negative for the other cases. It should be noted that many of the studies based on polarization measurements use the polarization ratio (polarized radiance divided by the total radiance) instead of the polarized radiance. This quantity cannot be used here since the polarization ratio combines 2of13

3 polarization and intensity measurements, which does not allow advantage to be taken of the spectral neutrality of surface polarization Multiband Polarization Algorithm Modeling of the Polarized Radiances and Retrieval Scheme [8] The principle of the MBP algorithm described below is based on the one developed for the POLDER I and II instruments [Deuzé etal., 2001]. Because primary scattering dominates polarized light [Bréon et al., 1995], it is possible to model the upwelling polarized radiance by considering only polarized light generated by single scattering by molecules and aerosols and reflection off the surface. Within this approximation, Lp c l is the polarized radiance measured at the instrument level z and can be modeled using a linear relationship as follows, Lp c l ¼ 1 d m l 4 cos q qm ðqþþ w a l da l qa l ðqþt 1 þ cos qs v and r surf p T 2 ð3þ d x l ¼ dx 0;lð 1 exp ð z=h xþþ; ð4þ where the superscript indicates whether we are considering aerosols (x = a) or molecules (x = m) and l indicates the spectral dependence of these equations. The molecular scale height, H m, is set to the standard value of 8 km and the aerosol scale height, Ha, is 2 km, which is a reasonable x value for typical boundary layer aerosols. d 0,l and d x l are the optical thicknesses at the top of the atmosphere and below the aircraft, respectively. q a is the aerosol polarized phase function and q m is the molecular polarized phase function computed as follows, q m ðqþ ¼ 3 4 ½1 cos2 QŠ: ð5þ Note that the molecular phase function does not include the depolarization factor for air molecules. w a is the aerosol surf single scattering albedo and r p is the surface polarized reflectance. The two transmission terms T 1 and T 2 in equation (3) are given by the formulae T 1 ¼ exp l dm o;l þ dm l ðþ z m s T 2 ¼ exp gdm o;l þ bda tot;l þ gdm l ðþþbda z l ðþ z m s m v m v ð6þ ; ð7þ where m s and m v are respectively the cosine of the solar and viewing zenith angles. The coefficients g and b account for diffuse transmission of light by aerosols and molecules which reduces their screening effect on the radiation [Bréon et al., 1995]. b depends on the aerosol model and is given by an empirical relation (0.3 < b < 0.6) whereas g is fixed and equal to 0.9 after Lafrance [1997]. In this study, we determined that the approximation given in equation (3) is accurate to about at 0.67 mm for aerosol optical thicknesses smaller than 0.25 and viewing angles smaller than 60. The accuracy of this approximation decreases when the aerosol and molecular optical thicknesses increase. As a consequence, the 0.49 mm channel is not used in the retrieval process described below. [9] Polarized radiance is computed following equation (3) for a given geometry (q s, q v, Q), at 0.67, 0.87 and 1.6 mm for a set of 15 aerosol models given their polarized phase function, single scattering albedo and aerosol optical thickness. We also introduce the Angström exponent, a, derived from the optical thicknesses of the particles at l 1 = 0.67 mm and l 0 = 0.87 mm as follows, d a 1 ¼ da 0ð l 1=l 0 Þ a : ð8þ The Angström exponent is indicative of the form of the polarized phase function as they both depend on the particle size distribution and refractive index with larger values of the Angström parameter being associated with smaller particles. Each aerosol model is described by a single mode lognormal distribution with a geometric standard deviation of and modal radii r g varying from 0.1 to 0.38 mm (corresponding to an Angström exponent ranging from 0.04 to 2.43). As POLDER mainly detects anthropogenic particles, a refractive index (m = i) is assumed for all retrievals, which is the mean value for urbanindustrial and biomass burning aerosols [Dubovik et al., 2001]. The properties of the 15 models are computed using the Mie theory. In the first step of the MBP method, the polarized reflectance at 2.2 mm is used to estimate the polarized surface contribution r p surf, assuming that the atmospheric contribution is negligible at this wavelength. Then, for each aerosol model m, polarized radiances are computed for the MICROPOL viewing geometry at 0.67, 0.87 and 1.6 mm with increasing values of aerosol optical thickness at 0.87 mm. For each model, we then obtain a value of aerosol optical thickness d a * that minimizes an error quadratic term e m computed between simulated and measured polarized radiances. The best fit (e m (d a *) minimum) defines the retrieved aerosol optical depth and aerosol model Information Content on Aerosol Properties [10] Figures 1a and 1b show the polarized phase functions obtained at 0.67 and 1.6 mm for the lognormal aerosol models previously defined. These figures show the angular and spectral variability of the polarized phase functions used in the MBP algorithm. The polarized phase functions presented above are classified by decreasing values of Angström exponent (i.e., increasing values of the particle size). We observe that the polarized phase function values at 0.67 and 1.6 mm increase with the Angström exponent values. Thus the smaller the particle is, the more the particle polarizes. The range of scattering angles observed by downward looking sensors in commonly used Sunsynchronous polar orbits (e.g., the POLDER, MISR and MODIS instruments) is typically between 80 and 160. At 0.67 mm and for scattering angles between 80 and 140 only smaller particles (a > 1.3) can be easily discriminated from one another based on variations in q a while at larger scattering angles, particularly close to 160, larger particles (a > 1.3) can be differentiated from one another. The large 3of13

4 Figure 1. Polarized phase functions for lognormal models (0.1 < r m < 0.38 mm, s = 0.403, m = i), computed at (a) 0.67 and (b) 1.6 mm. variations in polarized phase function observed near the backscattering direction are characteristic polarization features for large spherical particles [Deuzé et al., 2001]. As the wavelength increases (Figure 1b), the values of the polarized phase function become significantly higher, because the effective size (size divided by wavelength) is smaller. For instance, the maximum values of q a at 0.67 and 1.6 mm are 0.35 and 0.70, respectively, for a = We also observe that the largest values of q a at 1.6 mm move to larger values of scattering angle as particle size decreases with a general tendency toward Rayleigh like behavior (magnitude increasing and peak location moving toward 90 scattering angle). Thus the large spherical particles in our model database are likely to polarize more at 1.6 than at 0.67 mm over the available observed range of scattering angles. Note that the aerosol models included in our database are associated with effective radii (defined as the ratio of the third to the second moment of the size distribution) between roughly 0.1 and 0.6 mm and therefore cover the expected size range for accumulation mode aerosol particles, but do not extend to the expected effective radii of a few microns for coarse mode particles [Dubovik et al., 2001]. [11] Figure 2 shows polarized phase functions obtained for spherical and nonspherical coarse-mode particles at different wavelengths. Calculations for nonspherical particles were performed with the T-matrix code [Mishchenko and Travis, 1994] for randomly oriented mixtures of oblate and prolate particles (spheroids). We used the same size distribution and refractive index for both spheres and spheroids calculations. The microphysical and optical properties of the particle models are representative of coarsemode mineral dust properties [Dubovik et al., 2001]. We observe that spherical and nonspherical calculations both show low values of polarized phase functions over the scattering angle range. In comparison with small particles (see Figure 1, for a > 1.72), the polarized phase function values observed here are between 5 and 10 times lower. We also remark that oscillations near the backscatter direction are absent for the spheroid calculations. It should be noticed that such polarization features have never been observed with the POLDER instrument, in the case of mineral dust particles events [Herman et al., 2005]. This observation suggests that most of the mineral dust particles are probably nonspherical. [12] Figures 1 and 2 show that particles with effective radii above 0.6 mm do not polarize much and are therefore Figure 2. Polarized phase function calculated for a dust coarse-mode with spheres (solid lines) and spheroids (dashed lines). The wavelengths are 0.555, 0.67, 0.865, 1.24, 1.64 and 2.15 mm shown in turquoise, green, red, orange, purple and black, respectively. Computations are performed with a lognormal size distribution (r g = mm, s = and effective radius of mm). The refractive indices used were i, i, i, i, i and i in order of increasing wavelength. 4of13

5 Figure 3. Theoretical polarized spectral behavior within 0.41 to 2.2 mm for spherical lognormal aerosol models. (a) Angström exponent varying from 0.79 to 2.43 (0.1 < r g < 0.26 mm, s = and m = i). (b) Same as Figure 3a but for r g = 0.1 and 0.18 mm and for three values of particle real refractive index, m r = 1.40, 1.47 and not included in our model database since they cannot be accurately detected and characterized using polarized measurements. [13] According to equation (3), the aerosol contribution to polarization is roughly proportional to the d a.q a.w a product. In order to provide a simple depiction of the spectral behavior of the polarized light generated by aerosols, we introduce the ratio R defined as R ¼ ðd a q a w a ÞðlÞ= ðd a q a w a Þð0:87mmÞ: ð9þ This quantity is presented as a function of the wavelength in Figure 3a. Computations are performed for different aerosol models from our model database and for a scattering angle of 100. Contrasting spectral properties are observed in Figure 3a depending on the size parameter considered. As the Angström exponent decreases from 2.43 to 0.76, the spectral variation in the polarized aerosol contribution increases. This sensitivity is reduced when considering the spectral range used by POLDER for aerosol remote sensing ( mm). It should be noted that the inclusion of measurements at 1.6 mm increases the sensitivity of our retrieval to the Angström parameter for the largest particles considered in our model database. For instance a difference of 2 is observed between the values of R (1.6) computed for Angström exponents of 1.50 and 1.12, as compared to a difference of 0.5 between the R (0.67) values computed for the same Angström exponent values. The spectral dependence of the ratio R is also sensitive to the aerosol refractive index as shown in Figure 3b. The differences in this ratio calculated for particles of different size (r g = 0.10 and 0.18 mm) decrease as the particle real refractive index decreases and vice versa. It should also be noted that there is no sensitivity at 1.6 mm to the particle real refractive index for smallest particles (as one might expect since the particles are effectively Rayleigh scatterers at this wavelength). The variation in the ratio R with real refractive index is largest for the shortest wavelengths (i.e., 0.49 mm for MICROPOL) and so observations at this wavelength should be included in the retrieval process if the refractive index is to be retrieved. As shown by Deuzé etal.[2001], the angular dependence of polarized light is also of use in retrieving a particle size parameter and real refractive index. However, since MICROPOL is a single viewing instrument, we only evaluate in the following the capability of multispectral polarized measurements ( mm) to retrieve particle size and aerosol optical thickness Path Radiance Method [14] The algorithm developed to take advantage of the MICROPOL measurements in total radiance is based on that used for MODIS [Kaufman et al., 1997a]. This algorithm takes advantage of the 0.47, 0.66 and 2.13 mm MODIS channels and is adapted here to the MICROPOL wavelengths (0.49, 0.67 and 2.2 mm). The retrieval scheme can be divided into three steps: (1) the selection of dark targets and the estimation of their surface reflectance, (2) the modeling of the reflectances at the instrument level, and finally, (3) the use of a path radiance method to select an aerosol model in order to derive the aerosol optical thickness and Angström exponent between 0.49 and 0.67 mm. [15] 1. The measured reflectance at 2.2 mm is required to be less than 0.15 in order to restrict the retrievals to only dark, or semidark, targets. The surface reflectances at 0.49 and 0.67 mm are then estimated assuming that the atmospheric contribution at 2.2 mm is negligible and that the surface reflectances in the blue and red channels are linked to that at 2.2 mm by ratios of 0.25 and 0.50 [Kaufman et al., 1997b] respectively. [16] 2. The inversion is based on a look-up table approach. Computations are performed using the Successive Order of Scattering code [Deuzé etal., 1988], assuming a Lambertian 5of13

6 Figure 4. Polarized surface reflectances derived at 0.67 and 1.6 mm as a function of that at 2.2 mm. Set of observations performed close to the principal plane from forward scattering geometry to nadir views (q v 0, 8 r =±20, 45 < q s <60, 70 < Q < 135 ). R is the correlation coefficient associated with the linear regression. surface reflectance for several values of surface albedo, aerosol optical thickness and for a complete set of viewing geometry (q s, q v and 8 r ). A first estimation of the aerosol optical thickness is achieved assuming a continental aerosol model [Brogniez and Lenoble, 1984]. The retrieval process is stopped at this step if the aerosol optical thickness is smaller than 0.15 (at 0.55 mm). In this case, we conserve the aerosol optical thickness and the Angström exponent retrieved with the continental aerosol model. [17] 3. The properties of the continental model (phase function p a (Q) and single scattering albedo) are then used to compute the ratio of path radiances R* = L /L (where L 0 = d w o p a (Q)). This ratio qualitatively indicates whether the coarse mode particles or the fine mode ones are predominant in the atmosphere. Thresholds on R* are used to separate pure dust (> 0.90) from nondust (< 0.72) cases for further processing. A dust model from Shettle [1984] or an urban/industrial aerosol model from Remer et al. [1996] is then used depending on the value of R*. The aerosol optical thickness is independently derived in the blue and red channels using the selected aerosol model. For mixed cases (R* ranging from 0.72 to 0.90), a linear combination is used to estimate the total aerosol optical thickness and a mixing fraction following Remer et al. [2005]. As indicated in this paper, the thresholds given above are also slightly dependent on the scattering angle value. 3. Improvement of Surface Polarization Correction in the MBP Retrieval Scheme 3.1. Spectral Neutrality of the Surface Polarized Reflectance [18] Cairns et al. [2001] and Elias et al. [2004] have shown that spectral variations in surface polarized reflectance are very small for side and forward scattering geometries. We also observe a similarly flat spectral dependency in the surface polarized reflectance in our measurements. In Figure 4, we have plotted the surface polarized reflectances derived from MICROPOL measurements performed at low altitude (200 or 500 m) for small aerosol burden (0.07 < d a < 0.11 at 0.67 mm) and for an altitude of 3.5 km for very clear sky conditions (d a = 0.05). Observations were performed over urban and natural surfaces including forest and crop fields. Measurements are corrected for molecular scattering. The aerosol contribution is corrected by using aerosol optical thickness and microphysical model derived from PHOTONS/AERONET Sun photometers measurements performed close to the airborne observations. Measurements are also corrected for gaseous absorption using the 6S code [Vermote et al., 1997] and assuming a midlatitude atmospheric profile from the HITRAN database. When we compare the surface polarized reflectances at 0.67 and 1.6 mm with that at 2.2 mm we observe very high correlations (R > 0.93) and the spectral variation in the surface polarized reflectance is not larger than 3% (slope value). We also observe a rather large deviation from unity of the slope in the linear regression results obtained between the 1.6 and 2.2 channels (Figure 4b). An analysis of the MICROPOL measurements revealed that the cause of this deviation from unity of the regression slope was primarily low-altitude (200 m) measurements in the 0.87 and 1.6 mm channels. Such a deviation from spectral neutrality for natural surfaces has never been observed in the RSP measurements performed over land [Elias et al., 2004] nor is it apparent in atmospherically corrected MICROPOL measurements made at high altitude. At present we therefore regard these low-altitude observations of a deviation of surface polarized reflectance from spectral neutrality as anomalous. However, when we analyze the measurements by surface class between the 0.67 and 2.2 mm MICROPOL channels, we do observe a spectral effect over urban targets. This effect can reach a maximum of 15% and is beyond the range of measurement uncertainty indicated by the dispersion in Figures 4a and 4b. However, further investigations 6of13

7 Figure 5. Comparison between measured polarized reflectance (at 2.2 mm) and modeled surface polarized reflectances versus acquisition time (flight on 6 September 2004, anthropogenic aerosol case, two different IGBP land surface classes, NDVI varying from 0.15 to 0.75, flight level of 3.5 km). are required to determine if this behavior is universal, or not, for this surface type. The origin of the biases and an analysis of the spectral behavior of the surface polarized reflectance for various surface types and viewing geometries will be investigated in more detail in a separate paper Comparison Between Model and Measurements [19] In this section, we compare the surface polarized reflectance as measured by MICROPOL with that computed from the semiempirical model of Nadal and Bréon [1999], who have proposed a parametric scheme to model the surface polarized reflectance. This model does not depend on the wavelength and is used to estimate the surface polarized reflectance within the range 0.67 to 0.87 mm in the POLDER processing algorithm. The surface polarized reflectance r surf pol formula that defines this model is F p ðgþ pol ¼ 0 1 exp b 0 ; ð10þ cosðq s Þþcosðq v Þ r surf where F p (g) is the Fresnel reflection coefficient for polarized light, computed for a surface refractive index of 1.50 and g is the phase angle (g = p Q). a 0 and b 0 are empirical coefficients adjusted for 27 land surface classes according to the IGBP ground-type classification and Normalized Differential Vegetation Index (NDVI). The resolution of the land classification used for the POLDER II algorithm is km 2. [20] The modeled surface polarized reflectance varies between 0 near the backscattering direction (Q = 180 ) and a few percent in the forward direction. Figure 5 shows a comparison between measured and predicted surface reflectances for a flight track segment performed in the north of France. The measurements presented in Figure 5 were acquired between the cities of Valenciennes and Lille located 35 km from each other. The scattering angle remained constant over the whole transect and equal to 85 (± 2 ). This figure shows the large difference between the polarized reflectance measurements at 2.2 mm (thin solid line) that we assume to be the surface contribution, and the surface polarized reflectance computed from the model (equation (10), bold line). The dashed line corresponds to an atmospherically corrected polarized reflectance measurement at 2.2 mm which we will discuss further in section 3.3. In Figure 5 two distinct parts can be distinguished, one over urban area (9.14 to 9.17 UT (0908 to 0910 UT)) and the other over natural targets (mainly fields and a forest area between 9.19 and 9.21 UT (0911 and 0913 UT)). According to the land surface classification used in the POLDER algorithm the surfaces sampled here are associated with two different land surface classes labeled as natural vegetation and crop. [21] We observe that the MICROPOL measurements exhibit large variations in polarized reflectance due to surface type variability (e.g., a factor of 3 between urban and forest areas) whereas polarization predicted by the model is almost constant and about two times higher. The highest fluctuation observed in the modeled polarized reflectance, between 9.13 and 9.14 UT (0908 and 0908 UT), is due to a change in the land surface class whereas the smaller ones are due to variability in NDVI. The measurements shown here indicate that the surface model is not able to correctly predict the surface polarized reflectance for this region and for this viewing geometry. [22] Additional measurements for view zenith angles of 45, 30, 15 and 0 were performed at low altitude (200 m) over the flight track segment previously presented. We then observed that model-measurement differences progressively decrease from fifty to a few percent on average when the scattering angle increases from 85 to 130. The surfaces investigated above mainly belong to the surface class labeled as crop. A similar experiment was performed between the cities of Dunkerque and Lille where the surfaces overflown belong to the natural vegetation surface class. For this case we observed that modelmeasurement differences decrease from 25% to 20% over the same range of scattering angles. For the range of our observations (q s 50, 0<q v <10, 8 r =±10 ), the modeled surface polarized reflectance overestimated the measured one between a few percent and fifty percent on average depending on the viewing geometry and land surface class. For aerosol retrievals, an overestimation of the surface contribution necessarily causes an underestimation of the atmospheric contribution, and consequently leads to an underestimate of the aerosol optical thickness and an error in the estimated aerosol model, reflected in an error in the Angström parameter. The impact of an erroneous estimate of the polarized surface contribution on the aerosol retrievals is discussed further in section Atmospheric Effect in the 2.2 mm Channel [23] Although, in many cases, atmospheric scattering in the 2.2 mm channel is quite small (mainly the aerosol contribution), it should be accounted for. We have considered this perturbation in our scheme by means of an iterative process based on equation (3). We use the first estimation of 7of13

8 Table 1. Atmospheric Contribution (L atm+surf p,2.2mm L surf p )/L surf p (%) at 2.2 mm Computed for Several Atmospheric Conditions a Angström Exponent (a) Aerosol Optical Thickness at 0.67 mm (d a 0.67 ) d a 2.2 (d a 0.67 = 0.10) a A mean effect observed over a wide range of scattering angles (from 80 to 160 ) is reported. Computations were performed with the lognormal models described in section 2 and following equation (3) (r surf pol modeled as in Figure 5, flight level of 3.5 km, q s =50, 8 r =0 ). d a 2.2 is the aerosol optical thickness computed at 2.2 mm for an aerosol optical thickness of 0.1 (at 0.67 mm). This quantity can be derived for aerosol optical thickness of 0.05, 0.2 and 0.4 by multiplying d a 2.2 by factors of 0.5, 2 and 4, respectively. the aerosol model and optical thickness to estimate the atmospheric effect at 2.2 mm. Then, we restart the whole process with the corrected surface polarized reflectance. The iteration process is stopped after a couple of iterations when changes in the retrieved parameters are no longer significant. An example is given in Figure 5 where the dashed line is the measurement at 2.2 mm corrected for atmospheric effect. In this case (d a 0.15 at 0.67 mm, a 1.85), the average correction is about 18%. The accuracy of the iterative process is necessarily reduced when the atmospheric effect at 2.2 mm increases. Moreover, the accuracy of equation (3) is reduced as the aerosol optical thickness increases. The order of the atmospheric contribution at 2.2 mm depends on the aerosol optical thickness and aerosol size distribution (or Angström parameter). The amplitude of this contribution is given in Table 1 for various aerosol optical thicknesses and particle models from our model database. The values given in Table 1 do not vary much with the scattering angle since surface and atmospheric polarized contributions exhibit a similar angular behavior over the range of scattering angles considered here and a mean effect is therefore reported. [24] We observe that the mean atmospheric contribution at 2.2 mm increases as aerosol optical thickness and particle size increase (Angström parameter decreases). The aerosol optical thickness at 2.2 mm increases with particle size for a given optical depth at 0.67 mm (see Table 1) while the polarized phase function values decrease with particle size and increase with wavelength (as shown in Figures 1a and 1b). The aerosol contribution is proportional to the d a.q a.w a product which means that the increase of q a as size decreases partially compensates for the decrease of d a in the 2.2 mm channel. This behavior explains the fact that the effect of the atmosphere at 2.2 mm can become significant for small particles when strong optical thicknesses are considered. The quantity given in Table 1 increases as size increases (Angström parameter decreases) since the aerosol optical thickness at 2.2 mm increases more quickly with the size of the particle than the polarized phase function decreases. Note that we never retrieved Angström exponent values below 1.5 from our observations with the MBP method, as shown later, and that the quantities given in Table 1 for a > 1.50 (effective radius < 0.3 mm) are therefore suitable to estimate the atmospheric effect at 2.2 mm for fine mode particles in our retrievals. For even larger particles of several microns effective radius the polarized phase function becomes small or can even have negative values and the atmospheric contribution may therefore decrease for such large particles. The effect of the atmosphere at 2.2 mm for coarse particles is not further investigated here since the coarse mode particles discussed in section 4 are typically assumed to be nonspherical. The spheroid calculations, presented in Figure 2, show very small values of polarized phase function over a large range of scattering angle ( ) and in all MICROPOL bands. As a consequence, the effect of such particles on polarization would be relatively small and weakly spectrally dependent and is therefore effectively included in our characterization of the surface using the 2.2 mm measurement. The effect of the atmosphere at 2.2 mm on the MBP retrievals is investigated in the next section dedicated to aerosol retrievals. 4. MBP and PR Retrievals for Aerosol Optical Thickness and Angström Exponent: Validation Using AERONET [25] We present here some results for each method and a comparison between them, as well as a comparison with AERONET regional Sun photometer observations. For the purpose of this comparison we have chosen three distinct cases representative of aerosol conditions observed during our campaigns over France. The first case study corresponds to a monomodal size distribution dominated by anthropogenic particles observed in the north of France. The second case corresponds to a size distribution dominated by coarse mode particles of natural origin and the third case is a mixture of particles of both anthropogenic and natural origin. The aerosol properties were depicted for each flight using the AERONET/PHOTONS Sun photometers retrievals [Dubovik et al., 2001]. The summary of the flight characteristics is presented in Table 2. Each flight corresponds to a straight line flight transect performed for a constant viewing geometry. Two flights presented hereinafter Table 2. Main Characteristics of the Flights Considered in Our Study Date Time, UT 6 Sep ( ) 12 Oct ( ) 24 Mar ( ) Flight Level, km Location Transect Distance, km Q, deg Aerosol Type 0.5/3.5 north of France, Valenciennes-Lille (±2) pollutant particles 11 south of France (±3) dust 3.2 north of France, Lille (±3) mixed case 8of13

9 Figure 6. AOT retrieved with MBP, alternative MBP and path radiance methods and comparison with Sun photometer measurements. (a) Anthropogenic aerosol (6 September 2004), (b) mixed aerosol (24 March 2003) and (c) dust case (12 October 2001). were performed in the north of France (pollutant aerosols) on board a Piper aircraft and the third flight was in the south of France. The latter was performed as part of a field campaign presented by Waquet et al. [2005]. The MICROPOL instrument was set up on the French Research aircraft Falcon-20 and operated simultaneously with the nadir-looking backscattering lidar LEANDRE. During this campaign, two flights were performed in the south of France during the advection of a dust plume from the Saharan to Europe. We first observed the dust plume during a flight performed over the Mediterranean Sea, on 11 October A detailed description of the properties and origins of the observed particles is provided by Waquet et al. [2005]. The second flight presented here, corresponded to a northward flight over France, starting at the city of Tarbes (N ,E ), and performed on 12 October We then observed the same dust plume the day after, during its advection over France. The AERONET/PHOTONS Sun photometers located at Bordeaux (N ,W ) and Toulouse (N , E ) provided measurements at the beginning of the flight. They both indicated large values of aerosol optical thickness during the day (between 0.5 and 1.30) and Angström exponent values close to zero, which confirms the presence of mineral dust particles in the air. [26] The 6 September 2004 and the 24 March 2003 flights were performed during a regional field campaign performed in the north of France between October 2002 and October 2005, including seven flights. These two flights were performed close to the AERONET/PHOTONS Sun photometer located at Lille (50.61 N, 3.14E). For the 6 September 2004 flight, the Sun photometer retrievals indicate the presence of small particles in the air (Angström exponent equal to 1.85), associated with small aerosol optical thickness (0.2 at 0.67 mm) and low values of the real and imaginary parts of the refractive index. These parameters indicate a case of a low atmospheric pollution [Dubovik et al., 2001] that is representative of most of the situations that we observed during this regional campaign. For the 24 March 2003 flight, the Angström exponent is close to 1, which indicates a bimodal size distribution. The origin of the coarse mode particles remains undetermined for this case. The aerosol optical thickness of the coarse mode particles is small (0.07 at 0.67 mm) which suggests that these particles could be of maritime origin. [27] The data processed in our study were preliminarily filtered from any cloud contamination by visual screening. This basic cloud screening was also improved a posteriori by using MODIS cloud mask retrievals [King et al., 2003] and additional lidar LEANDRE data when available. [28] Figures 6a 6c show the aerosol optical thickness retrieved at 0.67 mm. This wavelength has been chosen because it is the only one common to the MBP and PR methods. For each of the plots, the retrievals using the MBP algorithm and the PR method are given. Some alternative MBP approaches, as described below, are also considered to show the usefulness of additional polarized measurements at 2.2 mm. The dots correspond to the averaged optical thickness at 0.67 mm measured by the PHOTONS/AERO- NET Sun photometers during each flight. We used the Sun photometers located as close as possible to the flight track segments presented here, which are the Sun photometer located in Lille for the 6 September 2004 and the 24 March 2003 flights and the Sun photometer located in Bordeaux for the 12 October 2001 flight. We also used additional handheld MICROTOPS II Sun photometer measurements [Morys et al., 2001] for the 6 September 2004 flight. This observation is shown in Figure 6a at 9.12 UT (0907 UT). The error bars associated with the dots correspond to a 0.01 absolute accuracy in the aerosol optical thickness derived from the Sun photometers. [29] In order to highlight the differences between the proposed method (MBP) and the method developed by Deuzé etal.[2001] which we will denote as the POLDERlike method, we present both retrievals. In the POLDER-like method, the polarized surface reflectance is modeled using the semiempirical surface model, described in section 3.2. It should be noted that for our data, only one viewing geometry is available for the retrieval, while in the standard POLDER-like method up to 12 viewing geometries can be included. For the sake of clarity, we present the POLDER-like method only for case (a). In Figure 6a, we observe that there is an underestimation in the Aerosol Optical Thickness (AOT) for the POLDER-like approach (dashed line) caused by an overestimation in the removed 9of13

10 surface polarized reflectance (see Figure 5). For this region, we observed that the differences between the modeled and the measured surface polarization progressively decrease when the scattering angle increases from 85 to 130 (see section 3.2). It can therefore reasonably be assumed that the multiangular measurements of POLDER would tend to reduce the errors shown here in the retrieved AOT. However, such errors in surface polarization modeling do limit the accuracy and robustness of the retrieved aerosol properties using the POLDER algorithm. [30] The MBP results (with and without 2.2 mm atmospheric correction, red and blue lines, respectively) are clearly better than POLDER-like ones and, moreover, quite consistent with Sun photometer measurements that were performed at the beginning and end of the flight. MICROPOL retrievals tend to indicate an increase in AOT during the flight, which is also detected by Sun photometers. The gradient of the retrieved MBP AOT is linked to a change in the retrieved aerosol model (i.e., Angström exponent). Between 9.14 and 9.17 UT (0908 and 0910 UT), the MBP method retrieves an average AOT equal to at 0.67 mm and an aerosol model associated to an Angström exponent equal to Between 9.17 UT (0910 UT) and the end of the axis, the retrieved Angström exponent is equal to 2.18 (with some variability) and the average AOT is equal to Over the whole transect, the average AOT retrieved by the Sun photometers is 0.15 whereas the MBP AOTs are 0.15 (atmospheric correction at 2.2 mm) and (no correction). In the following, we systematically take the effect of the atmosphere at 2.2 mm into account in the MBP retrieval scheme. [31] It can be noticed that the change on the MBP retrievals mainly appears over the urban area (between 9.14 and 9.17 UT (0908 and 0910 UT)). Over this part of the flight, MICROPOL measured, at low altitude, a departure of +15% between the surface polarized reflectances estimated at 0.67 and 2.2 mm. In order to evaluate the impact of such an effect on the retrievals, the quantity r p surf, in equation (3), is corrected by a coefficient 1.15 and the retrievals over the urban area are reevaluated. We then observe that the Angström exponent remains constant and that the retrieved average AOT is reduced by We also note that the spectral departure of 15% between the 0.67 and 2.2 mm bands could be reduced for the 0.87, 1.6 and 2.2 mm bands. We therefore consider correcting coefficients equal to 1.15, 1.1 and 1.05, respectively at 0.67, 0.87 and 1.6 mm. This modification introduces some changes to the retrieved Angström exponent, from 2.43 to 2.18, which causes an increase on the retrieved AOT of When the Angström exponent remains constant, the average AOT is only reduced by According to the previous analysis, the impact of a potential spectral effect of the surface polarization of 15% could lead to an error in the AOT of about This analysis shows that the change in the MBP retrievals could be related to a change in the surface properties due to a nonneutral behavior of the surface polarization. Additional measurements are required to quantify the spectral behavior of the polarized reflectance of urban surfaces and investigate its potential impact on the aerosol retrievals. [32] MODIS-like retrievals are also shown in Figure 6a (thin black line) and clearly overestimate AOT over the urban part of the flight between 9.14 and 9.17 UT (0908 and 0910 UT). The explanation for that bias comes from the empirical surface relationships assumed in the retrieval scheme. In the PR method, the ratio between the 0.49 mm and 2.2 mm surface reflectance is supposed to be However, we have observed during low-altitude flights (atmospheric corrections were applied according to Kaufman [1988]) that this ratio is close to 0.5 over urban-like area. The ratio between the 0.67 mm and the 2.2 mm channel is observed to be close to 0.8 for urban-like areas while the PR method uses a value of 0.5. As a consequence, the PR method selects an inappropriate particle model (dust model) leading to an overestimation of the AOT. From 9.17 to 9.28 UT (0910 to 0917 UT), the aircraft was over natural targets corresponding to dense and dark surfaces. In this case, the urban/industrial model is correctly selected and the PR method gives similar results to the MBP approach. [33] Figure 6b shows the inversion obtained in the case of a mix of accumulation and coarse-mode aerosols. Measurements were performed mainly over country soils and a few urban areas. The Sun photometer measurements were performed exactly in the middle of the flight track segment. For the MBP method (red line), we observe that the retrieved AOT is close to the fine mode AOT retrieved by the Sun photometer following the method proposed by Dubovik and King [2000] and using spherical particles. This result indicates that, at least for this viewing geometry, the MBP method is not sensitive to the properties of the coarse mode particles. Some peaks can also be noted on the MBP retrievals that are due to rapid fluctuations in the retrieved aerosol model. The MBP algorithm retrieves an Angström exponent equal to 2.18 in most of the cases (78%) whereas the fluctuations correspond to Angström exponent of 1.94 or We expect that additional angular measurements would increase the robustness of the MBP algorithm in terms of aerosol model retrieval. The POLDER-like method results are not shown here. Indeed, the AOT is largely underestimated with the single view POLDER-like approach, as shown in Figure 6a. As for this previous case, the measurements were acquired for a small scattering angle (Q =95 ) where we observed that the surface model overestimates the measured one (see section 3.2). [34] The PR method (thin black line) retrieves a combination of the urban/industrial and dust models and is sensitive to both coarse and fine mode particles. However, the mixing fraction shows rapid and large fluctuations from 0.0 to 1.0 over the whole transect which is not realistic. Thus the AOT retrieved using the PR method is highly variable and not particularly robust, e.g., AOT overestimation by a factor two between and UT (1048 and 1050 UT). [35] The Figure 6c shows the results for the dust case. The Sun photometer measurements were performed close to the beginning of the flight track segment. We observe that the MPB approach (red line) overestimates by a factor 2 the fine mode AOT of the dust plume. The Angström exponent of the fine mode particles retrieved by the AERONET/ PHOTON Sun photometer is significantly lower than for the two other cases and is equal to 0.60 (see Table 3). The MBP 10 of 13