THE GLI 380-NM CHANNEL APPLICATION FOR SATELLITE REMOTE SENSING OF TROPOSPHERIC AEROSOL

Transcription

1 THE GLI 380-NM CHANNEL APPLICATION FOR SATELLITE REMOTE SENSING OF TROPOSPHERIC AEROSOL Robert Höller, 1 Akiko Higurashi 2 and Teruyuki Nakajima 3 1 JAXA, Earth Observation Research and Application Center (EORC) Harumi Island Triton Square X 22F, Harumi, Chuo-ku, Tokyo , Japan hoeller@eorc.jaxa.jp 2 National Institute for Environmental Studies, Tsukuba, Japan 3 Center for Climate System Research, University of Tokyo, Japan ABSTRACT The Global Imager (GLI) onboard ADEOS-2 is the first medium-resolution Earth observation satellite sensor that is able to combine measurements from the near-uv to the IR wavelength region. This work investigates a newly available channel in the near-uv to retrieve the optical thickness and single-scattering albedo of aerosols. Due to the low reflectivity of most surfaces in the near-uv, this wavelength range is particularly useful to detect aerosols both over the oceans and lands. The 1-km resolution and wide spectral range of GLI data makes it possible to retrieve atmospheric aerosols parameters with higher accuracy due to accurate cloud screening. This paper describes the retrieval method, gives a sensitivity analysis, and presents examples of retrievals of aerosols. 1. INTRODUCTION The recent launch of several new satellites, which have sensors on board that include channels specifically designed for the observation of atmospheric aerosols are expected to greatly increase our understanding of the global aerosol system (Kaufman et al., 2002). The large amount and good quality of available data also gives spurs to improve on existing algorithms or develop new algorithms. In particular, several challenges remain concerning the satellite remote sensing of aerosols over land surfaces. Compared to observations over the oceans, where the surface reflectance is very homogeneous and low in the infrared, most land surfaces have a complex spectrally and spatially dependent reflectivity, which makes it difficult to decouple the atmospheric from the surface signal (King et al., 1998). Here, we describe a remote sensing method, which uses GLI channels in the near-uv to retrieve the aerosol optical thickness τ a and single-scattering albedo ω 0 of aerosols. The inversion method is based on a method developed for the TOMS instrument (Torres et al., 1998), but is adapted for the specific characteristics of the GLI instrument, and employs a different set of aerosol models. The near-uv offers several advantages for the remote sensing of aerosols over land. (a) Low reflectance of most surfaces. (b) Weak BRDF effects, which simplifies the radiative transfer problem. (c) Sensitivity to absorbing aerosol types, which derives from the large Rayleigh scattering component. Also, non-absorbing particles can be easily differentiated from absorbing material. (d) Absorption of trace gases is weak in this spectral region.

2 Compared to the TOMS instrument, which has a large instantaneous field-of-view (ca. 40 km), the spatial resolution of GLI is much higher (1km at nadir). Therefore, cloudy pixels can be rejected with much higher accuracy. Moreover, GLI has a range of channels in the visible and near-ir spectral region, which can be used for sophisticated cloud screening. Since the influence of cloud contamination is one of the largest error sources in the retrieval of aerosol optical properties from space, GLI should be able to retrieve aerosols with higher accuracy. This paper lays out the algorithm, and shows an example of the retrieved aerosol optical properties. 2. BRIEF DESCRIPTION OF THE GLI INSTRUMENT The Global Imager (GLI) instrument is one of five sensors onboard the ADEOS-2 satellite, that was launched from Tanegashima Space Center of JAXA (former NASDA) into a sun-synchronous polar orbit on December 14, 2002 (see the GLI webpage for more information). ADEOS-2 has an orbit period of 101 minutes, and a recurrent period of 4 days. GLI is a cross-track scanning spectroradiometer that observes the Earth in 36 spectral channels, spanning the visible and infrared spectrum from 0.38 to 12.0 µm at spatial resolutions of 250m and 1km (Nakajima et al., 1998). GLI has several unique wavelength bands at 380 nm (Ch1), 0.76 µm (Ch17), and 1.38 µm (Ch27) that previously were not available. Scanning is performed by mechanically rotating the scanning mirror (1600 km in the cross-track direction). A tilting mechanism ( 20 degrees along-track) enables enhanced observation of the ocean in mid latitudes. Data available from GLI span the seven-month period from April 2, 2003 to October 24, 2003, and were released to the public in December 2003 after a one-year calibration and validation period. In Figure 1, the response functions of all 36 VNIR, SWIR, and MTIR channels are shown. The third panel shows the LANDSAT/TM-like 250m channel, and the lower panel shows the MTIR channels. It has to be mentioned that the solar irradiance spectrum has a strong fluctuation in the near-uv. This fact made it necessary to use finer grid points of the channel response functions for GLI channels 1 and 2 to be able to accurately simulate the radiance measured by the satellite sensor. Figure 1. Response functions of all 36 GLI channels. Upper two panels: VNIR-1km channels; Third panel: 250m channels; Lower panel: MTIR-1km channels.

3 3. REMOTE SENSING METHOD 3.1 Aerosol retrieval concept Remote sensing of aerosols was largely restricted to the oceans or to dark land surface targets. Recently, multiple-views or polarized radiance information is successfully used for the detection of aerosols from space over land and oceans. A different approach was developed for the TOMS instrument that uses backscattered UV radiation for the remote sensing of aerosols (Torres et al., 1998). In this spectral region, most surfaces have a low and homogeneous reflection, therefore increasing the sensitivity to the aerosol. Surface reflectivity climatology from TOMS (Herman and Celarier, 1997) and GOME (Koelemeijer et al., 2002) showed that most Earth surfaces types (except snow and ice) have a reflectivity between 0.04 and 0.06 at 380 nm, while the oceans have a reflectivity of about Moreover, molecular scattering is strongly increasing with decreasing wavelength, which results in a very large background radiation component in the UV. The retrieval of aerosol optical properties in the near-uv is based on the fact that the presence of aerosols can change the spectral dependence of the change in radiance compared to a Rayleigh atmosphere (Torres et al., 1998). The higher the aerosol absorption, the smaller is the spectral contrast of the radiance change. To investigate the potential of aerosol remote sensing in the near-uv from GLI, extensive radiative transfer sensitivity calculations were performed. In particular, the effect of the larger width of the spectral response function of GLI (10 nm) compared to that of TOMS (1 nm), and the different wavelength pair was investigated. Figure 2 shows the relationship between the reflectance ratio R Ch1 /R Ch2 and the Channel 1 reflectance R Ch1, for 4 different values of the surface reflectance R s. As can be seen, an increasing optical thickness decreases the value of the reflectance ratio R Ch1 /R Ch2 for most aerosol types, except for highly absorbing aerosol above surfaces with a reflectance R s larger than about (a) (b) e t a d c b a (c) (d) Figure 2. Relationship between the GLI Ch1 reflectance, and the Ch1/Ch2 reflectance ratio for four different values of the surface reflectance (Rs = 0.04, 0.08, 0.12, and 0.16). The different colors (solid lines) represent different aerosol models: (a) 100% water-soluble (w 0 =0.97); (b) 100% rural (w 0 =0.95); (c) 96% rural+4% soot (w 0 =0.86); (d) 90% rural+10% soot (w 0 =0.76); and (e) 70% rural+30% soot (w 0 =0.55). Dotted lines connect points with the same aerosol optical thickness, which ranges between 0.0 and 2.2. The method is sensitive to absorbing and non-absorbing aerosols, but the sensitivity decreases with increasing surface reflectivity R s, which can be understood when comparing plots with different R s in Figure 2.

4 Also, the lower the surface reflectance, the smaller is the effect of uncertainties in the surface reflectance on the retrieved aerosol parameters. In Section 4 we give a detailed sensitivity analysis of the retrieval method. Figure 3 shows an RGB composite image of a scene acquired by GLI on May 20, 2003, and the Ch2/Ch1 radiance ratio for the same region. It can be clearly seen that the Ch2/Ch1 channel ratio is sensitive to the aerosol distribution, while the channel ratio is less influenced by a variation in the surface reflectance (especially when looking at the land-ocean difference). Figure 3. Left panel: GLI level 1B geolocated RGB composite imagery from bands 13, 8, and 5 (678nm, 545nm, 460nm) for May 20, 2003 around eastern China and the Korean peninsula. Right panel: GLI R ch2 /R ch1 reflectance ratio for the same area, to demonstrate the sensitivity of the near-uv channels to the aerosol loading. 3.2 Look-up-tables The retrieval of τ a and ω 0 is based on the look-up-table (LUT) concept, that is, satellite detected radiances are compared to theoretically pre-computed tables of radiances. τ a and ω 0 are retrieved by interpolating the observation values to the LUT values of different aerosol models and τ a values for given observation geometries. In the initial version of the algorithm, five aerosol models are used: (a) 100% water-soluble (ω 0 =0.97); (b) 100% rural (ω 0 =0.95); (c) 96% rural+4% soot (ω 0 =0.86); (d) 90% rural+10% soot (ω 0 =0.76); and (e) 70% rural+30% soot (ω 0 =0.55). The aerosol models will be updated with newly available knowledge and/or experience with the algorithm performance under different conditions. In particular, dust models are presently investigated and will be added in a following upgrade of the LUTs. 3.3 Satellite signal synthesis and ancillary data The theoretical reflectance is calculated from the reflectance function ρ* for a homogeneous Lambertian surface (Chandrasekhar, 1950): ρ * ( θ, θ, φ) ρ ( θ, θ, φ) + T( θ ) T( θ ) 0 ρ ρ ( θ, θ0, φ ) ( θ, θ, φ ) s = a 0 0, (1) s 0 where ρ a is the atmospheric contribution, and the second term is the surface contribution, with T the atmospheric transmittance, ρ s the surface reflection, S the spherical albedo of the atmosphere, θ the view zenith angle, θ 0 the solar zenith angle, and φ the relative azimuth angle. The assumption of a uniform target can be justified for sensors with a spatial resolution larger than about 500 m.

5 For an efficient construction of the LUTs, the atmospheric path radiance is synthesized following Higurashi and Nakajima (1998), i.e. we use the exact solution for single-scattering, and tabulate the aerosol and Rayleigh multiple scattering. Since the effect of polarization on the calculated TOA radiance can reach about 6% at 380 nm, we calculate the Rayleigh multiple scattering including polarization, but the aerosol multiple scattering without polarization, because the influence is very small. The azimuth dependence of the Rayleigh multiple scattering is expanded in a three term Fourier series. All LUTs are calculated for two surface pressure values, i.e. at sea level and at 6 km elevation. In the retrieval algorithm, the LUTs are interpolated using digital elevation model (GTOPO30) data coupled with meteorological objective analysis data for sea surface pressure (JMAOA) as ancillary datasets. In addition to the atmospheric path radiance, also the spherical albedo and the atmospheric transmittance are tabulated in separate LUTs. GOME LER data (Koelemeijer et al., 1998) are used as preliminary input data for the 380 nm surface reflection. Surface reflectance data from GLI observations are being developed and will be used in this algorithm. In addition to viewing geometry, aerosol models, surface reflectivity, and altitude, also the aerosol layer height is a necessary input parameter. This is due to the fact that the measured TOA radiance is sensitive to the layer height of absorbing aerosols types, i.e. dust and carbonaceous species. But the TOA signal is insensitive to the layer height of non-absorbing aerosols (Torres et al, 1998). In the present algorithm we use a constant aerosol layer height, but we plan to upgrade the algorithm to include layer height information. 4. SENSITIVITY ANALYSIS In this section, the influence of several factors on the retrieved aerosol properties is investigated. Table 1 shows channel characteristics of GLI channels 1, 2, and 3 in the near-uv and violet spectral region (Tanaka et al., 2004). In this work, for the inversion of aerosol parameters only channels in the near-uv are used. Measurements from channels in the visible and IR region are used for cloud screening of data (Ackermann et al., 1998). When comparing the use of channel pair 1-2 versus 1-3, we found that using pair 1-3 is about 1.6 times more sensitive than pair 1-2. Nevertheless, channel 3 saturates for high aerosol loadings (e.g. biomass burning plumes). Also, a larger wavelength separation of the two cannels leads to a possible larger difference in the surface reflectance of the two wavelengths, which adversely effects the aerosol retrieval. Therefore, channels 1 and 2 are used for the retrieval of aerosol properties. Table1. Characteristics and performance of GLI channels 1, 2, and 3. Channel No. Wavelength L st L max Ne L SNR F An interesting value for aerosol remote sensing is the noise-equivalent differential aerosol optical thickness Ne τ, since it gives information of the sensitivity of the instrument at the chosen channel to the aerosol loading. Assuming single-scattering, and assuming a continental aerosol model (ω 0 =0.94) under background concentrations (τ a =0.05) and unfavourable viewing conditions (nadir view, backscattering direction, and P(Θ)=0.01), a Ne τ value of 0.18 is obtained for channel 1. This results in a SNR value for the optical thickness of about This shows, that aerosols cannot be retrieved under these unfavorable conditions at single-pixel resolution. Nevertheless, when the analysis is performed over a larger area (10x10 pixel), the SNR value can be increased 10 times, which is about three times larger than the noise. To estimate the error of the surface reflectance on the retrieved aerosol optical thickness, a sensitivity calculation for the surface reflectance was performed. It is clear that the error of the aerosol optical thickness τ a is lower for higher aerosol optical thickness, and the error of τ a is smaller for lower surface reflectance. For a surface reflectance R s = %, the error of τ a at 0.5 is about 0.1, and about 0.05 at τ a = 2.0. A surface reflectance of about 20% is found to be an upper limit of this method. Since even highly reflecting deserts have surface reflectances lower than 20% in the near-uv and violet, snow and ice are the only surface types for that this method does can not be applied. The error of the aerosol optical thickness that results from an error in the assumed aerosol vertical distribution is estimate to be in the range of 10-20% for moderately absorbing aerosol, and errors up to 50% for highly absorbing aerosols.

6 5. RETRIEVAL OF A BIOMASS BURNING PLUME OVER THE SEA OF JAPAN During the spring and summer of 2003, large boreal forest fires took place in eastern Siberia. Figure 3 shows an example of a large smoke aerosol plume, which was transported over a large area of eastern Asia, covering parts of the Korean peninsula and of eastern China. Also Japan was swept several times during spring-summer 2003 by smoke aerosol plumes that were carried from the Asian continent to the western Pacific area. Figure 4 shows an example of a scene acquired by GLI on June 5, where two smoke plumes are transported over the Sea of Japan. In Figure 5, the retrieved aerosol optical thickness for the same scene is shown. It can be seen that the algorithm is able to retrieve the aerosol distribution both over the oceans and lands. Although coarse-resolution GOME surface reflectance data were used for this retrieval, the transition of the aerosol optical thickness from the oceans to the land is relatively small, which shows that the effect of a variation of Rs is relatively small in this spectral region. 6. SUMMARY AND FUTURE WORK We developed an algorithm for the remote sensing of tropospheric aerosols, which utilizes for the first time the unique GLI channels in the near-uv. The method seems promising to retrieve aerosol optical properties over most land surfaces, except highly reflecting surfaces such as snow and ice. For this paper, we analyzed a scene around the Sea of Japan with a high aerosol loading due to transport of smoke aerosol from Sigeria to the region. The algorithm performed well under most conditions. Future upgrades of the algorithm will include dust aerosol models, and information about the aerosol layer height. The surface reflectivity data used in the algorithm will be updated to GLI data at a higher spatial resolution than the GOME data, which are presently used. Validation of the satellite retrievals with ground-based skyradiometer measurements is underway. Figure 4. GLI level 1B geolocated RGB composite imagery from bands 13, 8, and 5 (678nm, 545nm, 460nm) for June 5, 2003 around Japan.

7 Figure 5. Retrieved aerosol optical thickness for the same scene as shown in Figure 4. Black areas are cloudy regions, which were screened out, or where no observation was made by GLI. 7. REFERENCES Ackerman, S. A., Strabala. K. I., Menzel, W. P., Frey, R. A., Moeller, C. C., and Gumley, L. E. (1998) Discriminating clear sky from clouds with MODIS, J. Geophys. Res., 103, Chandrasekhar, S., (1950) Radiative transfer, Oxford Univ. Press, London. GLI webpage: Herman, J. R., and E. A. Celarier (1997) Earth surface reflectivity climatology at nm from TOMS data, J. Geophys. Res., 102, Herman, J. R., P. K. Bhartia, O. Torres, C. Hsu, C. Seftor, and E. Celarier (1997) Global distribution of UVabsorbing aerosols from Nimbus 7/TOMS data, J. Geophys. Res., 102, Higurashi, A. and T. Nakajima (1999) Development of a two-channel aerosol retrieval algorithm on a global scale using NOAA AVHRR, J. Atmos. Sci., 56, Kaufman, Y. J., D. Tanre, H. T. Gordon, T. Nakajima, J. Lenoble, R. Frouin, H. Grassl, B. M. Herman, M. D. King, and P. M. Teillet (1999) Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect, J. Geophys. Res., 102,

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