Aerosol optical thickness determination by exploiting the synergy of TERRA and AQUA MODIS

Transcription

1 Remote Sensing of Environment 94 (2005) Aerosol optical thicness determination by exploiting the synergy of TERRA and AQUA MODIS Jiaui Tang a, Yong Xue a,b, *, Tong Yu c, Yanning Guan a a LARSIS, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, , China b Department of Computing, London Metropolitan University, Holloway Road, London N7 8DB, UK c Beijing Environmental Monitor Center, Beijing, PR China Received 23 March 2004; received in revised form 22 September 2004; accepted 25 September 2004 Abstract Aerosol retrieval over land remains a difficult tas because the solar light reflected by the Earth atmospheric system mainly comes from the ground surface. The dar dense vegetation (DDV) algorithm for MODIS data has shown excellent competence at retrieving the aerosol distribution and properties. However, this algorithm is restricted to lower surface reflectance, such as water bodies and dense vegetation. In this paper, we attempt to derive aerosol optical thicness (AOT) by exploiting the synergy of TERRA and AQUA MODIS data (SYNTAM), which can be used for various ground surfaces, including for high-reflective surface. Preliminary validation results by comparing with Aerosol Robotic Networ (AERONET) data show good accuracy and promising potential. D 2004 Elsevier Inc. All rights reserved. Keywords: Aerosol retrieval; Aerosol optical thicness; MODIS; TERRA; AQUA 1. Introduction Global aerosol characterization by satellite remote sensing arouses increasing interest, which is due to the mounting evidence of the importance of aerosol radiative forcing of climate (Charlson et al., 1992; Joseph, 1984; Kaufman et al., 2002; Kiehl & Briegleb, 1993; Tanré et al., 1984) and its effect on cloud microphysics and albedo (Coaley et al., 1987; Kaufman & Naajima, 1993; Twomey et al., 1984). Aerosol uncertainty in modeling radiative forcing is considered one of the largest uncertainties in modeling climate change (Hansen & Lacis, 1990; IPCC, 1995). Effective aerosol retrieval information is also essential to satellite imagery atmospheric correction (Kaufman & Tanré, 1996). The aerosol retrieval accuracy is improved since the first global aerosol maps were derived from the Advanced * Corresponding author. Department of Computing, London Metropolitan University, Holloway Road, London N7 8DB, UK. Tel.: ; fax: addresses: tangj@sohu.com (J. Tang)8 y.xue@londonmet.ac.u (Y. Xue) /$ - see front matter D 2004 Elsevier Inc. All rights reserved. doi: /j.rse Very High Radiometer/National Oceanic and Atmospheric Administration (AVHRR/NOAA; Higurashi & Naajima, 1999; Holben et al., 1992), due to new and more sensitive instruments available lie the Ocean Color and Thermal Scanner (OCTS; Higurashi & Naajima, 1999; Naajima & Higurashi, 1998), the Moderate Resolution Imaging Spectroradiometer (MODIS; Chu et al., 2002), the Along Trac Scanning Radiometer (ATSR; North et al., 1999), and Polarization and Directionality of Earth s Reflectance (POLDER; Claquin et al., 1999; Deuzé et al., 1999). However, remote sensing over land still remains a difficult tas, which is because the measured signal is a composite of reflectance of sunlight by the variable surface covers and bacscattering by the semitransparent aerosol layer (Kaufman et al., 1997a). The present approaches for remote sensing of aerosol over land can be grouped into two main categories: (1) retrieval based on detection of aerosol over dar surfaces from singlepass satellite images. It mainly relies on the use of low reflectance pixels or dar targets (Kaufman, 1993; Kaufman et al., 1990; Kaufman & Sendra, 1988) and needs the prior nowledge of accurate ground surface reflectance (King 转载

2 328 J. Tang et al. / Remote Sensing of Environment 94 (2005) et al., 1992). Therefore, the approaches were applied mainly to the regions with clear water bodies and dense vegetation coverage. This creates a serious limitation for urban areas where anthropogenic aerosol resources are usually located and deserts where dust aerosol mainly comes from. Furthermore, if surface visible reflectances (blue and red bands) are estimated from mid-ir channel using assumed ratios between the channels, a small error of in reflectance can result in an error in aerosol optical thicness (AOT) of F0.06 (Kaufman et al., 1997b). When no available mid-ir channel is available, it seems that aerosol can be only estimated over dar dense vegetation (DDV) targets (Kaufman & Sendra, 1988), which are obtained by using the NDVI and low NIR reflectance. However, the necessity of assuming the aerosol size distribution, single scattering albedo, and the refractive index will affect calculations of NDVI and therefore introduce errors in the retrieved AOT by ~30%. Another way to retrieve aerosol optical thicness is to use contrast reduction (or the blurring effect) of images from satellite multipasses (Tanré et al., 1988). Preliminary results of this approach (Sifais & Deschamaps, 1992) have shown promising potential. This approach can be applied for highly reflective surfaces but can only be applied for groups of images with the conditions that one of these images was obtained on a clear day and ground surface reflectance is invariant. For these reasons, further research wor has to be carried on. Until recently, the available new sensor with simultaneous multiangle observations, such as ATSR-2 radiometer on board the ESA satellite ERS-2, POLDER on the ADEOS spacecraft and MISR on TERRA, etc, mae it possible to retrieve terrestrial geophysical and biophysical parameters, such as aerosol properties with improved accuracy (Veefind et al., 2000) from remotely sensed data. Gonzalez et al. (2003) has retrieved successfully Aerosol Optical Thicness (AOT) and Angstrom coefficients over Europe using data from the ATSR-2 radiometer for August Taing advantage of the nadir and forward view of the ATSR-2, one dual view algorithm was used over land to eliminate the influence of the surface reflectance. Retrieval aerosol optical properties are in good agreement with those from groundbased sunphotometers. Deuze et al. (2003) has used the data form POLDER-1 onboard ADEOS-1 to retrieve tropospheric aerosol over oceanic and land surface. The results show, over the oceans, a good agreement between the optical thicness derived both from POLDER and groundbased photometers but Angstrom exponent was often underestimated. Over land, the sensor has the capability to detect the small spherical aerosols, mainly anthropogenic ones. Xue and Yu (2003) have developed an operational aerosol retrieval method, which relies on multiple view angle observations or multiple solar zenith angle observations of the surface. The results using data from ATSR over the middle area of the UK for August 1995 showed a promising potential. Hsu et al. (2004) proposed a new approach to retrieve aerosol properties over bright-reflectance source regions. They used the minimum reflectivity technique to construct a global surface reflectance database of 0.1 latitude by 0.1 longitude resolution over bright surfaces for visible wavelengths, then the aerosol optical thicness and aerosol type are determined simultaneously in the algorithm using looup tables to match the satellite observed spectral radiances. Good agreement results (i.e., within 30%) are indicated from their validation with groundbased Aerosol Robotic Networ (AERONET) sun/sy radiometer measurements. In this paper, we present a novel aerosol remote sensing method by exploiting the synergy of TERRA and AQUA MODIS data (SYNTAM) to accomplish part of the tas of aerosol retrieval over land, especially over higher reflective surfaces. Our method SYNTAM is described in Section 2. Section 3 describes the MODIS data sets and the procedure of processing. The results and validation are discussed in Section 4. In Section 5, we give the conclusion and directions for further development. 2. Models There are three main mechanisms by which the terrestrial atmosphere system perturb the measurement of ground reflectance from space as follows: (1) the aerosol and molecular bacscattering changes the measured target reflectance, (2) for nonuniform sites, the measured reflectance of target is contaminated by the contribution of the site bacground, and (3) the bidirectional properties of the target are partially smoothed out by the atmospheric scattering processes (Tanré et al., 1979). Much research progress has been achieved for these atmospheric effects aforementioned (Fraser & Kaufman, 1985). Atmospheric models assuming the ground to be uniform and Lambertian have been studied extensively (Conel, 1990; Tanré et al., 1979). According to the abovementioned mechanisms, the inference of the surface spectral reflectance using visible observations is complicated mainly because of atmospheric scattering effects, especially aerosol scattering effects. But the scattering effects mae it possible to determine the aerosol contribution to the reflectance of Top of Atmosphere (TOA) measured from space for aerosol retrieval and monitoring. The ey to determine the aerosol contribution is to eliminate the contribution of the ground surface, which is usually the main part of the total measured reflectance. The aerosol optical thicness depends not only on aerosol characteristics (size distribution and refractive index, etc.) but also on aerosol total loading. Angstrom suggested a single formula for aerosol scattering optical thicness evaluation generally nown as Angstrom s turbidity formula given by the following: s A =b a. In this formula, b is called Angstrom s turbidity coefficient, a is the wavelength exponent, and is the wavelength (Iqbal, 1983). The problem is to how to estimate the values of the parameters b and a. Angstrom s turbidity formula assumes linearity of

3 J. Tang et al. / Remote Sensing of Environment 94 (2005) aerosol optical thicness on a log log plot. Linearity is an approximation, a well-accepted approximation. Ec et al. (1999) have explored the nonlinearity of this relationship. The radiation pattern in the Earth atmosphere system can be described by integration of the radiative characteristics of a small volume of air through the entire atmosphere. Follow the analysis of Chandrasehar (1960) and Kontratyev (1969), the expression of the radiative-transfer equation becomes as follows: coshv q di ðz; rþ ¼ r Z dz 4p I ðz; rv Þc ðz; rv; rþdxv ð þ rþi ðz; rþ ð1þ where I (z,r) is the intensity of the radiation at height z and direction r. The other symbols are defined in the Appendix A. Eq. (1) cannot be solved analytically for I (z,r). Analytical approximations to solve this equation have been suggested by using several ways to eliminate the integral in the equation, and the computational methods that follow were based on the assumption that radiation attenuation is only determined by the primary scattering effects. However, in real conditions, multiple scattering taes place and its influence increases in proportion to the optical thicness of the scattering medium. Therefore, to solve the problem of radiative transfer while taing account of the multiple scattering, it is necessary to consider the integrodifferential transfer Eq. (1) with the corresponding boundary conditions. The general form of this problem is intricate and unwieldy. To solve it, various approximate methods have to be used. We confined our consideration only to one approximate method of reducing the problem to solving a set of differential equations in the application to the case of shortwave radiation transfer. For this, however, some cumbersome computations are required. To simplify and shorten these computations, many different variants of reduction of the integrodifferential transfer equation to differential equations have been proposed. The integration of the latter is a simpler problem than the solution of complex integrodifferential or integral equations. The main idea of the most frequently used approximate radiative transfer equations consists in substituting the exact integrodifferential equation for radiant intensity by common differential equations for the upward and incident radiation fluxes. The general solution of this problem has been given by Kontratyev (1969). Therefore, we can find the relation between the ground surface reflectance A and apparent reflectance (reflectance on the top of atmosphere) AV, which is proposed by Xue and Cracnell (1995) as follows: AVb a A ¼ ð Þþa ð 1 AV ðavb aþþbð1 AV a b Þe ð Þes 0 sechv ð2þ Þe ða bþes 0 sechv where a=sech and b=2, e is the bacscattering coefficient, typically 0.1. The solar zenith angle is calculated from latitude, longitude, and satellite pass time or the data set for MODIS. The atmospheric optical thicness s 0 is determined by the atmospheric turbidity state at passing time. For our model SYNTAM, only taing account of the scattering of atmospheric molecular and aerosol particles, we assume the atmospheric optical thicness s 0 consisting of two parts: the molecular Rayleigh scattering s M (l) and the scattering of aerosol particles s A (l). Therefore, the dimensionless quantity of the optical thicness of the whole atmosphere is as follows: s 0 ¼ s M ðl Þþs Að l Þ: ð3þ For the molecular Rayleigh scattering s M (l), Line (1956) has given an approximate expression which is sufficiently accurate for most application in remote sensing as follows: s M ðlþ ¼ 0: :09 : ð4þ For the scattering of aerosol particles s A (l), we tae Angstrom s turbidity formula as follows: s A ðlþ ¼ b a : ð5þ Substituting Eqs. (5), (4), and (3) into Eq. (2), we can obtained a new equation on the relation among the parameters of ground surface reflectance A, Angstrom s turbidity coefficient b and wavelength exponent a. Other parameters are from the data set of satellite images and the satellite Earth sun geometric information. For the deduction of our model, we need the assumptions as follows that are usually rational for real conditions: (1) For two pass observations of very short time interval, the ground surface bidirectional reflectance properties do not change other than rainfall or other events occurring between the two overpasses that change the ground surface bidirectional reflectance properties. (2) For the two pass observations of very short time interval, aerosol types and properties do not change. Accordingly, we assume that the wavelength exponent a is invariant and what may change is the concentration of aerosol particles, namely, Angstrom s turbidity coefficient b. Now, if we substitute bitemporal satellite data such as three visible spectral bands data, central wavelength of 0.47, 0.55, 0.66 Am, respectively, from TERRA and AQUA into Eq. (2), we can obtain one group of nonlinear equations as follows: A j;i ¼ A j; i Vb a j A j;i Vb a j þ aj 1 A j;i V þ b 1 Aj;i V e ða j b ð e ða j b ð Þe 0:00879i 4:09 þb j a i Þsech jv Þe 0: :09 i þb j a i Þsech jv ð6þ where j=1,2, respectively, stand for the observation of TERRA-MODIS and AQUA-MODIS; i=1,2,3, respectively,

4 330 J. Tang et al. / Remote Sensing of Environment 94 (2005) stand for three visible spectral bands of central wavelength of 0.47, 0.55, 0.66 Am; is the central wavelength. The other symbols are defined in the Appendix A. In real conditions, the bidirectional reflectance properties of the ground surface depend not only on the wavelength but also on the geometry. For two successive views of TERRA and AQUA, the geometries often are different, hence we have to tae account of this influence. Flowerdew and Haigh (1995) proposed that the surface reflectance be approximated by a part that describes the variation with the wavelength and a part that describes the variation with the geometry. Under this assumption, the ratio of two views surface reflectance can be written as follows: K i ¼ A 1;i =A 2;i ð7þ where A 1,i is the surface reflectance for the first view and A 2,i for the second view. The ratio K is assumed to depend only on the variation of the surface reflectance with the geometry and to be independent of the wavelength (Flowerdew & Haigh, 1995; Veefind et al., 1998, 2000). Because aerosol extinction decreases rapidly with wavelength, the AOT at 2.13 Am will be very small as compared to the AOT in the visible. This assumption will not be valid when the aerosol is dominated by the coarse mode, such as desert dust. Ignoring the atmospheric contribution at 2.13 Am, K =2.13 Am can be approximated as the ratio between the top of the atmosphere reflectances for the two overpasses at this wavelength. Since K is assumed independent of the wavelength, this value for K =2.13 Am can also be used for the visible channels (0.47, 0.55, 0.66 Am), which yields K i =K =2.13 Am. Fig. 1. Terra/MODIS reflectance RGB (R for Band 1; G for Band 4; B for Band 3) composed image (400æ400), Gaussian enhancement is made. Fig. 2. Aqua/MODIS reflectance RGB (R for Band 1; G for Band 4; B for Band 3) composed image (400æ400), Gaussian enhancement is made. Actually, it is very difficult to directly get the analytical solution of nonlinear Eq. (6). However, an approximate numerical solution can be obtained by means of many numerical methods. In this paper, Newton iteration algorithm is used for our solution. 3. Data and processing MODIS is one of the sensors on board EOS-AM1/ TERRA and EOS-PM1/AQUA, which are both sunsynchronous polar orbiting satellites. TERRA was launched on Dec. 12, 1999 and flies northward pass the equator at about local time 10:30 AM. AQUA, launched on May 4, 2002, flies southward pass the equator at about local time 1:30 PM. The time interval of their overpass at the same area is usually less than 3 h. MODIS is a new generation Imaging Spectroradiometer, which has moderate spectral resolution with 36 spectral bands that cover the wavelength range from 0.4 to 14 Am, and three spatial resolutions of 250, 500, and 1000 m, respectively, and a swath of 2330 m. In this paper, the date and time we have chose to validate our model were from MODIS data of TERRA and AQUA overpassing Beijing, China, at 03:28 and 05:07 GMT, respectively, on September 10, The image size is pixels with spatial resolution 1000 m. This image collocates the land area between N37830V 41830V and E115830V V. The RGB images for the area are shown in Figs. 1 and 2. The time interval of the two overpasses is 99 min. No rainfall or other events occurred between the two overpasses. We can assume that the ground surface bidirectional reflectance properties are invariant.

5 J. Tang et al. / Remote Sensing of Environment 94 (2005) the AOT of the northeast of Beijing is greater than of the others, which demonstrates the larger temporal variability of the aerosol. Fig. 3. The flowchart of aerosol retrieval by SYNTAM. The preprocessing of data mainly includes image radiation and geometric correction, two temporal image registrations, cloud detection and mas. The data used have been preprocessed at the MODIS data receiving station before acquired. The detailed flowchart of aerosol retrieval by SYNTAM is showed in Fig Results and validation The AOT retrieved by SYNTAM at 0.65 and 0.47 Am for TERRA Overpass are shown in Fig. 4a and b, respectively, and for AQUA Overpass shown in Fig. 4c and d, respectively. All of these images show relatively higher AOT value over Beijing city (about N398, E1168) than the surrounding country areas. Placing Beijing as a reference point, the AOT of the northeast areas are lower than of the southwest, and some of the northwest areas are higher than of Beijing. A clear demonstration shows the large spatial variability of the aerosol. Conversely, the AOT for the AQUA overpass is mostly less than the TERRA overpass other than over some areas to the northwest of Beijing, and the reduction in magnitude of Fig. 4. (a) Aerosol optical thicness derived at 650 nm of TERRA overpass by SYNTAM, water body in land and sea areas were not calculated (blac plot). (b) Aerosol optical thicness derived at 470 nm of TERRA overpass by SYNTAM, water body in land and sea areas were not calculated (blac plot). (c) Aerosol optical thicness derived at 650 nm of AQUA overpass by SYNTAM, water body in land and sea areas were not calculated (blac plot). (d) Aerosol optical thicness derived at 470 nm of AQUA overpass by SYNTAM, water body in land and sea areas were not calculated (blac plot), white plot means AOTN0.70.

6 332 J. Tang et al. / Remote Sensing of Environment 94 (2005) Fig. 5. AOT Level 1.5 data on 10 September 2003 from AERONET Beijing Site. The purple line refer to the value at 5:12 GMT, the cyan line refer to the value at 3:28 GMT. (Adjusted from the AERONET Web site). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). To validate capability of SYNTAM, the retrieval results were compared to the result of ground-based aerosol measurements by a sunphotometer at the AERONET Beijing site. In Fig. 5 is the curve of aerosol temporal variation observed by AERONET at the Beijing site. The AERONET Level 1.5 data and satellite retrieval data coordinated temporally and spatially are shown in Table 1. In Fig. 6, the AOT retrieved by SYNTAM and the AOT measured by sunphotometer of AERONET are plotted as a function of wavelength. Unfortunately, the sunphotometer data and our retrieval results are not available at the same wavelengths. However, it is reasonable to assume a smooth spectral variation of the AOT, thus the data points from each method can be interpolated. Application of this procedure shows that both the AOT at 0.65 and 0.47 Am by SYNTAM for TERRA overpass and sunphotometer agree to within 0.02, and for AQUA overpass to within 0.05 and 0.09, respectively. The AOT value retrieved by SYNTAM is overestimated from that the measured by sunphotometer, other than the AOT value at 0.47 Am for the AQUA overpass. Preliminary validation is encouraging, however, the difference in wavelength and time differences maes comparison difficult, and further validations are needed. 5. Conclusion A new aerosol optical thicness retrieval method proposed in this paper showed promising potential to address the aerosol retrieval over higher reflective surface over land. By exploiting the synergy of MODIS data from two successive orbit of lesser interval for the same area, nonlinear equations can be solved by means of numerical methods to retrieve Table 1 AOT comparison between the satellite retrieval by SYNTAM and Level 1.5 data from AERONET Time (GMT) AOT data\ wavelength 0.44 Am 0.47 Am 0.65 Am 0.67 Am 0312 AERONET SYNTAM AERONET SYNTAM Fig. 6. AOT for Beijing for 10 September 2003, plotted as function of the wavelength. Red squares for retrieval by SYNTAM; blue diamond for AERONET data. (a) 0328 GMT; (b) for 0507 GMT. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

7 J. Tang et al. / Remote Sensing of Environment 94 (2005) simultaneously the ground surface reflectance, Angstrom exponent, and aerosol optical thicness of two overpasses. Furthermore, no other parameters need to be assumed in our method, which allows the retrieval to be more objective and possibly more accurate. Because two successive MODIS swaths on the two satellites usually have some overlap for reasonably northern (or southern) latitudes, SYNTAM may be operationally used to retrieve global aerosol distribution over land, variation of aerosol distribution per day, or aerosol pollution monitoring over urban areas, which are often higher reflective. Uncertainties of our method are mainly introduced by factors, such as aerosol and water vapour spectral absorption, registration of two temporal images, subpixel cloud contamination, and our assumptions on invariant a and the ratio K for compensating the ground surface bidirectional properties effects, which should be taen in to account in future research. Further validation is ongoing. Acnowledgement The wor described in this paper is supported by the projects baerosol retrieval using MODIS data Q funded by Beijing Environment Monitor Center, NSFC, China ( ), bdynamic Monitoring of Beijing Olympic Environment Using Remote SensingQ funded by the Ministry of Science and Technology, China (2002BA904B07-2), and bhundred of Talents ProjectQ funded by Chinese Academy of Sciences. Many acnowledgements are addressed to members of Telegeoprocessing group in IRSA, CAS, China. The advice and assistance of Professor Shan Guo is gratefully acnowledged. Appendix A. List of symbols Symbol A AV r I (z,r) Z a b r (z,r,rv) e h hv q r s s s A s M s O xv Description The Earth s surface reflectance The Earth s system reflectance (apparent reflectance observed from space) The direction (zenith angle, azimuth angle) The intensity of the radiation at height z and direction r Height The wavelength exponent in angstrom s turbidity formula Angstrom s turbidity coefficient The scattering function that characterize the scattered light intensity distribution in the direction (z,r,rv) Bacscattering coefficient Solar zenith angle Zenith angle of the sensor The coefficient of absorption Wavelength The density of air The coefficient of scattering Optical thicness Optical thicness Aerosol optical thicness Rayleigh optical thicness Total atmospheric optical thicness Solid angle References Chandrasehar, S. (1960). Radiative transfer. New Yor7 Dover Publication. Charlson, R. J., Schwartz, S. E., Hales, J. M., Cess, R. D., Coacley, J. A., Hansen, J. E., et al. (1992). Climate forcing of anthropogenic aerosols. Science, 255, Chu, A. D., Kaufman, Y. J., Ichou, C., Remer, L. A., Tanré, D., & Holben, B. N. (2002). Validation of MODIS aerosol optical depth retrieval over land. Geophysical Research Letters, 29. Claquin, T., Schulz, M., & Balansi, Y. J. (1999). Modeling the mineralogy of atmospheric dust source. Journal of Geophysical Research, 104, Coaley, J. A., Bernstein, R. L., & Duree, P. A. (1987). Effect of ship stac effluents on cloud reflectance. Science, 237, Conel, J. E. (1990). Determination of surface reflectance and estimates of atmospheric optical depth and single scattering albedo from Landsat Thematic Mapper data. International Journal of Remote Sensing, 11, Deuze, J. L., Goloub, P., Herman, M., Roger, B., & Tanre, D. (2003). Aerosol remote sensing from POLDER measurements. IEEE International on Geoscience and Remote Sensing, Deuzé, J. L., Herman, M., Goloub, P., Tanre, D., & Marchand, A. (1999). Characterization of aerosols over ocean from POLDER/ADEOS-1. Geophysical Research Letters, 26, Ec, T. F., Holben, B. N., Reid, J. S., Dubovi, O., Smirnov, A., O Neill, N. T., et al. (1999). Wavelength dependence of the optical depth of biomass burning, urban and desert dust aerosols. Journal of Geophysical Research, 104, Flowerdew, R., & Haigh, J. (1995). An approximation to improve accuracy in the derivation of surface reflectance from multi-loo satellite radiometers. Geophysical Research Letters, 22, Fraser, R. S., & Kaufman, Y. J. (1985). The relative importance of aerosol scattering and absorption in remote sensing. IEEE Transactions on Geoscience and Remote Sensing, GE-23, Gonzalez, C. R., Schaap, M., de Leeuw, G., Builtjes, P. J. H., & van Loon, M. (2003). Spatial variation of aerosol properties over Europe derived from satellite observation and comparison with model calculations. Atmospheric Chemistry and Physics, 3, Hansen, J. E., & Lacis, A. A. (1990). Sun and dust versus greenhouse gases: An assessment of their relative roles in global climate change. Nature, 346, Higurashi, A., & Naajima, T. (1999). Development of a two-channel aerosol retrieval algorithm on a global scale using NOAA AVHRR. Journal of Atmospheric Sciences, 56, Holben, B. N., Vermote, E., Kaufman, Y. J., Tanre, D., & Kalb, V. (1992). Aerosol retrieval over land from AVHRR data Application for atmospheric correction. IEEE Transactions on Geoscience and Remote Sensing, 30(2), Hsu, N. C., Tsay, SI., King, M. D., & Herman, J. R. (2004). Aerosol properties over bright-reflecting source regions. IEEE Transactions on Geoscience and Remote Sensing, 42(3), IPCC, Intergovernmental Panel on Climate Change (1995). Radiative forcing of climate change. New Yor7 Cambridge University Press. Iqbal, M. (1983). An introduction to solar radiation. Toronto, Canada7 Academic Press. Joseph, J. H. (1984). The sensitivity of a numerical model of the global atmosphere to the presence of desert aerosol. In H. E. Gerber, & A. Deepa (Eds.), Aerosols and their climatic effects (pp ). Hampton, VA7 Deepa Publishing. Kaufman, Y. J. (1993). Measurements of the aerosol optical thicness and the path radiance Implications on aerosol remote sensing and atmospheric corrections. Journal of Geophysical Research, 98, Kaufman, Y. J., Fraser, R. S., & Ferrare, R. A. (1990). Satellite measurements of large-scale air pollution: Method. Journal of Geophysical Research, 95,

8 334 J. Tang et al. / Remote Sensing of Environment 94 (2005) Kaufman, Y. J., & Naajima, T. (1993). Effect of amazon smoe on cloud microphysics and albedo Analysis from satellite imagery. Journal of Applied Meteorology, 32, Kaufman, Y. J., & Sendra, C. (1988). Algorithm for atmospheric corrections. International Journal of Remote Sensing, 9, Kaufman, Y. J., & Tanré, D. (1996). Direct and indirect methods for correcting the aerosol effect on remote sensing. Remote Sensing of Environment, 55, Kaufman, Y. J., Tanre, D., & Boucher, O. (2002). A satellite view of aerosols in the climate system. Nature, 419, Kaufman, Y. J., Tanre, D., Naajima, T., Lenoble, J., Frouin, R., Grassl, H., et al. (1997a). Passive remote sensing of tropospheric aerosol and atmospheric correction for the aerosol effect. Journal of Geophysical Research, 102, Kaufman, Y. J., Wald, A. E., Remer, L. A., Gao, B. C., Li, R. R., & Lue, F. (1997b). The MODIS 2.1-um channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35, Kiehl, J. T., & Briegleb, B. P. (1993). The relative roles of sulfate aerosols and greenhouse gases in climate forcing. Science, 260, King, M. D., Kaufman, Y. J., Menzel, P., & Tanré, D. (1992). Remote sensing of cloud, aerosol and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS). IEEE Transactions on Geoscience and Remote Sensing, 30, Kontratyev, K. Ya. (1969). Radiation in the atmosphere. New Yor7 Academic Press. Line, F. (1956). Die Sonnestrahlung und ihre schwachung in der atmosphere. in handbuch der geopgyis, Bd VIII,herausgeg.von F.Line F.Moeller, (Berlin: Gebr. Borntraeger), ap.6, Naajima, T., & Higurashi, A. (1998). A use of two-channel radiances for an aerosol characterization from space. Geophysical Research Letters, 25, North, P. R. J., Briggs, S. A., Plummer, S. E., & Settle, J. J. (1999). Retrieval of land surface bidirectional reflectance and aerosol opacity from ATSR-2 multiangle imagery. IEEE Transactions on Geoscience and Remote Sensing, 37(1), Sifais, N., & Deschamaps, P. -Y. (1992). Mapping of air pollution using SPOT satellite data. Photogrammetric Engineering and Remote Sensing, 58(10), Tanré, D., Deschamps, P. Y., Devaux, C., & Herman, M. (1988). Estimation of Saharan aerosol optical thicness from blurring effects in Thematic Mapper data. Journal of Geophysical, Tanré, D., Geleyn, J. F., & Slingo, J. (1984). First results of the introduction of an advanced aerosol radiation interaction in ECMWF low resolution global model. In H. E. Gerber, & A. Deepa (Eds.), Aerosols and their climatic effects (pp ). Hampton, VA7 Deepa Publishing. Tanré, D., Herman, M., Deschamps, P. Y., & De Leffe, A. (1979). Atmospheric modeling for space measurements of ground reflectances including bi-directional properties. Applied Optics, 18, Twomey, S. A., Piepgrass, M., & Wolfe, T. L. (1984). An assessment of the impact of pollution on the global albedo. Tellus, 36b, Veefind, J. P., de Leeuw, G., & Duree, P. A. (1998). Retrieval of aerosol optical depth over land using two-angle view satellite radiometry during TARFOX. Geophysical Research Letters, 25, Veefind, J. P., de Leeuw, G., Koelemeijer, R. B. A., & Stammes, P. (2000). Regional distribution of aerosol over land derived from ATSR-2 and GOME data. Remote Sensing of Environment, 74, Xue, Y., & Cracnell, A. P. (1995). Operational bi-angle approach to retrieve the earth surface albedo from AVHRR data in the visible band. International Journal of Remote Sensing, 16, Xue, Y., & Yu, T. (2003). Aerosol optical depth determination from along trac scanning radiometer (ATSR) data. Proceedings of The IEEE 6th International Conference on Intelligent Transportation Systems, Shanghai, China, October 2003 (Piscataway: IEEE) (pp ).