**3. Solar and skylight radiation measurement**

For aerosols, network observation activities have been undertaken in terms of skyradiometer measurements (Takamura & Nakajima, 2004). Alternatively, the use of a compact, stand-alone spectroradiometer (EKO, MS-720) enables the spectral measurements of direct solar radiation (DSR), solar aureole (AUR) and scattered solar radiation (SSR) (Manago & Kuze, 2010; Manago et al., 2010). Since the instrument is powered by batteries with no PC requirement during measurements, it provides better portability compared to a skyradiometer. The wavelength coverage between 350 and 1050 nm with a resolution of 10 nm is useful for precise evaluation of the aerosol optical properties as well as that of the water vapor column amount. The wide dynamic-range measurement of both the direct and scattered solar radiation is attained by means of a thick diffuser and a stable photodiode array, in combination with the automatic exposure control equipped to the handy spectroradiometer (MS-720) (Manago & Kuze, 2011). In order to facilitate the radiation transfer calculation in the retrieval procedure, home-made baffle tubes are used to limit the field of view (FOV) of the observation to 20 deg (SSR) and 5 deg (DSR).

The radiation measurements were conducted at the CEReS site (35.62ºN, 140.10ºE) under clear-sky conditions, mostly around noon. The SSR measurements were made in 24 directions (north, east, south, and west directions, each with 6 elevation angles). The DSR and AUR components were measured before and after the SSR measurements. The total time required for a set of DSR, AUR, and SSR data was 30 - 40 minutes. Approximately 130 datasets were obtained during the observation period from August 2007 to March 2009.

Independent measurement of AOD was carried out with a sunphotometer (Prede, PSF-100). This instrument has four channels centred at 368, 500, 675, and 778 nm, each having the bandwidth of 5 nm. The wavelength dependence of AOT is analyzed with eq. (3) to obtain the Angstrom exponent. During the daytime the AOD is retrieved from the solar radiation intensity within a FOV of 1 deg at an interval of 10 s. From the sunphotometer measurement, *A*=ang and the coefficient *B* (turbidity constant at the reference wavelength 0= 550 nm in this case) can be retrieved.

In our ground observation with the battery-operated spectroradiometer, the direct solar irradiance, aureole radiance, and scattered solar radiance were measured in various directions as mentioned above. Even with these detailed measurements, however, usually it is not possible to determine the complete composition of aerosol particles. Thus, we rely on the three-component aerosol model (TCAM), in which three aerosol types of water soluble, oceanic, and soot components are considered as a basis set which is "quasi-complete" to describe the aerosol optical parameters, namely the wavelength dependence of extinction coefficient, single scattering albedo, asymmetry parameter, and scattering phase function. Figure 5 shows the wavelength dependence of the real and imaginary parts of the aerosol refractive index for the three aerosol components. It has been shown that most of the irradiance/radiance values are well reproduced by appropriately adjusting the total and

which is chosen to be 550 nm or some appropriate value within the observation wavelength range. Figure 4(b) shows the result of analysis based on eq. (4). As seen from Fig. 4(b) the temporal variation shows good agreement between the DOAS-derived aerosol optical

For aerosols, network observation activities have been undertaken in terms of skyradiometer measurements (Takamura & Nakajima, 2004). Alternatively, the use of a compact, stand-alone spectroradiometer (EKO, MS-720) enables the spectral measurements of direct solar radiation (DSR), solar aureole (AUR) and scattered solar radiation (SSR) (Manago & Kuze, 2010; Manago et al., 2010). Since the instrument is powered by batteries with no PC requirement during measurements, it provides better portability compared to a skyradiometer. The wavelength coverage between 350 and 1050 nm with a resolution of 10 nm is useful for precise evaluation of the aerosol optical properties as well as that of the water vapor column amount. The wide dynamic-range measurement of both the direct and scattered solar radiation is attained by means of a thick diffuser and a stable photodiode array, in combination with the automatic exposure control equipped to the handy spectroradiometer (MS-720) (Manago & Kuze, 2011). In order to facilitate the radiation transfer calculation in the retrieval procedure, home-made baffle tubes are used to limit the

The radiation measurements were conducted at the CEReS site (35.62ºN, 140.10ºE) under clear-sky conditions, mostly around noon. The SSR measurements were made in 24 directions (north, east, south, and west directions, each with 6 elevation angles). The DSR and AUR components were measured before and after the SSR measurements. The total time required for a set of DSR, AUR, and SSR data was 30 - 40 minutes. Approximately 130 datasets were obtained during the observation period from August 2007 to March 2009.

Independent measurement of AOD was carried out with a sunphotometer (Prede, PSF-100). This instrument has four channels centred at 368, 500, 675, and 778 nm, each having the bandwidth of 5 nm. The wavelength dependence of AOT is analyzed with eq. (3) to obtain the Angstrom exponent. During the daytime the AOD is retrieved from the solar radiation intensity within a FOV of 1 deg at an interval of 10 s. From the sunphotometer measurement, *A*=ang and the coefficient *B* (turbidity constant at the reference wavelength

In our ground observation with the battery-operated spectroradiometer, the direct solar irradiance, aureole radiance, and scattered solar radiance were measured in various directions as mentioned above. Even with these detailed measurements, however, usually it is not possible to determine the complete composition of aerosol particles. Thus, we rely on the three-component aerosol model (TCAM), in which three aerosol types of water soluble, oceanic, and soot components are considered as a basis set which is "quasi-complete" to describe the aerosol optical parameters, namely the wavelength dependence of extinction coefficient, single scattering albedo, asymmetry parameter, and scattering phase function. Figure 5 shows the wavelength dependence of the real and imaginary parts of the aerosol refractive index for the three aerosol components. It has been shown that most of the irradiance/radiance values are well reproduced by appropriately adjusting the total and

thickness and the SPM mass concentration observed from the ground sampling.

field of view (FOV) of the observation to 20 deg (SSR) and 5 deg (DSR).

**3. Solar and skylight radiation measurement** 

0= 550 nm in this case) can be retrieved.

relative contributions of these three basis components as well as the size distribution of each component (Manago et al, 2011). As seen from Fig. 5, the soot component shows remarkably high value of the imaginary part of the refractive index. This indicates that the absorption property is higher (single scattering albedo is lower) for aerosol with more contribution of soot particles. Figure 6 shows an example of the results of the irradiance and radiance observations. Figure 7 shows an example of aerosol optical parameters derived from the TCAM analysis of the data: Fig. 7(a) shows the wavelength dependence of the aerosol extinction coefficient (normalized to the value at 550 nm), (b) single scattering albedo, (c) asymmetry parameter, and (d) scattering phase function at wavelength 550 nm. In Sec. 5 below, we describe the application of these aerosol characteristics to the atmospheric correction of satellite remote sensing data.

Fig. 5. Real and imaginary parts of the complex refractive index of the three aerosol components: component 1, 2 and 3 refer to the water soluble, oceanic, and soot aerosol types, respectively.

Fig. 6. Spectra observed around noon on October 16, 2008: (a) direct solar radiation (DSR), (b) aureole (AUR), and (c) scattered solar radiation (SSR). Acceptance angle of the instrument is 5 deg for DSR, 5-20 deg for AUR, and 20 deg for SSR. Simulation curves based on the TCAM best fitting are also shown with data points (circles) used for the fitting.

Multi-Wavelength and Multi-Direction Remote Sensing of Atmospheric Aerosols and Clouds 287

 ( ) / ( ) 8.52 

is used as a constant. In eqs. (5) and (6), suffix 1 and 2 refer to aerosol and air molecule,

differential cross-section of aerosol scattering, respectively. The parameter *S*1 is often called the lidar ratio. In eq. (6), the range dependence of *S*2 is omitted, since the composition of air molecules is stable througout the troposphere. Under these assumptions, the lidar equation

*S X R*

c 1

*S R I R R R S*

 2 <sup>2</sup> 1 d *<sup>R</sup>*

1 2 exp d .

In eqs. (7), (9) and (10), *Rc* denotes the range of a far-end boundary, at which each integration is started. The reason that a far-end boundary value is assumed rather than a near-end boundary value is the stability of the numerical evaluation of eq. (7) (Fernald

Usually signals of a vertically looking lider are analyzed assuming that the aerosol property does not change with the altitude. Under this assumption, the range dependence in eq. (5) can be neglected. Even in this case, however, it is necessary to determine the value of lidar ratio as a function of wavelength [*S*1=*S*1()] in order to analyze multi- wavenength lidar data. One way to accomplish this is to use ancillary data from a sunphotometer (Kinjo et al., 1999), since the wavelength dependence of optical thickness provides a constraint to the intagration of 1(*R*, ) from *R*=0 to *R* =*R*c. Another approach is to employ the aerosol properties measured at the ground level. In the case of Fig. 8, for example, the *S*1 values of

derived from the chemical analysis of ground sampling data taken monthly at CEReS (Fukagawa et al., 2006). In Fig. 8, panel (a) shows the temporal variation of the aerosol extinction profile measured for 1064 nm and relative humidity (RH) at the ground level, while panel (b) depicts that of the profile of the Angstrom exponent, ang, as derived from the analysis of lidar data for the four wavelengths. The features in these panels indicate

1 1

 

2

*c c c*

*J R R R*

 

*c*

exp .

*S R S R XR IR*

 1 2 1 2

*SR S*

(sr) (6)

indicate the total cross-section and backward

<sup>2</sup> *XR RPR* () () (8)

= 355, 532, 756 and 1064 nm, respectively, as

(9)

*<sup>R</sup> JR S R XR IR R* (10)

(7)

22 2 *S RR* 

> 1/*d*)=

respectively. In eq. (5),

Here

and

1984).

can be analytically solved as

54.7, 53.0, 46.0 and 43.2 sr are used for

1(*R*) and (*d*

1 2

2

is the range-corrected signal, and functions *I*(*R*) and *J*(*R*) are defined as

*R*

*Rc*

 

*R R*

Fig. 7. Aerosol optical parameters derived from the TCAM analysis of the data shown in Fig. 6: (a) wavelength dependence of the aerosol extinction coefficient (normalized to the value at 550 nm), (b) single scattering albedo, (c) asymmetry parameter, and (d) scattering phase function at wavelength 550 nm.

#### **4. Lidar measurement of aerosols and clouds**

While the DOAS method and skylight/solar radiation measurement lead to the retrieval of atmospheric information integrated over optical paths, the lidar measurement makes it possible to measure aerosol and cloud distributions (profiles) along the optical path. Here we report the result of multi-wavelength lidar measurement conducted at CEReS. Conventionally lidar data have been analyzed by means of the Fernald method (Fernald, 1984), in which the lidar equation

$$P(R) = P\_0 \frac{c\tau}{2} AK \frac{G(R)}{R^2} \beta(R) \exp\left[-2 \int\_0^R \alpha(R') \, d\mathcal{R'}\right] \tag{4}$$

is solved by starting the integration from the far-end boundary. In eq. (4), *P*(*R*) is the power detected by the lidar system corresponding to a distance *R*, *P*0 is the power of the emitted laser radiation, *c* is the speed of light, is the time duration of the laser pulse, *A* is the area of the lidar telescope, *G*(*R*) is the function describing the overlap between the laser beam and telescope field of view, *R*) is the backscattering coefficient, and *R*) is the extinction coefficient. Since both air molecules and aerosol particles contribute to the scattering and extinction, one needs to separate these two components in solving the lidar equation. This can be achieved by introducing the ratio between the extinction coefficient and the backscattering coefficient. Thus, for aerosols,

$$S\_1(R) = \alpha\_1(R) / \beta\_1(R) = \sigma\_1(R) / \left(\frac{d\sigma\_1}{d\Omega}\right)\_{\theta = \pi} \tag{5}$$

is assumed, whereas for air molecules,

$$S\_2 = \alpha\_2(R) / \beta\_2(R) = 8.52 \quad \text{(sr)}\tag{6}$$

is used as a constant. In eqs. (5) and (6), suffix 1 and 2 refer to aerosol and air molecule, respectively. In eq. (5), 1(*R*) and (*d*1/*d*)= indicate the total cross-section and backward differential cross-section of aerosol scattering, respectively. The parameter *S*1 is often called the lidar ratio. In eq. (6), the range dependence of *S*2 is omitted, since the composition of air molecules is stable througout the troposphere. Under these assumptions, the lidar equation can be analytically solved as

$$a\_1(R) = -\frac{S\_1(R)}{S\_{\,\_2}} a\_2(R) + \frac{S\_1(R) \, X(R) \exp I(R)}{X(R\_c)} \,. \tag{7}$$

$$\frac{a\_1(R\_c)}{S\_{\,\_1}(R\_c)} + \frac{a\_2(R\_c)}{S\_{\,\_2}}}{S\_{\,\_2}}$$

Here

286 Remote Sensing – Applications

Fig. 7. Aerosol optical parameters derived from the TCAM analysis of the data shown in Fig. 6: (a) wavelength dependence of the aerosol extinction coefficient (normalized to the value at 550 nm), (b) single scattering albedo, (c) asymmetry parameter, and (d) scattering phase

While the DOAS method and skylight/solar radiation measurement lead to the retrieval of atmospheric information integrated over optical paths, the lidar measurement makes it possible to measure aerosol and cloud distributions (profiles) along the optical path. Here we report the result of multi-wavelength lidar measurement conducted at CEReS. Conventionally lidar data have been analyzed by means of the Fernald method (Fernald,

> <sup>0</sup> <sup>2</sup> <sup>0</sup> ( ) ( ) ( )exp 2 ( ) 2 *c G R R P R P AK R R dR*

is solved by starting the integration from the far-end boundary. In eq. (4), *P*(*R*) is the power detected by the lidar system corresponding to a distance *R*, *P*0 is the power of the emitted

the lidar telescope, *G*(*R*) is the function describing the overlap between the laser beam and

coefficient. Since both air molecules and aerosol particles contribute to the scattering and extinction, one needs to separate these two components in solving the lidar equation. This can be achieved by introducing the ratio between the extinction coefficient and the

1 11 1 ( ) ( )/ ( ) ( )/ *<sup>d</sup> SR R R R*

*R*) is the backscattering coefficient, and

 (4)

1

*d* 

is the time duration of the laser pulse, *A* is the area of

(5)

*R*) is the extinction

*R*

function at wavelength 550 nm.

1984), in which the lidar equation

laser radiation, *c* is the speed of light,

backscattering coefficient. Thus, for aerosols,

is assumed, whereas for air molecules,

telescope field of view,

**4. Lidar measurement of aerosols and clouds** 

$$X(R) = R^2 P(R) \tag{8}$$

is the range-corrected signal, and functions *I*(*R*) and *J*(*R*) are defined as

$$I(R) = 2\int\_{R}^{R\_r} \left[\frac{S\_1(R')}{S\_2} - 1\right] a\_2(R') \,\mathrm{d}R' \tag{9}$$

and

$$J(\mathbb{R}) \, := \, 2 \int\_{\cdot \mathbb{R}}^{\mathbb{R}\_c} S\_1(\mathbb{R}') X(\mathbb{R}') \exp I(\mathbb{R}') \, \mathrm{d}\mathbb{R}'. \tag{10}$$

In eqs. (7), (9) and (10), *Rc* denotes the range of a far-end boundary, at which each integration is started. The reason that a far-end boundary value is assumed rather than a near-end boundary value is the stability of the numerical evaluation of eq. (7) (Fernald 1984).

Usually signals of a vertically looking lider are analyzed assuming that the aerosol property does not change with the altitude. Under this assumption, the range dependence in eq. (5) can be neglected. Even in this case, however, it is necessary to determine the value of lidar ratio as a function of wavelength [*S*1=*S*1()] in order to analyze multi- wavenength lidar data. One way to accomplish this is to use ancillary data from a sunphotometer (Kinjo et al., 1999), since the wavelength dependence of optical thickness provides a constraint to the intagration of 1(*R*, ) from *R*=0 to *R* =*R*c. Another approach is to employ the aerosol properties measured at the ground level. In the case of Fig. 8, for example, the *S*1 values of 54.7, 53.0, 46.0 and 43.2 sr are used for = 355, 532, 756 and 1064 nm, respectively, as derived from the chemical analysis of ground sampling data taken monthly at CEReS (Fukagawa et al., 2006). In Fig. 8, panel (a) shows the temporal variation of the aerosol extinction profile measured for 1064 nm and relative humidity (RH) at the ground level, while panel (b) depicts that of the profile of the Angstrom exponent, ang, as derived from the analysis of lidar data for the four wavelengths. The features in these panels indicate

Multi-Wavelength and Multi-Direction Remote Sensing of Atmospheric Aerosols and Clouds 289

particles of natural origins (such as sea-salt and soil particles) and relatively fine particles of

In standard atmospheric correction, it is customary to assume some representative aerosol models such as maritime, rural, continental, or urban aerosol, to implement the radiative transfer calculation of a satellite scene. This approach has an obvious disadvantage that if the assumed aerosol properties are different from those of real aerosols included in the satellite scene, the resulting information on the ground reflectance is inaccurate. To overcome this difficulty, here we use the aerosol information derived from the ground observation implemented nearly simultaneously with the satellite overpass. Such ancillary information ensures better separation of the ground and atmospheric effects from satellite imagery. Figure 9 shows the schematic drawing of radiation components considered in the radiative transfer calculation (Kotchenova et al., 2006). In this scheme, the radiance originated from the target area is denoted as *L*tar, which consists of the ground direct (*L*gd) and ground indirect (*L*gi and *L*gi') components. The environmental radiance, *L*env, is the component associated with the surface reflection that takes place in adjacent pixels. The atmospheric radiance, *L*atm, consists of two terms, namely, the path radiance due to single

Fig. 9. Schematic drawing of radiation components considered in the radiative transfer calculation. See text for the explanation of radiance components shown in this figure.

In the present research, the ground measurement by means of a compact spectroradiometer was implemented in synchronous with the overpass of the satellite observation. The aerosol optical parameters were derived by analyzing both the direct solar radiation (DSR) and scattered solar radiation (SSR) through the Mie-scattering and radiative transfer calculations, as explained in Sec. 3 of this chapter. When the aerosol loading is relatively small (clear days), it is likely that the aerosol model resulting from this procedure can be applicable to the whole region of the Moderate Resolution Imaging Spectroradiometer (MODIS) image, and the atmospheric correction is applied to the image. Since this correction

anthropogenic origins (such as sulphate and soot particles).

scattering (*L*ps) and that due to multiple scattering (*L*pm).

Fig. 8. Analysis of vertical looking multi-wavelength lidar data: (a) extinction profile for wavelength = 1064 nm observed at CEReS on 17-18 November 2005, and Angstrom exponent derived from extinction coefficients observed for = 355, 532, 756 and 1064 nm. The analysis is based on the Fernald method with lidar parameters *S*1 = 54.7, 53.0, 46.0 and 43.2 sr for each lidar wavelength (based on sampling result at CEReS) and the reference altitude of *R*c = 5.5 km.

that relatively higher extinction near the ground level is observed when RH increases, and at the same time, smaller values of ang are observed. It is likely that both of these observations are due to the aerosol growth associated with the increase of RH.

#### **5. Atmospheric correction of satellite remote sensing data**

Images taken from satellite sensors are affected by both the ground reflectance and atmospheric conditions, which include the influence of scattering and absorption of air molecules and aerosol particles. The process of atmospheric correction, in which such atmospheric effects are precisely evaluated and removed, is indispensable for extracting the intrinsic information of the ground reflectance from satellite imagery (Tang et al., 2005; Kaufman et al., 1997). Although it is rather straightforward to make corrections on the Rayleigh scattering of air molecules, aerosol particles are quite variable both temporally and spatially. This is due to the variable origins of aerosols, consisting of relatively coarse

Fig. 8. Analysis of vertical looking multi-wavelength lidar data: (a) extinction profile for

The analysis is based on the Fernald method with lidar parameters *S*1 = 54.7, 53.0, 46.0 and 43.2 sr for each lidar wavelength (based on sampling result at CEReS) and the reference

that relatively higher extinction near the ground level is observed when RH increases, and at the same time, smaller values of ang are observed. It is likely that both of these observations

Images taken from satellite sensors are affected by both the ground reflectance and atmospheric conditions, which include the influence of scattering and absorption of air molecules and aerosol particles. The process of atmospheric correction, in which such atmospheric effects are precisely evaluated and removed, is indispensable for extracting the intrinsic information of the ground reflectance from satellite imagery (Tang et al., 2005; Kaufman et al., 1997). Although it is rather straightforward to make corrections on the Rayleigh scattering of air molecules, aerosol particles are quite variable both temporally and spatially. This is due to the variable origins of aerosols, consisting of relatively coarse

exponent derived from extinction coefficients observed for

are due to the aerosol growth associated with the increase of RH.

**5. Atmospheric correction of satellite remote sensing data** 

= 1064 nm observed at CEReS on 17-18 November 2005, and Angstrom

= 355, 532, 756 and 1064 nm.

wavelength

altitude of *R*c = 5.5 km.

particles of natural origins (such as sea-salt and soil particles) and relatively fine particles of anthropogenic origins (such as sulphate and soot particles).

In standard atmospheric correction, it is customary to assume some representative aerosol models such as maritime, rural, continental, or urban aerosol, to implement the radiative transfer calculation of a satellite scene. This approach has an obvious disadvantage that if the assumed aerosol properties are different from those of real aerosols included in the satellite scene, the resulting information on the ground reflectance is inaccurate. To overcome this difficulty, here we use the aerosol information derived from the ground observation implemented nearly simultaneously with the satellite overpass. Such ancillary information ensures better separation of the ground and atmospheric effects from satellite imagery. Figure 9 shows the schematic drawing of radiation components considered in the radiative transfer calculation (Kotchenova et al., 2006). In this scheme, the radiance originated from the target area is denoted as *L*tar, which consists of the ground direct (*L*gd) and ground indirect (*L*gi and *L*gi') components. The environmental radiance, *L*env, is the component associated with the surface reflection that takes place in adjacent pixels. The atmospheric radiance, *L*atm, consists of two terms, namely, the path radiance due to single scattering (*L*ps) and that due to multiple scattering (*L*pm).

Fig. 9. Schematic drawing of radiation components considered in the radiative transfer calculation. See text for the explanation of radiance components shown in this figure.

In the present research, the ground measurement by means of a compact spectroradiometer was implemented in synchronous with the overpass of the satellite observation. The aerosol optical parameters were derived by analyzing both the direct solar radiation (DSR) and scattered solar radiation (SSR) through the Mie-scattering and radiative transfer calculations, as explained in Sec. 3 of this chapter. When the aerosol loading is relatively small (clear days), it is likely that the aerosol model resulting from this procedure can be applicable to the whole region of the Moderate Resolution Imaging Spectroradiometer (MODIS) image, and the atmospheric correction is applied to the image. Since this correction

Multi-Wavelength and Multi-Direction Remote Sensing of Atmospheric Aerosols and Clouds 291

Fig. 11. Seasonal variation of surface reflectance at the MODIS pixel including the Chiba

reflectance and aerosol distribution images are shown in Fig. 12.

From the present TCAM analysis of MODIS data, monthly reflectance image (monthly) is generated for each month as a composite of pixels that exhibit the lowest reflectance. This process ensures the removal of cloud pixels that might contaminate the resulting map. These monthly maps, in turn, are employed in the radiative transfer analysis to derive the aerosol distribution (map) from images taken on relatively turbid days. Examples of the

Fig. 10. Surface reflectance at Chiba University (2008)

university campus (=550 nm).

is based on the aerosol model from the simultaneous measurement, the resulting distribution of the surface reflectance is considered to be more reliable than the result that would be obtained by assuming usually available "standard" aerosol models such as urban, rural, or oceanic models. The surface reflectance map (clear map) on such a "clear" day, in turn, can be used as a standard for that particular season of the year, and the atmospheric correction of MODIS data taken on more turbid days can be implemented on the basis of these standard clear maps. This process leads to the derivation of the distribution of aerosol optical thickness (map).

For each of the visible bands of MODIS, a lookup table of the radiance at the top of the atmosphere, *L*total(, 550), was constructed on the basis of the aerosol optical parameters and the geometric data describing the observational conditions of each image. Here, is the diffuse reflectance of each pixel, and 550 is the aerosol optical thickness (AOT) at wavelength 550 nm. The reflectance property of the surface was assumed to be Lambertian, and the radiative transfer calculation was carried out using the 6S code (Kotchenova et al., 2006).

The atmospheric correction was applied to channels 1 through 4 covering wavelength range between 0.450 and 0.876 m of the Terra/MODIS and Aqua/MODIS images. The ground resolution of the MODIS sensor is 0.5 km×0.5 km/pixel. The region of 600×600 pixels around Chiba University was extracted from each of the MODIS images, which were taken from the satellite data archiving system of CEReS, Chiba University. The ground observations using the spectroradiometer were carried out at CEReS around noon on nearly cloud-free days from 2007 to 2009 (around 130 days). In order to take the time lag of around 2 h between the satellite overpass (10:00 local time) and the spectroradiometer observation (12:00) into account, the sunphotometer data taken at CEReS were employed to examine the temporal stability of atmospheric conditions. If the AOT derived from the spectroradiometer was close to the AOT value measured with the sunphotometer at the time of satellite overpass, the data were employed in the atmospheric correction. Otherwise, the data were not included in the clear-day analysis lest the instability in the atmospheric condition might also degrade the regional stability (i.e. homogeneity) of the aerosol distribution. Figure 10 shows the wavelength dependence of the surface reflectance (clear map) of the pixel including the location of Chiba University for various months in the year 2008.

Figure 11 shows the seasonal variation of the surface reflectance for the Chiba University pixel obtained from the analysis of MODIS band 4 centred at 550 nm. For the sake of comparison, our previous result obtained from the Landsat-5 analysis is also depicted (Todate et al., 2004). Note that the Landsat reflectance was obtained assuming a standard aerosol model (maritime), whereas the TCAM aerosol model is used in the present MODIS analysis. Pixels with vegetation and soil coverage are shown for the Landsat data, since the ground resolution associated with this sensor (30 m) is much better than the MODIS resolution of 500 m. From Fig. 11, it is seen that the surface reflectance decreases from November to December, due to the decrease in the vegetation coverage during winter. In winter the reflectance shows no critical dependence on the aerosol model assumed in the atmospheric correction because of the fact that the AOT tends to be small. In summer, on the contrary, the AOT generally increases so that the resulting value of surface reflectance varies in accordance with the aerosol model employed in the analysis.

Fig. 10. Surface reflectance at Chiba University (2008)

is based on the aerosol model from the simultaneous measurement, the resulting distribution of the surface reflectance is considered to be more reliable than the result that would be obtained by assuming usually available "standard" aerosol models such as urban, rural, or oceanic models. The surface reflectance map (clear map) on such a "clear" day, in turn, can be used as a standard for that particular season of the year, and the atmospheric correction of MODIS data taken on more turbid days can be implemented on the basis of these standard clear maps. This process leads to the derivation of the distribution of aerosol

For each of the visible bands of MODIS, a lookup table of the radiance at the top of the atmosphere, *L*total(, 550), was constructed on the basis of the aerosol optical parameters and the geometric data describing the observational conditions of each image. Here, is the diffuse reflectance of each pixel, and 550 is the aerosol optical thickness (AOT) at wavelength 550 nm. The reflectance property of the surface was assumed to be Lambertian, and the radiative transfer calculation was carried out using the 6S code (Kotchenova et al.,

The atmospheric correction was applied to channels 1 through 4 covering wavelength range between 0.450 and 0.876 m of the Terra/MODIS and Aqua/MODIS images. The ground resolution of the MODIS sensor is 0.5 km×0.5 km/pixel. The region of 600×600 pixels around Chiba University was extracted from each of the MODIS images, which were taken from the satellite data archiving system of CEReS, Chiba University. The ground observations using the spectroradiometer were carried out at CEReS around noon on nearly cloud-free days from 2007 to 2009 (around 130 days). In order to take the time lag of around 2 h between the satellite overpass (10:00 local time) and the spectroradiometer observation (12:00) into account, the sunphotometer data taken at CEReS were employed to examine the temporal stability of atmospheric conditions. If the AOT derived from the spectroradiometer was close to the AOT value measured with the sunphotometer at the time of satellite overpass, the data were employed in the atmospheric correction. Otherwise, the data were not included in the clear-day analysis lest the instability in the atmospheric condition might also degrade the regional stability (i.e. homogeneity) of the aerosol distribution. Figure 10 shows the wavelength dependence of the surface reflectance (clear map) of the pixel

including the location of Chiba University for various months in the year 2008.

in accordance with the aerosol model employed in the analysis.

Figure 11 shows the seasonal variation of the surface reflectance for the Chiba University pixel obtained from the analysis of MODIS band 4 centred at 550 nm. For the sake of comparison, our previous result obtained from the Landsat-5 analysis is also depicted (Todate et al., 2004). Note that the Landsat reflectance was obtained assuming a standard aerosol model (maritime), whereas the TCAM aerosol model is used in the present MODIS analysis. Pixels with vegetation and soil coverage are shown for the Landsat data, since the ground resolution associated with this sensor (30 m) is much better than the MODIS resolution of 500 m. From Fig. 11, it is seen that the surface reflectance decreases from November to December, due to the decrease in the vegetation coverage during winter. In winter the reflectance shows no critical dependence on the aerosol model assumed in the atmospheric correction because of the fact that the AOT tends to be small. In summer, on the contrary, the AOT generally increases so that the resulting value of surface reflectance varies

optical thickness (map).

2006).

Fig. 11. Seasonal variation of surface reflectance at the MODIS pixel including the Chiba university campus (=550 nm).

From the present TCAM analysis of MODIS data, monthly reflectance image (monthly) is generated for each month as a composite of pixels that exhibit the lowest reflectance. This process ensures the removal of cloud pixels that might contaminate the resulting map. These monthly maps, in turn, are employed in the radiative transfer analysis to derive the aerosol distribution (map) from images taken on relatively turbid days. Examples of the reflectance and aerosol distribution images are shown in Fig. 12.

Multi-Wavelength and Multi-Direction Remote Sensing of Atmospheric Aerosols and Clouds 293

Fernald, F. G., Analysis of atmospheric lidar observation: some comments. Appl. Opt. Vol

Fukagawa, S., Kuze, H., Bagtasa, G., Naito, S., Yabuki, M., Takamura, T., Takeuchi, N.,

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Fig. 12. Analysis of MODIS data in November 2007: (a) surface reflectance map (540 - 570 nm), and (b) aerosol optical thickness at 550 nm on 24 November 2007.
