SPECTRAL REFLECTANCE OF CLOUDS IN

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include, MODIS, IKONOS , HYPERION, SPOT4 and SPOT 5 images. ... For HYPERION images, the spectral resolution was from 400 to 950 nm with a bandwith ...
SPECTRAL REFLECTANCE OF CLOUDS IN MULTIPLE-RESOLUTION SATELLITE REMOTE SENSING IMAGES C. W. Chang, S. V. Salinas, S. C. Liew and L. K. Kwoh Centre for Remote Imaging, Sensing and Processing, Faculty of Science, National University of Singapore, Blk SOC-1, Lower Kent Ridge Road, Singapore 119260 Tel: (+65) 65164411 Fax: (+65) 67757717 E-mail: [email protected] SINGAPORE

KEY WORDS: Modis, Cloud Reflectance,Ikonos ABSTRACT: Cloud microphysical, radiative and optical properties, are important parameters influencing the radiation budget of Earth. The high reflective nature of clouds, is responsible for reflecting a large part of the Sun’s energy back into space and therefore having a direct impact on Earth’s climate. Although clouds are generally not desired from a remote sensing perspective, they can be useful as reference parameters for other radiometric calculations since they exhibit high reflectance and a rather flat spectral response. In this paper, we analyze the spectral properties of clouds, such as reflectance, spectral shape and variability, inferred from images captured by several remote sensing satellites. 1.

INTRODUCTION

Clouds exhibit in a great variety of spatial and physical distribution around our planet. They are generally located at three different heights inside our atmosphere and mainly found at heights ranging from 6000 m and above for high clouds, 2000 m to 6000 m for middle clouds and 2000 m below for low clouds (Lutgens & Tarbuck, 2001). One parameter of importance for our current work is cloud albedo and its spectral variability. The albedo is directly dependent on the density, refractive index and size distribution of water droplets present inside a cloud system. Hence, the albedo of ice clouds would be different of those from water clouds for example since water and ice have a different set of physical properties. Remote sensing imagery holds a great potential for the study of spatial distribution and spectral behavior of cloud albedo in the visible to the NIR spectral range. In this paper, we used a variety of remote sensing images, at various spectral and spatial resolutions, to analyze the spectral shape and reflectance variability of cloud albedo. Our images were atmospherically corrected for Rayleigh scattering and absorption before any radiometric calculation was performed. Subsequently, cloud spectral shape and albedo obtained from different satelite images were compared to each other to ensure they are free of errors and independent of its source. Remote Sensing images used in this study include, MODIS, IKONOS , HYPERION, SPOT4 and SPOT 5 images. 2.

DATA USED IN THE STUDY

Several datasets were employed in the study. Two MODIS datasets were used as a “base map” to which a corresponding image from IKONOS and SPOT 4 and 5 could be compared. The two MODIS images are dated 2006-04-23 and 2006-05-04 and the image acquisition time is 04:04 UTC and 04:03 UTC respectively. The geographical position of the MODIS images we used are shown in Fig.(1) MODIS level 1B images, at a resolution of 250 m and 500 m respectively, were averaged over a 1 km spatial grid to make easier any geographical or geometrical correction that might be necessary thereafter. The spectral bands, we used, are centered at 470.0, 555.0, 648.0,858.0, 1240.0, 1640.0 and 2130.0 nm respectively. For IKONOS images, the spatial resolution was about 4 m and the wavelengths were centered at 480.3,550.7,664.8 and 805.0 nm. respectively. For HYPERION images, the spectral resolution was from 400 to 950 nm with a bandwith of 10 nm and a spatial resolution of 30 m. However, since we did not have a recent data set, an archived image dated back from 2001 was used for this study. As for SPOT 4 and SPOT 5 a recent image data set was readily available.

(a) MODIS image data 2006-04-23

(b) MODIS image data 2006-04-23

Figure 1: MODIS images used for this study 3.

COMPUTING CLOUD REFLECTANCE

The total radiance measured by a satellite for any cloud pixel can be written as,   ρc (λ)Ed (λ) cos(θs ) Lt (λ) = Tg (θv , θs ) T (θv )T (θs ) + Lpath (λ) , π

(1)

where Tg (θv , θs ) represents the gas transmittance part of the atmosphere, T (θv )T (θs ) is the atmospheric transmittance due to particle scattering and Lpath (λ) is the so called path reflectance (arising from diffuse radiation). θs and θv represents the Sun and the satellite’s viewing angle respectively. The total measured radiance can be converted to top-of-atmosphere reflectance by dividing each term in Eq.(1) by Ed (λ) cos(θs ) , π ρT OA (λ) = Tg (θv , θs )(λ) [T (θv )T (θs )ρc (λ) + ρpath (λ)]

(2)

The Rayleigh path reflectance(ρpath (λ)), the transmission terms T (θv )T (θs ) and Tg (θv , θs ) were computed with the 6s radiative transfer code(Vermote et al., 1997). Hence, ρc (λ) can be computed from cloud reflectance values as follow,  ρc (λ) =

  ρT OA (λ) 1 − ρpath (λ) Tg (θv , θs ) T (θv )T (θs )

(3)

Since clouds are located at different altitudes and different geographical locations inside the atmosphere, then its necessary to compute the transmission terms, ρpath (λ), T (θv ) and T (θs ) for a range of altitudes similar to those were we would normally expect to find clouds. Fig.(2(a)) shows Rayleigh path reflectance and transmittance values computed with the 6S radiative transfer code for 4 cases i.e. at 0, 2, 4 and 6 km above ground level. The case for zero height is include as a reference level for other cases. We can easily see that ρpath (λ) varies from around 0.065 up to 0.13 at 440 nm for the four altitudes considered. This variability is considerable lower at higher wavelengths however. If we take Rayleigh reflectance to be around 0.065 for example, the error in underestimating ρpath (λ) would be around 10% of the cloud total reflectance assuming that true Rayleigh reflectance is about 0.13 and cloud reflectance is of the order of 0.65. However, this example is a extreme case since clouds are generaly located at lower altitudes. On the other hand, the tranmission terms shown in Fig.(2(b)) indicates that underestimating or overestimating cloud height could lead to a potential error of the order of 20 % specially near the blue end of the spectrum. Both of this results, give us a rought idea of the potential source of errors arising from height under or over estimation.

(a) Rayleigh Path Reflectance

(b) Rayleigh Transmittance

Figure 2: Rayleigh ρpath (λ),T (θv )T (θs ) at different heights 4.

RESULTS

4.1.

Results from MODIS and IKONOS

Clouds pixels were identified and extracted from the various satellite images. For all bands, only pixels with corrected reflectance of the order of 0.2 and above were used. In order to obtain the spectral shape of clouds, we normalized each cloud spectrum to 555 nm to make easier to compare cloud spectra captured by different satellites.

(a) Mean Cloud Reflectance of IKONOS and MODIS on 230406

(b) Mean Cloud Reflectance of IKONOS and MODIS on 040506

Figure 3: Cloud reflectance obtained from concurrent MODIS and IKONOS images Fig.(3) shows the cloud reflectance range for both i.e. MODIS and IKONOS, when their images overlap each other. For the MODIS reflectance spectrum , the reflectance decreases at the short wave infrared. The observed reflectance values of clouds from the IKONOS image was lower than that of the MODIS image. This could be attributed to that fact that the saturated cloud pixels with digital number above 2000 was filtered out. For these pixels, we found that they had had a reflectance of the order of 0.7 and above for the visible bands and therefore they were deemed not to be usefull because of the uncertainty on their true reflectance value. The normalized reflectance spectra obtained from the both MODIS and IKONOS were observed to be fairly flat in the visible wavelength range i.e. from 400 to 800 nm. This result is usefull for our work. However, it needs to be confirmed by results obtained from other satellite images to make sure that this flat spectral response is independent of the satellite platform we are working with. 4.2.

Results from Hyperion Image

The HYPERION data we used also covered the same geographical foot prints as of MODIS and IKONOS images. Fig.(5) shows the mean cloud reflectance for all HYPERION bands together with error bars at one standard deviation.

(a) Normalized Reflectance spectrum of Clouds on 230406

(b) Normalized reflectance spectrum of Clouds on 050406

Figure 4: Normalized reflectance spectrum of Clouds obtained from concurrent MODIS and IKONOS images

(a) Mean Cloud Reflectance 020601

(b) Normalized reflectance spectrum of Clouds 020601

Figure 5: Cloud Reflectance and Normalized Cloud Reflectance of HYPERION Fig.(5(a)) illustrates, the reflectance spectrum obtained from a HYPERION image which geographically corresponds to that of MODIS and IKONOS. The spectral shape generated from the HYPERION image also shows the flat nature we observed on section (4.1.). However, the normalized spectrum shown in Fig.(5(b) also indicates that cloud pixels from HYPERION has low reflectance near the blue region and higher near the the infrared region. This effect could be due to the fact that the first four or five bands of HYPERION are rather noisy and hence this would explain the dips observed Fig.(5(b)). After 700 nm, the spectral shape also appear to be noisy. However, this effect could be due to water vapor and oxygen not being properly considered from our atmospheric correction scheme. 5.

CONCLUSION

Results from both, MODIS and IKONOS images, suggests that the normalized spectral shape of clouds are fairly flat over the spectrum specially at visible region. Similar results were obtained for HYPERION images but no conclusion could be drawn from SPOT data since most cloud pixels were found to be saturated. Further work need to be done to fully confirm this conclusion since only a limited set of images, from each satellite, was used. A full validation work is undergoing to fully confirm the results shown here since this result could be useful as a reference parameter for other radiometric calculations. ACKNOWLEDGMENTS THe authors would like to thank NUS/CRISP and A*STAR for funding the research conducted in this paper . REFERENCES Lutgens F., Tarbuck E., The atmosphere: an introduction to meteorology (Prentice Hall, 2001)

Vermote E., Tanre D., Deuze J., Herman M., Morcette J., 1997, Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview, Geoscience and Remote Sensing, IEEE Transactions on, 35, Nr. 3, 675–686