The Determination of Surface Albedo of Thailand Using Satellite Data

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1 The Determination of Surfae Albedo of Thailand Using Satellite Data Serm Janjai 1,*, Wathara Wanvong 2 and Jarungsaeng Laksanaboonsong 1 1 Laboratory of Tropial Atmospheri Physis, Department of Physis, Silpakorn University, Nakhon Pathom 73000, Thailand 2 Wad Rai Khing Shool, Sam Pran, Nakhon Pathom 73000, Thailand Abstrat: The surfae albedo of Thailand was determined using satellite data. The digital images of Thailand taken by the GMS 5 satellite at 12:30 loal time during were used in this work. These images obtained from the visible hannel of the satellite with a spatial resolution of 3x3 km 2 were transformed to a ylindrial projetion and then navigated using oastlines as referenes. The omposite image tehnique was used to onstrut minimum gray level images in whih louds were eliminated. The post-launhed alibration tables were employed to onvert gray level of the images into satellite band albedo. The atmospheri orretion of the albedo was then made using the Tanre et al. model. Finally, linear interpolation of the spetral refletane of vegetation was employed to onvert the satellite band ( µm) to the broad-band surfae albedo ( µm). The results show that the surfae albedo varies between for the forest areas and for agriultural areas in ourse of the year. Keywords: Surfae Albedo, Solar Radiation, Satellite Data, Radiative Transfer Model, Albedo Mapping 1. INTRODUCTION Surfae albedo is the ratio of solar radiation inident on the earth s surfae to solar radiation refleted from the surfae. It is an important parameter in the radiative balane of the earth s atmosphere. It is also required for the solar radiation mapping from satellite data beause it has a signifiant effet on the radiation deteted by the satellite sensors [1]. Surfae albedo is an essential data for alulating solar radiation on inlined plane [2]. In general, surfae albedo depends on types of the earth s surfae. For example sands and snows have higher surfae albedo than that of grass lands and forests. For the earth s surfae overed by vegetation, surfae albedo usually hanges with types of plants and stages of plant [3]. In priniple, surfae albedo an be measured by using an albedo meter. This meter omposes of two pyranometers plaed horizontally, one faing up and the other faing down. An albedo meter an be pratially used to measure surfae albedo of a small area. For a large area, balloon or airarfts equipped with albedo meters have to be used. This method is very ostly and time onsuming. In this work, we propose a method for determining surfae albedo from satellite data 2. DESCRIPTION OF THE METHOD The method proposed in this study an be shematially shown in Fig1. The details of eah step are desribed in the following setions. Proessing of satellite data Generation of lear sky i i Calulation of lear sky earthatmospheri albedo Preipitable water data Atmospheri orretion Aerosol data Conversion of satellite band to broad band surfae albedo Fig. 1 Shemati diagram of the method for determining surfae albedo Corresponding author: Serm@su.a.th 1

2.1 Proessing of satellite data Digital data in the visible hannel (0.55-0.90 µm) of the GMS5 satellite olleted during 1995 1998 were used in this work.

2 2.1 Proessing of satellite data Digital data in the visible hannel ( µm) of the GMS5 satellite olleted during were used in this work. The data displayed as images over the whole area of Thailand with a spatial resolution of 3x3 km 2. These images were transformed to the ylindrial projetion and then navigated using oast lines as referenes. Eah image obtained from this transformation and navigation has a pixel matrix of 500x800 pixels. The gray level of the pixels are in the range from 0 to 255. The distane in the image is linearly proportional to the orresponding distane at the ground. An example of suh image is shown in Fig. 2. Fig. 2 An image after the transformation and navigation 2.2 Generation of lear sky omposite images and alulation of earth-atmospheri albedo in satellite band In general, the satellite images are usually partially or totally ontaminated by loud, but only lear sky pixels are needed for the alulation of surfae albedo. To overome this problem, omposite images were onstruted. To reate a omposite image for eah month, the navigated satellite images taken at 12:30 loal time were used. The method is as follows Starting with the satellite data of January, gray level of pixel (1, 1) from the pixel matrix (500 x 800) of all 12:30h.-images were ompared and the minimum value obtained from the omparison was assigned to be the gray level of the pixel (1,1) of the omposite image. This value was assumed to be the gray level of that pixel under the lear sky ondition. The proess was repeated for the pixel (1, 2) to pixel (500,800) to omplete the onstrution of the omposite image of January. This proess was also repeated for February to Deember. Therefore, for eah year, twelve omposite images were reated with this proess. Diagram for reating a omposite image is shown in Fig. 3. Pixel (1,1) 1 January, 2002 Pixel (500,800) 2 January, January, 2002 Pixel (1,1) 31 January, 2002 Composite image for January, 2002 Pixel (500,800) Fig. 3 Diagram showing the proess for reating the omposite images 2

3 Then the gray level of eah pixel of the omposite images was onverted into earth-atmospheri albedo by using a post-launhed alibration urve (Fig. 4) Fig. 4 Calibration urve [4] 2.3 Atmospheri orretion As the earth-atmospheri albedo omposes of the albedo of the atmosphere and the albedo of the earth surfae, atmospheri orretion was made to obtain the surfae albedo. To aomplish this, the 5S radiative transfer model developed by Tanre et al. [5] was used to formulate an empirial model relating the surfae albedo to the earth-atmospheri albedo, preipitable water, visibility and solar zenith angle. This model an be written as follows. G = 0 + 1ρ EA + 2w + 3VIS 4θ z (1) ρ + where ρ G = surfae albedo in satellite band ρ EA = earth-atmospheri albedo in satellite band w = preipitable water (m) VIS = visibility (km) θ z = solar zenith angle (degree) 0,1,2,3, 4 are onstant whose values for January, April, July and Otober are shown in Table1. For the other months, they are obtained by interpolation. Table 1 Values of the onstant 0, 1, 2, 3 and 4 of Eq. (1) Month January April July Otober The values of preipiable water were alulated from relative humidity and temperature measured at 54 meteorologial stations in Thailand [1]. The visibility data were also retrieved from these meteorologial data. Preipitable water and visibility at these 54 stations were interpolated to obtain their values over the whole ountry. After having all input parameters, Eq. (1) was employed to onvert earth-atmospheri albedo into the satellite band surfae albedo over the ountry. 2.4 Conversion of satellite band surfae albedo into broad-band surfae albedo The surfae albedo alulated in the previous setion is in the satellite band ( µm). To onvert this satellite band into broad-band surfae albedo( µm), the 5S radiative transfer model was again used. It was assumed that the earth s surfae in Thailand is overed by vegetation whose refletane an be represented in Fig. 5 [5]. 3

4 Fig. 5 Spetral refletane of vegetation [5] The satellite band surfae albedo an be obtained from the following equation: 0.90 I λ ρ gλ dλ 0.55 ρg = 0.90 I λ dλ 0.55 and the broad-band surfae albedo an be alulated from: 4.00 I λ ρ gλ dλ 0.25 ρg = 4.00 I λ dλ 0.25 where ρ G = satellite band surfae albedo ρ G = broad-band surfae albedo ρ g λ = spetral refletane of vegetation I λ = solar spetral irradiane (2) (3) Applying the 5S model to Eqs. (2)-(3) yields the following relation: ρ G 0 92ρ =. G (4) This equation was used to onvert satellite band surfae albedo to broad-band surfae albedo. 3. RESULTS AND DISCUSSION The above-mentioned method was employed to alulated broad-band surfae albedo for every month overing the period of 4 years (Jan, De, 1998). The values of the monthly surfae albedo for eah year were averaged to obtain the long-term monthly average of surfae albedo. These values were displayed as monthly surfae albedo maps in Fig. 6. The yearly surfae albedo map is shown in Fig. 7. Due to the lak of surfae meteorologial data, surfae albedo over the Gulf of Thailand, Andaman Sea and neighboring ountries is not presented in the maps. From the monthly albedo maps, it is observed that the values of surfae albedo over the ountry are slightly varied from month to month. This is due to the fat that most surfaes of the ountry are overed by green vegetation whose refletivity has seasonal hange. In the dry season (November-April), most plants have less green leaves, ausing the inrease of refletivity. In addition in the dry season, grass and rie fields are dried and they have higher refletivity than those of green grass and green rie fields in the wet season (May-Otober). This effet an be learly seen in the Central region where most rie is grown. As the South has longer wet season and most of the land are overed by rubber trees and fruit trees, it yearly surfae albedo is relatively higher than that of the other regions. The dark spots in the yearly map (Fig. 7) orrespond to water reservoirs or irrigation dams. Their surfae albedo are lower than the land area. This is beause of the fat that water surfae has low refletivity. 4

5 January February Marh April May June July August September Otober November Deember Fig. 6 Maps showing geographial distribution of monthly averaged surfae albedo 5

Fig. 7 A map showing geographial distribution of yearly averaged surfae albedo 4. CONCLUSION A method for determining surfae albedo from satellite has been developed.

6 Fig. 7 A map showing geographial distribution of yearly averaged surfae albedo 4. CONCLUSION A method for determining surfae albedo from satellite has been developed. This method was used to ompute the values of surfae albedo over Thailand. It is observed that the surfae albedo for most areas of the ountry has seasonal hange. The values of surfae albedo in the dry season are relatively higher those in the wet season for most ases. The values of surfae albedo vary between for forest area and for the agriultural area in the ourse of the year. These values of surfae albedo are useful information for solar radiation mapping and radiative balane in the atmosphere. 5. ACKNOWLEDGEMENTS The authors would like to thank Dr. Manuel Nunez for his valuable advie. The authors would also like to aknowledge the Department of Alternative Energy Development and Effiieny for the finanial support to this work. The Department of Meteorology is gratefully aknowledged for providing temperature, relative humidity and visibility data. 6. REFERENCES [1] Janjai, S., Laksanaboonsong, J., Nunez, M. and Thongsathitya, A. (2005) Development of a method for generating operational solar radiation maps from satellite data for a tropial environment, Solar Energy, 78, pp [2] Hunn, B.D. and Calafell, D.D. (1977) Determination of average ground refletivity for solar olletors, Solar Energy, 19, pp [3] Iqbal, M. (1983) An introdution to solar radiation, Aademi Press, New York. [4] Marshall, J.L. (1993) Revision of GMS Strethed-VISSR Data Format, Japan Meteorologial Ageny. [5] Tanre, D., Deroo, C., Duhaut, P., Herman, N., Morrette, J. J., Perbos, J. and Deshamps, P. Y. (1986) Simulation of the Satellite Signal in the Solar Spetrum, Tehnial Report, Laboratoire d Optique Atmospherique, Universite des Siene et Tehnique de Lille, Villeneuve d Asq Cedex, Frane. 6

Available online at I-SEEC Proceeding - Science and Engineering (2013)

Available online at I-SEEC Proceeding - Science and Engineering (2013) Available online at www.iseec212.com I-SEEC 212 Proceeding - Science and Engineering (213) 281 285 Proceeding Science and Engineering www.iseec212.com Science and Engineering Symposium 4 th International