Determination of regional land surface heat fluxes over a heterogeneous landscape of the Jiddah area of Saudi Arabia by using Landsat-7 ETM data

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1 HYDROLOGICAL PROCESSES Published online in Wiley InterScience ( DOI: 1./hyp.639 Determination of regional land surface heat fluxes over a heterogeneous landscape of the Jiddah area of Saudi Arabia by using Landsat-7 ETM data Yaoming Ma, 1,2 *HuiTian, 2 Hirohiko Ishikawa, 3 Ryohji Ohba, Hiromasa Ueda 3 and Jun Wen 2 1 Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 1, People s Republic of China 2 Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 73, People s Republic of China 3 Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto 611, Japan Nagasaki R&D Center, Mitsubishi Heavy Industries, LTD, Fukahorimachi , Nagasaki, Japan Abstract: In this study, a parameterization method based on Landsat-7 Enhanced Thematic Mapper (ETM) data and field observations is presented and tested for deriving the regional land surface variables, vegetation variables and land surface heat fluxes over a heterogeneous landscape. As a case study, the method and two Landsat-7 ETM images are applied to the Jiddah area of Saudi Arabia. The regional distribution maps of surface reflectance, normalized difference vegetation index, modified soil adjusted vegetation index (MSAVI), vegetation coverage, leaf area index, surface temperature, net radiation flux, soil heat flux, sensible heat flux and latent heat flux have been determined over the Jiddah area. The derived results have been validated by using the ground truth. The results show that the more reasonable regional distributions of land surface variables (surface reflectance, surface temperature), vegetation variables (MSAVI and vegetation coverage), net radiation, soil heat flux and sensible heat flux can be obtained by using the method proposed in this study. Further improvement of the method is also discussed. Copyright 26 John Wiley & Sons, Ltd. KEY WORDS Landsat-7 ETM; regional land surface heat flux; arid area; Jiddah; Saudi Arabia Received October 2; Accepted 22 February 26 INTRODUCTION Arid areas (e.g. Jiddah area of Saudi Arabia) with an inhomogeneous landscape are characterized by extreme gradients in land surface properties, such as wetness, roughness and temperature, which have a significant but local impact on the atmospheric boundary layer (ABL). Observation of the actual extent over these areas is essential to understand the mechanisms through which inhomogeneous land surfaces may have a significant impact on the structure and dynamics of the overlying ABL. Progress in this research area requires spatial measurements of variables such as surface hemispherical reflectance, radiometric surface temperature, normalized difference vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI), vegetation coverage, leaf area index (LAI), local aerodynamic roughness length, etc. On-board radiometric imaging by satellite can provide useful estimates of most of these variables. In using these variables we can derive the distribution of land surface heat fluxes and evaporation over an inhomogeneous landscape. A parameterization method based on Landsat-7 Enhanced Thematic Mapper (ETM) data and field observations * Correspondence to: Yaoming Ma, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 1, People s Republic of China. ymma@itpcas.ac.cn (e.g. vertical profiles of air temperature, humidity and wind speed, surface radiation budget components and turbulent heat fluxes, etc.) will be presented and tested for deriving the regional land surface variables, vegetation variables and land surface heat fluxes over the heterogeneous landscape of the Jiddah area of Saudi Arabia. LANDSAT-7 ETM DATA AND FIELD OBSERVATION DATA Landsat-7 ETM provides a spectral radiance in seven narrow bands, with a spatial resolution of 3 ð 3 m 2 for three visible bands (bands 1, 2, 3) and three nearinfrared bands (bands,, 7), and 6 ð 6 m 2 for the thermal infrared band 6. The two ETM images used in this study are at 1 : (local time) June 22 and 1 January 23 over the Jiddah area of Saudi Arabia. Both of the days were very clear when the Landsat-7 ETM overpassed the Jiddah area. The most relevant data, collected at the Jiddah surface station to support the parameterization of land surface heat fluxes and analysis of the ETM images, consist of surface radiation budget components, surface radiation temperature, surface reflectance, vertical profiles of air temperature, humidity, wind speed and direction measured by radiosonde, turbulent fluxes measured by the Copyright 26 John Wiley & Sons, Ltd.

2 Y. MA ET AL. eddy-correlation technique, soil heat flux, soil temperature profiles, soil moisture profiles, and the vegetation state, etc. THEORY AND SCHEME The general concept of the methodology is shown in Figure 1. The surface reflectance for shortwave radiation r is retrieved from the Landsat-7 ETM data with atmospheric correction by a four-stream radiative transfer assumption in the solar spectral bands (Verhoef, 1997) using aerological observation data. The land surface temperature T sfc is also derived from Landsat-7 ETM data and aerological observation data. The radiative transfer model MODTRAN (Berk et al., 199) computes the downward shortwave and longwave radiation at the surface. With these results, the surface net radiation R n is determined. The soil heat flux G is estimated from R n,t sfc,r and MSAVI (Qi et al., 199), which is also derived from Landsat-7 ETM data. The sensible heat flux H is estimated from T sfc, surface and aerological data with the aid of so called blending height approach (Mason, 19), and regional latent heat flux E can be derived as the residual of the energy budget theorem for land surface. with ε s D 1 (Garratt, 1992). Surface reflectance r x, y can be derived from the integrated hemispherical planetary reflectance (e.g. Koepke et al., 19; Menenti et al., 199; Bastiaanssen, 199; Wang et al., 199). However, the linear regression relationships are in doubt, because: (1) the ground measurement is only a point value, whereas the satellite pixels are the average of many point values; (2) few ground observation data coincide with satellite data (Wen, 1999). A four-stream radiative transfer assumption for atmospheric correction in the solar spectral bands (Verhoef, 1997) is introduced to derive the surface reflectance over the Jiddah area in this study. The incoming shortwave radiation flux K # x, y in Equation (1) can be derived from the radiative transfer model MODTRAN (Kenizys et al., 1996; Ma et al., 22), where atmospheric shortwave transmittance sw is obtained. Hence, K # x, y can be obtained as K # x, y D sw K # TOA x, y 2 where the regional variation of radiation flux perpendicular to the top of atmosphere K # TOA x, y is a spectrally integrated form of in-band radiation flux perpendicular to the top of atmosphere K # TOA (b), and Net radiation The regional net radiation flux can be derived from K # TOA x, y D K# exo b cos sun x, y d 2 s 3 R n x, y D [1 r x, y ]K # x, y C ε s L # x, y ε x, y T sfc x, y 1 where ε x, y is the surface emissivity, K is the shortwave (Ð3 3 µm) radiation component and L is the longwave (3 1 µm) radiation component. In Equation (1), ε s is the longwave emissivity, and we have allowed for an amount 1 ε s L # of reflected longwave radiation, which equals zero if the surface is a black body where K # exo (b) is the averaged in-band solar exoatmospheric irradiance undisturbed by sun being zero, b is an abbreviation of in-band, d s is the relative sun Earth distance, and sun is the sun zenith angle. The incoming longwave radiation flux L # x, y in Equation (1) can also be derived from MODTRAN directly (Ma and Tsukamoto, 22). Surface temperature T sfc x, y in Equation (1) can be derived from the Landsat-7 ETM thermal infrared band-6 spectral radiance Landsat-7 ETM data Vegetation coverage MODTRAN surface and aero-logical data LAI MSAVI e r T sfc K L Z m kb 1,d blending height approach G R n H l E Figure 1. Diagram of the parameterization procedure by combining Landsat-7 ETM data with field observations Copyright 26 John Wiley & Sons, Ltd. DOI: 1./hyp

3 REGIONAL LAND SURFACE HEAT FLUX OVER A HETEROGENEOUS LANDSCAPE (Ma and Tsukamoto, 22). In other words, the distribution of land surface temperature is obtained as T sfc x, y D ε x, y 1/ T B x, y where T B is the brightness temperature at the surface, which can be derived from the surface upward longwave radiation flux (Ma and Tsukamoto, 22), and ε x, y is the surface emissivity, which can be derived from vegetation coverage P v (Valor and Caselles, 1996), i.e. ε x, y D ε v x, y P v x, y C ε g x, y [1 P v x, y ] C hεi[1 P v x, y ]P v x, y where ε v x, y and ε g x, y are the surface emissivities for full vegetation and bare soil respectively, hεi is the error item, and vegetation coverage (Carlson and Ripley, 1997) [ ] NDVI x, y 2 NDVImin P v x, y D 6 NDVI max NDVI min where NDVI min and NDVI max are the NDVI values for bare soil and full vegetation respectively. Soil heat flux The regional soil heat flux G x, y is determined through (Choudhury and Monteith, 19) G x, y D s C s T sfc x, y T s x, y r sh x, y 7 where s is the soil dry bulk density, C s is the soil specific heat, T s x, y is the soil temperature of a determined depth, and r sh x, y is the resistance of soil heat transportation. The regional soil heat flux G x, y cannot be mapped directly from satellite observations through Equation (7). Many investigations have shown that the midday G /R n ratio is reasonably predictable from special vegetation indices (Daughtry et al., 199). can be considered as a function F, which relates G /R n to other variables (Ma and Tsukamoto, 22). Some researchers have concluded that G /R n D D F NDVI (Clothier et al., 196; Kustas and Daughtry, 199). A better ratio of G /R n D D F r,t sfc, NDVI was also found (Choudhury et al., 197; Menenti et al., 1991; Bastiaanssen, 199). The parameterization scheme based on MSAVI found over the Jiddah area is G x, y D R n x, y T sfc x, y r x, y Ð 2 C Ð 36r C Ð 6r 2 [1 Ð97 92MSAVI x, y ] where r is the surface reflectance averaged over the period when the soil is heated from the sun. Surface temperature T sfc is expressed in degrees Celsius. MSAVI(x, y) is derived from the band reflectance of the Landsat-7 ETM as (Qi et al., 199). MSAVI x, y [2r x, y C 1] 2r x, y C 1 2 [r x, y r 3 x, y ] D 9 2 where r 3 and r are the band reflectances of the Landsat-7 ETM band-3 and band- on the land surface. Sensible heat flux The regional distribution of sensible heat flux is calculated from T sfc x, y T a x, y H x, y D C p 1 r a x, y where aerodynamic resistance is 1 r a x, y D ku Ł x, y { ] } ð ln C kb 1 x, y h x, y 11 [ z d x, y Z m x, y where k is the von Karman constant, u Ł is the friction velocity, z is the reference height, d is the zero-plane displacement height, Z m is the effective aerodynamic roughness, kb 1 is the excess resistance for heat transportation, and h is the stability correction function for heat. The friction velocity u Ł can be derived from u x, y D u { [ ] } Ł x, y z d x, y ln m x, y k Z m x, y where m is the stability correction function for momentum. From Equations (1) () H x, y D C p k 2 u x, y T sfc x, y T a x, y { [ ] } 13 z d x, y ln Z m x, y C kb 1 x, y h x, y { [ ] } z d x, y ln Z m x, y m x, y One approach to simulate sensible heat flux on a large scale is to scale up or aggregate the regional sensible flux by a weighted average of the contributions from different surface elements, based on the principle of flux conservation. The method of blending height is proposed to derive the regional sensible heat flux in this study. If the local-scale advection is comparatively small during the period of the Landsat-7 ETM observation taking place, then the development of a convection boundary layer may adjust the surface-disorganized variability at the blending height, where the atmospheric characteristics become proximately independent of the horizontal position. The corresponding effective surface variables can be determined accordingly (Mason, 19). This approach has proved successful in calculating areally averaged surface Copyright 26 John Wiley & Sons, Ltd. DOI: 1./hyp

4 Y. MA ET AL. fluxes in recent years (Lhomme et al., 199; Bastiaassen, 199; Wang et al., 199; Ma and Tsukamoto, 22; Ma et al., 23). Based on this approach, the regional sensible heat flux H x, y can be described as H x, y D C p k 2 u B T sfc x, y T a x, y { [ ] } 1 zb d ln x, y Z m x, y C kb 1 x, y h x, y { [ ] } zb d ln x, y Z m x, y m x, y where Z B is the blending height and u B is the wind speed at the blending height. In this study, Z B and u B are determined with the aid of field measurements of radiosonding system. Z m x, y in Equation (1) over the Jiddah area of Saudi Arabia includes the effect of topography, low vegetation (e.g. shrubs), and taller plants (e.g. trees). It is calculated using Taylor s model (Taylor et al., 199). kb 1 is the excess resistance, which can be determined from the LAI by using a widely used model (Qualls and Brutsaert, 199). d is the zero-plane displacement, which can be calculated from the LAI by using Raupach s model (Raupach, 199). T a x, y in Equation (1) is the regional distribution of air temperature at the reference height; it can be derived from an interpolation method (Ma and Tsukamoto, 22). h x, y and m x, y in Equation (1) are the integrated stability functions. For an unstable condition, the integrated stability functions h x, y and m x, y can be written as (Paulson, 197) { m x, y D 2ln[ 1 C X /2] C ln[ 1 C X 2 /2] 2arctan X C Ð h x, y D 2ln[ 1 C X 2 /2] where X Df1 [z d x, y ]/L x, y g Ð2.Forastable condition, the integrated stability function m x, y and h x, y become (Webb, 197) m x, y D h x, y D [z d x, y ]/L x, y The stability function [z d x, y ]/L x, y is calculated by Businger s method (Businger, 19): { [z d x, y ]/L x, y D R i x, y (unstable) [z d x, y ]/L x, y 17 D R i x, y /[1 Ð2R i x, y ] (stable) where R i x, y is the Richardson number; and according to the definition of the Richardson number, the approximate analytical solutions of R i found by Yang et al. (21) will be used here. Latent heat flux The regional latent heat flux E x, y can be derived as the residual of the energy budget theorem for land surface based on the condition of zero horizontal advection at z<z sur,i.e. E x, y D R n x, y H x, y G x, y 1 CASE STUDY AND VALIDATION Figures 2 and 3 show the distribution maps of surface reflectance, surface temperature and surface heat fluxes around the Jiddah area of Saudi Arabia. Their frequency distributions are shown in Figures and. The distribution maps of surface reflectance, NDVI, MSAVI and LAI in Figure 2 are based on 32 ð 32 pixels with asizeof3ð 3 m 2. The distribution maps of surface temperature in Figure 2 and land surface heat fluxes (net radiation, soil heat flux, sensible heat flux and latent heat flux) in Figure 3 are based on ð pixels with a size of 6 ð 6 m 2. Derived land surface heat fluxes are validated by field measurements. Since it is difficult to determine where the exact locations of the experimental sites are, the values of a ð pixel rectangle, surrounding the determined Universal Transverse Mercator coordinates, are compared with the field measurements. The mean absolute percent difference (MAPD) can quantitatively measure the difference between the derived results (H derived i ) and measured values (H measured i ): MAPD D 1 n n id1 The results show that: jh derived i H measured i j H measured i The derived surface variables (land surface reflectance and surface temperature) and surface heat fluxes (net radiation flux R n, soil heat flux G, sensible heat flux H and latent heat flux E) in two different months over the study area are in good accordance with the land surface status. These parameters show a wide range due to the strong contrast of surface features. Surface reflectance is from Ð7 to Ð62 in June and from Ð6 to Ð1 in January. Surface temperature ranged from 2 to 7 C in June and from to C in January. Net radiation changed from 2 to W m 2 in June and from to 6 W m 2 in January. Soil heat flux varied from 1 to 2 W m 2 in June and from 2 to 26 W m 2 in January. Sensible heat flux is from 7 to 37 W m 2 in June and from 2 to 29 W m 2 in January, and latent heat flux varied from 2 to C W m 2 in June and to C W m 2 in January (Figures 3 and ). The negative latent heat flux means that there is negative water vapour flux over the Jiddah area. This phenomenon occurs over the desertification area (Wang and Mitsuta, 199, 1992). 2. The derived pixel values (Figures 2 and 3) and average values (Figures and ) of surface reflectance, surface temperature, net radiation flux, soil heat flux, sensible heat flux and latent heat flux in June are higher than those in January. 3. The derived surface reflectance and surface temperature in this study are in good accordance with the field measurements, with MAPD less than 6% (Table I). Copyright 26 John Wiley & Sons, Ltd. DOI: 1./hyp

5 REGIONAL LAND SURFACE HEAT FLUX OVER A HETEROGENEOUS LANDSCAPE Study area (around Jiddah City of Saudi Arabia) Surface reflectance (1 January 23).6 Surface reflectance ( June 22) N Surface temperature (1 January 23) Surface temperature ( June 22) km Figure 2. Distributions of surface reflectance and surface temperature around the Jiddah area of Saudi Arabia Table I. Comparison of the derived results (Calibrated) versus those measured values (Measured) with MAPD Calibrated Measured MAPD (%) r Ð1 Ð171 Ð26 T s ( C) 3Ð 36Ð3 Ð6 R n Wm 2 37Ð 3Ð2 2Ð91 G W m 2 9Ð 9Ð2 Ð3 H W m 2 19Ð 9Ð 6Ð6 E W m 2 6Ð 67Ð Ð1. The derived net radiation flux over the study area is very close to the field measurement, with MAPD less than 3%(Table I).. Problems exist in the NDVI definition equation because of the external factor effect, such as soil background variations (Huete et al., 19; Huete, 199). To reduce the soil background effect, Qi et al. (199) proposed using MSAVI. Therefore, the parameterization method based on MSAVI for soil heat flux density is better than that based on NDVI on a heterogeneous land surface. Although the derived regional Copyright 26 John Wiley & Sons, Ltd. DOI: 1./hyp

6 Soil heat flux (1 January 23) 2 Soil heat flux ( June 22) 1 Y. MA ET AL. Net radiation (1 January 23) Net radiation ( June 22) N Sensible heat flux (1 January 23) 2 Sensible heat flux ( June 22) Latent heat flux (1 January 23) Latent heat flux ( June 22) km Figure 3. Distributions of surface heat fluxes around the Jiddah area of Saudi Arabia soil heat flux based on MSAVI is a bit higher than the measured value, the parameterization method based on MSAVI for soil heat flux is suitable for the inhomogeneous land surface of the Jiddah area, and the MAPD is about Ð3% (Table I). 6. The derived regional sensible heat flux and latent heat flux, with MAPD of around 6% at the validation site, are in good agreement with the field measurements (Table I). It is pointed that the proposed parameterization methodology for sensible heat flux and latent heat flux is reasonable, and it can be used over the Jiddah area of Saudi Arabia CONCLUDING REMARKS In this study, the regional distributions of land surface variables (surface reflectance and surface temperature), Copyright 26 John Wiley & Sons, Ltd. DOI: 1./hyp

7 REGIONAL LAND SURFACE HEAT FLUX OVER A HETEROGENEOUS LANDSCAPE January 23 Mean=.1 1 June 22 Mean= Surface reflectance r Surface reflectance r 1 January 23 Mean=3.7 C June 22 Mean=3.3 C Surface temperature T sfc ( C) Surface temperature ( C) Figure. Frequency distributions of surface reflectance and surface temperature around the Jiddah area of Saudi Arabia vegetation variables (NDVI, MSAVI, vegetation coverage and LAI) and land surface heat fluxes (net radiation, soil heat flux, sensible and latent heat flux) over heterogeneous arid areas of Jiddah, Saudi Arabia, were derived with the aid of Landsat-7 ETM data and field observations. This forms a sound basis for studying land surface variables, vegetation variables and land surface fluxes. Dealing with the regional land surface heat fluxes over heterogeneous landscape is not an easy issue. The parameterization method presented in this study is still in the development stage: 1. From Equation (1), it is better to use air temperature and wind speed at the reference height when we calculate the sensible heat flux, and we only propose one method to calculate regional air temperature at the reference height. The wind speed at the reference height over the study area should be derived by using some numerical models, and the spatial resolution of the models should be 3 m ð 3 m. 2. Only two Landsat-7 images at a specific time of a specific day are used in this study. To obtain more accurate regional land surface fluxes, their seasonal variations and even day-by-day variation over the Jiddah area of Saudi Arabia need to be examined and compared with other areas (e.g. the Tibetan Plateau area and the desertification area over northwest China), more field observations (PBL tower and radiation measurement system, radiosonde system, turbulent fluxes measured by the eddy-correlation technique, soil moisture and soil temperature measurement system, etc.), and other satellites, such as the Moderate Resolution Imaging Spectroradiometer and National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer, have to be used. It is also worth trying the Surface Energy Balance Index method (Menenti and Choudhury, 1993) and the Surface Energy Balance System (Su, 22). Vegetation variables cannot be validated in this study as no such measurements were made during the experiments. More attention should be paid to the measurements of vegetation variables, such as NDVI, LAI and vegetation coverage, in future experiments. ACKNOWLEDGEMENTS This work was under the auspices of the National Science Foundation of China (2333 and 216) and the cooperative research project between the Institute of Tibetan Plateau Research, Chinese Academy of Sciences and Mitsubishi Heavy Industries, Ltd, Japan, Research Revolution 22 of the Ministry of Education, Culture, Science and Technology of Japan (RR22-6). Most parts of this study were done as cooperative research work in the Disaster Prevention Research Institute, Kyoto University, Japan, and the Alterra Green World Research, Wageningen University and Research Centre. We thank Dr L. Jia, Dr Y. Oku, and Dr K. Tanaka for their kind help and useful discussions. Copyright 26 John Wiley & Sons, Ltd. DOI: 1./hyp

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