Estimating surface fluxes over middle and upper streams of the Heihe River Basin with ASTER imagery

Estimating surface fluxes over middle and upper streams of the Heihe River Basin with ASTER imagery W. Ma, Y. Ma, Z. Hu, B. Su, J. Wang, and H. Ishikawa Laboratory for Climate Environment and Disasters of Western China, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu 730000, China Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China International Institute for Geo-Information Science and Earth Observation, Enschede, The Netherlands Disaster Prevention Research Institute, Kyoto University, Kyoto, Japan


Introduction
Among many land surface experiments having been carried out so far, arid and cold regions were paid little attention.The land surface observations in arid and cold regions, both remotely sensed and in-situ, need to be strengthened for a better understand-Introduction

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Full ing of hydrological and ecological processes at different scales.The Watershed Allied Telemetry Experimental Research (WATER) is a simultaneous air-borne, satelliteborne, and ground-based remote sensing experiment conducted in the Heihe Basin, the second largest inland river basin in the northwest arid regions of China.The WA-TER is aiming at the research on water cycles, eco-hydrological and other land surface processes in catchment-scale.Data sets with high-resolution and spatiotemporal consistency will be generated based on this experiment.An integrated watershed model and a catchment-scale land/hydrological data assimilation system is proposed to be developed.The mission of WATER is to improve the observability, understanding, and predictability of hydrological and related ecological processes at catchmental scale, accumulate basic data for the development of watershed science and promote the applicability of quantitative remote sensing in watershed science studies (Li, 2008).
Remote sensing offers the possibility to derive regional distribution of land surface heat fluxes over heterogeneous land surface in combination with sparse field experimental stations.Remote sensing data provided by satellites are a means of obtaining consistent and frequent observations of spectral albedo and emittance of radiation at elements in a patch landscape and on a global scale (Sellers et al., 1990).The land surface variables and vegetation variables, such as surface temperature T sfc , surface hemispherical albedo r 0 , NDVI, MSAVI, LAI and surface thermal emissivity ε can be derived directly from satellite observations (e.g., Susskind et al., 1984;Che'din et al., 1985;Tucker, 1986;Wan and Dozier, 1989;Menenti et al., 1989;Becker andLi, 1990, 1995;Watson et al., 1990;Baret and Guyot, 1997;Price, 1992;Kahle and Alley, 1992;Li and Becker, 1993;Qi et al., 1994;Norman et al, 1995;Schmugge et al., 1995;Kustas and Norman, 1997;Sobrino and Raissouni, 2000;Su, 2002;Ma et al., 2003a, b;Oku and Ishikawa, 2004;Kato, 2005;Ma, 2006bMa, , 2007Ma, , 2009).The regional heat fluxes can be determined indirectly with the aid of these land surface variables and vegetation variables (Pinker, 1990).
Studies have explored several approaches to estimate the regional distribution of surface heat fluxes in recent years.These methods require specification of the vertical Introduction

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Full temperature difference between the surface temperature and the air temperature and an exchange resistance (e.g., Kustas et al.,1989;Kustas, 1990;Wang et al., 1995;Menenti et al., 1991;Menenti and Choudhury, 1993;Bastiaanssen, 1995;Kustas and Norman, 1997;Su, 2002).However, these remote sensing retrieval methods have been performed in homogeneous moist or semiarid regions, and investigations in heterogeneous landscape of arid and cold regions (e.g., the WATER area) are rare.NOAA/AVHRR, GMS and Landsat-7 ETM data were used to determine regional land surface heat fluxes over heterogeneous landscape of the Tibetan Plateau (Ma et al., 2003a(Ma et al., , b, 2005(Ma et al., , 2006;;Oku et al., 2007).However, the resolution of the NOAA/AVHRR and GMS data is about 1 km×1 km and sub-pixel heterogeneity has been omitted.So have Landsat-7 ETM data.The aim of this research is to upscale in-situ point observations of land surface variables and land surface heat fluxes to the regional scale using high-resolution (15 m×15 m) ASTER data.

Data
The recent availability of high-resolution, multi-band imagery from the ASTER sensor has enabled us to estimate surface fluxes.ASTER covers a wide spectral region with 14 bands from the visible to the thermal infrared with high spatial, spectral and radiometric resolution.The spatial resolution varies with wavelength: 15 m in the visible and near-infrared (VNIR, 0.52-0.86µm), 30 m in the short wave infrared (SWIR, 1.6-2.43µm), and 90 m in the thermal infrared (TIR, 8.1-11.6 µm) (Yamaguchi, 1998).
The most relevant data, collected at the WATER (Fig. 1) surface stations (sites) to support the parameterization of land surface heat fluxes and analysis of ASTER images in this paper, consist of surface radiation budget components, surface radiation temperature, surface albedo, humidity, wind speed and direction measured at the Atmospheric Boundary Layer (ABL) towers, Automatic Weather Stations (AWSs), turbu-Introduction

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Theory and scheme
A Surface Energy Balance System (SEBS, Su, 2002) is proposed for the estimation of atmospheric turbulent fluxes and evaporative fraction using satellite earth observation data, in combination with meteorological information at proper scales.SEBS consists of: a set of tools for the determination of the land surface physical parameters, such as albedo, emissivity, temperature, vegetation coverage etc., from spectral reflectance and radiance measurements; a model for the determination of the roughness length for heat transfer (Su, 2002).
In this study, the Surface Energy Balance System (SEBS) retrieval algorithm is used for the ASTER data (Su, 2002).The general concept of the methodology is shown in a diagram (Fig. 2).The surface albedo for shortwave radiation (r 0 ) is retrieved from narrowband-broadband conversion by Liang (Liang, 2001).The land surface temperature (T sfc ) is derived using a method developed by Juan (2006) from multispectral thermal infrared data.Juan (2006) also evaluated a technique to extract emissivity information from multispectral thermal infrared data adding vegetation information.The radiative transfer model SMAC (Rahman and Dedieu, 1994) computes the downward shortwave and longwave radiation at the surface.With these results the surface net radiation flux (R n ) is determined.On the basis of the field observations, the soil heat flux (G 0 ) is estimated from the net radiation flux (R n ).The sensible heat flux (H) is estimated from T sfc , and regional latent heat flux (λ E ) is derived as the residual of the energy budget theorem (Liou, 2004;Ma, 2006) for land surface.
The net radiation flux R n is estimated as (1) Introduction

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Full where ε 0 (x, y) is surface emissivity, K ↓ (Wm −2 ) represents the shortwave (0.3-3 µm) and L ↓ (Wm −2 ) is the longwave (3-100 µm) radiation components, respectively.Surface albedo r 0 (x, y) is derived from narrowband-broadband conversion method by Liang (2001).Since ASTER has nine bands, it is expected that so many bands should enable us to convert narrowband to broadband albedos effectively.Liang (2001) found that the conversions are quite linear.The resultant linear equations are collated in the following.
Where i (i =1-9) are the correspondent ASTER band surface reflectance.
The equation to calculate soil heat flux is parameterized as (Su, 2002): in which it is assumed that the ratio of soil heat flux to net radiation Γ c =0.05 for full vegetation canopy (Monteith, 1973) and Γ s =0.315 for bare soil (Kustas and Daughtry, 1989).An interpolation is then performed between these limiting cases using the fractional canopy coverage, f c .
In order to derive the sensible and latent heat flux, the similarity theory will be made use of.In ASL (Atmospheric Surface Layer), the similarity relationships for the profiles of the mean wind speed, u, and the mean temperature, θ 0 −θ a , are usually written in integral form as where z is the height above the surface, u * =(τ 0 /ρ) zero plane displacement height, z 0 m is the roughness height for momentum transfer, θ 0 is the potential temperature at the surface, θ a is the potential air temperature at height z, z 0 h is the scalar roughness height for heat transfer, ψ m and ψ h are the stability correction functions for momentum and sensible heat transfer respectively, and L is the Obukhov length defined as: where g is the acceleration due to gravity and θ v is the potential virtual temperature near the surface.
The latent heat flux λ E is the residual resulting from an application of the energy budget theorem to the land surface (Ma, 2006):

Case studies and validation
As a case study, 4 scenes of ASTER data over the mid-to-upstream sections of the Heihe River Basin are used.Figure 3 shows the distribution maps of surface heat fluxes around the WATER area.(Fig. 3).
2. The derived net radiation flux over the study area is very close to the field measurement.It is the result of the improvement on surface albedo and surface temperature.
3. The regional soil heat flux derived from the relationship between soil heat flux and net radiation flux is suitable for heterogeneous land surface of the WATER area, because the relationship itself was derived from the same area.
4. The derived regional sensible heat flux and latent heat flux at the validation sites in the WATER area is in good agreement with field measurements (Fig. 4).This is due to the fact that atmospheric boundary layer processes have been considered in more detail in our methodology and the proposed parameterization for sensible heat flux and latent heat flux can be used over the upper streams of the Heihe River Basin area.

Concluding remarks
In this study, the regional distributions of land surface heat fluxes (net radiation flux, soil heat flux, sensible heat flux and latent heat flux) over middle and upper streams of the Heihe River Basin were derived with the aid of ASTER data and field observations.Reasonable results of land surface heat fluxes were gained in this study.

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Interactive Discussion
The retrieval of regional land surface heat fluxes over heterogeneous landscape is not an easy task.
1.Only three ASTER images are used in this study.To obtain more accurate regional land surface fluxes (daily to seasonal variations) over a larger area (the Heihe River Basin), more field observations (ABL tower and radiation measurement system, radiosonde system, turbulent fluxes measured by eddy correlation technique, soil moisture and soil temperature measurement system, etc.) and other satellite sensors such as MODIS (Moderate Resolution Imaging Spectroradiometer) and NOAA (National Oceanic and Atmospheric Administration)/AVHRR (Advanced Very High Resolution Radiometer) with more frequent temporal coverage have to be used.
2. This study implies the SEBS method is only applicable to clear-sky days.In order to extend its applicability to cloudy skies, we should consider using microwave remote sensing data to derive surface temperature and other land surface variables.
SEBS has been developed to estimate atmospheric turbulent fluxes using satellite earth observation data, in combination with meteorological data from a proper reference height given by either in-situ measurements for application to a point, and radiosonde or meteorological forecasts for application at larger scales.On the basis of these experimental validations, SEBS can be used to estimate turbulent heat fluxes at different scales with acceptable accuracy.
by eddy correlation technique, soil heat flux, soil temperature profiles, soil moisture profiles, and the vegetation state.
Figure 4 shows the validation of the derived net radiation R n , soil heat flux G 0 , sensible heat flux H and latent heat flux λ E against ground measurements over the Watershed Airborne Telemetry Experimental Research (WA-TER) stations, Yingke, Huazhaizi, Guantan, Maliantan, A'rou, Binggou and Yakou with 1:1 line.The results show the following: 1.The derived surface heat fluxes (net radiation flux R n , soil heat flux G 0 , sensible heat flux H and latent heat flux λ E ) in different months over the study area are in good accordance with the land surface status.The experimental area includes a variety of land surfaces, such as a large area of grassy marshland, some , many small rivers; therefore these derived parameters show a wide range due to the strong contrast of surface features.Net radiation flux changed from 410 Wm −2 to 830 Wm −2 in May and from 410 Wm −2 to 870 Wm −2 in June.Soil heat flux varied from 30 Wm −2 to 260 Wm −2 in May and from 25 Wm −2 to 270 Wm −2 in June.Sensible heat flux is from 30 Wm −

Fig. 1
Fig. 1 Sketch map of studying area and the sites during the WATER Network of the automatic meteorological stations and flux towers in WATER (For regional meteorological stations, two elements indicate air temperature and precipitation; three elements indicate wind direction plus two elements; four elements indicate wind speed plus three elements; six elements indicate air pressure, global radiation plus four elements.SMTMS: Soil Moisture and Temperature Measurement System)

Fig. 1 .
Fig. 1.Sketch map of studying area and the sites during the WATER.Network of the automatic meteorological stations and flux towers in WATER (For regional meteorological stations, two elements indicate air temperature and precipitation; three elements indicate wind direction plus two elements; four elements indicate wind speed plus three elements; six elements indicate air pressure, global radiation plus four elements.SMTMS: Soil Moisture and Temperature Measurement System).

Fig. 2
Fig. 2 Diagram of parameterization procedure by combining ASTER data with field observations.