ETM+ images

Ecological Informatics 5 (2010) 348–358

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Ecological Informatics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l i n f

Estimating the regional evapotranspiration in Zhalong wetland with the Two-Source Energy Balance (TSEB) model and Landsat7/ETM+ images Wei Yao a, Min Han a,⁎, Shiguo Xu b a b

School of Electronic and Information Engineering, Dalian University of Technology, Dalian 116024, China School of Civil and Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China

a r t i c l e

i n f o

Article history: Received 22 January 2010 Received in revised form 3 June 2010 Accepted 5 June 2010 Keywords: Zhalong wetland Evapotranspiration Remote sensing Aerodynamic temperature

a b s t r a c t This paper aims to integrate a numerical model, vegetation index, and regional evapotranspiration estimation to assess the water cycle in Zhalong wetland, China with the aid of remote sensing technologies. An interdisciplinary analysis was performed to study the eco-hydrological characteristics of the wetland ecosystem. In particular, a new solution for solving the Two-Source Energy Balance (TSEB) was developed and applied. This new solution method is based on a definition of the aerodynamic temperature in a two-layer ground surface structure. Following this new solution for TSEB, the outputs of the Surface Energy Balance Algorithm (SEBAL) may be used as the inputs for TSEB to avoid the complex calculations of the bulk boundary layer and soil surface resistance. The new solution method makes the implementation of TSEB much easier when bi-directional thermal infrared remote sensing images are not available. Daily evapotranspiration and vegetation index of the wetland were calculated from six scenes of Landsat7/ETM+ remote sensing images acquired during 2001 and 2002 in our case study. Spatial distribution of daily evapotranspiration was also provided to help realize the wetland condition. The seasonal changes of vegetation index and evapotranspiration for some typical wetland ground surfaces were analyzed and presented as well to explore the multitemporal variations of plant species holistically. Ultimately, such integrative analysis aids in understanding the general biological patterns associated with these wetland vegetation cover, while specific situations of the wetland ecosystem can be recognized. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Wetlands are not only the important ecological resources of many species of wildlife but also the essential stabilizing factors in the climate evolution process. Since the values of wetlands are universally recognized (Ellison, 2004), the protection of wetlands is more and more important an issue for countries and civil organizations all around the world. Remote sensing provides effective tools to the study of wetlands (Pengra et al., 2007; Rivero et al., 2009). Studying the eco-hydrological features of wetlands oftentimes requires integrating remote sensing data with wetland models with respect to surface environmental conditions. The Zhalong wetland, one of the major wetlands in China, locates on the Songnen Plain of Songhua River Basin in Heilongjiang Province. The wetland is to the southeast of and about 26 km away from Qiqihaer City (Fig. 1). The Zhalong wetland reserve lies between 46°52′N to 47°32′N in latitudes and 123°47′E to 124°37′E in longitudes. The whole area of the reserve is around 2100 km2. The average altitude of the wetland region is 144.0 m. It is in a temperate ⁎ Corresponding author. School of Electronic and Information Engineering, Dalian University of Technology, Linggong Lu 2, Ganjingzi Qu, Dalian 116024, China. Tel./fax: +86 0411 84707847. E-mail address: [email protected] (M. Han). 1574-9541/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoinf.2010.06.002

continental monsoon climate zone. The mean annual temperature is about 3.9 °C and the annual precipitation is approximately 402.7 mm. Marsh, lake and paddy fields are the main land cover types in the natural reserve (Wang et al., 2006). Reed marshes compose the central region and the dense reeds cover 80–90% of the ground surface of the wetland. Emergent and shoreline plants such as common reed and typha are the main components of the ecosystem within the wetland, while the peripheral farmland ecosystem is mainly composed of wheat (Cui, 2002; Zheng et al., 2008; Wang et al., 2009; Wo and Sun, 2010). Zhalong wetland became the Wetland Natural Reserve of Heilongjiang Province in 1979 and the Wetland Natural Reserve of China in 1988. In 1992, the wetland was put onto the list of Wetlands of International Importance. In the wetland there are about 265 species of birds including large wading bird species and swimming bird species. Most of those birds belong to the Palearctic species and the rest belong to the Oriental species. In terms of residence types, the main part is the migratory birds of which the majority are summer migratory birds, such as ducks and sparrows; the number of the passing birds such as yans are also considerable. Besides, there are also a few resident birds (Zou et al., 2003, 2007; Yang et al., 2008). Unfortunately, some highly rare species, such as the white-napped crane and the red-crowned crane, are now on the edge of extinction in this area because of the shrinking of the wetland itself. During the past

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Fig. 1. Location of Zhalong wetland.

two decades, due to the warmer and drier regional climate as well as the disturbance of intensive and irrational human activities, the ecological environment in Zhalong has become more vulnerable and the region continued degrading and shrinking. Excessive felling of forests in the upper basin led to a rapid decrease of upstream runoff amount. And as the demand on the water resources for the agricultural development increased, the water that should enter Zhalong wetlands was intercepted. As a result, the water level in the water land continued decreasing. In particular, the continuing severe drought in 1999–2001 caused severe damage to the Zhalong wetland ecosystem. The environmental degradation and the destruction of habitat caused by water level decrease are serious threats to the survival of these cranes in the wetland (Han et al., 2007; Huang et al., 2007; Yuan et al., 2009). Since the lack of water is the main driving factor that causes the degeneration of the wetland during recent years, it's of great importance to study the water cycle and how water is consumed in the wetland. Evapotranspiration (Shukla and Mintz, 1982) is the sum of the water lost to the atmosphere from the soil surface through evaporation and from plant tissues via transpiration. Therefore, evapotranspiration is a key component in the water cycle and a vital way by which the ground water is consumed. In order to study the water cycle characteristics of the wetland ecosystem, evapotranspiration from the ground surface for the whole wetland region should be estimated and analyzed. The development of the state-of-the-art remote sensing technologies enables us to assess multitemporal evapotranspiration in a large-scale region, such a regional wetland, and integration of different numerical models with remote sensing technologies has yet been fully investigated based on their synergistic potential. In this

study the research mainly focuses on the eco-hydrological characteristics of the wetland vegetations. The amount of transpiration from wetland vegetations is of great interest, so more detailed information rather than a total value of evapotranspiration of the wetland ground surface is pursued. A two-layer model, which was developed as the Two-Source Energy Balance (TSEB) model (Norman et al., 1995; Kustas and Norman, 1999a,b), can be employed to support the wetland assessment with the aid of remote sensing images. In TSEB, a substrate surface layer and a canopy layer are used to represent different sources of evapotranspiration for the vegetation covered ground surface in the wetland, so the evaporation and transpiration values can be estimated respectively. This is very important for the research of the eco-hydrological characteristics of wetland ecosystems.

2. Materials and methods Methods for the estimation of evapotranspiration from remote sensing images can be classified into two main categories, namely the statistical/empirical methods (Roerink et al., 2000) and the mechanistic modeling methods (Moran et al., 1996; Su, 2002). The statistical methods make use of the regression relationships to derive the evapotranspiration estimations from some routine remote sensing products, such as normalized differential vegetation index (NDVI) and brightness temperature. On the other hand, most mechanistic modeling methods, including Surface Energy Balance Algorithm (SEBAL) proposed by Bastiaanssen et al. (1998a,b) and TSEB (Norman et al., 1995; Kustas and Norman, 1999a,b), are based on the ground surface energy balance theory. SEBAL and TSEB are among

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the best designed and practically reliable methods for the information retrieval of regional evapotranspiration. SEBAL is a one-layer model because in the model the land surface conditions are described with a simple one-layer structure. The model performs an “internal calibration”, which eliminates a priori calibration and the need for ancillary data during parameterization process, and hence shows a notable robustness to input noises. Advantages of SEBAL have already been verified through worldwide applications (Allen et al., 2001; Mohamed et al., 2004; Akbari et al., 2007; Bastiaanssen et al., 2005; Zwart and Bastiaanssen, 2007). However, since the land surface is modeled as a single surface layer, it's impossible for SEBAL to distinguish different sources of evapotranspiration. TSEB is a prominent two-layer model. It models the land surface with a two-layer structure. TSEB has also been widely used (Kustas et al., 2004; Crow and Kustas, 2005; Anderson et al., 2007). For homogeneous land surface conditions, a one-layer approach may be suitable. However, in most cases the landscape is under partial vegetation canopy cover so that both substrate and canopy components contribute to the flux exchanges. For the complex land surface conditions in Zhalong wetland, two-layer modeling schemes provide a more realistic representation of the turbulent and radiation exchanges between the land surface and the lower atmosphere. Furthermore, two-layer models can separate transpiration from evaporation. Therefore, TSEB is a good choice in our study of the Zhalong wetland ecosystem. However, the parameterization process for a two-layer model is often quite complex. The requirement for a big load of auxiliary data can hardly be fully satisfied by conventional meteorological measurements. As in TSEB, to obtain the values of the heat fluxes from the substrate surface, a complex calculation procedure for the soil surface resistance is involved. The complexity of such parameterization processes demands a lot of additional handling efforts in applications. In the original TSEB model, a parallel resistance network which is quite different from the classical two-layer structures is applied to simplify the calculation procedure of the sensible heat fluxes. However, this parallel two-layer model is controversial: arguments arose about whether this TSEB model is a layer model or a patchy model (Lhomme and Chehbouni, 1999; Kustas and Norman, 1999a,b). To eliminate the confusion, Norman et al. (1995) also offered algorithms based on the more realistic series resistance network to take into consideration the coupling of fluxes from different components of ground surface, but the solution is much more complex. Furthermore, to make this series network TSEB solvable, some approximations are used, which makes the model much less precise. In order to solve the above-mentioned problem, a two-layer structure with a series resistance network is used to describe different sources of evapotranspiration in our study. A new solution for the TSEB model is developed based on the series two-layer structure and the consistency between TSEB and SEBAL. According to Timmermans et al. (2007), applications of both TSEB and SEBAL have obtained similar results for the estimation of evapotranspiration and other ground surface fluxes. These applications all use the data obtained in two large-scale field experiments of SGP '97 (Jackson et al., 1999) and Monsoon '90 (Kustas et al., 1994) which cover subhumid grassland and semi-arid rangeland respectively. This similarity could be considered as a proof for the consistency between TSEB and SEBAL. According to this consistency, the coupling of these two models is feasible. Using the outputs of SEBAL as the inputs to TSEB, heat fluxes from both layers in the two-layer structure can be estimated without calculating the soil surface resistance. Furthermore, since the estimation results of SEBAL are input into TSEB, the robustness of SEBAL and its insensitivities to land cover varieties can be inherited by TSEB. Therefore, the new solution for TSEB cannot only simplify the calculation procedure but also improve the robustness of TSEB. Using the new solution for TSEB, reliable

evapotranspiration estimations for various ground surfaces can be obtained. The theory of the new solution for TSEB is explained in the following sections. In Section 2.1, a discussion about the definition of the ground surface aerodynamic temperature in a two-layer model is provided. Following this definition, a theoretical explanation for the combination of TSEB and SEBAL is offered. Furthermore, according to a graphical understanding, the two-layer structure and the one-layer structure are integrated in a unified schematic diagram. Based on this definition of aerodynamic temperature and the new understanding of the links between one and two-layer structures, the new solution for TSEB is proposed in Section 2.2. Calculation steps are also described. In Section 2.3, essential difference between the implementation mechanisms of the new solution and the original one is discussed.

2.1. Definition of surface aerodynamic temperature in TSEB The remote sensing of ground surface temperature is critical in energy balance models which are the basis for the estimating of the ground surface fluxes. When the ground surface temperature is mentioned, two different kinds of temperature definitions, namely aerodynamic temperature and radiometric temperature, can be referred to. Though these two temperatures are both averaged values for a certain scale area corresponding to a remote sensing image pixel, they are defined by considering totally different aspects of the surface process. Aerodynamic temperature describes the thermodynamic property of the ground surface while radiometric temperature concerns the radiometric property. The radiometric temperature can be calculated directly from a remote sensing image, which records radiation from the ground surface. However, as illustrated in Fig. 2(a), when the sensible heat flux is to be calculated, the value of aerodynamic temperature is required. There is hardly a truly reliable method to derive aerodynamic temperature from remote sensing images directly, or to convert radiometric temperature into it. Usually, a remarkable difference between the values of aerodynamic temperature and radiometric temperature exist for a heterogeneous land surface, so the empirical relations between these two temperatures (Seguin et al., 1989; Rosema and Fiselier, 1990) have been utilized to handle the problem. However, in two-layer models like TSEB where component temperatures have been introduced, the aerodynamic temperature is considered as a directional integral temperature. Using a series twolayer structure the ground surface is divided into two components, the canopy and the substrate soil (or water). Temperatures of these two components are named component temperatures. The relation

Fig. 2. Schematic diagrams illustrating (a) a one-layer surface structure and (b) a twolayer structure with a series resistance network.

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between the two component temperatures and the integral radiometric temperature of the ground surface can be expressed as εBðTR Þ = ½1−fC ðϕÞεS BðTS Þ + fC ðϕÞε C BðTC Þ;

ð1Þ

where fC(ϕ) is the vegetation coverage fraction and it depends on the view zenith angle ϕ. TS and TC represent soil surface temperature and canopy layer temperature respectively. TR is the directional radiometric temperature and it also depends on the view zenith angle ϕ, though the dependence is not explicitly expressed here. εS and εC are the emissivities of soil and canopy, and a single emissivity ε is used to represent the combined soil and canopy. B(·) is the Planck blackbody radiation function and it is usually substituted by some simpler approximate functions without causing significant errors. In TSEB, the relation is approximately simplified as 4

4

4

TR = ½1−fC ðϕÞTS + fC ðϕÞTC :

ð2Þ

The relation given by Eq. (2) is used to calculate the component temperatures in TSEB. In Fig. 2, a series two-layer structure used in TSEB and a one-layer structure are shown together for comparison. Accordingly the relation between the aerodynamic temperature and the component temperatures can be expressed as ρCp

TO −TA T −TB T −TB = ρCp S + ρCp C ; RA RS RX

TB −TA T −TB T −TB = ρCp S + ρCp C : RA RS RX

flux is calculated. Considering the inner structure of the ground surface layer, the layer can be divided into two sub layers representing different components of the ground surface. Correspondingly, the total value of heat flux is divided into two heat flux values corresponding to these two components.

ð3Þ 2.2. A new solution for TSEB

where TB is the air temperature at the blending height and TO is the aerodynamic temperature of the ground surface. ρCp is the volumetric heat capacity of air. RA, Rx and Rs are the aerodynamic resistance of air, bulk boundary layer resistance and aerodynamic resistance of substrate surface respectively. Both sides of the equation give the total value of sensible heat flux H from the ground surface. Though these two sensible heat flux values are derived according to different surface structures, as illustrated by Fig. 2(a) and (b) respectively, they still refer to the same thing and should be equal. It should be noted that when the blending height is lower than the canopy height, as is usually the case, RA in Fig. 2(a) and RA in Fig 2(b) are equal. On the other hand, based only on the series two-layer structure as illustrated in Fig. 2(b), another relation between these temperatures can be obtained, which goes like ρCp

Fig. 3. Schematic illustration of ground surface layer divided into canopy and substrate sub layers.

ð4Þ

this equation describes the coupling of the sensible heat fluxes from the two layers in TSEB. Comparing Eqs. (3) and (4), a conclusion can be drawn that TO = TB, namely the aerodynamic temperature in one-layer models and the air temperature at the blending height in a series two-layer model represent the same temperature measure. And this equivalence also gives a physical and practical meaning to the aerodynamic temperature, which used to be only a virtual concept in a temperature form. Consequently, by defining the aerodynamic temperature in a series TSEB model, consistency between TSEB and the one-layer models such as SEBAL is obtained. In fact, considering only the sensible heat fluxes above ground surface, the series two-layer structure and the one-layer structure can be unified in the same schematic diagram as illustrated in Fig. 3. In this uniform schematic diagram, the canopy layer and the substrate layer are expressed as sub layers of the ground surface layer. Therefore, when dealing with the ground heat flux problems, a two step approach can be adopted. First, a one-layer structure is used to model the thermo dynamical interactions between the ground surface and the atmosphere boundary layer. The total value of sensible heat

When bi-directional thermal radiation measurements are available, the component temperatures can be directly calculated and TSEB can be perfectly solved. However, only a limited number of remote sensing systems, like the Along Track Scanning Radiometer (ATSR), can offer such a convenience (Li et al., 2001). When bi-directional thermal radiation measurements are not available, the Priestley– Taylor equation (Priestley and Taylor, 1972) is introduced to make TSEB complementary and solvable. The component temperatures are calculated using the relationship given by Eq. (2) (Kustas and Norman, 1999a,b). This section however, gives an alternative approach to solve TSEB. By using this approach, there is no need to calculate component temperatures and the ground surface resistance. First of all, the difference in sources of evapotranspiration is ignored and a simple one ground surface layer is used to describe all the components on the ground within a pixel. As in SEBAL, an energy balance equation is established to manifest the thermodynamic equilibrium between the turbulent transport processes in the atmosphere and the laminar processes in the sub-surface. Rn = H + G + λET

ð5Þ

Rn is the net radiation flux on the ground surface. H, G and λET are the sensible, soil and latent heat fluxes respectively. This equation is the key equation and the algebraic expression for a one-layer model. It gives a global description for the surface processes of the whole pixel. Net radiation flux Rn is calculated according to a radiation equilibrium relationship concerning all the incoming and outgoing radiation fluxes. Firstly, incoming short wave radiation flux from the sun and the incoming long wave radiation flux from the atmosphere are summed up. Secondly, the outgoing long wave radiation flux taking energy away from the ground surface is subtracted from the sum. Finally, the value of the net radiation flux is obtained. Sensible heat flux H is derived from a ‘temperature gradient/ resistance’ approach. The temperature gradient means the difference between the surface aerodynamic temperature and air temperature. As discussed before, the surface aerodynamic temperature can hardly be obtained directly from the remote sensing images. However, an approximate linear relationship between the surface radiation

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temperature and the temperature gradient exists according to some field experiments in Egypt, China and USA (Roerink et al., 2000). This linear relationship made use of SEBAL to calculate the ground/air temperature gradient from the surface radiation temperature. The aerodynamic resistance of the air is calculated using synoptic data. Then the total value of the sensible heat flux H is obtained. Detailed calculation steps for Rn and H are listed in Appendix A, where the calculation methods for related surface parameters such as NDVI and the surface emissivity are also provided. To separate transpiration from evaporation in the total estimation of evapotranspiration, the original ground surface layer is divided into two sub layers, namely the substrate layer and the vegetation canopy layer. A two-layer structure with a series resistance network is applied. The fluxes from both layers are coupled and the air temperature at the blending height equals to the aerodynamic temperature of the ground surface. Total value of the sensible heat flux derived in SEBAL is divided into fluxes from both layers as H = HC + HS ;

ð6Þ

where HC and HS represent the sensible heat fluxes of the vegetation canopy layer and substrate layer respectively. The soil layer and the canopy layer have their own energy balance equations similar to Eq. (5). Rns = HS + λETS + G

ð7Þ

Rnc = HC + λETC

ð8Þ

Labels in Eq. (7) with subscript S represent fluxes of the soil surface and C refers to the canopy. In two-layer models, the canopy layer and the ground soil is separated by the ground surface air, so the soil heat conduction flux G is a monopolistic term of the substrate layer energy balance. Following the original TSEB approach, a beer's law type equation is used to estimate the divergence of the net radiation in the canopy covered ground surface. !!

Rnc =

LAI 1− exp −k pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 cosðθÞ

Rns = Rn −Rnc

⋅Rn

ð9Þ ð10Þ

θ is the solar zenith angel, LAI is the leaf area index, and k is the canopy extinction coefficient. LAI is derived from NDVI (see Appendix A). By solving Eqs. (9) and (10), values of net radiation for both layers are obtained. Then Priestley–Taylor equation (Priestley and Taylor, 1972), which is originally established to estimate evapotranspiration for the saturated surface conditions only, is used to calculate the latent heat flux from the canopy layer. λETC = aPT ⋅fG

S R S +γ nc

ð11Þ

S is the slope of the saturation vapor pressure–temperature curve at the temperature of vegetation canopy, γ is the psychometric constant, fG is the fraction of LAI that is actively transpiring and is assumed to equal unity. aPT is the P–T constant. Values of Rnc, Rns and H are already known at this stage, and G is proportional related to Rns as described in Appendix A. Assuming the canopy is saturated, an initial value of the latent heat flux from the canopy layer is obtained. Based on the series two-layer structure, Eqs. (6), (7), (8) and (11) give a comprehensive description of the ground surface process. Sensible and latent heat fluxes from the two layers are the only remainders to be calculated, and these equations are mathematically solvable.

As explained in Appendix B, by solving the four equations listed above, the initial estimations of evaporation and transpiration are obtained. Then according to a common sense that no condensation happens during daytime, a correction process is introduced to equal the evaporation value to zero for those pixels producing a minus initial value of evaporation and this backward calculation will give a new estimation of transpiration from the vegetation canopy. Fig. 4 is the flowchart illustrating the procedure of calculating evaporation and transpiration initials. 2.3. Comparison and discussion For convenience, the TSEB approach which is based on a series resistance network and solved using the new approach as described in Section 2.2 will be referred to as the modified TSEB approach hereafter. In the original TSEB approach, conditions of the canopy and substrate are connected with a radiation temperature as shown in Eq. (2), while in the modified TSEB, the connection is built on a new definition of the aerodynamic temperature. In the modified TSEB approach, algorithms in SEBAL are used to calculate the ground surface aerodynamic temperature (or ground– air temperature difference, to be precise), and then to calculate a total value of the sensible heat flux from the ground. In the original TSEB model, the sensible heat fluxes of different layers are related through the relation of the component temperatures, but in the modified TSEB approach, the relation between these sensible heat fluxes is much more direct. Calculation procedures from component temperatures to component sensible heat fluxes and backward are no longer involved in the modified TSEB approach. The calculation steps for the soil surface resistance and the bulk boundary layer resistance are also avoided. Considering the facts given above, the modified TSEB approach can be much more convenient than the original one. 3. Data collection, analysis and synthesis Six scenes of LandSat7/ETM+ images have been selected in this study. These images were acquired on six different days in the years 2001 and 2002. The dates of acquisition are reported in Table 1. The path and the row number of these images are 120 and 27. The satellite overpass time was 14:00 local time. Using a vector boundary graph for the area of interest, a manual segmentation has been applied to these images to get the sub-images which cover only the wetland and the nearby rural area. Meteorological data were collected by an automatic weather station installed inside the kernel zone of the wetland. Air conditions including humidity, temperature and wind speed have been recorded hourly by the station during a 4 year discontinuous working phase from 2001 to 2005. Field experiments were carried out at a place near the location of the automatic weather station, where a soil moisture meter had been installed in the soil to measure the soil moisture and a set of specially designed lysimeters had been installed to measure evapotranspiration directly from the water level change (Xu and Wang, 2007). The region of the Zhalong wetland is relatively flat and the impact of land surface slope on evapotranspiration can be ignored, therefore no digital elevation map (DEM) data has been used in our research. And since the model is insensitive to land cover differences, no land cover map has been used as the input data neither. 4. Experimental results 4.1. Daily evapotranspiration of Zhalong wetland Algorithms in SEBAL do not enforce closure because the net radiation flux and the sensible heat flux are calculated independently and can sometimes give a minus latent heat flux. Therefore a closure method was used to assign the sensible heat flux H to the available

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Fig. 4. Flowchart showing the logic for calculating evaporation and transpiration.

energy Rn − G when H N Rn − G. Results of the total sensible heat flux were then obtained and these results were made use of as the inputs for TSEB to calculate the components' heat fluxes, following Eq. (6). When no rapid weather change happens, the evaporative fraction, defined as the ratio of latent heat flux to available energy, will not change much and can be considered as a constant during the daytime period (Hoedjes et al., 2008). Based on this common assumption, daily evapotranspiration values of Zhalong wetland were derived from the six scenes of remote sensing images. A list of the regional mean values and the standard deviations of daily evapotranspiration as well as NDVI for the whole wetland area are reported in Table 2. Daily evapotranspiration at the wetland area varied from 3.5 mm in spring, to 4.5 mm in summer, and to 2.5 mm in autumn. The daily water consumption of the 2100 km2 wetland reserve is about 7,350,000 t, 9,450,000 t and 5,250,000 t per day in spring, summer and autumn, respectively. The spatial distributions of evapotranspiration in the wetland are shown in Fig. 5. The kernel part of the wetland where dense reeds and other wetland vegetation grew can be easily recognized as those areas with the largest amount of evapotranspiration in July and September. Those parts where evapotranspiration is constantly high are open water surfaces. 4.2. Estimations of transpiration and evaporation Using the method described in Section 2.2, daily evaporation and daily transpiration of Zhalong wetland were calculated from the six scenes of remote sensing images of the wetland area. Daily values of evaporation and transpiration were derived from instantaneous heat flux estimations, based on an assumption that the ratio of evaporation Table 1 Measurements of solar zenith angles and air condition records at satellite pass time. Year/Month/Day 2002/04/15 2001/04/28 2002/05/17 2002/07/04 2002/09/22 2002/10/08

to evapotranspiration is constant during the daytime period. This assumption is similar to the constant evaporative fraction assumption and is also fairly reliable when the weather condition is ordinary. During the night time period, transpiration and evaporation are both tiny and can be ignored. By multiplying daily evapotranspiration value with the ratio derived from instantaneous values, daily evaporation and daily transpiration were obtained. Five typical sub-regions are selected for a comparative analysis on evapotranspiration patterns of different kinds of land surface. Locations of these regions are labeled as 1 ∼ 5 as shown in Fig. 6. These regions consist of basically homogeneous land cover types. And these land cover types are open water, dense reed marsh and crop field in the wetland, respectively. Some information about these subregions is given in Table 3. Eco-hydrological situations of these reed marshes are the main concern of our research, while evapotranspiration behaviors of the open water area and the crop field system are considered as the references for comparison. As illustrated in Fig. 7, temporal changes of area-averaged NDVI for the three reed marshes are identical. An unnatural decrease of NDVI can be observed on the third day of the sequence. This decrease of NDVI is a result of the water level raise in the wetland during April of 2002. A fire broke out in August of 2001 and lasted into the first few months of 2002. The fire caused massive damage to the wetland ecosystem. In order to help the recovery of the damaged reed communities, a large mount of water has been inflooded into the wetland during April of 2002. A large part of these reed marshes was flooded by water. As a result, it gives negative NDVI and the regionally averaged NDVI for these regions are small on May 17th 2002. Except for this very unusual condition, trends of NDVI changes for the crop

Table 2 Regional means and standard deviations of NDVI and daily evapotranspiration values.

Solar zenith (°)

Air temperature (°C)

Wind speed (m/s)

Relative humidity (%)

Year/Month/Day

NDVI

Daily ET (mm)

37.2 41.2 32.1 30.2 49.4 55.6

19.9 12.6 24.2 29.4 21.8 12.7

6.8 4.5 2.8 2.8 3.0 2.3

14 20 33 43 25 33

2002/04/15 2001/04/28 2002/05/17 2002/07/04 2002/09/22 2002/10/08

0.130 ± 0.075 0.139 ± 0.049 0.159 ± 0.120 0.479 ± 0.173 0.281 ± 0.117 0.187 ± 0.073

3.423 ± 1.283 3.545 ± 1.481 3.445 ± 2.150 4.451 ± 1.909 2.624 ± 1.338 2.456 ± 0.782

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Fig. 5. Distributions of daily evapotranspiration in Zhalong wetland on the date of (a) 2002/4/15, (b) 2001/4/28, (c) 2002/5/17, (d)2002/7/4, (e)2002/9/22 and (f)2002/10/8.

field and the reed marshes are similar. However, NDVI changes of the crop filed are smoother and the peak value is much smaller than those of reed marshes. Fig. 8 shows the temporal changes of daily evapotranspiration of these five sub-regions through a typical growth cycle of wetland vegetations. Evapotranspiration of the lake area, which can be considered totally as evaporation, is constant for the best part of the period, while only a slow decline can be observed during September and October. Daily evapotranspiration values of the crop field and the three reed marsh regions all reach their peaks in July. This is not surprising since the corresponding NDVI peak values also happen at the same time. These peak values of daily evapotranspiration and NDVI demonstrate the fast growth of crops and wetland vegetations. In April daily evapotranspiration of reed marsh regions is considerably smaller than evaporation of the lake area. This shows the low level of Table 3 Information about the five sub-regions selected to represent typical land cover types in a wetland. Label Land Cover Type Dominant Vegetation

Fig. 6. Five sub-regions representing typical land cover classes in Zhalong wetland Reserve.

1 2 3 4 5

Lake Farm land Reed marsh Reed marsh Reed marsh

Area (km2) Center Latitude

24.6528 Wheat 11.1618 Common reed 16.7688 Common reed 10.9125 Common reed 17.7156

47°18′38″N 47°18′20″N 47°11′39″N 47°08′50″N 47°06′18″N

Center Longitude 124°18′10″E 124°35′34″E 124°28′44″E 124°11′33″E 124°17′18″E

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Fig. 7. Temporal changes of area-averaged NDVI of the sub-regions as a crop field and three reed marshes.

the vegetation life activity and the comparatively smaller surface humidity in the reed marshes. From May to July, when the wetland area is getting humid and reeds are growing rapidly, evapotranspiration of the reed regions increases a lot and exceeds the evaporation of the lake area. This result is consistent with the field experiments and measurements inside the kernel area of the wetland (Xu and Wang, 2007). In September and October, evapotranspiration of the reed marsh regions decreases along with the open water evaporation of the lake area. Noticeable difference between open water evaporation and reed marsh evapotranspiration are shown in the figures. This difference can be considered as an evidence for the fact that when wetland vegetations are at the very beginning of wilting, they are still green and NDVI of the vegetation field can still be high, but they no longer produce remarkable amount of transpiration. On the contrary, the existence of those inactive canopies can be obstacles to the evaporation of the substrate wet surface. More detailed information is given in Figs. 9 and 10. The areaaveraged values and associated standard deviations of daily evaporation and transpiration at the three reed marsh regions and the crop field were plotted respectively, to signify the temporal variability. From April to July, a continuous increase of daily transpiration is obvious in Fig. 9. Seasonal changes of crop conditions and intensities of the vegetation life activities can be recognized from the changes of transpiration values. It can be concluded from Fig. 9, that the spatial consistency of transpiration is much better than that of evaporation in the crop field, because the relative standard deviations of transpiration are much smaller compared to those of evaporation. As illustrated in Fig. 10, daily transpiration values in the reed marsh regions change in a similar way as in the crop field, except for the unusual decrease in May. Like the decrease of NDVI in May, this unusual decrease of transpiration also results from the water level raise in the wetland during this period. Spatial consistency of transpiration is also good for these reed marshes. This implies a spatial equally distributed growing pattern of the reeds and other wetland vegetations within these regions. Curves describing the changes of daily transpiration for the three different reed marsh regions are highly identical. This identity basically holds true for the evaporation changes though the reed region named ‘marsha’ has an obviously lower evaporation than the other two reed marsh regions on April 28th, the 118th day of the year. An interesting fact is that in

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all the three reed marsh regions, when the lowest NDVI values are observed in May the largest amounts of daily evaporation occur, and when the highest NDVI values are reached in July the strongest transpiration happens. Both the peak values of daily evaporation and the bottom values of NDVI can be ascribed to the before mentioned fact that the water level in the wetland was getting higher and the substrate surfaces were getting wetter because of an inflood activity implemented during April of 2002. The coincidence between the largest NDVI values and the largest daily transpiration amounts is much more understandable, because these two phenomena both indicate the vigorous growth of those wetland vegetations in July. During this period of the year, dense canopies formed gradually in those marsh regions. The evaporations from the substrate surface are badly restrained by these dense vegetation canopies consequently. Therefore, rapid decreases of daily evaporation values in these regions are observed. 4.3. Validation A comparison between the original TSEB and the new solution for TSEB is performed to validate the reliability of the new solution and the results listed above. Using the image data set of Zhalong wetland acquired on October 8th, 1999, evaporation and transpiration values are calculated following both the original TSEB and the new solution for TSEB. As a result, the total amounts of evapotranspiration derived following both the original TSEB and the new solution are basically equal, with an average deviation at about 5%. The average deviation between estimations of transpiration at 12.7% is relatively small, because even higher deviations between model estimations and field measurements are acceptable according to Timmermans et al. (2007). Furthermore, because the Priestley–Taylor equation has been used in both of the two procedures, the average relative deviation of evaporation estimations is only 1%. Based on all these facts stated above, it could be concluded that the new solution for TSEB proposed in this paper holds an obvious consistency with the original TSEB. 5. Conclusions A new solution for the TSEB approach is developed in this paper. This new solution is based on an inference that the air temperature at the blending height in the two-layer structure is equivalent to the aerodynamic temperature originally defined in a one-layer structure. Therefore, the sensible heat flux which is estimated by using a onelayer evapotranspiration model can be considered as a combination of sensible heat fluxes from different sources, namely the canopy and the substrate surface. The two-layer structure can be considered as the inner structure of a one-layer model. This new solution for TSEB is more convenient than the original one when the bi-directional thermal infrared remote sensing images are not available. The reason is that the component temperatures and the ground surface resistance no longer need to be calculated, although the concept of the component temperature is still used.

Fig. 8. Temporal changes of daily evapotranspiration amounts of all the five sub-regions.

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Fig. 9. Temporal changes of daily evaporation and transpiration of the crop field sub-region.

This new solution for TSEB was used to estimate the daily evapotranspiration of Zhalong wetland from six scenes of LandSat7/ ETM+ images acquired during 2001 and 2002. Spatial distribution

and temporal changes of evapotranspiration were obtained and analyzed. From the spatial distribution of evapotranspiration, the kernel zone of the wetland where the densest reeds grow can be

Fig. 10. Temporal changes of daily evaporation and transpiration of the three reed marsh sub-regions.

W. Yao et al. / Ecological Informatics 5 (2010) 348–358

identified. From a detailed analysis and the comparison between the temporal change tracks of NDVI and evapotranspiration from different ground surfaces inside and around the wetland area, life activity patterns of the wetland vegetations were captured. Furthermore, a specific situation of the water level raise in the wetland during May of 2002 was also recognized. Acknowledgments This research is supported by the project (2006CB403405) of the National Basic Research Program of China (“973” Program) and the project (60674073) of the National Nature Science Foundation of China. All of these supports are appreciated. And we would like to express our gratitude to Professor Ni-Bin Chang for his patient review and helpful suggestions on the writing of this paper. Appendix A

the satellite. L ↓ and L ↑ are the downwelling and upwelling long radiation, which can be calculated using ↓

L = εLatm ↑

4

L = σ εTR

   e 4 Latm = σ 1−0:35 exp − TA 10Ta

ðA1Þ

τ2sw

where ra is the fractional path radiance that is assigned the value of 0.03, and the single way transmittance τsw is set to 0.77. rp is the broadband directional planetary reflectance. Normalized Difference Vegetation Index (NDVI) can be calculated as r  rRED NDVI = NIR rNIR + rRED

ðA2Þ

where rRED and rNIR are the spectral reflectance for the red channel and the near infrared channel respectively. Surface emissivity is estimated from NDVI as 8 0:96; > < ε = 1:009 + 0:047 log NDVI; > : 0:985;

0:16bNDVI≤0:74

ðA3Þ

NDVI N 0:74

Radiometric temperature of the ground surface is calculated using the mono window algorithm (Qin et al., 2001). TR =

−67:355351ð1−C−DÞ + ½0:458606ð1−C−DÞ + C + DÞT6 + DTatm C

ðA4Þ where T6 is the brightness temperature of ground surface, and the two parameters C and D can be calculated from surface emissivity and the total atmospheric transmittance τ (let τ = 0.8) as C = ετ;

D = ð1−τÞ½1 + ð1−εÞτ

dT RA

ðA11Þ

dT = aTR + b

ðA12Þ

The linear relationship is calibrated inverting the sensible heat flux over both the wet and dry areas. These areas are selected considering respectively the minimum and maximum temperatures over the whole region. LAI is calculated from NDVI following Choudhury et al. (1994) as   1 NDVImax −NDVImin log k NDVImax −NDVI

ðA13Þ

where k represent the canopy extinction coefficient (let k = 0.45). NDVImax and NDVImin are the minimal and maximal value for NDVI over the whole region. In both SEBAL and TSEB, soil heat conductance flux is calculated according to empirical relations with net radiation. In SEBAL, the relation is expressed as follow: G = Rn ⋅

Tr 2 4 ð0:0032albedo + 0:0062albedo Þð1−0:978⋅NDVI Þ albedo ðA14Þ

In TSEB, a simpler relation is used that ðA15Þ

ðA5Þ

G = CG ⋅Rns

ðA6Þ

For the sake of consistency, the second type of equation is used and the constant CG is set to 0.15, which is the mean ratio of the output from the first equation to the net radiation of the soil layer. In this way, the situation in which soil heat conductance flux overvalues net radiation of the soil layer is avoided.

Tatm represents the mean atmospheric temperature given by Tatm = 16:011 + 0:92621TA

ðA10Þ

The temperature gradient d T is defined as the difference between surface aerodynamic temperature TO and near surface air temperature TA. Surface aerodynamic temperature can hardly be obtained directly from remote sensing images, but the temperature gradient can be estimated from the surface radiation temperature as

LAI = NDVI≤0:16

ðA9Þ

e is the vapor pressure of near surface air. Sensible heat flux is calculated based on the equation H = ρCp

rp −ra

ðA8Þ

where σ is the Stefan Boltzmann constant, and the atmosphere long wave radiation Latm can be calculated as

Surface albedo is calculated after Koepke et al. (1985) that albedo =

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Net radiance is calculated from the incoming and outgoing all wave radiation fluxes

Appendix B ↓

↓

Rn = ð1−albedoÞK + L −L

↑

ðA7Þ

Solar radiation K ↓ can be calculated from the solar constant, the earth–sun distance and the solar zenith angler at the overpass time of

There are four variables to be solved in Eqs. (6), (7), (8) and (11). These variables are λETc and λETs, together with Hc and Hs. Initial value of λETc can be calculate following Eq. (11). Then according to Eq. (8), initial value of Hc can be calculated as Hc = Rnc −λETc

ðB1Þ

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Subsequently, initial value of Hs is given by Eq. (6) that Hs = H−Hc

ðB2Þ

Initial value of λETs can be obtained following Eq. (7) that λETs = Rns −G−Hs

ðB3Þ

For some pixels, these initial estimations should be corrected to avoid negative values of λETs and λETc. First of all, negative λETs is set to zero, then according to Eqs. (7) and (6), Hs and Hc are recalculated as Hs = Rns −G−λETs = Rns −G

ðB4Þ

Hc = H−Hs

ðB5Þ

The new estimation for λETc is given by Eq. (8) that λETc = Rnc −Hc

ðB6Þ

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