Terrestrial Evapotranspiration

C H A P T E R 18 Terrestrial Evapotranspiration O U T L I N E 18.1. Introduction 557 18.2. Basic Theories of lE 562 18.2.1. The Monin-Obukhov Simil...

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C H A P T E R

18 Terrestrial Evapotranspiration O U T L I N E 18.1. Introduction

557

18.2. Basic Theories of lE 562 18.2.1. The Monin-Obukhov Similarity Theory (MOST) 562 18.2.2. The Penman-Monteith Equation 563 18.3. Satellite lE Algorithms 18.3.1. One-Source Models 18.3.2. Two-Source Models 18.3.3. Ts-VI Space Methods 18.3.4. Empirical Models 18.3.5. The Empirical PenmanMonteith (P-M) Equation

565 565 568 569 572

18.3.6. Assimilation Methods and Temporal Scaling Up

575

18.4. Observations for Algorithm Calibration and Validation 576 18.4.1. Eddy Covariance Technique (EC) 576 18.4.2. The Energy Balance Bowen Ratio Method (BR) 577 18.4.3. The Scintillometer Method 578 18.5. Conclusions and Discussion

579

Acknowledgements

581

574

Abstract

18.1. INTRODUCTION

This chapter provides the theoretical basis for measurement and estimation of terrestrial evapotranspiration. It includes the Monin-Obukhov similarity theory (MOST) and the Penman-Monteith equation. We focus on the application of these theories to satellite remote sensing. The accuracy of a given model is usually the ﬁrst consideration in its development, but the required ancillary data and the method’s suitability for different conditions are considerations for satellite remote methods as well. Satellite remote sensing can now reliably determine the spatial variability of land-surface variables and terrestrial evapotranspiration; however, it still has difﬁculty in providing long-term, stable estimates.

Terrestrial evapotranspiration (E) is the transfer of water from the soil or the surface of canopies, stems, branches, soils and paved areas to the atmosphere. This water exchange usually involves the phase change of water from liquid to gas; in the process, energy is absorbed and the surface is cooled. The latent heat absorbed by the process, which is the source of the ﬂux to the atmosphere, is lE, where l is the latent heat of vaporization. In some publications, ET also has the units of Wm2 under these

Advanced Remote Sensing DOI: 10.1016/B978-0-12-385954-9.00018-6

557

Copyright Ó 2012 Elsevier Inc. All rights reserved.

558

18. TERRESTRIAL EVAPOTRANSPIRATION

conditions, ET represents the latent heat ﬂux. lE is required by short-term numerical weather predication models and longer-term climate simulations. Under the assumption that the energy stored by the canopy is negligible, lE can be calculated as a residual of the available energy at the surface (Rn), the sensible heat ﬂux (H), and the ground heat ﬂux (G; see Figure 18.1): Rn ¼ lE þ H þ G

(18.1)

Rn is determined from the sum of the incident downward and upward shortwave and longwave radiation (see Figure 18.1): Rn ¼ SY S[ þ LY L[ ¼ SY ð1 aÞ þ LY L[ ¼ Sn þ LY L[ (18.2) where SY and S[ are the surface downward and upward shortwave radiation, respectively; LY and L[ are the surface downward and upward

FIGURE 18.1 The background values of radiation or energy ﬂows are based on observations 2000 to 2005. Superposed, with the key on the lower left, are values from the various reanalyses for the 2002-2008 period except for ERA-40, which is for the 1990s, color coded in W m2. On top of the ﬁgure, values are given for albedo in %, absorbed solar radiation ASR, and Net TOA (Top of Atmosphere) radiation, and the box labeled SFC near the bottom gives the net ﬂux absorbed at the surface. For the 1990s the latter value is 0.6 Wm2. (Trenberth et al., 2011)

559

18.1. INTRODUCTION

longwave radiation, respectively; a is the surface albedo; and Sn is the surface net shortwave radiation. On average, lE uses approximately 59% (from 48% to 88% for different models) of the surface net energy (Trenberth et al., 2009). The energy required to evaporate a given mass of liquid water corresponds to approximately 600 times the energy required to increase its temperature by 1 K and to 2400 times the energy required to increase the temperature of a corresponding

mass of air by 1 K (Seneviratne et al., 2010). If the terrestrial lE were zero, then the Northern Hemisphere would be 15-25 C warmer (Shukla and Mintz, 1982). lE is a major component not only of the surface energy balance but also of the terrestrial water cycle. Therefore, it can be estimated from the surface water balance at basin-wide or continental scale using (see Figure 18.2): E ¼ P Q dw=dt

(18.3)

FIGURE 18.2 The background ﬁgure shows the estimates of the observed hydrological cycle from 2002 to 2008, with units

in 1000 km3 for storage, and 1000 km3 yr-1 for exchanges. Superposed are values from the eight reanalyses for 2002-2008, color coded as given at top right. The exception is for ERA-40 which is for the 1990s. (Trenberth et al., 2011)

560

18. TERRESTRIAL EVAPOTRANSPIRATION

FIGURE 18.3 Annual average lE from six coupled global circulation models and two reanalyses.

18.1. INTRODUCTION

where P is the amount of precipitation, Q is the amount of river discharge, and dw/dt is the change in terrestrial water storage. E accounts for approximately two-thirds of the 700-mm/yr average precipitation that falls over the land (Chahine, 1992; Oki and Kanae, 2006). The global average of the E/P ratio varies from 56 to 82% for eight reanalyses (Figure 18.2), and the differences at the regional scale are much greater (Figure 18.3). Water and heat are transferred from the land surface to the atmosphere through turbulence, which transports the quantities involved with several orders of magnitude greater efﬁciency than does molecular diffusivity. Turbulence can be assumed to comprise eddies of many different sizes that are superimposed on each other (Figure 18.4). Large eddies can be thermals of rising warmer air caused by solar heating of the ground during sunny days. Turbulence has been recognized for a long time, but its understanding is still advancing (Lumley and Yaglom, 2001). The Monin-Obukhov similarity theory (MOST) was developed in the 1950s (Monin and Obukhov, 1954) to estimate lE and the sensible heat ﬂuxes (H). MOST is similar to

561

Ohm’s Law, which describes the current through a conductor between two points, because MOST relates the turbulence ﬂuxes to the mean gradients of the wind, temperature, and humidity. A number of methods provide reliable point measurements of lEdfor example, the eddy covariance (EC) and the energy balance Bowen ratio (BR) tower systems (see Section 18.4). Satellite remote sensing does not provide direct measurement of lE. Rather, most satellite lE algorithms relate the satellite-derived landsurface variables to lE using either the MOST or Penman-Monteith equations (Kalma et al., 2008; Li et al., 2009b). The methods that use the surface-air temperature gradient require unbiased Ts retrievals and interpolation of the Ta values from ground-based point measurements (Timmermans et al., 2007). Two approaches have been proposed to reduce the sensitivity of the ﬂux estimates to uncertainties of Ts and Ta : (1) methods that use the temporal variation of Ts (Anderson et al., 1997; Caparrini et al., 2003, 2004b; Norman et al., 2000) and (2) methods that use the spatial variation of Ts (Carlson, 2007; Jiang and Islam, 2001; Wang et al., 2006). The above approaches require input that is not

FIGURE 18.4 Air ﬂow can be imagined as a horizontal ﬂow of numerous rotating eddies, that is, turbulent vortices of various sizes, with each eddy having horizontal and vertical components. The situation looks chaotic, but vertical movement of the components can be measured from the tower. (Burba and Anderson, 2008)

562

18. TERRESTRIAL EVAPOTRANSPIRATION

easily obtained. Therefore, many empirical and semi-empirical models relate lE to more easily obtained data, including radiation, temperature, the satellite-derived vegetation index, and the water vapor deﬁcit based on meteorological observations (Fisher et al., 2008; Jung et al., 2009; Shefﬁeld et al., 2010; Wang et al., 2010a; Wang and Liang, 2008). This chapter includes a critical review of the basic theories of lE, including the MOST and Penman-Monteith Equations in Section 18.1. Their assumptions and requirements are discussed in detail to clarify the strengths and weaknesses of the existing remote-sensing methods. The chapter also includes a review of the current remote-sensing methods in Section 18.2. This review largely focuses on the physical aspects of methods that are typically used, but more exhaustive and complete reviews have been provided by others (Carlson, 2007; Kalma et al., 2008; Li et al., 2009b). A brief introduction to the data used to calibrate and evaluate lE methods (Section 18.3) is also included, as is a discussion of current issues and future prospects (Section 18.4).

18.2. BASIC THEORIES OF lE 18.2.1. The Monin-Obukhov Similarity Theory (MOST) Within the near-surface layer, turbulent ﬂuxes are assumed to be height-independent (for this reason, this layer is termed the constant-ﬂux layer). The layer is approximately 10% of the depth of the daytime atmospheric boundary layer (i.e., typically 0.1 km thick during the daytime). MOST relates the turbulent ﬂuxes to the differences between the mean temperature and humidity at two levels in the constant-ﬂux layer through its universal similarity functions (Businger et al., 1971; Dyer, 1974). MOST (Monin and Obukhov, 1954) was the starting point for modern micrometeorology, including the

development of new measurement devices and the execution of several important experiments. This method of estimating lE and H from timeaveraged variables in the surface layer is termed the ﬂux-proﬁle method and uses: Hp ¼ rCp u q

(18.4)

lEp ¼ rlu q

(18.5)

where the subscript p refers to the ﬂux-proﬁle method; Cp is the speciﬁc heat at constant pressure; r is the air density; and u , q , and q are the friction velocity, scale potential temperature, and scale humidity, respectively. According to MOST, the following expressions are valid for the horizontally homogeneous and stationary surface layers: u1 u2 zm1 ln jm ðxm1 ; xm2 Þ zm2 T1 T2 q ¼ k z h1 ln jh ðxh1 ; xh2 Þ zh2 q1 q2 q ¼ k z q1 ln jh ðxq1 ; xq2 Þ zq2

u ¼ k

(18.6)

(18.7)

(18.8)

where u, q, and q are the mean wind speed, potential temperature, and humidity measurements, respectively. These parameters are mean values over typical averaging times of between 10 min and approximately 1 hour. jm and jh are the integral proﬁle functions of the stability, x ¼ z=L, where L is the Monin-Obukhov length. jm and jh are MOST universal functions (Businger et al., 1971; Dyer, 1974). The subscripts 1 and 2 refer to levels. Under ideal conditions, the theory has an accuracy of approximately 10e20% (Foken, 2006; Hogstrom and Bergstrom, 1996). Its assumptions of steady, horizontally homogeneous ﬂow (Monin and Obukhov, 1954) are not always applicable. The temperature and humidity gradients over the surface used by MOST can be very small, especially for forest, irrigated croplands, and

563

18.2. BASIC THEORIES OF lE

grasslands. After the similarity theory was developed for the constant-ﬂux layer, much experimental effort was applied to determining its universal functions. The widely used Businger universal functions (Businger et al., 1971) are based on eddy covariance (EC) observations from the 1968 KANSAS experiment. However, some of the results from this experiment have been seriously questioned, in particular, the value of the von Kármán constant (0.35) found by the Kansas group (Businger et al., 1971) because of ﬂow distortion problems caused by the tower, overspeeding of cup anemometers, and the unstable performance of the phase-shift sonic anemometers (Wieringa, 1980). Improved data have led to the reformulation of the Businger universal function (Hogstrom, 1988). Since the late 1980s, scientists have realized that the sum of H and lE measured by the EC method is generally less than the available energy (Figure 18.5). The closure problem is always characterized by low turbulent ﬂuxes (Foken, 2008). Some reasons for this underestimation include averaging and coordinate rotation, and these coordinate systems have been recognized and corrected (Finnigan, 2004; Finnigan et al., 2003). Consequently, the MOST universal functions from Equations (18.4) and (18.5) must be reformulated to avoid underestimating H and lE.

The universal stability functions are not valid in the roughness sublayer (Sun et al., 1999) of vegetated surfaces (canopy height > 0.1 m), and the proﬁle equations must be modiﬁed using a universal function that depends on the thickness of this roughness sublayer (Garratt et al., 1996). For tall vegetation or in urban areas, the roughness sublayer may be tens of meters thick, whereas the constant-ﬂux layer, for which the similarity theory is valid, may be shallow. This distinction has particularly important implications for retrieving lE from satellite-derived land-surface temperature (Ts) because Ts is the radioactive temperature within the roughness layer of surface.

18.2.2. The Penman-Monteith Equation The Penman equation (Penman, 1948) describes lE from an open water surface. Monteith (Monteith, 1972) modiﬁed the Penman equation by including stomatal resistance to make the equation more suitable for terrestrial surfaces. The Penman-Monteith equation was derived from MOST by assuming that surface energy is balanced (Snyder and Paw U, 2002): H ¼ Rn G lE

According to MOST, H and lE have the following forms, depending on the humidity and temperature at the surface and the reference layer: To Ta rh rCp es ðTo Þ e lE ¼ $ g rh H ¼ rCp $

FIGURE 18.5 Histogram of the annual mean energy balance closure at FLUXNET sites. (Beer et al., 2010)

(18.9)

(18.10) (18.11)

where g ¼ ðrv =rh Þ$g (g is the psychrometric constant and rv is the aerodynamic resistance to water vapor transfer from the surface to the atmosphere), To is the aerodynamic air temperature at the surface, Ta is the air temperature above the surface at a reference level, and rh is the aerodynamic resistance to heat transfer

564

18. TERRESTRIAL EVAPOTRANSPIRATION

from the surface to the air, which can be calculated using the MOST universal function. If D ¼ des =dT is the derivative of the saturated vapor pressure (es ) with respect to Ta, then a ﬁrst-order approximation of es ðTo Þ is: es ðTo Þ ¼ es ðTa Þ þ D$ðTo Ta Þ

(18.12)

lE ¼

ðRn GÞ þ rCp $½es ðTa Þ e=rh D þ ð1 þ rs =rh Þ$g

(18.14)

Equation (18.14) is the Penman-Monteith equation, which is widely regarded as an accurate model (Shuttleworth, 2007). The expression ½es ðTa Þ e in Equations (18.13) and (18.14) is called the water vapor pressure deﬁcit (VPD):

Substituting Equations (18.12), (18.10), and (18.9) into Equation (18.11) and rearranging the terms of Equation (18.8), we obtain:

es ðTa Þ e ¼ es ðTa Þ es ðTa Þ RH=100 ¼ es ðTa Þð1 RH=100Þ ¼ VPD (18.15)

ðRn GÞ þ rCp $½es ðTa Þ e=rh lE ¼ D þ g

(18.13)

Assuming that rv ¼ rh , then g ¼ g, and Equation (8.13) is the Penman equation. Under the assumption of a big-leaf canopy (Deardorff, 1978), rv can be separated into the stomatal resistance (rs) and the aerodynamic resistance (rh), and g ¼ ½ðrs þ rh Þ=rh $g ¼ ð1 þ rs =rh Þ$g; then, we obtain the following equation:

FIGURE 18.6 The scatterplot of D/

1

(Dþg) in Equation (18.16). as a function of air temperature (Ta). (Wang et al., 2006)

0.9

The factors D and g in Equations (18.13) and (18.14) depend solely on Ta at a given location (Wang et al., 2006). The Penman-Monteith equation depends on vegetation-speciﬁc parameters, for example, the stomatal conductance (Beven, 1979), which are difﬁcult to measure directly. Therefore, for a long time, the Penman-Monteith equation was regarded as a diagnostic equation to estimate rs (Alves and Pereira, 2000).

Δ/(Δ+γ) Δ/(Δ+γ)=0.0127*Tair+0.3464

0.8

Δ/(Δ+γ)

0.7 0.6 0.5 0.4 0.3 0.2 −10

0

10 20 Air temperature (centigrade)

30

40

565

18.3. SATELLITE lE ALGORITHMS

When the water supply is sufﬁcient, the available energy term dominates the equation. Hence, in the case of moist soil, Priestley and Taylor simpliﬁed the Penman-Monteith equation to the following (Priestley and Taylor, 1972): lE ¼ a

D $ðRn GÞ Dþg

(18.16)

where a is the so-called Priestley-Taylor parameter. The value of a is typically 1.2 to 1.3 (Agam et al., 2010), but it can range from 1.0 from 1.5 (Brutsaert and Chen, 1995; Chen and Brutsaert, 1995). Equation (18.16) does not consider the roles of the VPD or stomatal resistance that are used in Equations (18.13) and (18.14). D/(Dþg) is a function of Ta (Figure 18.6).

18.3. SATELLITE lE ALGORITHMS Satellite remote sensing does not provide a direct observation of lE; rather, it estimates TABLE 18.1

lE by relating it to land-surface variables, such as Ts, the vegetation index (VI), the soil moisture (SM), clouds, and the radiation budget. The MOST and the Penman-Monteith equation are the basic formulas for this application. Accordingly, we divide the methods into different categories based on how the methods relate the land-surface variables to lE. Tables 18.1 and 18.2 summarize the existing satellite remotesensing methods for estimating lE, along with their strengths and weaknesses.

18.3.1. One-Source Models The one-source model was ﬁrst proposed to estimate lE and H from satellite thermal infrared observations in the 1980s. It uses Ts obtained by the satellite at its view angle to replace the aerodynamic temperature (To) in Equation (18.10) for estimating H (Figure 18.7). lE is then estimated as a residual of the surface energy budget when the ground heat ﬂux G is known or has been estimated (Clothier et al., 1986; Jacobsen,

A Summary of Estimation ET Methods Using Satellite Remote Data

Model

Advantages

One-source models

Disadvantages

Conditions for best performance

Require parameterized excessive resistance; high sensitivity to errors of Ts and Ta; only available for clear sky conditions

Dry and sparse surface with large Ts-Ta

Two-source models

Local calibration not requested

High sensitivity to errors of Ts and Ta; only available for clear sky conditions

Dry and sparse surface with large Ts-Ta

Ts-VI models

Low sensitivity to errors of Ts, does not require Ta or wind speed

Relationship between ET and Ts complicated with temperature and energy control on ET, key parameters; only available for clear sky conditions

Middle latitude where soil moisture determines ET

Empirical models

Simple, low requirements of accuracy of Ts, Ta

Most models need local calibration

Depends on calibration data

Pemnan-Montheith Equation

Simple, low requirements of accuracy of Ts, Ta

Need local calibration

Depends on calibration data

Assimilation method

Temporal integrated estimation

High sensitivity to errors of Ts and Ta

Dry and sparse surface with large Ts-Ta

566 TABLE 18.2

18. TERRESTRIAL EVAPOTRANSPIRATION

Validation Results of Remote-Sensing Techniques for Estimating Evaporation modiﬁed from (Kalma et al., 2008) RMSE (W m-2)

R2

Method

Source

Validation

Surface type

One-source method

(Kustas and Daughtry, 1990)

(Kustas and Daughtry, 1990)

Furrowed cotton

24-85 (10%-25%)

One-source method

(Su, 2002)

(Su et al., 2005)

Corn

47

0.89

One-source method

(Su, 2002)

(Su et al., 2005)

Soybean

40

0.84

One-source method

(Su, 2002)

(McCabe and Wood, 2006)

Corn and soybean

99

0.66

One-source method

(Su, 2002)

(McCabe and Wood, 2006)

Corn and soybean

68

0.77

One-source method

(Su, 2002)

(Su et al., 2007)

Grassland, crops, (rain) forest

44 (25%)

Two-source method

(Norman et al., 1995)

(Kustas and Norman, 1999)

Furrowed cotton

37-47 (12%-15%)

Two-source method

(Norman et al., 1995)

(Norman et al., 2000)

Shrub, rangeland, pasture, salt cedar

105 (27%)

Two-source method

(Norman et al., 1995)

(French et al., 2005)

Corn and soybean

94 (26%)

Two-source method

(Norman et al., 1995)

(Li et al., 2006)

Corn and soybean

50-55 (10%-15%)

Two-source method

(Anderson et al., 1997; Norman et al., 2000)

(Norman et al., 2003)

Wheat, pasture

40-50 (20%)

Two-source method

(Norman et al., 2000)

(Norman et al., 2000)

Shrub,

65 (17%)

Two-source method

(Anderson et al., 1997; Norman et al., 2000)

(Anderson et al., 2007a; Anderson et al., 2007b)

Water, forest, woodland, shrub, grassland, crops, bare, built-up

58 (25%)

Triangle method

(Bastiaanssen et al., 1998a)

(French et al., 2005)

Corn and soybean

55 (15%)

Triangle method

(Roerink et al., 2000)

(Verstraeten et al., 2005) Forests

Triangle method

(Carlson et al., 1995)

(Gillies et al., 1997)

Tall grass prairie, 25-55 grassland, steppe shrub (10%-30%)

0.800.90

Triangle method

( Jiang and Islam, 2001)

( Jiang and Islam, 2001)

Mixed farming, forest, tall & short grass

85 (30%)

0.64

Triangle method

( Jiang and Islam, 2001)

( Jiang and Islam, 2001)

Mixed farming, forest, tall & short grass

50 (17%)

0.9

Triangle method

( Jiang and Islam, 2001)

( Jiang and Islam, 2003)

Mixed farming, forest, tall & short grass

59 (15%)

0.79

Triangle method

( Jiang and Islam, 2001)

(Batra et al., 2006)

Mixed farming, forest, tall & short grass

51-56 (22%-28%)

0.770.84

35 (24%)

567

18.3. SATELLITE lE ALGORITHMS

TABLE 18.2

Validation Results of Remote-Sensing Techniques for Estimating Evaporation modiﬁed from (Kalma et al., 2008) (Cont'd) RMSE (W m-2)

R2

Method

Source

Validation

Surface type

Triangle method

(Nishida et al., 2003)

(Nishida et al., 2003)

Forest, corn, soybean, 45 wheat, shrub, rangeland, tall grass

0.86

Empirical method

(Wang et al., 2007b)

(Wang et al., 2007b)

Forest, grassland, cropland

32 (36%)

0.81

Trad and climate data (McVicar and Jupp, 2002)

(McVicar and Jupp, 2002)

Cropping

88-72 (27%-28%)

0.520.81

Trad and compl. approach

(Venturini et al., 2008)

(Venturini et al., 2008)

Rangelands, pasture, wheat

34 (15%)

0.79

Assimilation of Trad

(Caparrini et al., 2004b)

(Caparrini et al., 2004b)

Tall-grass prairie

56

Assimilation of Trad

(Caparrini et al., 2004b)

(Caparrini et al., 2004b)

Tall-grass prairie

20

(a)

0.96

(b)

FIGURE 18.7 Schematic of resistance network for the one-source model and two-source model formulations for computing sensible heat ﬂux, H. (Kustas and Anderson, 2009)

1999; Kustas and Daughtry, 1990; Kustas et al., 1993) as in the following equation: G ¼ Rn $ða$VI þ bÞ

(18.17)

where VI is the vegetation index, and a and b are constants.

The aerodynamic temperature (To) at the surface used in Equation (18.10) is a “ﬁctitious” temperature at the surface from where the surface turbulent exchanges of water and heat in the constant-ﬂux layer originate. The satellite-derived value of Ts is a radiative temperature of the land

568

18. TERRESTRIAL EVAPOTRANSPIRATION

surface for a homogeneous surface separated from To by a roughness layer in which the water vapor and heat exchange primarily depend on molecular diffusion; this diffusion requires much larger gradients than the turbulent exchange in the constant-ﬂux layer. During the daytime, Ts can be much greater than To, especially for bare soil or sparsely vegetated surfaces (Chehbouni et al., 1996; Friedl, 2002). Therefore, directly replacing To with Ts may result in a significant overestimation of H. The universal stability functions that are used to calculate the aerodynamic rh in Equation (18.10) are not valid in the roughness layer (Sun et al., 1999). The system is more complicated for heterogeneous vegetation, for which Ts is a view-angle-dependent mixture of the canopy and soil radiative temperatures. This scenario raises serious doubts about the utility of the satellite-derived Ts for estimating lE (Hall et al., 1992; Shuttleworth, 1991). Its overestimation can be corrected by adding an excess heat exchange resistance into Equation (18.10), rex ¼ kB1 ¼ lnðzm =zh Þ, which is a function of the roughness height for the momentum transfer (zm) and the roughness height for the heat transfer (zh) (Blümel, 1999; Kustas and Anderson, 2009; Su et al., 2001; Verhoef et al., 1997). Over sparse vegetation, kB-1 can be large and variable. This parameter is a function of the structural characteristics of the vegetation (e.g., the leaf area index, LAI), the level of water stress, the view angle of the radiometer, and climatic conditions (Blümel, 1999; Kustas and Anderson, 2009; Lhomme and Chehbouni, 1999; Lhomme et al., 2000; Su et al., 2001; Verhoef et al., 1997; Yang et al., 2009). A widely used one-source model is the Surface Energy Balance Algorithm for Land (SEBAL) (Bastiaanssen et al., 1998a, 1998b). This model requires only ﬁeld information about shortwave atmospheric transmittance, surface temperature, and vegetation height, as well as empirical relationships for different geographical regions and times of image acquisition. The empirical relationships used in the

model require local calibration (Teixeira et al., 2009a, 2009b). Another well-known one-source model is the Surface Energy Balance System (SEBS), which estimates H and lE from satellite data and routinely available meteorological data (Su, 2002). This model has been widely used with high-resolution satellite data (Landsat and ASTER) and with modest resolution data (MODIS).

18.3.2. Two-Source Models Two-source models have been proposed to improve the accuracy of lE estimates from satellite remote-sensing data, especially over sparse surfaces. A typical, widely used, two-source model was ﬁrst proposed by Norman et al. (1995). This model and its improvements are reviewed here. This model divides the surface into soil and vegetation components, each of which transfers H and lE into the atmosphere above its surface (Figure 18.7). The satellite-derived surface directional radiometric temperature, Ts ðqv Þ, is considered to be a composite of the soil (Tsoil) and canopy temperatures (Tveg) (Figure 18.7): εg s½Ts ðqv Þ4 ¼ ½1 f ðqv Þεs sTsoil4 þ f ðqv Þεc sTveg4 (18.18) where the StefaneBoltzmann constant s ¼ 5:6697 108 Wm2K4, f ðqv Þ is the vegetation cover fraction at the view zenith angle qv , and εg , εs , and εc are the surface emissivities of the composite surface, soil and vegetation, respectively. If εt ¼ εs ¼ εc , then 1

4 4 þ ½1 f ðqv ÞTsoil g4 Trad ðqv Þ ¼ f f ðqv ÞTveg

(18.19) H and lE are also divided into soil and vegetation components, as follows: Tveg Ta rhc T Ta ¼ rCp soil rhv

Hveg ¼ rCp

(18.20)

Hsoil

(18.21)

18.3. SATELLITE lE ALGORITHMS

where rhc and rhs are the aerodynamic resistances from the canopy and the soil to the atmosphere, respectively. The canopy lE (lEveg) has been estimated using the Priestley-Taylor equation (Priestley and Taylor, 1972) (Equation (18.16)). The twosource model iterates to obtain Tsoil and Tveg from the satellite-derived value of Ts using an initial value of 2.0 for the Priestley-Taylor parameter (Anderson et al., 2008; Kustas and Anderson, 2009). This initial high value of the Priestley-Taylor parameter overestimates lEveg and gives a negative soil evaporation value (lEsoil), which is a nonphysical solution. Therefore, the effective Priestley-Taylor parameter is obtained by iterative reduction until lEsoil approaches zero, producing a ﬁnal PriestleyTaylor constant; hence, a ﬁnal Tr and Tc. lE and H are then calculated using these estimates. This modeling scheme has been demonstrated to provide reasonable estimates of lE over a wide range of climatic and vegetation cover conditions (Anderson et al., 2004; Li et al., 2008b; Norman et al., 1995, 2003). However, this scheme may be limited by its accuracy in the partitioning of lE between lEsoil and lEveg because soil evaporation is usually a signiﬁcant component of the total lE and is not zero (Anderson et al., 2008). The above iteration may result in an overestimation of lEveg. In a recent study, the canopy evaporation term was replaced by (Anderson et al., 2008): lEc ¼ l

es ðTc Þ e rs þ rhc

(18.22)

Both the one- and two-source models are sensitive to the use of temperature differences to estimate H, as mentioned in Section 18.3.1. For example, Timmermans et al. (2007) showed that over subhumid grassland and semiarid rangeland, a 3 K error in Ts results in an average error of approximately 75% in H for a typical

569

one-source model (Bastiaanssen et al., 1998a, 1998b) and an average error of approximately 45% for the two-source model (Norman et al., 1995). The methods require unbiased Ts retrievals and a value of Ta that is interpolated from ground-based point measurements. Attempts to estimate the spatial variability of Ta at regional scales with remote sensing suggest an uncertainty of 3-4 K (Goward et al., 1994; Prince et al., 1998). The uncertainties associated with the Ts retrievals are on the order of several K (Wang et al., 2007a; Wang and Liang, 2009). Consequently, except in areas containing low vegetation cover, the derived values of Ts-Ta may be comparable to its uncertainty (Caselles et al., 1998; Norman et al., 2000). Consequently, several methods have used a time series of satellite-derived values of Ts to reduce the sensitivity of the ﬂux estimation to errors (Anderson et al., 1997, 2007a, 2007b; Mecikalski et al., 1999; Norman et al., 2000); however, considerable sensitivity to temperature errors (in Ts and Ta) remain. Most validation experiments have considered sparse grasslands, croplands, or shrubs (Table 18.3). For these surfaces, Ts-Ta is relatively large; therefore, this term has a relatively low sensitivity to temperature errors. No capability to predict lE and H over forests where the value of Ts-Ta is small has been established. Furthermore, the parameterizations of aerodynamic resistance may also cause large deviations in the modeling of H (Liu et al., 2007; van der Kwast et al., 2009).

18.3.3. Ts-VI Space Methods In contrast to the one- and two-source models, Ts-VI methods use the spatial variation of Ts to partition Rn into lE and H. If a sufﬁciently large number of satellite pixels are present, then the shape of the pixel envelope resembles a triangle or trapezoid in the Ts-VI space (Figure 18.8). The Ts-VI method was ﬁrst introduced by Price (1990). Its principle is simple (Gao and Long,

570 TABLE 18.3

18. TERRESTRIAL EVAPOTRANSPIRATION

A Summary of the Satellite Methods that Provide an Estimate of Global Terrestrial lE Surface type

RMSE (Wm-2, relative error)

R2

Method type

Source

Validation and application

Empirical P-M

(Zhang et al., 2009)

(Zhang et al., 2010a)

82 FLUXNET stations

12.5-14.6

0.8-0.84

Empirical P-M

(Zhang et al., 2009)

(Zhang et al., 2010a)

261 river basins globally

14.8

0.8

Empirical P-M

(Wang et al., 2010a)

(Wang et al., 2010a; Wang et al., 2010b)

64 globally distributed FLUXNET sites

17.0 (25%)

0.88

Empirical method (Jung et al., 2009)

(Jung et al., 2010)

112 river basins

11.8

0.92

Empirical P-M

(Zhang et al., 2008)

120 catchments in Australia

6.4

0.67

Empirical method (Wang and Liang, 2008)

(Wang and Liang, 2008)

12 sites in U. S.

28.6 (daytime)

0.85

Empirical P-M

(Fisher et al., 2008)

(Fisher et al., 2008)

16 FLUXNET sites

15.2 (28%)

0.90

Empirical P-M

(Mu et al., 2007)

(Shefﬁeld et al., 2010)

19 FLUXNET sites

27.3

0.70-0.76

(Leuning et al., 2008)

Tmax

min

A

Ts or

Ts

Warm edge

NDVIi, Ti, i

B

E +

Tmin

max

F

C 0

G max

NDVI

max

D 1

cold edge

FIGURE 18.8 A schematic plot for the linear interpretation of the Priestley-Taylor parameter using Ts-VI space. (Wang et al., 2006)

18.3. SATELLITE lE ALGORITHMS

2008; Wang et al., 2006): a surface that absorbs solar radiation experiences surface heating during the daytime. However, the changes in Ts for wet surfaces are relatively small because more energy is expended on the lE of a wet surface and such surfaces have higher thermal inertia. The greater cooling effect of lE and the thermal inertia of wet surfaces produce small or varying values of Ts. Therefore, the warm edge of the Ts-VI space has the lowest evaporative fraction (EF) or lE, and the cold edge of the space represents the highest EF or lE (Murray and Verhoef, 2007; Nemani and Running, 1997; Verhoef et al., 1996). The Ts-VI method is based on an interpretation of the image (pixel) distribution in Ts-VI space (Carlson, 2007). Figure 18.8 is a schematic plot of the linear interpretation of the Priestley-Taylor parameter in Ts-VI space (Equation (18.16)). The trapezoid ABCD represents the spatial variation of Ts-VI; CD is the “cold edge” and AB is the “warm edge” of the spatial variation. The linear interpolation of Ts-VI was ﬁrst proposed by Jiang and Islam (2001) to parameterize a. For a pixel i at a point E in Ts-VI space, A and E can be connected, and AE can be extended to G. Because a at point A is equal to amin and the “cold edge” has a maximum of amax , the length of AG is amax amin , and the length of AE is ai amin . Because the triangle EFG is similar to the triangle ACG, the following equation can be derived: jEFj jEGj ¼ jACj jAGj

(18.23)

Equation (18.23) can be rewritten as: Ti Tmin ðamax amin Þ ðai amin Þ ¼ Tmax Tmin amax amin (18.24) ai at ðTi ; NDVIi Þ is equal to: ai ¼

Tmax Ti ðamax amin Þ þ amin (18.25) Tmax Tmin

571

where T is Ts in Ts-VI space. Therefore, lE is parameterized as follows (Wang et al., 2006): D Tmax Ti lE ¼ ðRn GÞ$ $ D þ g Tmax Tmin (18.26) ðamax amin Þ þ amin The values of Tmax and Tmin are derived from a visual inspection of the Ts-VI space (Figure 18.8). amax is the maximum Priestley-Taylor parameter without surface water stress, and amin is often assumed to be zero, corresponding to the energy fraction used for evaporating the driest bare soil. Because the Ts-VI method makes use of only Ts spatial information, its requirement for Ts accuracy is low. Batra et al. (2006) (demonstrated that lE (or EF) can be accurately derived from satellite brightness temperature observations (without the use of atmospheric and emissivity corrections to convert Ts). Similarly, other valid methods interpret the spatial variation of Ts, such as the albedo-Ts method (Sobrino et al., 2007). Because of its simplicity, this method has been widely accepted. It has been reviewed in detail by Petropoulos et al. (2009) and Carlson (2007). Some well-known, one-source models, such as SEBS and METRIC (Mapping EvapoTranspiration with High Resolution and Internalized Calibration) (Allen et al., 2007a, 2007b; Tittebrand et al., 2005), also rely on Ts-VI space to estimate lE. However, some model simulations have shown that the Priestley-Taylor parameter a is not linear as Equation (18.25) would suggest; rather, it is highly curved (Carlson, 2007; Mallick et al. 2009). In addition, as reported by Wang et al. (2006) the information used in Ts-VI space is the change of Ts after the absorption of solar radiation. The dependence of lE on Ts or changes of Ts is further complicated by the series of processes that are mixed together, that is, the cooling effect of lE; the change in thermal inertia with vegetation, soil moisture and soil texture; and the impact of soil moisture, Ta and available

572

18. TERRESTRIAL EVAPOTRANSPIRATION

energy on the lE process (Nemani et al., 2003; Wang and Liang, 2008). A key assumption of the Ts-VI method is that lE is negatively correlated with temperature because of the cooling effect from lE and the thermal inertia effect of SM (Figure 18.8). However, observations and model simulations have shown that this assumption is not always true. At high latitudes and in cold areas, temperature is a major controlling factor for lE and is generally positively correlated with lE (Iwasaki et al., 2010; Nemani et al., 2003). Therefore, the Ts-VI method is suitable for use only during the growing season (when the range of VI is sufﬁciently large) in middlelatitude regions where soil moisture, rather than Ta or available energy, is the key factor controlling lE. The following additional sources of uncertainty affect the operational application of the Ts-VI method to the estimation of lE (Carlson, 2007; Mallick et al., 2009). (1) The triangle may not be fully determined if the area of interest does not include the full range of land-surface types and conditions. (2) The main weakness of the triangle method is that it requires some subjectivity in identifying the warm edge and the dense vegetation and bare soil extremes. Identiﬁcation is more easily obtained from high-resolution imagery. Tang et al. proposed an algorithm to automatically determine the edges of the Ts-VI triangle (Tang et al., 2010). Finally, (3) Ts and NDVI depend on the surface type due to differences in aerodynamic resistance, which is not considered in this method.

18.3.4. Empirical Models Because satellites can provide only limited information pertaining to lE, a major task in remote sensing is to identify key factors that inﬂuence the processes involved and their parameterization from satellite data. Long-term continuous measurements collected by globally distributed

projects (as reviewed by Shuttleworth, 2007) provide an opportunity to identify these factors, especially with the data obtained from the FLUXNET and Atmospheric Radiation Measurements (ARM) projects (see also Section 18.3). By analyzing the long-term surface measurements of lE collected by the ARM project, Wang et al. (2006, 2007b) found that the dominant parameters controlling lE are the surface net radiation (Rn), the temperature (either as air temperatures (Ta) or land-surface temperature (Ts)), and the vegetation cover, which is quantiﬁed using vegetation indices (VI) (Figure 18.9). Therefore, a method was developed to parameterize the daily value of lE using Rn, Ta or Ts and VI from satellite data (Figure 18.10) (Wang et al., 2007b): lE ¼ Rn ða0 þ a1 $T þ a2 $VIÞ

(18.27)

In its simplest form, Equation (18.27) expresses the dependence of the variation of lE on the vegetation and is consistent with the Priestley-Taylor equation (Equation (18.16)) while incorporating the inﬂuence of vegetation control on lE (through the stomatal conductance). A similar equation was proposed to relate the vegetation fraction to lE (Anderson and Goulden, 2009; Choudhury, 1994; Kim and Kim, 2008; Schuttemeyer et al., 2007): lE ¼ VF$a

D $ðRn GÞ Dþg

(18.28)

where VF is a linear function of VI. Equations (18.27) and (18.28) do not include the effects of soil water stress on lE. These effects were parameterized by adding a dependence on the Diurnal Temperature Range (DTR) (Wang and Liang, 2008): lE ¼ Rn ðb0 þ b1 $T þ b2 $VI þ b3 $DTRÞ (18.29) A comparison of Equation (18.29) with the land model simulations showed that it works well on a global scale, at spatial resolutions of several kilometers to hundreds of kilometers (Wang and

18.3. SATELLITE lE ALGORITHMS

(a)

(b)

(c)

(d)

573

FIGURE 18.9 Scatterplots of lE (or ET) as a function of (a) the daytime-averaged air temperature (Tair), (b) the daytime averaged land-surface temperature (Ts), (c) enhanced vegetation index (EVI) and (d) surface net radiation (Rn) at a site in the Southern Great Plains. (Wang et al., 2007b)

Liang, 2008). A relative humidity-based Priestley-Taylor parameter is derived from ARM measurements (Yan and Shugart, 2010). The above empirical methods seek to relate lE to vegetation and key environmental control factors based on the analysis of ground measurements. The so-called crop coefﬁcient methods (Gordon et al., 2005; Rana and Katerji, 2000) are similar methods that empirically link lE with vegetation conditions and potential lE. The microwave emissivity difference vegetation index (EDVI), which is deﬁned as the difference between the microwave land-surface emissivity at 19 and 37 GHz, was also used to estimate lE (Becker and Choudhury, 1988; Li et al., 2009a; Min and Lin, 2006). The EDVI values represent the physical properties of crown vegetation, such as the vegetation water content of crown canopies.

Some methods that use artiﬁcial neural networks or support vector machine techniques have been used to relate satellite retrievals, such as Rs, Ts, Ta, VI, and land cover, with lE measurements and to apply their training results spatially. The methods are naturally empirical, although their application in other areas is limited because the methods do not provide explicit formulas to follow (Jung et al., 2009; Lu and Zhuang, 2010; Yang et al., 2006). The above simple empirical methods are as accurate as more complicated models, as shown in the comparison between the lE models (Jiménez et al., 2010; Kalma et al., 2008). This simplicity allows global application of the model (Wang and Liang, 2008), although further improvements are needed (Wang et al., 2010a) (see Table 18.3).

574

18. TERRESTRIAL EVAPOTRANSPIRATION

(a)

(b)

(c)

(d)

FIGURE 18.10 Scatterplots of lE (or ET) normalized by surface net radiation (ET/Rn) as a function of (a) the daytimeaveraged air temperature (Tair), (b) the daytime-averaged land-surface temperature (Ts), (c) the enhanced vegetation index (EVI), and (d) the normalized difference vegetation index (NDVI) at a site in the Southern Great Plains. (Wang et al., 2007b)

Empirical methods often require local calibration. To reduce this requirement, some universal empirical methods have been proposed that are suitable for different types of land cover (Jung et al., 2009; Wang et al., 2010a; Wang and Liang, 2008). However, experiments have shown that the composition of tree species strongly inﬂuences lE; for example, the value of lE of deciduous species is much greater than that of coniferous species (Margolis and Ryan, 1997; Yuan et al., 2010), and physiological limitations to transpiration in boreal conifers reduce lE and increase H over large regions, even when soil water is abundant (Margolis and Ryan, 1997). Therefore, land-cover dependent coefﬁcients may be a better choice, such as the tabulated maximum values of gs for various land covers used in land-surface models (Sellers et al., 1997).

18.3.5. The Empirical PenmanMonteith (P-M) Equation The Penman-Monteith equation is widely used in land process modeling to estimate vegetation transpiration or soil evaporation because it requires only conventional observations as input and does not require input from two levels. This equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations for estimating lE (Allen et al., 1998). Its usefulness for the satellite remote sensing of lE has not been fully recognized until recently, most likely because it does not explicitly use a satellitederived Ts and its parameterization of rs is complex. Most Penman-Monteith-like methods empirically relate satellite-derived variables with rs.

18.3. SATELLITE lE ALGORITHMS

Cleugh et al. (2007) directly related the stomatal conductance (the inverse of stomatal resistance) to the satellite-derived leaf area index (LAI) using the Penman-Monteith equation (Equation (18.14)) to estimate lE. The model was modiﬁed with adjustments to the stomatal conductance based on environmental controls, and the algorithm was applied globally (Mu et al., 2007): gs ¼ gs;min $LAI$f ðTmin Þ$f ðVPDÞ

(18.30)

Similarly, Wang et al. (2010a) used VI and the relative humidity deﬁcit (RHD ¼ 1-RH/100) to parameterize the stomatal conductance as follows: (18.31) gs zð1 RH=100Þ$ða0 þ a1 $VIÞ Equation (18.30) linearly relates gs to LAI; consequently, it may overestimate gs when LAI is greater than 3 or 4. The equation may be improved by using the vegetation indices in Equation (18.31) (Bucci et al., 2008; Glenn et al., 2007; Lu et al., 2003; Suyker and Verma, 2008). Leuning et al. (2008) replaced Equation (18.30) with a biophysical two-parameter model that requires local calibration, a requirement that limits its application. Equation (18.30) is also improved by simulations of the Variable Inﬁltration Capacity (VIC) model (Shefﬁeld et al., 2010).

18.3.6. Assimilation Methods and Temporal Scaling Up Remotely sensed data are acquired instantaneously and provide the spatial variation of land-surface variables that relate to lE. However, some of these variables can be accurately estimated only from satellite optical and thermal observations under clear sky conditions, and such retrievals are impossible under cloudy conditions (i.e., albedo, VI, and Ts) (Baroncini et al., 2008). The satellite retrievals must be temporally and spatially interpolated

575

to provide temporally and spatially continuous values. Land data assimilation (LDA) provides a physical solution to this problem by merging satellite measurements with estimates from land-surface models (Li et al., 2009b; Reichle, 2008). A typical assimilation method proposed by Caparrini et al. (2003) is discussed here. This method assimilates the satellite value of Ts into the following force-restore equation: pﬃﬃﬃﬃﬃﬃﬃ dTs 2 pu 1 ¼ Rn H 2puðTs Td Þ dt P 1 EF (18.32) H ¼ r$Cp $CHN $½1 þ 2ð1 e10RiB Þ$U$ðTs Ta Þ (18.33) where CHN is the bulk transfer coefﬁcient for heat under neutral conditions, RiB is the bulk Richardson number, and U is the wind speed. This variational assimilation algorithm uses a bulk transfer model (a one-source model) and Ts Ta to parameterize H, and it connects Ts, the radiation components and the ground heat ﬂux G. The variational problem is solved by obtaining an adjoint state model for Equation (18.32) and utilizing the model to efﬁciently search for values of CHN and EF that minimize the root mean square difference between the predictions of Ts obtained from Equation (18.33) and the observed Ts values (Boni et al., 2001a; Castelli et al., 1999; Crow and Kustas, 2005). These approaches have a number of advantages over purely diagnostic approaches, as shown in Sections 18.3.1-18.3.3. Most importantly, they provide ﬂux estimates that are continuous in time using a physically realistic force-restore prognostic equation to interpolate between the sparse Ts observations (Boni et al., 2001b). The above variational approach demonstrates promise for lE and H retrievals at dry and lightly vegetated sites. However, its results

576

18. TERRESTRIAL EVAPOTRANSPIRATION

suggest that a simultaneous retrieval of both EF and CHN by this variational approach will be difﬁcult for wet or heavily vegetated land surfaces (Crow and Kustas, 2005). The singlesource nature of the variational approach hampers the physical interpretation of the retrieved CHN (Crow and Kustas, 2005). Consequently, the algorithm was modiﬁed to incorporate the two-source model concept by dividing the EF into soil and canopy components (Caparrini et al., 2004b). The impact of precipitation on lE was explicitly incorporated using the antecedent precipitation index (API) (Caparrini et al., 2004a): arctanðK$APIÞ (18.34) p K is another parameter that must be optimized using the variational approach. These improvements add more parameters to be solved from the satellite Ts observations. However, incorporating Equation (18.34) does not change the bulk transfer model of H and depends on Ts Ta ; therefore, the algorithm is more accurate for dry and lightly vegetated sites where Ts Ta is relatively large, and the sensitivity of the algorithm to errors of Ts or Ta is relatively small. The algorithm’s requirement for accurate estimates of both Ta and Ts at pixel scale hampers its wide application. Recently, satellite-based remotely sensed Ts was assimilated into a coupled atmosphere/ land global data assimilation system (Jang et al., 2010), with explicit accounting for biases in the model state. However, some studies have reported that Ts assimilation does not substantially improve lE simulations (Bosilovich et al., 2007). One likely reason for this lack of improvement is the fact that the satellite-derived value of Ts represents only a surface skin temperature (Crow and Wood, 2003; Reichle, 2008), whereas the land-surface model uses the temperature of a surface layer, whose thickness is approximately 5-20 cm for various models (Tsuang et al., 2009). EF ¼ a þ b

18.4. OBSERVATIONS FOR ALGORITHM CALIBRATION AND VALIDATION Estimating lE from satellite-derived instantaneous variables requires instantaneous observations to calibrate and validate the methods. Currently, observations collected by the eddy covariance technique (EC), energy balance Bowen ratio (BR) tower systems, and scintillometer systems meet this requirement. The measurements are brieﬂy discussed here.

18.4.1. Eddy Covariance Technique (EC) The EC technique measures H and lE through statistical covariance between the heat and moisture variation and vertical velocity using rapid response sensors at frequencies that are typically equal to or greater than 10 Hz (Figure 18.11). It was ﬁrst developed in the 1950s by scientists from CSIRO in Australia (Garratt and Hicks, 1990; Hogstrom and Bergstrom, 1996). Now, it is regarded as the best method to directly measure H and lE, and it is widely accepted in many important boundary-layer experiments (Aubinet et al., 1999; Baldocchi et al., 1996, 2001; Wilson et al., 2002), in particular FLUXNET (Figure 18.12). The typical error of lE is about 5-20% or 20-50 Wm2 (Foken, 2008; Vickers et al., 2010). The EC method suffers from an energy closure problem, that is, H þ lE < Rn G. If the energy closure ratio is deﬁned as R ¼ ðH þ lEÞ=ðRn GÞ, it has values about 0.8 (Wilson et al., 2002). See also Figure 18.5. Several reasons for this energy closure problem have been reported and corrected (Foken, 2008; Foken et al., 2006), including averaging and coordinate rotation, and coordinate systems (Finnigan, 2004; Finnigan et al., 2003; Fuehrer and Friehe, 2002; Gockede et al., 2008; Massman and Lee, 2002; Mauder et al., 2008). These corrections substantially increase lE and H estimates and improve energy closure ratio R

18.4. OBSERVATIONS FOR ALGORITHM CALIBRATION AND VALIDATION

577

FIGURE 18.11 Multiple eddy covariance systems were installed at the Southern Great Plains Central Facility to collect data and compare latent and sensible heat ﬂuxes. This photo was downloaded from http://www.ﬂickr.com/photos/ armgov/4621337701/in/photostream.

(Finnigan et al., 2003; Kanda et al., 2004; Oncley et al., 2007), but the closure ratio R is still less than unit after these corrections. Foken (2008) argues that the eddy covariance technique can only measure small eddies, but large eddies in the lower boundary layer provide an important contribution to the energy balancedalthough they do not touch the surface or are steady state and therefore cannot be measured with the eddy covariance method. Given our current (limited) understanding of the nature of the energy imbalance (Foken, 2008), it may be better to preserve the Bowen-ratio and close the energy balance on a larger time scale (Wohlfahrt et al., 2009).

18.4.2. The Energy Balance Bowen Ratio Method (BR) The energy balance Bowen ratio (BR) method uses simultaneous measurements of vertical air temperature and humidity gradients

to partition the energy balance (Figure 18.13) (Bowen, 1926). Under moist conditions, the BR approach for determining lE from plant communities can provide good results, but this method may be less accurate under very dry conditions or with considerable advection of energy in moist conditions (Angus and Watts, 1984). The primary challenge in implementing the BR method is the difﬁculty in measuring the small gradients of temperature and humidity over surfaces with efﬁcient turbulent transfer, especially forests, irrigated croplands, and grasslands. There are several assumptions or requirements for using the BR method (Angus and Watts, 1984; Kanemasu et al., 1992). The ﬁrst is that the turbulent transfer coefﬁcients for heat and water vapor are identical; this assumption is known to hold for conditions that are not too far from neutral, but it is not valid in very strong lapse (or inversion) conditions (Angus and Watts, 1984; Blad

578

18. TERRESTRIAL EVAPOTRANSPIRATION

FIGURE 18.12 A map of global FLUXNET sites.

and Rosenber, 1974). The two levels at which the temperature and humidity are measured must be within a constant-ﬂux layer. The second requirement for the BR method is that the surface energy must be closed; this requirement can be met for homogeneous surfaces or periods that are daily or longer. The components of the radiation and ground heat ﬂuxes measured in BR systems are point measurements, especially for the upward radiation components. However, the turbulent ﬂuxes related to the landscape, are affected by its heterogeneities, and fetch in the upwind direction when the air ﬂow over the surface is extensive (at least 100 times the maximum height of the measurement) (Wieringa, 1993).

18.4.3. The Scintillometer Method One important issue with the use of EC and BR measurements to calibrate and evaluate satellite-derived values of lE is the mismatch between the estimate scales. EC and BR measurements usually represent a small scale of several hundreds of meters, including the upwind fetch; however, satellite lE estimates usually represent pixel averages covering kilometers, which may produce a substantial difference in the estimates, even when both estimates are accurate (Li et al., 2008a; McCabe and Wood, 2006). This issue has been addressed using scintillometers to take the measurements (Figure 18.14). Recently, the use of scintillometers to determine H and lE has become widely

18.5. CONCLUSIONS AND DISCUSSION

579

FIGURE 18.13 An energy balance Bowen ratio (EBBR) system installed at the Southern Great Plains. Flux estimates are calculated from observations of net radiation, soil surface heat ﬂux, and the vertical gradients of temperature and relative humidity. The photo was downloaded from http://www.ﬂickr.com/photos/armgov/4857704844.

accepted because of its ability to quantify energy distributions at the landscape scale. They can calculate H (Moene et al., 2009; Solignac et al., 2009) over distances of several kilometers. Scintillometers are cost-effective and simple to operate (De Bruin, 2009). However, scintillometry does not provide a direct measurement of lE and its sign, (i.e., positive or negative) (Lagouarde et al., 2002). Scintillometry relies on the validity of MOST for calculating surface ﬂuxes (de Bruin et al., 1993; Solignac et al., 2009). Zhang et al. (2010b) showed that the overestimation of H using scintillometry (Chehbouni et al., 2000; Hoedjes et al., 2007; Lagouarde et al., 2002; Von Randow et al., 2008) is associated with a higher frictional velocity estimation by MOST. Furthermore, studies have reported signiﬁcant differences of up to 21% between

six Kipp & Zonen large-aperture scintillometers (Kleissl et al., 2008, 2009).

18.5. CONCLUSIONS AND DISCUSSION This chapter has discussed the basic theories for measuring and estimating lE, including the MOST and the Penman-Monteith equation, and reviewed how the theories have been applied to the satellite remote-sensing ﬁeld. The MOST and Penman-Monteith equation methods depend on turbulence; therefore, they are partly empirical, and assumptions are needed. Further parameterizations and assumptions are required to relate the satellite-derived land surface variables to lE because satellites do not supply direct measurements of lE. No substantial difference exists

580

18. TERRESTRIAL EVAPOTRANSPIRATION

(a)

(b)

(c)

FIGURE 18.14 Layout of the deployed large-aperture scintillometer and two eddy covariance systems in the experimental site near Las Cruces, New Mexico. (Zeweldi et al., 2010)

between the two formulas because the PenmanMonteith equation was derived from the MOST. In practical applications, however, there is a large difference between the two types of methods. MOST-type methods use Ts eTa; therefore, they require accurate estimates of Ts and Ta. This sensitivity is substantially reduced using the PenmanMonteith-type methods that require the available energy and stomatal conductance, which are usually parameterized as a function of the vegetation index or leaf area index. Satellite instantaneous retrieval of Ts, which is a key parameter that controls both available

energy and lE, is available only under clear sky conditions and for limited periods. The data assimilation method can be used to ﬁll temporal and spatial gaps. As a result, data assimilation is a useful tool for integrating satellite-derived instantaneous lE estimates over daily, monthly, or longer periods. Obtaining satellite-derived instantaneous value of lE requires instantaneous observations to calibrate and validate the methods. Currently, observations collected using the eddy covariance technique (EC), the energy balance Bowen Ratio (BR) system, and the

581

REFERENCES

scintillometer system meet this requirement. The EC method directly measures the turbulence and uses a statistical method to calculate lE; however, it usually underestimates the value. The BR technique uses point measurements of the available energy (surface net radiation minus soil heat ﬂux) and the observed Bowen ratio that is affected by the upwind fetch. This approach makes the energy balance requirement of the BR technique a problem because of the different scales of the available energy and the Bowen ratio. An important issue with using EC or BR measurements for calibrating and evaluating satellite-derived lE values is the mismatch in the estimate scales. EC and BR measurements usually are of a smaller scale than that of satellite lE retrievals (Li et al., 2008b; McCabe and Wood, 2006). This issue has been addressed using scintillometer measurements because scintillometers can quantify H and lE at a scale of several kilometers (Solignac et al., 2009). Signiﬁcant progress has been made toward the satellite derivation of lE in recent years, with experiments showing systematic agreement between ground-based estimates and aircraft measurements over several test areas (French et al., 2005; Kustas et al., 2005; McCabe and Wood, 2006; Su et al., 2005). Evaluation studies have shown that satellite-derived lE values have accuracies of approximately 15-30% (Kalma et al., 2008), whereas the accuracy of EC measurements is 5-20% (Foken, 2008). A number of methods require ancillary data to obtain the spatial variability of lE, for example, near-surface air temperature, and wind speed. This requirement hinders their application. Methods have been provided to reduce the need for ancillary data. Some methods rely solely on satellite-derived variables. Other methods have attempted to reduce the sensitivity of the method to the ancillary data, such that the ancillary data can be interpolated from weather station observations without substantially reducing their accuracy. The

model’s accuracy is usually the ﬁrst consideration in choosing a method. However, the ancillary data requirement and the method’s suitability over different conditions are also considerations when using satellite-based remote methods. The balance of the accuracy of the methods with their capability for practical application is also an issue to be considered in evaluating satellite-based remote-sensing methods for lE. Simple empirical methods are of comparable accuracy to the more complicated models, as demonstrated by comparisons between various global lE models (Jiménez et al., 2010; Kalma et al., 2008). The evaluation and comparison of different methods remains signiﬁcant issues. Currently, most existing methods are developed and calibrated using local data. The capability of these methods to describe other land-cover types and climates must be evaluated. The lack of standard reference data also limits the critical evaluation of remote-sensing methods. Satellite remote sensing has an obvious advantage for monitoring spatial variability. However, the ability of satellite-based remote sensing to provide long-term stable estimates of lE is controversial. Most satellite-based remote-sensing methods for lE are limited to their ancillary data requirement; thus, they are not applicable for obtaining long-term estimates.

ACKNOWLEDGEMENTS This study is funded by the National Basic Research Program of China (2012CB955302) and the National Natural Science Foundation of China (41175126).

References Agam, N., et al. (2010). Application of the Priestley-Taylor approach in a two-source surface energy balance model. Journal of Hydrometeorology, 11(1), 185e198. Allen, R. G., et al. (2007a). Satellite-based energy balance for mapping evapotranspiration with internalized

582

18. TERRESTRIAL EVAPOTRANSPIRATION

calibration (METRIC)dApplications. Journal of Irrigation and Drainage Engineering-Asce, 133(4), 395e406. Allen, R. G., Tasumi, M., & Trezza, R. (2007b). Satellitebased energy balance for mapping evapotranspiration with internalized calibration (METRIC)dModel. Journal of Irrigation and Drainage Engineering-Asce, 133(4), 380e394. Alves, I., & Pereira, L. S. (2000). Modelling surface resistance from climatic variables? Agricultural Water Management, 42(3), 371e385. Anderson, M. C., Norman, J. M., Diak, G. R., Kustas, W. P., & Mecikalski, J. R. (1997). A two-source time-integrated model for estimating surface ﬂuxes using thermal infrared remote sensing. Remote Sensing of Environment, 60(2), 195e216. Anderson, M. C., et al. (2008). A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy ﬂuxes from ﬁeld to regional scales. Remote Sensing of Environment, 112(12), 4227e4241. Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., & Kustas, W. P. (2007a). A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 1. Model formulation. Journal of Geophysical Research-Atmospheres, 112(D10), D10117. Anderson, M. C., Norman, J. M., Mecikalski, J. R., Otkin, J. A., & Kustas, W. P. (2007b). A climatological study of evapotranspiration and moisture stress across the continental United States based on thermal remote sensing: 2. Surface moisture climatology. Journal of Geophysical Research-Atmospheres, 112(D11), D11112. Anderson, M. C., et al. (2004). A multiscale remote sensing model for disaggregating regional ﬂuxes to micrometeorological scales. Journal of Hydrometeorology, 5(2), 343e363. Anderson, R. G., & Goulden, M. L. (2009). A mobile platform to constrain regional estimates of evapotranspiration. Agricultural and Forest Meteorology, 149(5), 771e782. Angus, D. E., & Watts, P. J. (1984). EvapotranspirationdHow good is the Bowen ratio method? Agricultural Water Management, 8(1-3), 133e150. Aubinet, M., et al. (1999). Estimates of the annual net carbon and water exchange of forests: The EUROFLUX Methodology. Advances in Ecological Research. Academic Press. 113e175. Baldocchi, D., et al. (2001). FLUXNET: A new tool to study the temporal and spatial variability of ecosystem scale carbon dioxide, water vapor, and energy ﬂux densities. Bulletin of the American Meteorological Society, 82(11), 2415e2434. Baldocchi, D., Valentini, R., Running, S., Oechel, W., & Dahlman, R. (1996). Strategies for measuring and

modelling carbon dioxide and water vapour ﬂuxes over terrestrial ecosystems. Global Change Biology, 2(3), 159e168. Baroncini, F., Castelli, F., Caparrini, F., & Ruffo, S. (2008). A dynamic cloud masking and ﬁltering algorithm for MSG retrieval of land surface temperature. International Journal of Remote Sensing, 29(12), 3365e3382. Bastiaanssen, W. G. M., Menenti, M., Feddes, R. A., & Holtslag, A. A. M. (1998a). A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation. Journal of Hydrology, 213(1-4), 198e212. Bastiaanssen, W. G. M., et al. (1998b). A remote sensing surface energy balance algorithm for land (SEBAL)-2. Validation. Journal of Hydrology, 213(1-4), 213e229. Batra, N., Islam, S., Venturini, V., Bisht, G., & Jiang, J. (2006). Estimation and comparison of evapotranspiration from MODIS and AVHRR sensors for clear sky days over the Southern Great Plains. Remote Sensing of Environment, 103(1), 1e15. Becker, F., & Choudhury, B. J. (1988). Relative sensitivity of normalized difference vegetation index (NDVI) and microwave polarization difference index (MPDI) for vegetation and desertiﬁcation monitoring. Remote Sensing of Environment, 24(2), 297e311. Beer, C., et al. (2010). Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate. Science, 329(5993), 834e838. Beven, K. (1979). Sensitivity analysis of the PenmanMonteith Actual Evapotranspiration Estimates. Journal of Hydrology, 44(3-4), 169e190. Blümel, K. (1999). A simple formula for estimation of the roughness length for heat transfer over partly vegetated surfaces. Journal of Applied Meteorology, 38(6), 814e829. Blad, B. L., & Rosenber, N. J. (1974). Lysimetric calibration of Bowen ratio-energy balance method for evapotranspiration estimation in Central Great Plains. Journal of Applied Meteorology, 13(2), 227e236. Boni, G., Castelli, F., & Entekhabi, D. (2001a). Sampling strategies and assimilation of ground temperature for the estimation of surface energy balance components. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 165e172. Boni, G., Entekhabi, D., & Castelli, F. (2001b). Land data assimilation with satellite measurements for the estimation of surface energy balance components and surface control on evaporation. Water Resources Research, 37(6), 1713e1722. Bosilovich, M. G., Radakovich, J. D., da Silva, A., Todling, R., & Verter, F. (2007). Skin temperature analysis and bias correction in a coupled land-atmosphere data assimilation system. Journal of the Meteorological Society of Japan, 85A, 205e228.

REFERENCES

Bowen, I. S. (1926). The ratio of heat losses by conduction and by evaporation from any water surface. Physical Review, 27(6), 779. Brutsaert, W., & Chen, D. (1995). Desorption and the two stages of drying of natural tallgrass prairie. Water Resources Research, 31(5), 1305e1313. Bucci, S. J., et al. (2008). Controls on stand transpiration and soil water utilization along a tree density gradient in a Neotropical savanna. Agricultural and Forest Meteorology, 148(6-7), 839e849. Burba, G., & Anderson, D. (2008). Introduction to the eddy covariance method: general guidelines and conventional workﬂow. available at. http://www.licor.com/env/ 2010/applications/eddy_covariance.jsp, p. 141. Businger, J. A., Wyngaard, J. C., Izumi, Y., & Bradley, E. F. (1971). Flux-proﬁle relationships in atmospheric surface layer. Journal of the Atmospheric Sciences, 28(2), 181e189. Caparrini, F., Castelli, F., & Entekhabi, D. (2003). Mapping of land atmosphere heat ﬂuxes and surface parameters with remote sensing data. Boundary-Layer Meteorology, 107(3), 605e633. Caparrini, F., Castelli, F., & Entekhabi, D. (2004a). Estimation of surface turbulent ﬂuxes through assimilation of radiometric surface temperature sequences. Journal of Hydrometeorology, 5(1), 145e159. Caparrini, F., Castelli, F., & Entekhabi, D. (2004b). Variational estimation of soil and vegetation turbulent transfer and heat ﬂux parameters from sequences of multisensor imagery. Water Resources Research, 40(12), W12515. Carlson, T. (2007). An overview of the “triangle method” for estimating surface evapotranspiration and soil moisture from satellite imagery. Sensors, 7(8), 1612e1629. Carlson, T. N., Gillies, R. R., & Schmugge, T. J. (1995). An interpretation of methodologies for indirect measurement of soil water content. Agricultural and Forest Meteorology, 77(3-4), 191e205. Caselles, V., Artigao, M. M., Hurtado, E., Coll, C., & Brasa, A. (1998). Mapping actual evapotranspiration by combining landsat TM and NOAA-AVHRR images: Application to the Barrax area, Albacete, Spain. Remote Sensing of Environment, 63(1), 1e10. Castelli, F., Entekhabi, D., & Caporali, E. (1999). Estimation of surface heat ﬂux and an index of soil moisture using adjoint-state surface energy balance. Water Resources Research, 35(10), 3115e3125. Chahine, M. T. (1992). The hydrological cycle and its inﬂuence on climate. Nature, 359(6394), 373e380. Chehbouni, A., Lo Seen, D., Njoku, E. G., & Monteny, B. M. (1996). Examination of the difference between radiative and aerodynamic surface temperatures over sparsely vegetated surfaces. Remote Sensing of Environment, 58(2), 177e186. Chehbouni, A., et al. (2000). Estimation of heat and momentum ﬂuxes over complex terrain using a large

583

aperture scintillometer. Agricultural and Forest Meteorology, 105(1-3), 215e226. Chen, D. Y., & Brutsaert, W. (1995). Diagnostics of land surface spatial variability and water vapor ﬂux. Journal of Geophysical Research-Atmospheres, 100(D12), 25595e25606. Choudhury, B. J. (1994). Synergism of multispectral satelliteobservations for estimating regional land-surface evaporation. Remote Sensing of Environment, 49(3), 264e274. Cleugh, H. A., Leuning, R., Mu, Q., & Running, S. W. (2007). Regional evaporation estimates from ﬂux tower and MODIS satellite data. Remote Sensing of Environment, 106(3), 285e304. Clothier, B. E., et al. (1986). Estimation of soil heat ﬂux from net radiation during the growth of alfalfa. Agricultural and Forest Meteorology, 37(4), 319e329. Crow, W., & Kustas, W. (2005). Utility of assimilating surface radiometric temperature observations for evaporative fraction and heat transfer coefﬁcient retrieval. BoundaryLayer Meteorology, 115(1), 105e130. Crow, W. T., & Wood, E. F. (2003). The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman ﬁltering: A case study based on ESTAR measurements during SGP97. Advances in Water Resources, 26(2), 137e149. De Bruin, H. A. R. (2009). Time to think: reﬂections of a prepensioned scintillometer researcher. Bulletin of the American Meteorological Society, 90(5), ES17eES26. de Bruin, H. A. R., Kohsiek, W., & Hurk, B. J. J. M. (1993). A veriﬁcation of some methods to determine the ﬂuxes of momentum, sensible heat, and water vapour using standard deviation and structure parameter of scalar meteorological quantities. Boundary-Layer Meteorology, 63(3), 231e257. Deardorff, J. W. (1978). Efﬁcient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation. Journal of Geophysical Research, 83(C4), 1889e1903. Dyer, A. J. (1974). A review of ﬂux-proﬁle relationships. Boundary-Layer Meteorology, 7(3), 363e372. Finnigan, J. J. (2004). A re-evaluation of long-term ﬂux measurement techniquesdPart II: Coordinate systems. Boundary-Layer Meteorology, 113(1), 1e41. Finnigan, J. J., Clement, R., Malhi, Y., Leuning, R., & Cleugh, H. A. (2003). A re-evaluation of long-term ﬂux measurement techniquesdPart I: Averaging and coordinate rotation. Boundary-Layer Meteorology, 107(1), 1e48. Fisher, J. B., Tu, K. P., & Baldocchi, D. D. (2008). Global estimates of the land-atmosphere water ﬂux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment, 112(3), 901e919. Foken, T. (2006). 50 years of the Monin-Obukhov similarity theory. Boundary-Layer Meteorology, 119(3), 431e447. Foken, T. (2008). The energy balance closure problem: An overview. Ecological Applications, 18(6), 1351e1367.

584

18. TERRESTRIAL EVAPOTRANSPIRATION

Foken, T., Wimmer, F., Mauder, M., Thomas, C., & Liebethal, C. (2006). Some aspects of the energy balance closure problem. Atmospheric Chemistry and Physics, 6, 4395e4402. French, A. N., et al. (2005). Surface energy ﬂuxes with the Advanced Spaceborne Thermal Emission and Reﬂection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote Sensing of Environment, 99(1-2), 55e65. Friedl, M. A. (2002). Forward and inverse modeling of land surface energy balance using surface temperature measurements. Remote Sensing of Environment, 79(2-3), 344e354. Fuehrer, P. L., & Friehe, C. A. (2002). Flux corrections revisited. Boundary-Layer Meteorology, 102(3), 415e457. Gao, Y. C., & Long, D. (2008). Intercomparison of remote sensing-based models for estimation of evapotranspiration and accuracy assessment based on SWAT. Hydrological Processes, 22(25), 4850e4869. Garratt, J. R., Hess, G. D., Physick, W. L., & Bougeault, P. (1996). The atmospheric boundary layerdAdvances in knowledge and application. Boundary-Layer Meteorology, 78(1-2), 9e37. Garratt, J. R., & Hicks, B. B. (1990). Micrometeorological and PBL experiments in Australia. Boundary-Layer Meteorology, 50(1), 11e29. Gillies, R. R., Kustas, W. P., & Humes, K. S. (1997). A veriﬁcation of the “triangle” method for obtaining surface soil water content and energy ﬂuxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface. International Journal of Remote Sensing, 18(15), 3145e3166. Glenn, E. P., Huete, A. R., Nagler, P. L., Hirschboeck, K. K., & Brown, P. (2007). Integrating remote sensing and ground methods to estimate evapotranspiration. Critical Reviews in Plant Sciences, 26(3), 139e168. Gockede, M., et al. (2008). Quality control of CarboEurope ﬂux datadPart 1: Coupling footprint analyses with ﬂux data quality assessment to evaluate sites in forest ecosystems. Biogeosciences, 5(2), 433e450. Gordon, L. J., et al. (2005). Human modiﬁcation of global water vapor ﬂows from the land surface. Proceedings of the National Academy of Sciences of the United States of America, 102(21), 7612e7617. Goward, S. N., Waring, R. H., Dye, D. G., & Yang, J. L. (1994). Ecological Remote-Sensing at Otter - Satellite Macroscale Observations. Ecological Applications, 4(2), 322e343. Hall, F. G., Huemmrich, K. F., Goetz, S. J., Sellers, P. J., & Nickeson, J. E. (1992). Satellite remote sensing of surface energy balance: success, failures, and unresolved issues in FIFE. Journal of Geophysical Research, 97(D17), 19061e19089. Hoedjes, J. C. B., Chehbouni, A., Ezzahar, J., Escadafal, R., & De Bruin, H. A. R. (2007). Comparison of large aperture

scintillometer and eddy covariance measurements: Can thermal infrared data be used to capture footprintinduced differences? Journal of Hydrometeorology, 8(2), 144e159. Hogstrom, U. (1988). Non-dimensional wind and temperature proﬁles in the atmospheric surface-layerda reevaluation. Boundary-Layer Meteorology, 42(1-2), 55e78. Hogstrom, U., & Bergstrom, H. (1996). Organized turbulence structures in the near-neutral atmospheric surface layer. Journal of the Atmospheric Sciences, 53(17), 2452e2464. Iwasaki, H., Saito, H., Kuwao, K., Maximov, T. C., & Hasegawa, S. (2010). Forest decline caused by high soil water conditions in a permafrost region. Hydrology and Earth System Sciences, 14(2), 301e307. Jacobsen, A. (1999). Estimation of the soil heat ﬂux/net radiation ratio based on spectral vegetation indexes in high-latitude Arctic areas. International Journal of Remote Sensing, 20(2), 445e461. Jang, K., et al. (2010). Mapping evapotranspiration using MODIS and MM5 four-dimensional data assimilation. Remote Sensing of Environment, 114(3), 657e673. Jiang, L., & Islam, S. (2001). Estimation of surface evaporation map over Southern Great Plains using remote sensing data. Water Resources Research, 37(2), 329e340. Jiang, L., & Islam, S. (2003). An intercomparison of regional latent heat ﬂux estimation using remote sensing data. International Journal of Remote Sensing, 24(11), 2221e2236. Jiménez, C., et al. (2010). Global inter-comparison of 12 land surface heat ﬂux estimates. Journal of Geophysical Research, 116. D02102, doi: 10.1029/2010ID014545. Jung, M., Reichstein, M., & Bondeau, A. (2009). Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model. Biogeosciences, 6(10), 2001e2013. Jung, M., et al. (2010). Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature, 467(7318), 951e954. Kalma, J. D., McVicar, T. R., & McCabe, M. F. (2008). Estimating land surface evaporation: a review of methods using remotely sensed surface temperature data. Surveys in Geophysics, 29(4-5), 421e469. Kanda, M., Inagaki, A., Letzel, M. O., Raasch, S., & Watanabe, T. (2004). LES study of the energy imbalance problem with Eddy covariance ﬂuxes. Boundary-Layer Meteorology, 110(3), 381e404. Kanemasu, E. T., et al. (1992). Surface ﬂux measurements in FIFEdan overview. Journal of Geophysical Research-Atmospheres, 97(D17), 18547e18555. Kim, S., & Kim, H. S. (2008). Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. Journal of Hydrology, 351(3-4), 299e317.

REFERENCES

Kleissl, J., et al. (2008). Large aperture scintillometer intercomparison study. Boundary-Layer Meteorology, 128(1), 133e150. Kleissl, J., Watts, C. J., Rodriguez, J. C., Naif, S., & Vivoni, E. R. (2009). Scintillometer intercomparison study-continued. Boundary-Layer Meteorology, 130(3), 437e443. Kustas, W., & Anderson, M. (2009). Advances in thermal infrared remote sensing for land surface modeling. Agricultural and Forest Meteorology, 149(12), 2071e2081. Kustas, W. P., & Daughtry, C. S. T. (1990). Estimation of the soil heat-ﬂux net-radiation ratio from spectral data. Agricultural and Forest Meteorology, 49(3), 205e223. Kustas, W. P., Daughtry, C. S. T., & Van Oevelen, P. J. (1993). Analytical treatment of the relationships between soil heat ﬂux/net radiation ratio and vegetation indices. Remote Sensing of Environment, 46(3), 319e330. Kustas, W. P., Hatﬁeld, J. L., & Prueger, J. H. (2005). The soil moisture-atmosphere coupling experiment (SMACEX): Background, hydrometeorological conditions, and preliminary ﬁndings. Journal of Hydrometeorology, 6(6), 791e804. Kustas, W. P., & Norman, J. M. (1999). Evaluation of soil and vegetation heat ﬂux predictions using a simple twosource model with radiometric temperatures for partial canopy cover. Agricultural and Forest Meteorology, 94(1), 13e29. Lagouarde, J. P., Bonnefond, J. M., Kerr, Y. H., McAneney, K. J., & Irvine, M. (2002). Integrated sensible heat ﬂux measurements of a two-surface composite landscape using scintillometry. Boundary-Layer Meteorology, 105(1), 5e35. Leuning, R., Zhang, Y. Q., Rajaud, A., Cleugh, H., & Tu, K. (2008). A simple surface conductance model to estimate regional evaporation using MODIS leaf area index and the Penman-Monteith equation. Water Resources Research, 44(10), W10419. Lhomme, J. P., & Chehbouni, A. (1999). Comments on dualsource vegetation-atmosphere transfer models. Agricultural and Forest Meteorology, 94(3-4), 269e273. Lhomme, J. P., Chehbouni, A., & Monteny, B. (2000). Sensible heat ﬂux-radiometric surface temperature relationship over sparse vegetation: Parameterizing B-1. Boundary-Layer Meteorology, 97(3), 431e457. Li, F., et al. (2006). Comparing the utility of microwave and thermal remote-sensing constraints in two-source energy balance modeling over an agricultural landscape. Remote Sensing of Environment, 101(3), 315e328. Li, F. Q., Kustas, W. P., Anderson, M. C., Prueger, J. H., & Scott, R. L. (2008a). Effect of remote sensing spatial resolution on interpreting tower-based ﬂux observations. Remote Sensing of Environment, 112(2), 337e349.

585

Li, R., Min, Q. L., & Lin, B. (2009a). Estimation of evapotranspiration in a mid-latitude forest using the Microwave Emissivity Difference Vegetation Index (EDVI). Remote Sensing of Environment, 113(9), 2011e2018. Li, S., et al. (2008b). A comparison of three methods for determining vineyard evapotranspiration in the and desert regions of northwest China. Hydrological Processes, 22(23), 4554e4564. Li, Z. L., et al. (2009b). A review of current methodologies for regional evapotranspiration estimation from remotely sensed data. Sensors, 9(5), 3801e3853. Liu, S. M., Lu, L., Mao, D., & Jia, L. (2007). Evaluating parameterizations of aerodynamic resistance to heat transfer using ﬁeld measurements. Hydrology and Earth System Sciences, 11(2), 769e783. Lu, H., Raupach, M. R., McVicar, T. R., & Barrett, D. J. (2003). Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series. Remote Sensing of Environment, 86(1), 1e18. Lu, X. L., & Zhuang, Q. L. (2010). Evaluating evapotranspiration and water-use efﬁciency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data. Remote Sensing of Environment, 114(9), 1924e1939. Lumley, J. L., & Yaglom, A. M. (2001). A century of turbulence. Flow Turbulence and Combustion, 66(3), 241e286. Mallick, K., Bhattacharya, B. K., & Patel, N. K. (2009). Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI. Agricultural and Forest Meteorology, 149(8), 1327e1342. Margolis, H. A., & Ryan, M. G. (1997). A physiological basis for biosphere-atmosphere interactions in the boreal forest: an overview. Tree Physiology, 17(8-9), 491e499. Massman, W. J., & Lee, X. (2002). Eddy covariance ﬂux corrections and uncertainties in long-term studies of carbon and energy exchanges. Agricultural and Forest Meteorology, 113(1-4), 121e144. Mauder, M., et al. (2008). Quality control of CarboEurope ﬂux datadPart 2: Inter-comparison of eddy-covariance software. Biogeosciences, 5(2), 451e462. McCabe, M. F., & Wood, E. F. (2006). Scale inﬂuences on the remote estimation of evapotranspiration using multiple satellite sensors. Remote Sensing of Environment, 105(4), 271e285. McVicar, T. R., & Jupp, D. L. B. (2002). Using covariates to spatially interpolate moisture availability in the MurrayDarling Basin: A novel use of remotely sensed data. Remote Sensing of Environment, 79(2-3), 199e212. Mecikalski, J. R., Diak, G. R., Anderson, M. C., & Norman, J. M. (1999). Estimating ﬂuxes on continental scales using remotely sensed data in an atmospheric and exchange model. Journal of Applied Meteorology, 38(9), 1352e1369.

586

18. TERRESTRIAL EVAPOTRANSPIRATION

Min, Q. L., & Lin, B. (2006). Remote sensing of evapotranspiration and carbon uptake at Harvard Forest. Remote Sensing of Environment, 100(3), 379e387. Moene, A. F., Beyrich, F., & Hartogensis, O. K. (2009). Developments in Scintillometry. Bulletin of the American Meteorological Society, 90(5), 694e698. Monin, A. S., & Obukhov, A. M. (1954). Basic laws of turbulent mixing in the ground layer of the atmosphere (in Russian). Tr. Geoﬁz. Inst. Akad. Nauk SSSR, 151, 163e187. Monteith, J. L. (1972). Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9, 747e766. Mu, Q., Heinsch, F. A., Zhao, M., & Running, S. W. (2007). Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sensing of Environment, 111(4), 519e536. Murray, T., & Verhoef, A. (2007). Moving towards a more mechanistic approach in the determination of soil heat ﬂux from remote measurementsdII. Diurnal shape of soil heat ﬂux. Agricultural and Forest Meteorology, 147(1-2), 88e97. Nemani, R., & Running, S. (1997). Land cover characterization using multitemporal red, near-IR, and thermal-IR data from NOAA/AVHRR. Ecological Applications, 7(1), 79e90. Nemani, R. R., et al. (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625), 1560e1563. Nishida, K., Nemani, R. R., Running, S. W., & Glassy, J. M. (2003). An operational remote sensing algorithm of land surface evaporation. Journal of Geophysical Research, 108(D9), 4270. Norman, J. M., et al. (2003). Remote sensing of surface energy ﬂuxes at 10(1)-m pixel resolutions. Water Resources Research, 39(8), 1221. Norman, J. M., Kustas, W. P., & Humes, K. S. (1995). Source approach for estimating soil and vegetation energy ﬂuxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology, 77(3-4), 263e293. Norman, J. M., Kustas, W. P., Prueger, J. H., & Diak, G. R. (2000). Surface ﬂux estimation using radiometric temperature: A dual temperature-difference method to minimize measurement errors. Water Resources Research, 36(8), 2263e2274. Oki, T., & Kanae, S. (2006). Global hydrological cycles and world water resources. Science, 313(5790), 1068e1072. Oncley, S. P., et al. (2007). The Energy Balance Experiment EBEX-2000. Part I: Overview and energy balance. Boundary-Layer Meteorology, 123(1), 1e28. Penman, H. L. (1948). Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 193(1032), 120e145. Petropoulos, G., Carlson, T. N., Wooster, M. J., & Islam, S. (2009). A review of T-s/VI remote sensing based

methods for the retrieval of land surface energy ﬂuxes and soil surface moisture. Progress in Physical Geography, 33(2), 224e250. Price, J. C. (1990). Using spatial context in satellite data to infer regional scale evapotranspiration. IEEE Transactions on Geoscience and Remote Sensing, 28(5), 940e948. Priestley, C. H. B., & Taylor, R. J. (1972). On the assessment of surface heat ﬂux and evaporation using large-scale parameters. Monthly Weather Review, 100(2), 81e92. Prince, S. D., Goetz, S. J., Dubayah, R. O., Czajkowski, K. P., & Thawley, M. (1998). Inference of surface and air temperature, atmospheric precipitable water and vapor pressure deﬁcit using Advanced Very High-Resolution Radiometer satellite observations: comparison with ﬁeld observations. Journal of Hydrology, 212-213, 230e249. Rana, G., & Katerji, N. (2000). Measurement and estimation of actual evapotranspiration in the ﬁeld under Mediterranean climate: a review. European Journal of Agronomy, 13(2-3), 125e153. Reichle, R. H. (2008). Data assimilation methods in the Earth sciences. Advances in Water Resources, 31(11), 1411e1418. Roerink, G. J., Su, Z., & Menenti, M. (2000). S-SEBI: A simple remote sensing algorithm to estimate the surface energy balance. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 25(2), 147e157. Schuttemeyer, D., Schillings, C., Moene, A. F., & De Bruin, H. A. R. (2007). Satellite-based actual evapotranspiration over drying semiarid terrain in West Africa. Journal of Applied Meteorology and Climatology, 46(1), 97e111. Sellers, P. J., et al. (1997). Modeling the exchanges of energy, water, and carbon between continents and the atmosphere. Science, 275(5299), 502e509. Seneviratne, S. I., et al. (2010). Investigating soil moistureclimate interactions in a changing climate: A review. Earth-Science Reviews, 99(3-4), 125e161. Shefﬁeld, J., Wood, E. F., & Munoz-Arriola, F. (2010). Longterm regional estimates of evapotranspiration for Mexico based on downscaled ISCCP data. Journal of Hydrometeorology, 11(2), 253e275. Shukla, J., & Mintz, Y. (1982). Inﬂuence of land-aurface wvapotranspiration on the Earth’s xlimate. Science, 215(4539), 1498e1501. Shuttleworth, W. J. (1991). Insight from large-scale observational studies of land/atmosphere interactions. Surveys in Geophysics, 12(1), 3e30. Shuttleworth, W. J. (2007). Putting the “vap” into evaporation. Hydrology and Earth System Sciences, 11(1), 210e244. Snyder, R. L., & K. T. Paw, U (2002). Penman-Monteith Equation Derivation. available at. http://biomet.ucdavis. edu/Evapotranspiration/PMDerivation/PMD.htm. Sobrino, J. A., Gomez, M., Jimenez-Munoz, C., & Olioso, A. (2007). Application of a simple algorithm to estimate

REFERENCES

daily evapotranspiration from NOAA-AVHRR images for the Iberian Peninsula. Remote Sensing of Environment, 110(2), 139e148. Solignac, P. A., et al. (2009). Uncertainty analysis of computational methods for deriving sensible heat ﬂux values from scintillometer measurements. Atmospheric Measurement Techniques, 2(2), 741e753. Su, H., McCabe, M. F., Wood, E. F., Su, Z., & Prueger, J. H. (2005). Modeling evapotranspiration during SMACEX: Comparing two approaches for localand regional-scale prediction. Journal of Hydrometeorology, 6(6), 910e922. Su, H., Wood, E. F., McCabe, M. F., & Su, Z. (2007). Evaluation of remotely sensed evapotranspiration over the CEOP EOP-1 reference sites. Journal of the Meteorological Society of Japan, 85A, 439e459. Su, Z. (2002). The Surface Energy Balance System (SEBS) for estimation of turbulent heat ﬂuxes. Hydrology and Earth System Sciences, 6(1), 85e99. Su, Z., Schmugge, T., Kustas, W. P., & Massman, W. J. (2001). An evaluation of two models for estimation of the roughness height for heat transfer between the land surface and the atmosphere. Journal of Applied Meteorology, 40(11), 1933e1951. Sun, J. L., Massman, W., & Grantz, D. A. (1999). Aerodynamic variables in the bulk formulation of turbulent ﬂuxes. Boundary-Layer Meteorology, 91(1), 109e125. Suyker, A. E., & Verma, S. B. (2008). Interannual water vapor and energy exchange in an irrigated maize-based agroecosystem. Agricultural and Forest Meteorology, 148(3), 417e427. Tang, R., Li, Z.-L., & Tang, B. (2010). An application of the Ts-VI triangle method with enhanced edges determination for evapotranspiration estimation from MODIS data in arid and semi-arid regions: Implementation and validation. Remote Sensing of Environment, 114(3), 540e551. Teixeira, A., Bastiaanssen, W. G. M., Ahmad, M. D., & Bos, M. G. (2009a). Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle Sao Francisco River basin, Brazil Part A: Calibration and validation. Agricultural and Forest Meteorology, 149(3-4), 462e476. Teixeira, A., Bastiaanssen, W. G. M., Ahmad, M. D., & Bos, M. G. (2009b). Reviewing SEBAL input parameters for assessing evapotranspiration and water productivity for the Low-Middle Sao Francisco River basin, Brazil Part B: Application to the regional scale. Agricultural and Forest Meteorology, 149(3-4), 477e490. Timmermans, W. J., Kustas, W. P., Anderson, M. C., & French, A. N. (2007). An intercomparison of the surface energy balance algorithm for land (SEBAL) and the twosource energy balance (TSEB) modeling schemes. Remote Sensing of Environment, 108(4), 369e384.

587

Tittebrand, A., Schwiebus, A., & Berger, F. H. (2005). The inﬂuence of land surface parameters on energy ﬂux densities derived from remote sensing data. Meteorologische Zeitschrift, 14(2), 227e236. Trenberth, K. E., Fasullo, J. T., & Kiehl, J. (2009). Earth’s global energy budget. Bulletin of the American Meteorological Society, 90(3), 311e324. Trenberth, K. E., Fasullo, J. T., & Mackaro, J. (2011). Atmospheric moisture transports from ocean to land and global energy ﬂows in reanalyses. Journal of Climate, 24(18), 4907e4924. Tsuang, B.-J., et al. (2009). A more accurate scheme for calculating Earth’s skin temperature. Climate Dynamics, 32(2), 251e272. van der Kwast, J., et al. (2009). Evaluation of the durface energy balance system (SEBS) applied to ASTER imagery with ﬂux-measurements at the SPARC 2004 site (Barrax, Spain). Hydrology and Earth System Sciences, 13(7), 1337e1347. Venturini, V., Islam, S., & Rodriguez, L. (2008). Estimation of evaporative fraction and evapotranspiration from MODIS products using a complementary based model. Remote Sensing of Environment, 112(1), 132e141. Verhoef, A., De Bruin, H. A. R., & Van Den Hurk, B. J. J. M. (1997). Some practical Notes on the parameter kB-1 for sparse vegetation. Journal of Applied Meteorology, 36(5), 560e572. Verhoef, A., van den Hurk, B. J. J. M., Jacobs, A. F. G., & Heusinkveld, B. G. (1996). Thermal soil properties for vineyard (EFEDA-I) and savanna (HAPEX-Sahel) sites. Agricultural and Forest Meteorology, 78(1-2), 1e18. Verstraeten, W. W., Veroustraete, F., & Feyen, J. (2005). Estimating evapotranspiration of European forests from NOAA-imagery at satellite overpass time: Towards an operational processing chain for integrated optical and thermal sensor data products. Remote Sensing of Environment, 96(2), 256e276. Vickers, D., Gockede, M., & Law, B. E. (2010). Uncertainty estimates for 1-h averaged turbulence ﬂuxes of carbon dioxide, latent heat and sensible heat. Tellus Series BChemical and Physical Meteorology, 62(2), 87e99. Von Randow, C., Kruijt, B., Holtslag, A. A. M., & de Oliveira, M. B. L. (2008). Exploring eddy-covariance and large-aperture scintillometer measurements in an Amazonian rain forest. Agricultural and Forest Meteorology, 148(4), 680e690. Wang, K., Dickinson, R. E., Wild, M., & Liang, S. (2010a). Evidence for decadal variation in global terrestrial evapotranspiration between 1982 and 2002: 1. Model development. Journal of Geophysical Research, 115(D20), D20112. Wang, K., Dickinson, R. E., Wild, M., & Liang, S. (2010b). Evidence for decadal variation in global terrestrial

588

18. TERRESTRIAL EVAPOTRANSPIRATION

evapotranspiration between 1982 and 2002: 2. Results. Journal of Geophysical Research, 115(D20), D20113. Wang, K., & Liang, S. (2008). An improved method for estimating global evapotranspiration based on satellite estimation of surface net radiation, vegetation index, temperature, and soil moisture. Journal of Hydrometeorology, 9(4), 712e727. Wang, K., et al. (2007a). Evaluation and improvement of the MODIS land surface temperature/emissivity products using ground-based measurements at a semi-desert site on the western Tibetan Plateau. International Journal of Remote Sensing, 28(11), 2549e2565. Wang, K. C., Li, Z. Q., & Cribb, M. (2006). Estimation of evaporative fraction from a combination of day and night land surface temperatures and NDVI: A new method to determine the Priestley-Taylor parameter. Remote Sensing of Environment, 102(3-4), 293e305. Wang, K. C., & Liang, S. L. (2009). Evaluation of ASTER and MODIS land surface temperature and emissivity products using long-term surface longwave radiation observations at SURFRAD sites. Remote Sensing of Environment, 113(7), 1556e1565. Wang, K. C., Wang, P., Li, Z. Q., Cribb, M., & Sparrow, M. (2007b). A simple method to estimate actual evapotranspiration from a combination of net radiation, vegetation index, and temperature. Journal of Geophysical Research-Atmospheres, 112(D15), D15107. Wieringa, J. (1980). A revaluation of the Kansas mast inﬂuence on measurements of stress and cup anemometer overspeeding. Boundary-Layer Meteorology, 18(4), 411e430. Wieringa, J. (1993). Representative roughness parameters for homogeneous terrain. Boundary-Layer Meteorology, 63(4), 323e363. Wilson, K., et al. (2002). Energy balance closure at FLUXNET sites. Agricultural and Forest Meteorology, 113(1-4), 223e243. Wohlfahrt, G., et al. (2009). On the consequences of the energy imbalance for calculating surface conductance to

water vapour. Agricultural and Forest Meteorology, 149(9), 1556e1559. Yan, H., & Shugart, H. H. (2010). An air relative-humiditybased evapotranspiration model from eddy covariance data. Journal of Geophysical Research-Atmospheres, 115, D16106. Yang, F. H., et al. (2006). Prediction of continental-scale evapotranspiration by combining MODIS and AmeriFlux data through support vector machine. IEEE Transactions on Geoscience and Remote Sensing, 44(11), 3452e3461. Yang, K., Chen, Y. Y., & Qin, J. (2009). Some practical notes on the land surface modeling in the Tibetan Plateau. Hydrology and Earth System Sciences, 13(5), 687e701. Yuan, W., et al. (2010). Impacts of precipitation seasonality and ecosystem types on evapotranspiration in the Yukon River Basin, Alaska. Water Resources Research, 46(2), W02514. Zeweldi, D. A., et al. (2010). Intercomparison of sensible heat ﬂux from large aperture scintillometer and eddy covariance methods: Field experiment over a homogeneous semi-arid region. Boundary-Layer Meteorology, 135(1), 151e159. Zhang, K., et al. (2009). Satellite based analysis of northern ET trends and associated changes in the regional water balance from 1983 to 2005. Journal of Hydrology, 379(1-2), 92e110. Zhang, K., Kimball, J. S., Nemani, R. R., & Running, S. W. (2010a). A continuous satellite-derived global record of land surface evapotranspiration from 1983 to 2006. Water Resources Research, 46(9), W09522. Zhang, X. D., Jia, X. H., Yang, J. Y., & Hu, L. B. (2010b). Evaluation of MOST functions and roughness length parameterization on sensible heat ﬂux measured by large aperture scintillometer over a corn ﬁeld. Agricultural and Forest Meteorology, 150(9), 1182e1191. Zhang, Y. Q., Chiew, F. H. S., Zhang, L., Leuning, R., & Cleugh, H. A. (2008). Estimating catchment evaporation and runoff using MODIS leaf area index and the Penman-Monteith equation. Water Resources Research, 44(10), W10420.

Terrestrial Evapotranspiration

Terrestrial Evapotranspiration

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