Estimating evapotranspiration from terrestrial ...

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International Journal of Digital Earth Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tjde20

Estimating evapotranspiration from terrestrial groundwater-dependent ecosystems using Landsat images a

a

b

Xihua Yang , Peter L. Smith , Tao Yu & Hailiang Gao

b

a

Department of Environment, Climate Change and Water, PO Box 3720, Parramatta, NSW, 2124, Sydney, Australia b

Institute of Remote Sensing Application, Chinese Academy of Sciences, Beijing, China Available online: 15 Jun 2010

To cite this article: Xihua Yang, Peter L. Smith, Tao Yu & Hailiang Gao (2011): Estimating evapotranspiration from terrestrial groundwater-dependent ecosystems using Landsat images, International Journal of Digital Earth, 4:2, 154-170 To link to this article: http://dx.doi.org/10.1080/17538947.2010.491561

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International Journal of Digital Earth, Vol. 4, No. 2, March 2011, 154170

Estimating evapotranspiration from terrestrial groundwater-dependent ecosystems using Landsat images Xihua Yanga*, Peter L. Smitha, Tao Yub and Hailiang Gaob a

Department of Environment, Climate Change and Water, PO Box 3720, Parramatta, NSW 2124, Sydney, Australia; bInstitute of Remote Sensing Application, Chinese Academy of Sciences, Beijing, China

Downloaded by [University of Sydney] at 02:19 24 May 2012

(Received 25 December 2009; final version received 4 May 2010) Understanding and mitigating against the impact of groundwater extraction on groundwater-dependent ecosystems (GDE) requires information of evapotranspiration (ET) of these ecosystems. In this pilot study, we tested two remotesensing methods, Surface Energy Balance Algorithms for Land (SEBAL) and Vegetation Index/Temperature Trapezoid (VITT), for ET estimation from terrestrial GDEs. Multi-temporal Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) images were used to derive vegetation indices and land surface temperatures for ET estimation. Radiative transfer model was used for atmospheric correction of the Landsat images. Field measurements were used to validate the remote sensing estimation of VI and surface temperature. Both methods have been implemented in a geographic information system (GIS) using automated scripts and ancillary GIS data for quality control process. Comparison of predicted ET by SEBAL to VITT model indicates relatively good agreement (R2 0.90) and promise for use in groundwater management. The average ET from woodland GDEs within the zone of influence of the pumping stations is in general lower than similar woodlands outside of the pumping area, particularly in summer seasons which demonstrates that the pumping regime has an impact on those GDEs. The study also demonstrates that even a simple physical ET model can provide useful information for groundwater management, and more broadly other applications in hydrologic modelling and digital earth studies. Keywords: remote sensing; evapotranspiration; surface energy balance; Landsat; groundwater-dependent ecosystems; digital earth, GIS

1. Introduction In recent years water availability has become an issue of global concern. Global water studies help to identify the areas of present and future water scarcity, and areas of potential water-related conflicts, as well as help set priorities for international financing of water projects (e.g. Acreman 2001). However, most of these studies have been limited to assessing if human water needs (domestic, industrial and agricultural) can be satisfied by the total renewable water resources in a country or a river basin. These studies often do not consider the water requirements of freshwater-dependent ecosystems or groundwater-dependent ecosystems (GDE) (Smakhtin 2007).

*Corresponding author. Email: [email protected] ISSN 1753-8947 print/ISSN 1753-8955 online # 2011 Taylor & Francis DOI: 10.1080/17538947.2010.491561 http://www.informaworld.com


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In many semi-arid and sub-humid areas, groundwater constitutes the primary water source that sustains human habitation, agriculture and riparian systems (Wadge and Archer 2003). In Australia, the driest inhabited continent on Earth, there is a strong recognition that we must learn to make better use of its water resources. Groundwater allocation is currently a contentious issue in Australia. Arguably, the main concern is how much water should be allocated to the environment (Murray et al. 2008). For example, a Water Management Act was introduced in the State of New South Wales (NSW), Australia in 2000 by the NSW Government as part of on-going major reforms of water resource management. A major component of this reform was the requirement under the Act to prepare Water Sharing Plans (WSP) for regulated and unregulated rivers as well as groundwater. An important focus of these WSP was to ensure that the water needs of ecological assets, such as rivers, wetlands and GDEs are identified, and the protection of the ecological integrity of these assets balanced with the socio-economic benefits of water extraction and use. As such, WSP provide the framework and rules for managing water shares and extraction under an access license. Groundwater is a relatively small but increasingly important component of water supply within NSW, and for some areas a major and critical component of urban water supplies. Across NSW there are 108 groundwater sources from which approximately 330 gigaliters of groundwater is extracted annually. This is roughly equivalent to 15% of the agricultural and urban water supply demand for the whole State. Eamus et al. (2006) classified GDEs on the basis of where and how the ecosystem accesses or utilises the groundwater resource. As such they classified GDEs into three broad classes: (1) aquifer and cave ecosystems, where stygofauna (groundwaterinhabiting organisms) reside within the groundwater resource; (2) all ecosystems dependent on the surface expression of groundwater; and (3) all ecosystems dependent on the subsurface presence of groundwater, often accessed via the capillary fringe. This type of classification is useful for management as it allows us to separate GDEs into readily recognisable ecosystem types where we can use similar techniques and approaches for the identification of the GDE and the assessment of ecological risk. For this study we are concentrating on identifying and assessing the ecological risk arising from existing groundwater management to class 3 or Terrestrial GDEs. In an attempt to obtain information on the terrestrial GDE distribution and water uses, the NSW Government has developed an assessment procedure which is based on an iterative, interdisciplinary, risk-based approach. One of the key assumptions of this approach is that if groundwater is within the potential rooting zone of these woodland species then those species are highly likely to be somewhat groundwater-dependent (Eamus et al. 2006). To test this approach, we have established a pilot project in the sub-humid woodlands in the lower Macquarie Groundwater Management Area (GWMA) of central western NSW near Narromine. The pilot study is to determine if the growth of tree species within the groundwater drawdown radius are different to those outside of these affected areas. If the growth patterns within in the zones are less than that of the trees outside of the drawdown radius then we can be confident that the groundwater pumping is having an effect on these ecosystems. Field-based techniques can be used for determining plant water use and plant stress, but these processes are very labour intensive and time consuming, thus not applicable for large


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areas. Additionally, they also have severe spatial limitations in that the measurements for one tree within a woodland patch are not readily transferable to woodland patches that may be some distance apart. We therefore propose that as an alternative to using field-based measurements of plant water use, we adopt remote sensing-based techniques using multi-temporal Landsat images and the application of Surface Energy Balance Algorithms for Land (SEBAL, Bastiaanssen et al. 1998a). SEBAL uses spectral radiances recorded by satellite-based sensors and meteorological data, to determine the energy balance for a point on the earth’s surface. SEBAL can be used to determine evapotranspiration (ET) and biomass production of agricultural crops and native vegetation, pixel by pixel (Bastiaanssen et al. 1998a, 1998b). The key input data for SEBAL consists of spectral radiance in the visible, near-infrared (NIR) and thermal infrared part of the spectrum. SEBAL computes a complete radiation and energy balance along with the resistances for momentum, heat and water vapour transport. The resistances are a function of state conditions, such as soil water potential, wind speed and air temperature, and change from day-to-day. More recently, a variation of the SEBAL, named Mapping EvapoTRanspiration at high resolution and with Internalized Calibration (METRIC) has been developed (Allen et al. 2007). Unlike SEBAL, METRIC uses ground-based, reference ET which makes the best use of existing technology in agricultural areas. Both SEBAL and METRIC use Landsat and MODIS data as the primary sources of input data, as well as Digital Elevation Model (DEM) and ground-based weather data. The dominant applications of SEBAL and METRIC have been on a regional scale in agriculture (e.g. Dodds et al. 2005, Tasumi et al. 2005, Kalma et al. 2008) and hydrological modelling (e.g. Morse et al. 2004, Bastiaanssen et al. 2005). Recently, these models are increasingly applied in forest and riparian vegetation (e.g. Bastiaanssen et al. 2002, Ahmad et al. 2005, Teixeira de Castro et al. 2008), but there have been few applications in relation to GDE (Mu¨nch and Conrad 2007). In addition to SEBAL, a simplified approach based on the Vegetation Index/ Temperature Trapezoid (VITT) concept (Moran et al. 1994) is also assessed and compared in this study. VITT is based on the simple negative relationship between vegetation cover and surface temperature, and it is easier to calculate and implement compared with SEBAL (Yang et al. 1997). This paper presents the applications of these two approaches using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) images, and geographic information system (GIS) to derive land surface temperatures and ET mapping for targeting ecosystems and species. The estimated ET is used to compare quantitatively the actual water use of woodlands within the pumping zones and similar woodlands in areas unaffected by the pumping regime. If we find that the water use within the pumping zones is less than those of the same type of woodland in areas unaffected then we can assume that the pumping regime is having an impact on those woodlands. The implementation of these methods and the automated processing are also discussed. The methodology developed in this pilot study is expected to be extended to other GWMAs and adopted as an institutional tool for groundwater and GDE monitoring, and management in the State of NSW and other areas. More broadly, these techniques are also useful in hydrologic modelling and digital earth studies since ET is an important element in the hydrologic circle.


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2. The study area The pilot study area is located at lower Macquarie GWMA, which lies west from Dubbo in central western NSW (Figure 1). It is a tableland area with elevation ranging from 217 to 420 m above the sea level. The climate is sub-humid with mean annual precipitation of about 500 mm. The dominant land uses are cropping and pasture which account for about 70%; other land uses include forestry (about 27%), residential and infrastructure (about 3%). Agriculture mainly includes irrigated cotton and pasture at lower elevations. The native vegetation consists of Poplar Box (Eucalyptus populnea) woodlands on the flats and drainage depressions, and White Cypress Pine (Callitris glaucophylla) woodlands on the low rises and small areas of aeolian dunes. Along with the major water courses and grey-cracking clay soils are River Red Gum (Eucalyptus camaldulensis) forests and woodlands, and the steeper hills and ridges tend to be dominated by Dwyer’s Red Gum (Eucalyptus dwyeri) and Iron Bark (Eucalyptus sideroxylon) woodlands. In the study area, the groundwater is primarily used for irrigation of cotton but also for town water supply purposes. With the growing demand for water supply there are increased pressures on utilities to extract more water from the water supply borefields. The location of most of these municipal water supply borefields is within national parks or nature reserve areas with native vegetation. The spatial and temporal data on the distribution of the depth to water table was supplied by the regional groundwater hydrologists of the then NSW Department of Natural Resources using a two-layer analytical model with a leaky aquitard that used the aquifer parameters observed due to pumping in this area. In general the groundwater drawdowns from pumping under the target woodland GDEs are in the order of

Figure 1.

The pilot study area at central west of New South Wales, Australia.

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34 m over the 4-month pumping season (NovemberFebruary). Due to the good hydraulic connection between groundwater and the Macquarie river, groundwater levels usually recover rapidly to pre-pumping levels within a month after the cessation of pumping. In the drawdown zones groundwater levels are being affected during the summer period when ET rates are higher and water stress is often more extreme. However, the water table, even during these periods, is still within the potential rooting depth of these species (Eamus et al. 2006). The study area is fully covered by a full scene of Landsat image (Path/Row 92/82, about 185 km by 185 km with a scene centre of 147.958 Easting and 32.088 Northing).


3. Satellite images and datasets We obtained seven full-scene Landsat images, five TM and two ETM images, which cover different seasons between 2002 and 2008 (Table 1). There are four summer images (February/March) when groundwater usage expected to be high and three images in late winter or early spring (AugustOctober). All these images are cloud free or contain less than 5%. Landsat images were chosen because they have adequate spatial (30 m) and radiometric resolutions (NIR and thermal bands) required for vegetation and surface temperature-related studies, also they are widely available. Land cover data is not directly necessary for SEBAL, but it does improve estimates of surface roughness, soil heat flux and surface emissivity (Allen et al. 2007). The most recent land use maps, produced from aerial photograph interpretation (API) at this Department, were used in this study and the land use classes are grouped into two broad classes as woodland and non-woodland. This information was useful to confirm location and extent of GDEs within the study area and further calculate the water use of the woodland GDEs. Weather data were obtained from the Bureau of Meteorology at the nearby station (Narromine, Station ID 51049) included wind speed, solar radiation, air temperature, humidity and reference ET data. Climate data were used as inputs to the SEBAL model and for image radiometric correction. Rainfall data are also obtained and used to examine rainfall impacts on groundwater level and drawdown. A DEM was also used in this project to account for temperature differences in slope, aspect and elevation. The DEM was obtained from the Department of Lands Table 1. Landsat images obtained for the study and the ground weather conditions at the time of the satellite overpasses. Date

19/10/02 24/02/03 27/08/03 19/02/04 14/09/04 21/02/05

Season Day of year (DOY) Satellite sensor Time (Local) Sun azimuth (DD) Sun altitude (DD) Ta (8C) Wind speed (m/s)

Winter 1 292 ETM 09:45:00 64.25 50.16 23.6 5.0

Summer 1 Winter 2 55 239 ETM TM 09:45:00 09:38:26 70.36 46.16 44.55 34.99 25.1 11 7.8 4.7

Summer 2 Winter 3 50 257 TM TM 09:40:36 09:44:53 70.51 49.61 47.65 41.65 33.4 14 2.5 3.6

17/03/08

Summer 3 Summer 4 52 76 TM TM 09:48:08 09:51:00 67.62 55.25 48.61 44.66 25.2 24.4 2.5 7.2


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and has a ground resolution of 25 m, with vertical accuracy better than 10 m. The DEM was further processed to remove any gaps and artefacts. The pumping zones were prepared from the borefield points with varying buffer distances determined by groundwater experts and stored in Arc Shape polygon files. Groundwater drawdown contours were also available from groundwater modelling produced by this Department. In addition, field measurements were carried out at 32 woodland sites within the study area which contain the dominant species. These measurements included tree species, canopy heights (m), crown cover (%), surface temperatures of various targets (trees, soils, litter, grass and rocks) and air temperature. These measurements are on a regular basis (twice a year) and they provide baseline information for the calculation and validation of the surface energy balance methods. 4. Methodology Two methods have been tested in this study, one is based on the surface energy balance model (SEBAL, Bastiaanssen et al. 1998a, 1998b), another is a simplified model based on the VITT concept (Moran et al. 1994, Yang et al. 1997). For forestryrelated application, there is essentially no difference between SEBAL and its variation METRIC (primarily for use with irrigated crops), thus only SEBAL is used and discussed in this study. 4.1. The Surface Energy Balance Algorithms for Land (SEBAL) method For surface energy balance-based calculation, ET is generally determined from satellite imagery by applying an energy balance at the surface, where energy consumed by the ET process is calculated as a residual of the surface energy equation: LE ¼ Rn H G

(1)

where LE is the latent or moisture flux (the energy used to evaporate water, Wm 2), Rn is the net radiation at the surface (Wm 2), H is the sensible heat flux to the air (Wm 2) and G is the soil heat flux (the energy flux into ground, Wm 2). In most remote-sensing applications, Rn is computed from satellite-measured reflectance and surface temperature; G is estimated from Rn, surface temperature and vegetation indices; and H is estimated from surface temperature ranges, surface roughness and wind speed. The characteristics of this well-known equation shall not be discussed further, but the emphasis is put on how to make use of this equation for ET estimation and how to obtain these components from remotely sensed data. The following text gives details on the application of the energy equation for regional ET estimation. The SEBAL models solve Equation (1) by estimating the different components separately using satellite images and weather data (e.g. Norman et al. 1995, Li and Kustas 2005). The theoretical and computational basis of SEBAL is described by Bastiaanssen et al. (1998a, b). An improved SEBAL model (METRIC) is described in detail by Allen et al. (2007). Specific details and equations for applications of SEBAL are contained in Bastiaanssen et al. (1998a, 1998b), Allen et al. (2007). The


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computation details are not repeated here, but the general computation process is illustrated (Figure 2). The radiative transfer model MODTRAN (Moderate spectral resolution atmospheric transmittance model, PcModWin4.0, http://www.ontar.com) was used with atmospheric profile data to atmospherically correct the Landsat data (the red, NIR and thermal band for this study). The radio-sounding atmospheric profiles, such as atmospheric pressure, air temperature and humidity, at the time of the satellite overpass were obtained from the Bureau of Meteorology (Sydney) as model inputs. However, these radio soundings are only about 16 km above the ground, and no other data was available above that height. In this case, the standard Midlatitute profile was used for the rest of the profile (1650 km above the ground). Regarding the location and the atmospheric condition, a typical profile for rural conditions (rural aerosols) was selected to represent the boundary layer (02 km). Vegetation indices (such as NDVI and SAVI) were calculated from the radiometrically corrected surface reflectance from the red and NIR bands (bands 3 and 4) (Allen et al. 2007). The surface temperature was calculated from the thermal infrared band (band 6) after radiometric correction (e.g. Li et al. 2004). The radiation balance for each pixel of the image was calculated from surface temperature, short wave reflectance, atmospheric transmissivity and emittance (Allen et al. 2007). Soil heat flux (G) is predicted as a ratio of net radiation (Rn) as a function of surface temperature and soil-adjusted vegetation index (SAVI, Huete 1988), an improved normalized difference vegetation index (NDVI). Sensible heat (H) is the most critical one to calculate compared with Rn and G, and the provision of a model for the sensible heat flux is the key to computing ET through an energy balance method. H is computed here using a resistance model expressed as follows: H ¼ qair Cp ðTs Ta Þ=rah ¼ qair Cp dT=rah

Landsat V/NIR data

Landsat thermal data

Digital elevation model

(2)

Meteorology data

Modtran 4.1 NDVI Albedo emissivity

Surface temperature

Rs

Ma

Rn

SEBAL

RL

G

H

VITT VITT ET

Figure 2.

LE

SEBAL ET

Computation procedures for evapotranspiration using SEBAL and VITT methods.


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where rair is air density (kg m 3), Cp is the specific heat of air (1004 Jkg 1K 1, rair Cp 1200 Jm 3 K 1), Ts is the effective surface temperature (K), Ta is air temperature (K) at reference height Z2 (set to 2 m), dT is the temperature gradient over the two heights of Z2 and Z1 (zero plane displace height, set to 0.1 m), rah is the aerodynamic resistance (sm 1) to exchange of sensible heat between the surface and the reference height Z2. The calculation of dT can be approximated as a relatively simple linear function of Ts as follows:


dT ¼ a þ bTs

datum

(3)

where a and b are empirically determined constant for a given satellite image, and Ts datum is surface temperature adjusted to a common elevation datum for each pixel using a DEM and customised laps rate (Allen et al. 2007). The calculation of rah followed Bastiaanssen et al. (1998a) where the near surface aerodynamic resistance is computed using a measurement of wind speed at the time of the image, and surface roughness is determined as an empirical function of NDVI (Allen et al. 2007). DEM was used in the computation of the surface temperature gradient (dT). Due to the instability in the planetary boundary layer, MoninObukhov similarity theory was adopted in which the Obukhov length L (or stability parameter), linking the parameters of rah, H, dT and rair, can be used to support the iterative procedure for the H calculation when the value of rah becomes stable. Finally, based on the energy balance Equation (1), the instantaneous latent heat flux can be computed, and extrapolated to hourly ET and an average ET over a period (e.g. 24 h). In this study, the remote-sensing derived instantaneous latent flux (LE) for each pixel was converted to daily value (mm day 1) based on Jackson et al. (1983), which is assumed that the daily course of ET would generally follow the trend of solar irradiation throughout the daylight period. The above energy balance-based algorithms were implemented in an Arc Macro Language (AML) and ran in ESRI’s ArcGIS workstation. The primary inputs are Landsat images (bands 3, 4 and 6) or any other images with the required bands (Red, NIR and Thermal), DEM, land use map (optional) and ground-based weather data measured at the nearby station. The outputs include energy balance components, vegetation indices, surface temperature and the final ET maps. The AML programme allows automated calculation of the energy balance components and ET, so that the whole process can be fast and repeatable. One of the advantages of the programme is the calculation of dT in which the ‘hot’ (dry) and ‘cold’ (wet) spots within an image can be automatically located and used to determine the coefficients a and b in Equation (3) from two pairs of dT values and Ts. 4.2. The Vegetation Index/Temperature Trapezoid (VITT) approach The Vegetation Index/Temperature Trapezoid (VITT)-based ET was calculated based on Moran et al. (1994), Yang et al. (1997). The method is based on widely observed negative correlation between Ts and remotely sensed measurements of actively transpiring vegetation, such as NDVI (Moran et al. 1994) (Figure 3). One hypothesis to explain this correlation is that the relationship between Ts and NDVI is an

X. Yang et al. a

Fractional vegetation cover,Vc

162 1

2

1

0.8 B

A

C

0.6 0.4 0.2 0 –8

3 –6

–4

4 –2

0

2

4

6

8

10

12

Ts – Ta(°C) Vegetation index temperature trapezoid (VITT) (Landsat-5 TM, 14-09-2004)

NDVI


b 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0

( Ts – Ta )max = a0 + a1NDVI a0 = 11.7,a1 = –13.9

(Ts – Ta)min = b0+b1NDVI

b0 = –11.7, b1 = –9.1

–12 –10 –8

–6

–4

–2

0 2 Ts-Ta

4

6

8

10

12

14

Figure 3. The hypothetical trapezoidal shape that would result from the relation between (Ts Ta) and the fractional vegetation cover (a) and the trapezoidal shape calculated from a scene of Landsat image (b).

indicator of evaporative cooling by plant transpiration. With a measurement of Ts Ta at point C, it would be possible to equate the ratio of actual to potential ET with the ratio of distance CB and AB (Figure 3a). Using physical energy balance equations described by Jackson et al. (1981) and later by Moran et al. (1994) (termed here the JacksonMoran equations), it is possible to compute the values of the four vertices of the trapezoid for a specific time and vegetation. More practically, the VITT shape can be constructed from field or remote-sensing measurements and empirical equation can be established to calculate Ts Ta from NDVI (Yang et al. 1997) (Figure 3b). Based on the VITT concept, a water availability index (Ma) can be defined here as follows: Ma ¼ 1 WDI ¼

ðTs Ta Þmax ðTs Ta Þr k Et ¼ ðTs Ta Þmax ðTs Ta Þmin k Ep

(4)

where WDI is the water deficit index (Moran et al. 1994); lEt is the surface ET rate (mm h 1); lEp is the potential surface ET rate (mm h 1); and r is the subscript refers to the measured value. The potential ET rate (lEp) at a specific time and location can be computed using standard methods (e.g. Allen et al. 1998). Thus, at a given time, if surface


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temperature is known, then Ma and subsequently surface ET rate can be calculated using Equation (4). The actual ET rate estimated using Equation (4) was an instantaneous value at the time of satellite overpasses and it was converted to a daily value using the same methods described by Jackson et al. (1983). In Figure 3a, Vc can be substituted with a vegetation index computed from remotely sensed imagery (e.g. NDVI, SAVI or MSAVI) as a good linear correlation is observed between these indices and the Vc. Therefore, it is possible to compute the maximum and minimum (Ts Ta) using a vegetation index, such as NDVI: ðTs Ta Þmax ¼ a0 þ a1 NDVI

(5)

ðTs Ta Þmin ¼ b0 þ b1 NDVI

(6)

where a0 and a1 are the offset (8C) and slope of the line connecting 2 and 4, and b0 and b1 are the offset (8C) and slope of the line connecting 1 and 3 in Figure 3a. Note that the maximum and minimum temperatures are still relative values since the extreme conditions (very wet and dry, fully vegetated and completely bare surfaces) are not always present on a given scene of image. The above relationships (Equations 5 and 6) provide more practical means to construct the VITT using remote sensing. It can be validated using the four VITT vertices calculated from the JacksonMoran equations (Jackson et al. 1981, Moran et al. 1994) which use the estimated or measured maximum and minimum crop-specific values, net radiation (Rn), vapour pressure deficit (VPD), wind speed (U) and air temperature (Ta) during the period of estimation. The Normalized Difference Temperature Index (NDTI) developed by McVicar and Jupp (2002) is an alternative for water availability index (Ma, Equation 4) and it provides an estimate of the relative evaporation. Nearly 9000 single-pass afternoon AVHRR images for south-east Australia have been processed using the NDTI approach since 1992 (McVicar et al. 2007, Guerschman et al. 2009a) and these NDTI series, though at a resolution of 1000 m, are useful for broad validation of the VITT and SEBAL-based methods. Surface temperature (Ts) is computed for Landsat thermal images using a modified Plank equation following Wukelic et al. (1989) with atmospheric and surface emissivity correction: Ts ¼

ln

K2

K1 Rc

þ1

(7)

where K1 and K2 are calibration constants in Wm 2sr 1mm 1 (K1 607.76 and K2 1260.56 for Landsat 5, K1 666.09 and K2 1282.91 for Landsat 7), Rc is the corrected thermal radiance from the surface using spectral radiance (RU) from Landsat band 6 (the thermal band). The corrected spectral radiance (Rc) can be determined as follows: Ru LP 1 1 Rsky (8) Rc ¼ e es where LP and t are the path radiance and transmittance calculated using MODTRAN4.1 code, o is surface emissivity at 10.4412.42 mm band and it was set to 0.98 (for forest area); Rsky downwelling sky irradiance (Wm 2mm 1) for a

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clear sky in the thermal band (10.4412.42 mm) and it was estimated from an empirical formula as follows: o n 2 Rsky ¼ 1:807 1010 Ta4 1 0:26 exp½7:77 104 ð273:15 Ta Þ (9) The spectral radiance (Ru) is calculated from the digital number (DN) of each pixel for TM or ETM band 6 by:


Ru ¼

DN 255

ðRmax Rmin Þ þ Rmin

(10)

where DN is digital number of the thermal band; Rmax and Rmin are the maximum and minimum spectral radiance (Wm 2sr 1mm 1) which are in-band values from the image header files. The above processes for VITT-based ET estimation (Equations 410) were also implemented into an AML programme which runs in an Arc/Info GIS (GRID module) environment. It produces ET maps from Landsat images and the required meteorological data. The programme also generates sampling files in ASCII format which list all estimated energy balance components and final ET results. The sampling points were 5000 random points evenly distributed in and outside the groundwater drawdown zones. These data are useful for statistic analysis outside GIS using software, such as Microsoft Excel and Access. 5. Results and discussion Application of the SEBAL and VITT models to the NSW central west woodlands produced 25 m resolution ET maps (daily average) from all selected seven Landsat images for an area of 185 km by 185 km (full scene images). An example of these ET maps for the pilot study area is presented in Figure 4. Despite the relatively sparse temporal resolution (16 days or even longer for cloud-free images), Landsat images can be used to produce snapshot ET maps with useful information on water usage for GDE and the seasonal changes. The methodology can be applied to satellite images with higher temporal resolution, such as MODIS, to produce time-series ET products at monthly interval or less. It is also possible that the remote sensing estimated ET be linked with the time-series (e.g. daily) ET based on a water balance model to provide continuous measurements of ET in both spatial and temporal contexts at desired interval (e.g. monthly or daily). Major SEBAL components and the resulting ET values have been sampled randomly (5000 points) across the study area for statistical analyses. Comparisons of predicted ET by SEBAL to VITT model indicate relatively good agreement (R2 0.91) and promise for use in groundwater management. To examine the pumping effects on groundwater GDEs, we produced a groundwater depth contour map showing the spatial extent of groundwater affected by the existing pumping regime based on hydrologic modelling and the groundwater drawdown data. We then obtained existing vegetation or land use maps and produced a ‘woody/non-woody’ vegetation layer to spatially identify woodlands and forests that are within the regions of influence from groundwater pumping. Comparison shows that the average woodland ET within the pumping (pumped)



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Figure 4. Example map of spatially distributed ET (mm/day) estimated from Landsat TM image on 19 February 2004.

area appears less than that outside the pumping (non-pumped) area (Figure 5). The differences are about 0.64 mm/day in the summer season and about 0.17 mm/day in the winter season. This demonstrates that the pumping regime has an impact on those woodlands. One of the concerns is the possible impacts of rainfall on ET since this may introduce uncertainties in the ET estimation results and consequently in the conclusion that the water use in the pumping area is less than that in non-pumping areas. However, the rainfall records at the nearby meteorological stations show that there was no rainfall within 24 h of the satellite overpass times for all seven images. Nor was rainfall recorded in the 5 days prior to all satellite overpasses except for the two images in 2003 (with rainfall less than 10 mm in previous 5 days). This suggests that the impact of rainfall on actual ET at the study area was minimal since the sky was generally clear and there was essentially no precipitation near the times of satellite overpass. However, rainfall can have big spatial variation even within a scene image and its impact on ET estimation should be more closely examined using more

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6 Pumped

Non-pumped

ET (mm/day)

5

4

3

2


1

0 Winter 1

Summer 1

Winter 2

Summer 2

Winter 3

Summer 3

Summer 4

Figure 5. Comparison of average daily ET (mm/day) at pumped and non-pumped zones at different seasons (refer to Table 1 for dates).

dense rainfall records if available. In addition, a more robust method of determining the location and volume of recharge and pumping rates across the basin and the borefields is required to provide more satisfactory and accurate results. Due to the lack of field data on actual ET measurements, we were unable to assess the absolute accuracy of the final ET product by remote-sensing methods. However, we measured the spectral reflectance (with ADS FieldSpec† HandHeld spectrometer) and surface temperature (IR thermometer), at the same time of the satellite overpass, for ground targets at the pilot study area in Narromine, central west of NSW. The field measurements were used to compare those important components, such as NDVI and surface temperature estimated from satellite images, as a measure to validate and improve the remote-sensing estimation. The remotely sensed vegetation indices and surface temperature are in good agreement with the measured values (relative error B 5%), though on average there was an underestimation on NDVI and overestimation of surface temperature (Figure 6). In most cases, the spectral reflectance of GDE (or woodlands) can be easily distinguishable from the background objects, such as grass and bare soils, particularly in the NIR wavelength area. Such difference helped to accurately classify GDEs and apply appropriate coefficients in the computation of energy balance components and ET. A land use layer was also useful as ancillary data in the GDE identification and quality control processes. SEBAL is emerging technology and has the potential to become widely adopted and used in groundwater management. However, the computation and implementation of these energy balance-based models are rather difficult and not practicable without automated or semi-automated programming. VITT approach is relatively simpler and easier to implement, yet still maintains good accuracy and correlation with the energy balance-based model. It indicates substantial promise as an alternative procedure to predict the actual ET from GDEs.


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0.7 0.6 0.5

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45 40 Field Surface temperature (°C)


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Figure 6. Comparison of NDVI (top) and temperature (bottom) values of ground targets between field measurements at the time of satellite overpass and calculation from satellite images.

6. Conclusion and further studies In this study we compared the ET estimation for GDEs from satellite images by using two different models with different levels of complexity. The successful implement of SEBAL and VITT methods in an ArcGIS environment provides an operational means for quantifying ET and its distribution for GDEs. We demonstrated that even a simple physical ET model can provide good performances to estimate ET from GDEs when compared to the complicated land energy balance models (i.e. SEBAL) which have been widely used and validated in many parts of the world. This pilot application of VITT model in NSW GWMA indicates substantial promise as an efficient and inexpensive procedure to predict the ET fluxes from different GDEs at different seasons. Predicted ET has been compared with modelled ET using energy balance-based SEBAL method with good agreement. Though the estimated ET is strictly still a relative value, it provides adequate information to assist in monitoring the effects of groundwater management on the water uses and the indicative health of these terrestrial groundwater ecosystems. The pilot study also


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demonstrates that Landsat images can be used to effectively quantify, spatially and temporarily, the ET on a pixel by pixel basis over a region or catchment area. These methods are still to be fully assessed using more accurate field measurements. Then we will use it to predict total, net depletion of water from the groundwater system resulting from pumping for all GWMAs in the State of NSW. While the remotely sensed data currently used are Landsat TM/ETM, the methodology is applicable to any sensor with both thermal and reflective capacity, such as MODerate Resolution Imaging Spectroradiometer (MODIS), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and China Brazil Earth Resources Satellite-03/04 (CBERS). It is a significant milestone to demonstrate remote-sensing capabilities to support the water user community for informative decision making in GDE management. The successful implementation of SEBAL and VITT approaches in an ArcGIS environment using automated scripts (AML and Python) enables these algorithms to be incorporated as an institutional tool for operational water resource administration, such as the groundwater management for terrestrial GDEs at NSW Department of Environment, Climate Change and Water (DECCW). The VITT approach is currently being used in GDE mapping to produce national Atlas of GDE across Australia. The techniques can also be readily modified for applications in other areas, such as hydrologic modelling and digital earth studies. Further studies should be conducted at a range of GDE species and landscapes for longer periods to judge the robustness of the VITT model associated with ground and remotely sensed ground cover products. One of our immediate further studies is to use time-series MODIS products, such as the fractional vegetation cover (Guerschman et al. 2009b) and NDTI products (monthly since February 2000) in both coastal and inland GWMA areas across the State of NSW. The accuracy of ET estimation and GDE mapping is expected to be further improved with these validated time-series products and other relevant datasets (e.g. hydrologic soil landscape and vegetation mapping) which are likely to be available in the near future. Acknowledgements This project was funded by the GDE project from the NSW Government. Many DECCW staff helped and supported this project. Mustak Shaikh, at DECCW, Parramatta office provided the archive Landsat images. Mark Mitchell from DECCW Wagga Wagga office helped with project management and remote-sensing equipment requisition. Gerry Smit, John Wood and Darren Shelly from DECCW Dubbo office helped with field work. Dr. Fuqin Li from Geosciences Australia reviewed the paper and provided useful comments. All of this support is greatly appreciated.

Notes on contributors Dr. Xihua Yang received an MSc in remote sensing from China Agricultural University in 1988, and a PhD in remote sensing and GIS from the University of New South Wales, Australia in 1998. He has over 20 years of experience in remote sensing and GIS applications with focus on agriculture and natural resources. He is currently a research scientist at NSW Department of Environment, Climate Change and Water in Australia. His main research interests include soil erosion and ecosystem response modelling using remote sensing and GIS. He has published about 60 scientific papers in various journals and conferences.


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Dr. Peter L. Smith has a background in landscape ecology research, conservation assessment and climate change impact assessment with a particular interest in biodiversity conservation in production landscapes. He has over 20 years of experience in ecological research and natural resource management and has numerous technical and scientific publications. He is currently the Manager of Climate Change Science for the NSW Department of Environment, Climate Change and Water. In that role he heads a specialist research team that has developed and oversees the implementation of a climate change science programme for the NSW Government. His current research interests include how our ecological systems respond and have responded to climate variability as a guide to how they may deal with climate change. Dr. Tao Yu received an MSc in remote sensing and mapping from the Institute of Remote Sensing Applications within Chinese Academy of Sciences in 1996 and a PhD in atmospheric remote sensing from University of Science and Technology of Lille, France in 2002. He is currently a Professor and the Head of the Remote Sensing Calibration and Validation Laboratory at Institute of Remote Sensing Applications. His research interests include demonstration of space remote sensing, calibration, validation, emulation of remote-sensing images, new sensor and surface radiative transfer model development. He and his group publish more than 30 journal papers each year on average. Hailiang Gao received his BSc in geographic information systems in 2005 from Jilin University, China. He is currently a PhD student under Professor Yu’s supervision at the Institute of Remote Sensing Applications within Chinese Academy of Sciences. His main research interests include radiometric calibration and hyperspectral remote sensing.

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