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Measuring water stress in a wheat crop on a spatial scale using airborne thermal and multispectral imagery
Field Crops Research 112 (2009) 55–65
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Measuring water stress in a wheat crop on a spatial scale using airborne thermal and multispectral imagery Mohammad Abuzar a,*, Garry O’Leary b, Glenn Fitzgerald b a b
Future Farming Systems Research, Department of Primary Industries, 32 Lincoln Square North, Carlton, Victoria 3053, Australia Department of Primary Industries, 110 Natimuk Road, Horsham, Victoria 3400, Australia
A R T I C L E I N F O
A B S T R A C T
Article history: Received 15 July 2008 Received in revised form 5 February 2009 Accepted 5 February 2009
A modified stress index is proposed that accounts for both chronic and acute water stress. Current trapezoid methods that use vegetation cover and temperature indices do not necessarily measure chronic conditions. The modified method describes the chronic stress as the ratio of actual crop cover to its potential expressed such that zero stress occurs when actual cover equals or exceeds the potential as determined by a simulation model. The advantage of such a definition is that in areas where full groundcover is rarely achieved, such as semi-arid regions, a more realistic and conservative stress condition will be observed. Airborne thermal and multispectral images were acquired at four growth stages of a wheat crop from a site in Victoria, Australia with experimental plots having rain-fed and irrigated regimes over two seasons (2005 and 2006). The theoretical basis of vector determination was adopted for trapezoidal extent per season. The relationship between such chronic stress and acute stress is explored and show that in any 2 years large differences between these stresses exist. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Crop water deficiency Chronic water stress Acute water stress Airborne imagery Precision agriculture
1. Introduction Understanding crop water stress is important for in-season management of irrigated and rain-fed crops. One method that has long been used in measuring water stress is crop surface temperature (Idso et al., 1981; Jackson et al., 1981; GonzalezDugo et al., 2006). As crops transpire, water loss reduces leaf temperature through evaporative cooling (Allen et al., 1998). In well-watered situations, the leaf surface temperature often becomes much lower than that of the surrounding air. Conversely, water-stressed crops transpire less and crop surface temperature increases, typically rising above the surrounding air temperature (Jackson, 1982). The difference between the crop surface and air temperatures (Ts Ta) is therefore a good indication of crop water stress. The first conclusive demonstration that leaf temperature can be cooler than air temperature was given by Ehler (1973). Using thin wire thermocouples to measure leaf temperature, it became evident from his experiments that Ts Ta was linearly related to vapour pressure deficit (VPD). The VPD is the difference between water vapour pressures of the surrounding air and saturated air at the same temperature. Idso et al. (1981) and Jackson et al. (1981) made use of Ehler’s findings in developing a crop water stress index
* Corresponding author. Tel.: +61 3 8341 2435; fax: +61 3 9347 6056. E-mail address: [email protected] (M. Abuzar). 0378-4290/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2009.02.001
(CWSI) for canopies at full cover, a measure which later received wide acceptance. In their work, canopy temperature was measured by infrared thermometers (IRT) instead of thermocouples. Distinction needs to be made between individual leaves and the leaves arranged in a crop canopy even though the mechanism of temperature differences is essentially the same (Idso et al., 1967). When an instrument like an IRT provides point measurements, the intent is generally to characterise a whole canopy, which usually comprises multiple point measurements. With the advent of thermal scanners and imagers and their use as hand-held, vehicledriven, airborne or space-borne instruments, point or small sample information has largely been replaced by the more complete fullsample coverage of canopy temperature. The theoretical approach to CWSI (Jackson et al., 1981) and its practical applications (Idso et al., 1981; Idso, 1982) require that baselines for non-stressed and fully stressed canopies are defined. Baselines are crop and/or variety specific in relation to Ts Ta and VPD (Idso, 1982). The concept of the CWSI is confined to full canopies only and does not provide an immediate solution for incomplete cover where bare soil is included. In order to overcome this limitation, Moran et al. (1994) introduced the concept of a vegetation index versus temperature (VIT) trapezoid. This provides a blended surface temperature response comprising a crop and soil temperature mix. A trapezoidal shape is generally formed when Ts Ta is plotted against vegetation cover, provided sufficient data points are available and adequate spatial variability exists in water status and crop cover. According to Moran et al. (1994) the water
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deficit index (WDI) of a point is its distance from cold front in proportion to the distance between cold and warm fronts. WDI for any full cover point is equivalent to Idso–Jackson’s CWSI at a given VPD. Clarke (1997) provided a pragmatic approach to the VIT trapezoid by reducing certain conditions on the definition of vertices proposed by Moran et al. (1994) but his proposed approach also relied on field information. The empirical approaches to VIT framework help implementation of the trapezoid models to some extent (Mendez-Barrosa et al., 2008). As the purpose of the VIT trapezoid is to determine the stressed part of the crop at a particular time, Clarke (1997) divided the trapezoid into sections and identified a region of the trapezoid representing a stressed crop. A limitation of the CWSI is its focus on the acute crop water status and it does not necessarily consider chronic water stress conditions. Similarly, the trapezoid approach uses an instantaneous (acute) measure of crop temperature and, whilst accounting for incomplete cover assumes a linear relationship between soil temperature and crop temperature displacement as soil is covered or uncovered by the crop canopy. For perennial vegetation we assumed that any cover less than unity indicates a chronic stressed condition, but for annual crops this is not necessarily true and in some environments crops never fully cover the ground (O’Connell et al., 2004). We investigate and propose an annual crop version for the trapezoid where vegetative cover is relative to a potential cover predicted at that stage of development through a simple crop model. In this way, both chronic and acute water stress status can
Table 1 Airborne acquisitions and crop growth stages. Flight dates
DOY
Local time (hh:mm)
Solar zenith (8)
Crop growth stage (Zadoks)
21 07 17 21
July 2005 September 2005 October 2005 November 2005
202 250 290 325
12:46 14:25 13:34 12:02
63 45 31 34
13 (3-leaf) 22 (tillering) 60 (anthesis) 80 (dough development)
08 30 30 24
August 2006 August 2006 September 2006 October 2006
220 242 273 297
11:34 10:55 12:31 11:13
69 69 44 49
15 24 32 65
(5-leaf) (tillering) (stem elongation) (mid-anthesis)
Table 2 Weather data for the site close to flight times. Dates
Time (hh:mm)
Air temperature (8C)
Relative humidity (%)
Vapour pressure deficit (kPa)a
21 07 17 21
July 2005b September 2005b October 2005b November 2005b
12:46 14:25 13:34 12:02
8.2 20.4 23.9 20.2
74.7 48.0 31.5 48.5
0.3 1.2 2.0 1.2
08 30 30 24
August 2006c August 2006c September 2006c October 2006c
11:30 11:00 12:30 11:15
15.5 14.0 16.1 28.0
49.8 68.7 44.5 12.5
0.9 0.5 1.0 3.3
a b c
Calculated using equations by Allen et al. (1998). Bureau of Meteorology. Onsite weather station.
be assessed from one graph and appropriate management intervention implemented as needed. 2. Materials and methods A field study was established on an experimental farm near Horsham, Victoria, Australia (36.748S latitude, 142.108E longitude, elevation 126 m). Crops of wheat (Triticum aestivum L., cv Chara) were sown in two consecutive years. The experiment was set up comprising 48 plots in 2005 and 16 plots in 2006 (Fig. 1). Each plot was of 12 m 20 m in size. Half of the plots were irrigated and the
Fig. 1. The identification and the irrigation status of plots under study are shown on an airborne multispectral image taken with Duncan MS3100 imager on 30 September 2006. Band combination (RGB) includes NIR, red-edge and red. All 48 plots were investigated in 2005 but in 2006 only 16 plots (17–32) were sown.
Fig. 2. A hypothetical trapezoid defines crop water status. Acute water stress index (ASI) = AC/AB. Chronic water stress index (CSI) = DC/DE.
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Fig. 3. Illustration shows an example of determining the trapezoidal vertices for the purpose of estimating crop water stress. This illustration is based on the actual data set of 30 September 2006 of the study area. The vertices were determined as per procedure given by Moran et al. (1994) and Colaizzi et al. (2003b). Crop cover was scaled in reference to the potential crop cover determined by a crop model (O’Leary and Connor, 1996) as shown on the left.
remaining plots were rain-fed. The crop was sown on the 2nd of June in 2005 and on the 31st of May in 2006. The study area was overflown four times each year to acquire high-resolution multispectral and thermal image datasets of the crops. The dates of flights were chosen to coincide with key stages of crop growth (Table 1). 2.1. Airborne multispectral data The two imagers used in the airborne acquisitions were a Redlake MS3100 3-CCD multispectral imager (Redlake MASD Inc., Tucson, AZ, USA) and a Therma CAM P40 (FLIR, Danderyd, Sweden). The MS3100 had three 8-bit narrow wavebands centred at 670 nm (red), 720 nm (red edge) and 790 nm (infrared). The bands widths were 25, 10 and 25 nm respectively. The FLIR imager had a spectral range of 7.5–13 mm and output K as 16-bit pixels. The imagers were custom-mounted in a survey port in the floor of a Cessna 182-RG light aircraft. Flight altitude was around 1000 m above ground level and both imagers had fields of view near 158 208. All flights occurred within 2 h of solar noon. Both multispectral and thermal images were acquired within 4 s of each other. Skies were clear except on 24 October 2006 when it was partly cloudy (5 Oktas), but images were collected where the crop plots were fully illuminated and fully shaded. All images were geo-referenced in ERDAS Imagine (Leica Geosystems, Norcros, GA, USA) to MGA94 (Zone 54, Datum: GDA94, Spheroid: GRS1980) coordinate system. All the images were first resampled close to their original resolution using the nearest neighbour (NN) method. Pixel resolution of the MS3100 images was close to 0.2 m. These were resampled to match the coarser resolution of FLIR images (2 m in 2005 and 1 m in 2006). For this resampling, a bilinear interpolation method was used, which allowed the original pixel values to be averaged. Prior to geo-referencing, the MS3100 images were pre-processed in ENVI (ITT, Boulder, CO, USA) for flat field correction, inter-band coregistration, spatial noise filtering (Minimum Noise Fraction) and converted to reflectance using known values of ground panels placed adjacent to the experiment. A total of 8508 data points were extracted from the imagery in 2005 and 2435 data points in 2006.
2.2. Field measurements A handheld IRT (Model 100.3ZL, Everest Interscience, Tucson, AZ, USA) was used during and near flight times to measure surface temperature transects on all plots and calibration patches. The IRT used has a bandwidth of 8–14 mm with adjustable emissivity between 0.2 and 0.98. Nadir and oblique (foliage) measurements were acquired by walking along the E side of each plot, pointing the IRT obliquely towards the west. When compared with Therma CAM values, the IRT observations were marginally higher. An automatic weather station (AWS Model WXT510, Measurement Engineering Australia, Magill, Australia) located at the study site recorded air temperature, relative humidity, global solar radiation, wind speed, and rainfall at 15 min intervals in 2006. For 2005, the weather data was obtained from the Bureau of Meteorology
Fig. 4. Relationship between SAVI and crop cover is shown. SAVI data points were derived using airborne multispectral imagery and crop cover was determined using ground-based photos from field measurements during 2004–2006.
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(www.bom.gov.au) from the local recording stations (Polkemmet – # 79023 and Horsham Aerodrome – #079100) within 10 km. The observations from Bureau of Meteorology and the in-field weather station can be considered comparable (Bureau of Meteorology, 2005). The saturated and actual vapour pressure values were calculated using these weather records (Table 2). The crop was machine harvested on 20th December in 2005 and on 11th December in 2006 using a Kingaroy Header Cut Harvester with a width of 1.8 m. Calibration panels (2.8 m 2.8 m) were also established to convert the raw digital number data from the multispectral images to reflectance. Reflectance of the panels was calculated by measurement several times during the season with a FieldSpec Pro (Analytical Spectral Devices, Boulder, Colorado, USA) referenced to a 99% Spectralon (Labsphere, North Sutton, NH, USA) panel. Plot reflectance values were also acquired at or near flight times with the FieldSpec Pro and the soil-adjusted vegetation index (SAVI) (Huete, 1988) calculated. 2.3. Methods of quantifying crop water stress 2.3.1. Chronic stress Chronic water stress was measured as the ratio of the difference between actual crop cover and potential crop cover, such that the stress index is zero when the actual crop cover is equal to the potential cover on the day of measurement (Figs. 2 and 3): viz; Chronic water stress index ðCSIÞ ¼
DC DE
(1)
Airborne multispectral imagery and field measurements collected over the sites during 2004–2006 were used to measure crop cover by establishing its relationship with soil-adjusted vegetation index (SAVI) and green leaf area index (GLAI). The crop cover is highly correlated with SAVI (R2 = 0.89, Fig. 4). Potential crop cover was calculated using the crop simulation model of O’Leary and Connor (1996). SAVI was estimated using the near infrared (NIR) and red wavebands from the MS3100 imager: SAVI ¼
ð1 þ LÞ ðP 790 P 670 Þ ðL þ P 790 þ P 670 Þ
(2)
L is a constant taken here as 0.5, which is suitable for intermediate vegetation cover (Huete, 1988). P790 and P670 are wavebands in the NIR (790 nm) and red (670 nm) parts of the spectrum, respectively. 2.3.2. Acute stress Crop water stress occurs when the crop cannot meet its transpiration demand at the current VPD. This can be problematic when considering the differences between chronic and acute stress because various crop processes have different thresholds for stress. For example, leaf expansion is reduced much earlier than photosynthesis during progressive water deficit. Nevertheless, the degree of stress is therefore the relative portion of the unmet overall demand reflected in a decreased Ts Ta. Irrespective of current VPD, if the transpiration demand is met the crop is deemed to not be under acute water stress, despite various lower efficiencies of water use that might exist, particularly at high VPD. This is often expressed as a transpiration ratio, commonly used in many simulation models, or the Ts Ta difference over the maximum possible difference. Thus, for a fully stressed crop with full canopy (i.e. a dead crop with zero transpiration), Ts will be close to Ta or slightly hotter depending on the albedo differences between the soil and crop. The acute stress index is the ratio of the Ts Ta temperature difference from the unstressed Ts Ta over the
maximum possible difference (Fig. 2), viz; Acute water stress index ðASIÞ ¼
AC AB
(3)
A scatter plot of Ts Ta versus crop cover usually shows a cloud of data points (pixels) forming the shape of a trapezoid. The bounds of the shape require four vertices which can be defined analytically using the established relationships (e.g. Idso–Jackson model), the derived parameters (Moran model) or direct field measurements. The upper bound of the trapezoid represents full crop cover whereas the lower bound represents bare soil. Thus the crop cover for the four vertices is known. It is the Ts Ta which needs to be determined. The vertices of the trapezoid represent four contrasting situations: (1) well-watered full crop cover, (2) water-stressed full crop cover, (3) saturated bare soil, and (4) dry bare soil. The line joining the vertices representing the water-stressed full cover and the dry bare soil defines the warm front, indicating the status of maximum acute water stress. The line joining the vertices representing well-watered full cover and saturated bare soil defines the cold front indicating a situation of no acute water stress. All data points are expected to lie within the trapezoid (except certain man-made objects). The procedure of determining the trapezoidal vertices are based on energy balance equations (Monteith, 1973). The derived formulations for the full crop cover (Jackson et al., 1981) and for the partial cover (Moran et al., 1994) have been considered appropriate for the present study. In an attempt to Table 3 Values used to define trapezoid vertices as shown in Figs. 5 and 6. Dates
Vertexa
21 July 2005
1 2 3 4
0.90 3.36 1.46 5.23
0 0 1 1
07 September 2005
1 2 3 4
2.87 2.73 3.00 8.29
0 0 1 1
17 October 2005
1 2 3 4
3.33 5.29 2.40 19.70
0 0 1 1
21 November 2005
1 2 3 4
0.30 8.00 0.95 16.25
0 0 1 1
8 August 2006
1 2 3 4
0.10 6.43 1.11 11.70
0 0 1 1
30 August 2006
1 2 3 4
0.01 5.95 1.10 12.28
0 0 1 1
30 September 2006
1 2 3 4
0.76 7.86 3.97 19.01
0 0 1 1
24 October 2006
1 2 3 4
5.00 5.34 2.00 17.70
0 0 1 1
Ts Ta (8C)
‘‘1 - Actual/Potential’’ crop cover
a 1. Cold front vertex at cover = 1, 2. Warm front vertex at cover = 1, 3. Cold front vertex at cover = 0, 4. Warm front vertex at cover = 0 (Cover here denotes ‘‘1 - Actual/ Potential’’ crop cover).
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Fig. 5. Scatter diagrams of surface-air temperature differences versus actual/potential crop cover ratio as captured on four occasions during the 2005 wheat crop. Each dot represents an area of 2 m 2 m on ground.
explore the alternative procedure, the empirical approach for the full crop cover (Idso, 1982) and the field calibration data for the bare soils (wet and dry) were tested but the resultant vertices were deficient in some instances to encompass the trapezoidal spread of the study area. The four terms of (Ts Ta) that refer to the four vertices are (Moran et al., 1994; Colaizzi et al., 2003b): ðT s T a Þ1 ¼
g 1 ð1 þ r cp =ra1 Þ r a1 ðRn1 G1 Þ C v1 D1 þ g 1 ð1 þ rcp =r a1 Þ
ðT s T a Þ2 ¼
VPD
D1 þ g 1 ð1 þ rcp =r a1 Þ
Table 4 Summary of potential and actual GLAI and crop cover for 2005 and 2006.
(4)
r a2 ðRn2 G2 Þ g 2 ð1 þ r cx =ra2 Þ C v2 D2 þ g 2 ð1 þ rcx =ra2 Þ
VPD
D2 þ g 2 ð1 þ rcx =ra2 Þ
1013 J kg1 8C1 and r = 1000P/(1.01Ta 287); P denotes atmospheric pressure (kPa). ra is aerodynamic resistance (s m1) based on Campbell model without stability correction (Kjelgaard et al., 1996). Rn is net radiation (W m2); G is soil heat flux (W m2); g is psychrometric parameter (kPa 8C1); rcp is the canopy resistance at potential transpiration; and rcx is the upper limit of canopy resistance; and D is the slope of the saturated vapour pressure–
(5)
ðT s T a Þ3 ¼
r a3 ðRn3 G3 Þ g3 VPD C v3 D3 þ g 3 D3 þ g 3
(6)
ðT s T a Þ4 ¼
r a4 ðRn4 G4 Þ C v4
(7)
Cv is the volumetric heat capacity of the air (J 8C1 m3) as product of air specific heat (Cp) and air density (r) where Cp is constant
Date
DOY
Potential GLAIa
Potential covera
Actual GLAIb
Actual Coverb
Sowing 21 July 2005 7 September 2005 17 October 2005 21 November 2005 Harvest
153 202 250 290 325 354
0.11 2.60 5.16 0.26
0.05 0.73 0.92 0.12
0.16 2.30 2.87 0.43
0.13 0.41 0.46 0.19
Sowing 8 August 2006 30 August 2006 30 September 2006 21 October 2006 Harvest
151 220 242 273 297 345
0.25 1.50 4.87 3.69
0.12 0.53 0.91 0.84
0.10 0.57 1.35 1.31
0.11 0.23 0.33 0.32
Note: Actual cover refers to the mean of crop cover of all plots. a From O’Leary and Connor (1996) model. b Ground-based biophysical data.
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Fig. 6. Scatter diagrams of surface-air temperature differences versus actual/potential crop cover ratio as captured on four occasions during the 2006 wheat crop. Each dot represents an area of 2 m 2 m on ground.
temperature relation (kPa 8C1). rcp and rcx have been taken as constant 15 and 250 s m1, respectively (Shen et al., 2002; Niyogi and Raman, 1997) The average of Ta and Ts replacing Ta was recommended by Jackson et al. (1981). This has been adopted for the vectors representing the extreme dry conditions (i.e. vectors 2 and 4)
only, as in the extreme wet conditions, air and surface temperatures are assumed equal. The minimum wind speed taken is 2 m s1 as the lower value may result in an unrealistic ra (Colaizzi et al., 2003a). For full cover, 0.92 m is taken as crop height as estimated for Chara variety (Stapper, 2004). Rn and G were taken as specific fraction of incoming short-wave solar
Fig. 7. The plotted mean values of CSI and ASI show the seasonal changes at a glance. Bars show the standard deviation. (a) 2005 and (b) 2006.
M. Abuzar et al. / Field Crops Research 112 (2009) 55–65
radiation (Colaizzi et al., 2003b). An example of term calculation has been shown in Appendix A. Our modified chronic stress trapezoid is summarised diagrammatically (Fig. 3). Table 3 lists the respective vertices for each sampling time. We further developed a compound stress index (COSI) as a way of expressing the relative magnitude of the likely overall stress impact. COSI ¼ ASI CSI
(8)
As such, strongly positive values represent a dominant chronic condition and strongly negative values reflect a dominating acute condition. A zero compound index reflects equal acute and chronic conditions. 3. Results 3.1. Chronic and acute water stress differences The modified trapezoid of Ts Ta versus ‘‘1 - actual/potential’’ cover ratio is shown in Figs. 5 and 6. It represents a wide range in crop growth stage from sampling four dates over 2 years (Table 3).
61
It is noted that ‘‘potential’’ was less than ‘‘actual’’ in two cases referring to an early crop stage (21 July) and a late stage (21 November) in 2005. At both stages the overall crop cover itself was very low. Even though the difference between potential and actual was very small in both cases it is not considered significant. It was however, considered important to maintain the range of calculated CSI between 0 and 1, hence as a procedure, the applicable ‘‘potential’’ cover should be more than or equal to the ‘‘actual’’. The irrigated and rain-fed crops in 2005 can easily be distinguished (Fig. 5). The position of any point along the Ts Ta axis in the scatter diagrams indicates the acute stress which is determined by reference to the cold and warm fronts. The position of a point along the axis of actual/potential cover ratio, indicates chronic stress. At the early crop (3-leaf) stage in 2005, there was very low acute stress shown (Fig. 5a). At that stage, the average crop cover for the whole experimental site was only 0.13 whereas the potential cover for that stage was even lower, 0.05 (Table 4). The irrigated and the rain-fed areas showed a large overlap that is indicative of the uniform ground conditions at that growth stage. The tillering stage (Fig. 5b) shows more variation in chronic stress than acute. Around anthesis, however, acute stress was more varied with some distinction between irrigated and rain-fed areas
Fig. 8. Scatter plots showing chronic stress versus acute stress in 2005 season.
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(Fig. 5c). By the time of dough development stage (Fig. 5d), the crop has started to senesce. The acute stress was the highest at this stage. Similarly in 2006, Fig. 6 shows the varying march of the chronic and acute stress across the sown areas. At the 5-leaf stage (Fig. 6a) the acute stress was low. The irrigated and the rain-fed areas
overlapped almost entirely, indicating a lack of variation in ground conditions at that stage. Crop cover increased through tillering and stem elongation but remained much lower than expected (Table 4). Hence the data indicate more chronic than acute stress (Fig. 6b and c). Around mid-anthesis (Fig. 6d) however, acute stress increased mainly in the rain-fed areas.
Fig. 9. Scatter plots showing chronic stress versus acute stress in 2006 season.
Fig. 10. Average difference between chronic stress and acute stress over 2005 and 2006 seasons. (a) 2005 and (b) 2006.
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3.2. Temporal expression of water stress
Table 5 Total seasonal irrigation, rain, stress measures, nitrogen treatment and crop yield.
The relative relationship between chronic and acute stress is shown in Figs. 8 and 9 preceded by a summary diagram (Fig. 7) as an X–Y graph and as a compound index (COSI) over crop phenological development time (Fig. 10). The crop at the early growth stage (3-leaf) in 2005 appeared largely free from any water stress where both ASI and CSI values were low (predominantly <0.2) with little spatial variations across the plots (data not shown). At the stage of tillering (7 September 2005), chronic stress was high (predominantly 0.3–0.6). Around anthesis (17 October), there was a uniform very high chronic stress, whereas acute stress was mostly low (the main concentration of points below about 0.4 represented 94% of the data points). The rain-fed plots had relatively high ASI values (Fig. 8c). Around the crop stage of dough development (21 November 2005), the ASI values were generally high except in some irrigated areas (not shown). In contrast, chronic stress was low to moderate at this stage (Fig. 8d). In 2006, the crop at the early stage (5-leaf) showed more chronic stress than acute. There were noticeable variations within and across the plots (data not shown). At tillering stage (30 August), chronic stress increased uniformly across all plots whereas acute stress reduced in irrigated areas but increased in some rain-fed areas. At the stem elongation stage (30 September), acute water stress was uniformly low in all plots whereas chronic stress increased to a very high level. Around mid-anthesis (24 October), both chronic and acute stresses were higher with considerable spatial variations. The rain-fed plots showed the maximum stress as expected. In 2005 near growth stage 30, we conclude that the crop was mildly dominantly chronically stressed and in 2006 it was severely chronically water stressed (Fig. 10). Only at the end of the season was the crop considered to be under a dominantly acute condition. Similarly, in 2006 the crop exhibited a strong chronic stressed condition but with equal acute and chronic conditions early and late in the season.
Treatment
3.3. Spatial expression of water stress The combined index was calculated for individual pixels from our experimental site and mapped for 17 October 2005 (Fig. 11). This shows significant spatial variation indicating greater chronic stress (blue) in the irrigated plots compared to the lesser chronic
Fig. 11. Difference maps of chronic stress and acute stress (COSI) for 17 October 2005.
Rain-fed without N
Rain-fed with N
Irrigated without N
Irrigated with N
(a) 2005 Crop Irrigation (mm)a Rain (mm)b N application (kg/ha)c CSId ASId Yield (t/ha)
Nil 203 Nil 0.53 0.38 2.84
Nil 203 87 0.50 0.39 3.00
150 203 Nil 0.50 0.22 3.44
150 203 87 0.47 0.21 3.48
(b) 2006 Crop Irrigation (mm)e Rain (mm)f N application (kg/ha)g CSIh ASIh Yield (t/ha)
Nil 130 Nil 0.70 0.74 2.06
Nil 130 196 0.67 0.73 1.75
240 130 Nil 0.58 0.62 3.68
240 130 196 0.52 0.59 3.89
a b c d e f g h
Irrigation on (DOY): 100, 132, 235 and 265. Rainfall during active crop season. N application on (DOY): 253. Image acquired on (DOY): 290. Irrigation on (DOY): 114, 180, 231, 254, 257, 282 and 303. Rainfall during active crop season. N application on (DOY): 264. Image acquired on (DOY): 297.
stressed (light blue) dryland plots. Similarly in 2006, there was significant spatial variation in COSI seen between the irrigated and dryland plots (data not shown). The edge effects seen in the plots are noteworthy, indicating greater acute stress, especially in the rain-fed plots. Application of irrigation has apparent influence on stress condition and final yield as expected whereas nitrogen application has no or negligible influence (Table 5). 4. Discussion The extension of the acute water stress index method of Idso et al. (1981), Idso (1982) and subsequent variants by Moran et al. (1994) and Clarke (1997) to include a measure of chronic water stress progresses toward a unique non-destructive method of assessing crop water stress in a spatial and temporal context. This is particularly important when considering annual crops that do not normally attain full groundcover as is common in much of the semi-arid regions of the world. The spatial aspect is particularly suited to remote sensing for applications in precision agriculture where knowledge of the type of water stress will dictate the kinds of intervention that would be suitable for a profitable outcome. We have assumed that the ratio of the actual groundcover of the crop to its potential groundcover is indicative of its chronic stress status for rain-fed crops. This is not unreasonable as leaf area expansion is one of the first physiological processes affected by water stress and the nonlinear relationship (Beer’s Law) between groundcover and LAI is well known. Of course, other factors than water shortage can reduce groundcover. One of these is late sowing, but contemporary simulation models can account for this. Low nutrient status may also reduce leaf area expansion under adequate water supply. Our experiments were all conducted on soil with large amounts of soil N and had adequate P applied. One of the problems of our combined index is that the relative components become obscured. A zero COSI could occur because of either a very low but equal chronic and acute stress or very high but equal chronic and acute stress. Clearly, the X–Y graphs of each index are more informative, but the combined index highlights any asymmetry between the relative stresses (Fig. 11). One important issue that we have not addressed in this analysis is determining the intervention point. This will no doubt be
M. Abuzar et al. / Field Crops Research 112 (2009) 55–65
64 Table 6 Parameters and equation terms for 30 September 2006. Description
Notation
1
Wind speed (m s ) Height of measurement (m) Crop height (m) Air temperature (8C) Average surface temperature (8C)b Applicable Ta (8C) Aerodynamic resistance (m s1) Vapour pressure deficit (kPa) Incoming solar radiation (W m2) Net radiation (W m2) Soil heat flux (W m2) Volumetric heat capacity of the air (J 8C1 m3) Heat capacity of the air (J kg1 8C1) Air density (kg m3) Psychrometric constant (kPa 8C1) Theoretical surface-air difference (8C) a b
uz zm, zh h Ta T 0s T 0a ra VPD Rs Rn G Cv Cp
r g (Ts Ta)
Verticesa 1
2
3
4
2.5 2 0.92 16.1 25.4 16.1 28.67 1.01 776 543.2 54.32 1205.7 1013 1.19 0.067 0.76
2.5 2 0.92 16.1 25.4 20.75 28.67 1.36 776 543.2 54.32 1186.6 1013 1.17 0.067 7.86
2.5 2 0.12 16.1 25.4 16.1 83.06 1.01 776 543.2 162.96 1205.7 1013 1.19 0.067 3.97
2.5 2 0.12 16.1 25.4 20.75 83.06 1.36 776 388.0 116.4 1186.6 1013 1.17 0.067 19.01
1. Wet vegetation, 2. Dry Vegetation, 3. Wet soil, and 4. Dry soil. Average surface temperature of the study site obtained from the airborne Therma CAM.
dependent on the management options under consideration. For example, in rain-fed agriculture topdressing of N fertiliser is an important post-sowing option available to farmers if there is a profitable potential to utilise the additional fertiliser. That is, there is a sufficient soil water reserve or likelihood of imminent rainfall and there is sufficient capacity to grow. Our combined stress index might also have application with respect to non-water stresses like nutrient deficiency or diseases that affect the ability of the plant to transpire, but this remains to be tested. 5. Conclusions A modified trapezoid water stress model that accounts for chronic and acute water stress is proposed. The input required is largely the same as traditionally used for the existing trapezoid models for acute water stress, but additionally requires potential crop cover that can be derived readily by any suitable crop model. The model is applicable to both irrigated and dryland crops. Our combined stress index might also have application with respect to non-water stresses like nutrient deficiency and diseases but this remains to be tested. Airborne multispectral and thermal image data provides a good basis of measuring and monitoring crop water stress both in irrigated and rain-fed situations. Measurements are effective at various scales ranging from micro-level (1–2 m) to plot or field level. However, the proposed methods of water stress measurement are also likely appropriate in larger scale applications involving satellite data both at farm and regional scales. Acknowledgements The authors gratefully acknowledge the financial support for this work through the Victorian Government’s Our Rural Landscape initiative. We also thank an anonymous referee for helpful comments on an earlier draft. Appendix A The aerodynamic resistance (ra) used in Eqs. (4)–(7) was calculated as thus (Allen et al., 1998): ra ¼
ln½ðzm dÞ=Z om ln½ðZ h dÞ=Z oh 2
k uz
where ra = aerodynamic resistance (s mS1); zm = height of wind measurement (m); zh = the height of humidity measurement (m); d = zero plane displacement height in m = 2/3h; h = the crop height (m); zom = roughness length governing momentum transfer (m) = 0.123h; zoh = roughness length governing transfer of heat and vapour (m) = 0.1zom; uz = wind speed (m sS1) at zm; k = Karman’s constant = 0.41. For each VIT trapezoid vertices, Rn and G were taken as the specific fractions of the incoming solar radiation (Rs) as noted by Colaizzi et al. (2003b): Rn1, Rn2 and Rn3 as seven-tenths of Rs whereas Rn4 as half of Rs; G1 and G2 as one-tenth of the respective Rn; G3 and G4 as threetenths of the respective Rn. Table 6 represents an example of the calculation of the terms used in Eqs. (4)–(7). References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration. Food and Agriculture Organisation (FAO), Rome. Bureau of Meteorology, 2005. Automatic weather stations for agricultural and other applications. www.bom.gov.au/inside/services_policy/pub_ag/aws/aws.shtml (accessed December 2008). Clarke, T.R., 1997. An empirical approach for detecting crop water stress using multispectral airborne sensors. HortTechnology 7 (1), 9–16. Colaizzi, P.D., Barnes, E.M., Clarke, T.R., et al., 2003a. Estimating soil moisture under low frequency surface irrigation using crop water stress index. J. Irrig. Drain EASCE 129 (1), 27–35. Colaizzi, P.D., Barnes, E.M., Clarke, T.R., et al., 2003b. Water stress detection under high frequency sprinkler irrigation with water deficit index. J. Irrig. Drain EASCE 129 (1), 36–43. Ehler, W.L., 1973. Cotton leaf temperature as related to soil depletion and meteorological factors. Agron. J. 65 (3), 404–409. Gonzalez-Dugo, M.P., Moran, M.S., Mateos, L., Bryant, R., 2006. Canopy temperature variability as an indicator of crop water stress severity. Irrigation Sci. 24, 233– 240. Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25, 295–309. Idso, S.B., Baker, Donald, G., 1967. Relative importance of reradiation, convection, and transpiration in heat transfer from plants. Plant Physiol. 42, 631–640. Idso, S.B., Jackson, R.D., Pinter, P.J., Reginato, R.J., Hatfield, J.L., 1981. Normalizing the stress-degree-day parameter for environmental variability. Agric. Meteorol. 24, 45–55. Idso, S.B., 1982. Non-water-stressed baselines: a key to measuring and interpret ting plant water stress. Agric. Meteorol. 27, 59–70. Jackson, R.D., Idso, S.B., Reginato, R.J., Pinter, P.J, 1981. Canopy temperature as a crop water stress indicator. Water Resour. Res. 17 (4), 1133–1138. Jackson, R.D., 1982. Canopy temperature and crop water stress. Advances in Irrigation, vol. 1. D. Hillel. New York, Academic Press, pp. 43–85. Kjelgaard, J.F., Stockle, C.O., Evans, R.G., 1996. Accuracy of canopy temperature energy balance for determining daily evapotranspiration. Irrig. Sci. 16, 149–157.
M. Abuzar et al. / Field Crops Research 112 (2009) 55–65 Mendez-Barrosa, L.G., Garutuza-Payan, J., Vivoni, E.R., 2008. Quantifying water stress on wheat using remote sensing in the Yaqui Valley, Sonora, Mexico. Agric. Water Manage. 95, 725–736. Monteith, J.L., 1973. Principles of Environmental Physics. Edward Arnold, London. Moran, M.S., Clarke, T.R., Inoue, Y., Vidal, A., 1994. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sens. Environ. 49, 246–263. Niyogi, D.S., Raman, S., 1997. Comparison of four different stomatal resistance schemes using FIFE observations. J. Appl. Meteorol. 36, 903–917. O’Connell, M.G., O’Leary, G.J., Whitfield, D.M., Connor, D.J., 2004. Interception of photosynthetically active radiation and radiation-use efficiency of wheat,
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field pea and mustard in a semi-arid environment. Field Crops Res. 85, 111–124. O’Leary, G., Connor, D.J., 1996. A simulation model of the wheat crop in response to water and nitrogen supply. I. Model construction. Agric. Syst. 52, 1–29. Shen, Y., Kondoh, A., Tang, C., et al., 2002. Measurement and analysis of evapotranspiration and surface conductance of a wheat canopy. Hydrol. Process. 16, 2173–2187. Stapper, M., 2004. Achieving higher irrigated wheat yields. Available online at http://www.irec.org.au/reser_f/04_pdf/Achieving%20higher%20irrigated%20wheat%20yeilds.pdf (accessed 30 October 2008).
Contents lists available at ScienceDirect
Field Crops Research journal homepage: www.elsevier.com/locate/fcr
Measuring water stress in a wheat crop on a spatial scale using airborne thermal and multispectral imagery Mohammad Abuzar a,*, Garry O’Leary b, Glenn Fitzgerald b a b
Future Farming Systems Research, Department of Primary Industries, 32 Lincoln Square North, Carlton, Victoria 3053, Australia Department of Primary Industries, 110 Natimuk Road, Horsham, Victoria 3400, Australia
A R T I C L E I N F O
A B S T R A C T
Article history: Received 15 July 2008 Received in revised form 5 February 2009 Accepted 5 February 2009
A modified stress index is proposed that accounts for both chronic and acute water stress. Current trapezoid methods that use vegetation cover and temperature indices do not necessarily measure chronic conditions. The modified method describes the chronic stress as the ratio of actual crop cover to its potential expressed such that zero stress occurs when actual cover equals or exceeds the potential as determined by a simulation model. The advantage of such a definition is that in areas where full groundcover is rarely achieved, such as semi-arid regions, a more realistic and conservative stress condition will be observed. Airborne thermal and multispectral images were acquired at four growth stages of a wheat crop from a site in Victoria, Australia with experimental plots having rain-fed and irrigated regimes over two seasons (2005 and 2006). The theoretical basis of vector determination was adopted for trapezoidal extent per season. The relationship between such chronic stress and acute stress is explored and show that in any 2 years large differences between these stresses exist. ß 2009 Elsevier B.V. All rights reserved.
Keywords: Crop water deficiency Chronic water stress Acute water stress Airborne imagery Precision agriculture
1. Introduction Understanding crop water stress is important for in-season management of irrigated and rain-fed crops. One method that has long been used in measuring water stress is crop surface temperature (Idso et al., 1981; Jackson et al., 1981; GonzalezDugo et al., 2006). As crops transpire, water loss reduces leaf temperature through evaporative cooling (Allen et al., 1998). In well-watered situations, the leaf surface temperature often becomes much lower than that of the surrounding air. Conversely, water-stressed crops transpire less and crop surface temperature increases, typically rising above the surrounding air temperature (Jackson, 1982). The difference between the crop surface and air temperatures (Ts Ta) is therefore a good indication of crop water stress. The first conclusive demonstration that leaf temperature can be cooler than air temperature was given by Ehler (1973). Using thin wire thermocouples to measure leaf temperature, it became evident from his experiments that Ts Ta was linearly related to vapour pressure deficit (VPD). The VPD is the difference between water vapour pressures of the surrounding air and saturated air at the same temperature. Idso et al. (1981) and Jackson et al. (1981) made use of Ehler’s findings in developing a crop water stress index
* Corresponding author. Tel.: +61 3 8341 2435; fax: +61 3 9347 6056. E-mail address: [email protected] (M. Abuzar). 0378-4290/$ – see front matter ß 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.fcr.2009.02.001
(CWSI) for canopies at full cover, a measure which later received wide acceptance. In their work, canopy temperature was measured by infrared thermometers (IRT) instead of thermocouples. Distinction needs to be made between individual leaves and the leaves arranged in a crop canopy even though the mechanism of temperature differences is essentially the same (Idso et al., 1967). When an instrument like an IRT provides point measurements, the intent is generally to characterise a whole canopy, which usually comprises multiple point measurements. With the advent of thermal scanners and imagers and their use as hand-held, vehicledriven, airborne or space-borne instruments, point or small sample information has largely been replaced by the more complete fullsample coverage of canopy temperature. The theoretical approach to CWSI (Jackson et al., 1981) and its practical applications (Idso et al., 1981; Idso, 1982) require that baselines for non-stressed and fully stressed canopies are defined. Baselines are crop and/or variety specific in relation to Ts Ta and VPD (Idso, 1982). The concept of the CWSI is confined to full canopies only and does not provide an immediate solution for incomplete cover where bare soil is included. In order to overcome this limitation, Moran et al. (1994) introduced the concept of a vegetation index versus temperature (VIT) trapezoid. This provides a blended surface temperature response comprising a crop and soil temperature mix. A trapezoidal shape is generally formed when Ts Ta is plotted against vegetation cover, provided sufficient data points are available and adequate spatial variability exists in water status and crop cover. According to Moran et al. (1994) the water
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M. Abuzar et al. / Field Crops Research 112 (2009) 55–65
deficit index (WDI) of a point is its distance from cold front in proportion to the distance between cold and warm fronts. WDI for any full cover point is equivalent to Idso–Jackson’s CWSI at a given VPD. Clarke (1997) provided a pragmatic approach to the VIT trapezoid by reducing certain conditions on the definition of vertices proposed by Moran et al. (1994) but his proposed approach also relied on field information. The empirical approaches to VIT framework help implementation of the trapezoid models to some extent (Mendez-Barrosa et al., 2008). As the purpose of the VIT trapezoid is to determine the stressed part of the crop at a particular time, Clarke (1997) divided the trapezoid into sections and identified a region of the trapezoid representing a stressed crop. A limitation of the CWSI is its focus on the acute crop water status and it does not necessarily consider chronic water stress conditions. Similarly, the trapezoid approach uses an instantaneous (acute) measure of crop temperature and, whilst accounting for incomplete cover assumes a linear relationship between soil temperature and crop temperature displacement as soil is covered or uncovered by the crop canopy. For perennial vegetation we assumed that any cover less than unity indicates a chronic stressed condition, but for annual crops this is not necessarily true and in some environments crops never fully cover the ground (O’Connell et al., 2004). We investigate and propose an annual crop version for the trapezoid where vegetative cover is relative to a potential cover predicted at that stage of development through a simple crop model. In this way, both chronic and acute water stress status can
Table 1 Airborne acquisitions and crop growth stages. Flight dates
DOY
Local time (hh:mm)
Solar zenith (8)
Crop growth stage (Zadoks)
21 07 17 21
July 2005 September 2005 October 2005 November 2005
202 250 290 325
12:46 14:25 13:34 12:02
63 45 31 34
13 (3-leaf) 22 (tillering) 60 (anthesis) 80 (dough development)
08 30 30 24
August 2006 August 2006 September 2006 October 2006
220 242 273 297
11:34 10:55 12:31 11:13
69 69 44 49
15 24 32 65
(5-leaf) (tillering) (stem elongation) (mid-anthesis)
Table 2 Weather data for the site close to flight times. Dates
Time (hh:mm)
Air temperature (8C)
Relative humidity (%)
Vapour pressure deficit (kPa)a
21 07 17 21
July 2005b September 2005b October 2005b November 2005b
12:46 14:25 13:34 12:02
8.2 20.4 23.9 20.2
74.7 48.0 31.5 48.5
0.3 1.2 2.0 1.2
08 30 30 24
August 2006c August 2006c September 2006c October 2006c
11:30 11:00 12:30 11:15
15.5 14.0 16.1 28.0
49.8 68.7 44.5 12.5
0.9 0.5 1.0 3.3
a b c
Calculated using equations by Allen et al. (1998). Bureau of Meteorology. Onsite weather station.
be assessed from one graph and appropriate management intervention implemented as needed. 2. Materials and methods A field study was established on an experimental farm near Horsham, Victoria, Australia (36.748S latitude, 142.108E longitude, elevation 126 m). Crops of wheat (Triticum aestivum L., cv Chara) were sown in two consecutive years. The experiment was set up comprising 48 plots in 2005 and 16 plots in 2006 (Fig. 1). Each plot was of 12 m 20 m in size. Half of the plots were irrigated and the
Fig. 1. The identification and the irrigation status of plots under study are shown on an airborne multispectral image taken with Duncan MS3100 imager on 30 September 2006. Band combination (RGB) includes NIR, red-edge and red. All 48 plots were investigated in 2005 but in 2006 only 16 plots (17–32) were sown.
Fig. 2. A hypothetical trapezoid defines crop water status. Acute water stress index (ASI) = AC/AB. Chronic water stress index (CSI) = DC/DE.
M. Abuzar et al. / Field Crops Research 112 (2009) 55–65
57
Fig. 3. Illustration shows an example of determining the trapezoidal vertices for the purpose of estimating crop water stress. This illustration is based on the actual data set of 30 September 2006 of the study area. The vertices were determined as per procedure given by Moran et al. (1994) and Colaizzi et al. (2003b). Crop cover was scaled in reference to the potential crop cover determined by a crop model (O’Leary and Connor, 1996) as shown on the left.
remaining plots were rain-fed. The crop was sown on the 2nd of June in 2005 and on the 31st of May in 2006. The study area was overflown four times each year to acquire high-resolution multispectral and thermal image datasets of the crops. The dates of flights were chosen to coincide with key stages of crop growth (Table 1). 2.1. Airborne multispectral data The two imagers used in the airborne acquisitions were a Redlake MS3100 3-CCD multispectral imager (Redlake MASD Inc., Tucson, AZ, USA) and a Therma CAM P40 (FLIR, Danderyd, Sweden). The MS3100 had three 8-bit narrow wavebands centred at 670 nm (red), 720 nm (red edge) and 790 nm (infrared). The bands widths were 25, 10 and 25 nm respectively. The FLIR imager had a spectral range of 7.5–13 mm and output K as 16-bit pixels. The imagers were custom-mounted in a survey port in the floor of a Cessna 182-RG light aircraft. Flight altitude was around 1000 m above ground level and both imagers had fields of view near 158 208. All flights occurred within 2 h of solar noon. Both multispectral and thermal images were acquired within 4 s of each other. Skies were clear except on 24 October 2006 when it was partly cloudy (5 Oktas), but images were collected where the crop plots were fully illuminated and fully shaded. All images were geo-referenced in ERDAS Imagine (Leica Geosystems, Norcros, GA, USA) to MGA94 (Zone 54, Datum: GDA94, Spheroid: GRS1980) coordinate system. All the images were first resampled close to their original resolution using the nearest neighbour (NN) method. Pixel resolution of the MS3100 images was close to 0.2 m. These were resampled to match the coarser resolution of FLIR images (2 m in 2005 and 1 m in 2006). For this resampling, a bilinear interpolation method was used, which allowed the original pixel values to be averaged. Prior to geo-referencing, the MS3100 images were pre-processed in ENVI (ITT, Boulder, CO, USA) for flat field correction, inter-band coregistration, spatial noise filtering (Minimum Noise Fraction) and converted to reflectance using known values of ground panels placed adjacent to the experiment. A total of 8508 data points were extracted from the imagery in 2005 and 2435 data points in 2006.
2.2. Field measurements A handheld IRT (Model 100.3ZL, Everest Interscience, Tucson, AZ, USA) was used during and near flight times to measure surface temperature transects on all plots and calibration patches. The IRT used has a bandwidth of 8–14 mm with adjustable emissivity between 0.2 and 0.98. Nadir and oblique (foliage) measurements were acquired by walking along the E side of each plot, pointing the IRT obliquely towards the west. When compared with Therma CAM values, the IRT observations were marginally higher. An automatic weather station (AWS Model WXT510, Measurement Engineering Australia, Magill, Australia) located at the study site recorded air temperature, relative humidity, global solar radiation, wind speed, and rainfall at 15 min intervals in 2006. For 2005, the weather data was obtained from the Bureau of Meteorology
Fig. 4. Relationship between SAVI and crop cover is shown. SAVI data points were derived using airborne multispectral imagery and crop cover was determined using ground-based photos from field measurements during 2004–2006.
M. Abuzar et al. / Field Crops Research 112 (2009) 55–65
58
(www.bom.gov.au) from the local recording stations (Polkemmet – # 79023 and Horsham Aerodrome – #079100) within 10 km. The observations from Bureau of Meteorology and the in-field weather station can be considered comparable (Bureau of Meteorology, 2005). The saturated and actual vapour pressure values were calculated using these weather records (Table 2). The crop was machine harvested on 20th December in 2005 and on 11th December in 2006 using a Kingaroy Header Cut Harvester with a width of 1.8 m. Calibration panels (2.8 m 2.8 m) were also established to convert the raw digital number data from the multispectral images to reflectance. Reflectance of the panels was calculated by measurement several times during the season with a FieldSpec Pro (Analytical Spectral Devices, Boulder, Colorado, USA) referenced to a 99% Spectralon (Labsphere, North Sutton, NH, USA) panel. Plot reflectance values were also acquired at or near flight times with the FieldSpec Pro and the soil-adjusted vegetation index (SAVI) (Huete, 1988) calculated. 2.3. Methods of quantifying crop water stress 2.3.1. Chronic stress Chronic water stress was measured as the ratio of the difference between actual crop cover and potential crop cover, such that the stress index is zero when the actual crop cover is equal to the potential cover on the day of measurement (Figs. 2 and 3): viz; Chronic water stress index ðCSIÞ ¼
DC DE
(1)
Airborne multispectral imagery and field measurements collected over the sites during 2004–2006 were used to measure crop cover by establishing its relationship with soil-adjusted vegetation index (SAVI) and green leaf area index (GLAI). The crop cover is highly correlated with SAVI (R2 = 0.89, Fig. 4). Potential crop cover was calculated using the crop simulation model of O’Leary and Connor (1996). SAVI was estimated using the near infrared (NIR) and red wavebands from the MS3100 imager: SAVI ¼
ð1 þ LÞ ðP 790 P 670 Þ ðL þ P 790 þ P 670 Þ
(2)
L is a constant taken here as 0.5, which is suitable for intermediate vegetation cover (Huete, 1988). P790 and P670 are wavebands in the NIR (790 nm) and red (670 nm) parts of the spectrum, respectively. 2.3.2. Acute stress Crop water stress occurs when the crop cannot meet its transpiration demand at the current VPD. This can be problematic when considering the differences between chronic and acute stress because various crop processes have different thresholds for stress. For example, leaf expansion is reduced much earlier than photosynthesis during progressive water deficit. Nevertheless, the degree of stress is therefore the relative portion of the unmet overall demand reflected in a decreased Ts Ta. Irrespective of current VPD, if the transpiration demand is met the crop is deemed to not be under acute water stress, despite various lower efficiencies of water use that might exist, particularly at high VPD. This is often expressed as a transpiration ratio, commonly used in many simulation models, or the Ts Ta difference over the maximum possible difference. Thus, for a fully stressed crop with full canopy (i.e. a dead crop with zero transpiration), Ts will be close to Ta or slightly hotter depending on the albedo differences between the soil and crop. The acute stress index is the ratio of the Ts Ta temperature difference from the unstressed Ts Ta over the
maximum possible difference (Fig. 2), viz; Acute water stress index ðASIÞ ¼
AC AB
(3)
A scatter plot of Ts Ta versus crop cover usually shows a cloud of data points (pixels) forming the shape of a trapezoid. The bounds of the shape require four vertices which can be defined analytically using the established relationships (e.g. Idso–Jackson model), the derived parameters (Moran model) or direct field measurements. The upper bound of the trapezoid represents full crop cover whereas the lower bound represents bare soil. Thus the crop cover for the four vertices is known. It is the Ts Ta which needs to be determined. The vertices of the trapezoid represent four contrasting situations: (1) well-watered full crop cover, (2) water-stressed full crop cover, (3) saturated bare soil, and (4) dry bare soil. The line joining the vertices representing the water-stressed full cover and the dry bare soil defines the warm front, indicating the status of maximum acute water stress. The line joining the vertices representing well-watered full cover and saturated bare soil defines the cold front indicating a situation of no acute water stress. All data points are expected to lie within the trapezoid (except certain man-made objects). The procedure of determining the trapezoidal vertices are based on energy balance equations (Monteith, 1973). The derived formulations for the full crop cover (Jackson et al., 1981) and for the partial cover (Moran et al., 1994) have been considered appropriate for the present study. In an attempt to Table 3 Values used to define trapezoid vertices as shown in Figs. 5 and 6. Dates
Vertexa
21 July 2005
1 2 3 4
0.90 3.36 1.46 5.23
0 0 1 1
07 September 2005
1 2 3 4
2.87 2.73 3.00 8.29
0 0 1 1
17 October 2005
1 2 3 4
3.33 5.29 2.40 19.70
0 0 1 1
21 November 2005
1 2 3 4
0.30 8.00 0.95 16.25
0 0 1 1
8 August 2006
1 2 3 4
0.10 6.43 1.11 11.70
0 0 1 1
30 August 2006
1 2 3 4
0.01 5.95 1.10 12.28
0 0 1 1
30 September 2006
1 2 3 4
0.76 7.86 3.97 19.01
0 0 1 1
24 October 2006
1 2 3 4
5.00 5.34 2.00 17.70
0 0 1 1
Ts Ta (8C)
‘‘1 - Actual/Potential’’ crop cover
a 1. Cold front vertex at cover = 1, 2. Warm front vertex at cover = 1, 3. Cold front vertex at cover = 0, 4. Warm front vertex at cover = 0 (Cover here denotes ‘‘1 - Actual/ Potential’’ crop cover).
M. Abuzar et al. / Field Crops Research 112 (2009) 55–65
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Fig. 5. Scatter diagrams of surface-air temperature differences versus actual/potential crop cover ratio as captured on four occasions during the 2005 wheat crop. Each dot represents an area of 2 m 2 m on ground.
explore the alternative procedure, the empirical approach for the full crop cover (Idso, 1982) and the field calibration data for the bare soils (wet and dry) were tested but the resultant vertices were deficient in some instances to encompass the trapezoidal spread of the study area. The four terms of (Ts Ta) that refer to the four vertices are (Moran et al., 1994; Colaizzi et al., 2003b): ðT s T a Þ1 ¼
g 1 ð1 þ r cp =ra1 Þ r a1 ðRn1 G1 Þ C v1 D1 þ g 1 ð1 þ rcp =r a1 Þ
ðT s T a Þ2 ¼
VPD
D1 þ g 1 ð1 þ rcp =r a1 Þ
Table 4 Summary of potential and actual GLAI and crop cover for 2005 and 2006.
(4)
r a2 ðRn2 G2 Þ g 2 ð1 þ r cx =ra2 Þ C v2 D2 þ g 2 ð1 þ rcx =ra2 Þ
VPD
D2 þ g 2 ð1 þ rcx =ra2 Þ
1013 J kg1 8C1 and r = 1000P/(1.01Ta 287); P denotes atmospheric pressure (kPa). ra is aerodynamic resistance (s m1) based on Campbell model without stability correction (Kjelgaard et al., 1996). Rn is net radiation (W m2); G is soil heat flux (W m2); g is psychrometric parameter (kPa 8C1); rcp is the canopy resistance at potential transpiration; and rcx is the upper limit of canopy resistance; and D is the slope of the saturated vapour pressure–
(5)
ðT s T a Þ3 ¼
r a3 ðRn3 G3 Þ g3 VPD C v3 D3 þ g 3 D3 þ g 3
(6)
ðT s T a Þ4 ¼
r a4 ðRn4 G4 Þ C v4
(7)
Cv is the volumetric heat capacity of the air (J 8C1 m3) as product of air specific heat (Cp) and air density (r) where Cp is constant
Date
DOY
Potential GLAIa
Potential covera
Actual GLAIb
Actual Coverb
Sowing 21 July 2005 7 September 2005 17 October 2005 21 November 2005 Harvest
153 202 250 290 325 354
0.11 2.60 5.16 0.26
0.05 0.73 0.92 0.12
0.16 2.30 2.87 0.43
0.13 0.41 0.46 0.19
Sowing 8 August 2006 30 August 2006 30 September 2006 21 October 2006 Harvest
151 220 242 273 297 345
0.25 1.50 4.87 3.69
0.12 0.53 0.91 0.84
0.10 0.57 1.35 1.31
0.11 0.23 0.33 0.32
Note: Actual cover refers to the mean of crop cover of all plots. a From O’Leary and Connor (1996) model. b Ground-based biophysical data.
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M. Abuzar et al. / Field Crops Research 112 (2009) 55–65
Fig. 6. Scatter diagrams of surface-air temperature differences versus actual/potential crop cover ratio as captured on four occasions during the 2006 wheat crop. Each dot represents an area of 2 m 2 m on ground.
temperature relation (kPa 8C1). rcp and rcx have been taken as constant 15 and 250 s m1, respectively (Shen et al., 2002; Niyogi and Raman, 1997) The average of Ta and Ts replacing Ta was recommended by Jackson et al. (1981). This has been adopted for the vectors representing the extreme dry conditions (i.e. vectors 2 and 4)
only, as in the extreme wet conditions, air and surface temperatures are assumed equal. The minimum wind speed taken is 2 m s1 as the lower value may result in an unrealistic ra (Colaizzi et al., 2003a). For full cover, 0.92 m is taken as crop height as estimated for Chara variety (Stapper, 2004). Rn and G were taken as specific fraction of incoming short-wave solar
Fig. 7. The plotted mean values of CSI and ASI show the seasonal changes at a glance. Bars show the standard deviation. (a) 2005 and (b) 2006.
M. Abuzar et al. / Field Crops Research 112 (2009) 55–65
radiation (Colaizzi et al., 2003b). An example of term calculation has been shown in Appendix A. Our modified chronic stress trapezoid is summarised diagrammatically (Fig. 3). Table 3 lists the respective vertices for each sampling time. We further developed a compound stress index (COSI) as a way of expressing the relative magnitude of the likely overall stress impact. COSI ¼ ASI CSI
(8)
As such, strongly positive values represent a dominant chronic condition and strongly negative values reflect a dominating acute condition. A zero compound index reflects equal acute and chronic conditions. 3. Results 3.1. Chronic and acute water stress differences The modified trapezoid of Ts Ta versus ‘‘1 - actual/potential’’ cover ratio is shown in Figs. 5 and 6. It represents a wide range in crop growth stage from sampling four dates over 2 years (Table 3).
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It is noted that ‘‘potential’’ was less than ‘‘actual’’ in two cases referring to an early crop stage (21 July) and a late stage (21 November) in 2005. At both stages the overall crop cover itself was very low. Even though the difference between potential and actual was very small in both cases it is not considered significant. It was however, considered important to maintain the range of calculated CSI between 0 and 1, hence as a procedure, the applicable ‘‘potential’’ cover should be more than or equal to the ‘‘actual’’. The irrigated and rain-fed crops in 2005 can easily be distinguished (Fig. 5). The position of any point along the Ts Ta axis in the scatter diagrams indicates the acute stress which is determined by reference to the cold and warm fronts. The position of a point along the axis of actual/potential cover ratio, indicates chronic stress. At the early crop (3-leaf) stage in 2005, there was very low acute stress shown (Fig. 5a). At that stage, the average crop cover for the whole experimental site was only 0.13 whereas the potential cover for that stage was even lower, 0.05 (Table 4). The irrigated and the rain-fed areas showed a large overlap that is indicative of the uniform ground conditions at that growth stage. The tillering stage (Fig. 5b) shows more variation in chronic stress than acute. Around anthesis, however, acute stress was more varied with some distinction between irrigated and rain-fed areas
Fig. 8. Scatter plots showing chronic stress versus acute stress in 2005 season.
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(Fig. 5c). By the time of dough development stage (Fig. 5d), the crop has started to senesce. The acute stress was the highest at this stage. Similarly in 2006, Fig. 6 shows the varying march of the chronic and acute stress across the sown areas. At the 5-leaf stage (Fig. 6a) the acute stress was low. The irrigated and the rain-fed areas
overlapped almost entirely, indicating a lack of variation in ground conditions at that stage. Crop cover increased through tillering and stem elongation but remained much lower than expected (Table 4). Hence the data indicate more chronic than acute stress (Fig. 6b and c). Around mid-anthesis (Fig. 6d) however, acute stress increased mainly in the rain-fed areas.
Fig. 9. Scatter plots showing chronic stress versus acute stress in 2006 season.
Fig. 10. Average difference between chronic stress and acute stress over 2005 and 2006 seasons. (a) 2005 and (b) 2006.
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3.2. Temporal expression of water stress
Table 5 Total seasonal irrigation, rain, stress measures, nitrogen treatment and crop yield.
The relative relationship between chronic and acute stress is shown in Figs. 8 and 9 preceded by a summary diagram (Fig. 7) as an X–Y graph and as a compound index (COSI) over crop phenological development time (Fig. 10). The crop at the early growth stage (3-leaf) in 2005 appeared largely free from any water stress where both ASI and CSI values were low (predominantly <0.2) with little spatial variations across the plots (data not shown). At the stage of tillering (7 September 2005), chronic stress was high (predominantly 0.3–0.6). Around anthesis (17 October), there was a uniform very high chronic stress, whereas acute stress was mostly low (the main concentration of points below about 0.4 represented 94% of the data points). The rain-fed plots had relatively high ASI values (Fig. 8c). Around the crop stage of dough development (21 November 2005), the ASI values were generally high except in some irrigated areas (not shown). In contrast, chronic stress was low to moderate at this stage (Fig. 8d). In 2006, the crop at the early stage (5-leaf) showed more chronic stress than acute. There were noticeable variations within and across the plots (data not shown). At tillering stage (30 August), chronic stress increased uniformly across all plots whereas acute stress reduced in irrigated areas but increased in some rain-fed areas. At the stem elongation stage (30 September), acute water stress was uniformly low in all plots whereas chronic stress increased to a very high level. Around mid-anthesis (24 October), both chronic and acute stresses were higher with considerable spatial variations. The rain-fed plots showed the maximum stress as expected. In 2005 near growth stage 30, we conclude that the crop was mildly dominantly chronically stressed and in 2006 it was severely chronically water stressed (Fig. 10). Only at the end of the season was the crop considered to be under a dominantly acute condition. Similarly, in 2006 the crop exhibited a strong chronic stressed condition but with equal acute and chronic conditions early and late in the season.
Treatment
3.3. Spatial expression of water stress The combined index was calculated for individual pixels from our experimental site and mapped for 17 October 2005 (Fig. 11). This shows significant spatial variation indicating greater chronic stress (blue) in the irrigated plots compared to the lesser chronic
Fig. 11. Difference maps of chronic stress and acute stress (COSI) for 17 October 2005.
Rain-fed without N
Rain-fed with N
Irrigated without N
Irrigated with N
(a) 2005 Crop Irrigation (mm)a Rain (mm)b N application (kg/ha)c CSId ASId Yield (t/ha)
Nil 203 Nil 0.53 0.38 2.84
Nil 203 87 0.50 0.39 3.00
150 203 Nil 0.50 0.22 3.44
150 203 87 0.47 0.21 3.48
(b) 2006 Crop Irrigation (mm)e Rain (mm)f N application (kg/ha)g CSIh ASIh Yield (t/ha)
Nil 130 Nil 0.70 0.74 2.06
Nil 130 196 0.67 0.73 1.75
240 130 Nil 0.58 0.62 3.68
240 130 196 0.52 0.59 3.89
a b c d e f g h
Irrigation on (DOY): 100, 132, 235 and 265. Rainfall during active crop season. N application on (DOY): 253. Image acquired on (DOY): 290. Irrigation on (DOY): 114, 180, 231, 254, 257, 282 and 303. Rainfall during active crop season. N application on (DOY): 264. Image acquired on (DOY): 297.
stressed (light blue) dryland plots. Similarly in 2006, there was significant spatial variation in COSI seen between the irrigated and dryland plots (data not shown). The edge effects seen in the plots are noteworthy, indicating greater acute stress, especially in the rain-fed plots. Application of irrigation has apparent influence on stress condition and final yield as expected whereas nitrogen application has no or negligible influence (Table 5). 4. Discussion The extension of the acute water stress index method of Idso et al. (1981), Idso (1982) and subsequent variants by Moran et al. (1994) and Clarke (1997) to include a measure of chronic water stress progresses toward a unique non-destructive method of assessing crop water stress in a spatial and temporal context. This is particularly important when considering annual crops that do not normally attain full groundcover as is common in much of the semi-arid regions of the world. The spatial aspect is particularly suited to remote sensing for applications in precision agriculture where knowledge of the type of water stress will dictate the kinds of intervention that would be suitable for a profitable outcome. We have assumed that the ratio of the actual groundcover of the crop to its potential groundcover is indicative of its chronic stress status for rain-fed crops. This is not unreasonable as leaf area expansion is one of the first physiological processes affected by water stress and the nonlinear relationship (Beer’s Law) between groundcover and LAI is well known. Of course, other factors than water shortage can reduce groundcover. One of these is late sowing, but contemporary simulation models can account for this. Low nutrient status may also reduce leaf area expansion under adequate water supply. Our experiments were all conducted on soil with large amounts of soil N and had adequate P applied. One of the problems of our combined index is that the relative components become obscured. A zero COSI could occur because of either a very low but equal chronic and acute stress or very high but equal chronic and acute stress. Clearly, the X–Y graphs of each index are more informative, but the combined index highlights any asymmetry between the relative stresses (Fig. 11). One important issue that we have not addressed in this analysis is determining the intervention point. This will no doubt be
M. Abuzar et al. / Field Crops Research 112 (2009) 55–65
64 Table 6 Parameters and equation terms for 30 September 2006. Description
Notation
1
Wind speed (m s ) Height of measurement (m) Crop height (m) Air temperature (8C) Average surface temperature (8C)b Applicable Ta (8C) Aerodynamic resistance (m s1) Vapour pressure deficit (kPa) Incoming solar radiation (W m2) Net radiation (W m2) Soil heat flux (W m2) Volumetric heat capacity of the air (J 8C1 m3) Heat capacity of the air (J kg1 8C1) Air density (kg m3) Psychrometric constant (kPa 8C1) Theoretical surface-air difference (8C) a b
uz zm, zh h Ta T 0s T 0a ra VPD Rs Rn G Cv Cp
r g (Ts Ta)
Verticesa 1
2
3
4
2.5 2 0.92 16.1 25.4 16.1 28.67 1.01 776 543.2 54.32 1205.7 1013 1.19 0.067 0.76
2.5 2 0.92 16.1 25.4 20.75 28.67 1.36 776 543.2 54.32 1186.6 1013 1.17 0.067 7.86
2.5 2 0.12 16.1 25.4 16.1 83.06 1.01 776 543.2 162.96 1205.7 1013 1.19 0.067 3.97
2.5 2 0.12 16.1 25.4 20.75 83.06 1.36 776 388.0 116.4 1186.6 1013 1.17 0.067 19.01
1. Wet vegetation, 2. Dry Vegetation, 3. Wet soil, and 4. Dry soil. Average surface temperature of the study site obtained from the airborne Therma CAM.
dependent on the management options under consideration. For example, in rain-fed agriculture topdressing of N fertiliser is an important post-sowing option available to farmers if there is a profitable potential to utilise the additional fertiliser. That is, there is a sufficient soil water reserve or likelihood of imminent rainfall and there is sufficient capacity to grow. Our combined stress index might also have application with respect to non-water stresses like nutrient deficiency or diseases that affect the ability of the plant to transpire, but this remains to be tested. 5. Conclusions A modified trapezoid water stress model that accounts for chronic and acute water stress is proposed. The input required is largely the same as traditionally used for the existing trapezoid models for acute water stress, but additionally requires potential crop cover that can be derived readily by any suitable crop model. The model is applicable to both irrigated and dryland crops. Our combined stress index might also have application with respect to non-water stresses like nutrient deficiency and diseases but this remains to be tested. Airborne multispectral and thermal image data provides a good basis of measuring and monitoring crop water stress both in irrigated and rain-fed situations. Measurements are effective at various scales ranging from micro-level (1–2 m) to plot or field level. However, the proposed methods of water stress measurement are also likely appropriate in larger scale applications involving satellite data both at farm and regional scales. Acknowledgements The authors gratefully acknowledge the financial support for this work through the Victorian Government’s Our Rural Landscape initiative. We also thank an anonymous referee for helpful comments on an earlier draft. Appendix A The aerodynamic resistance (ra) used in Eqs. (4)–(7) was calculated as thus (Allen et al., 1998): ra ¼
ln½ðzm dÞ=Z om ln½ðZ h dÞ=Z oh 2
k uz
where ra = aerodynamic resistance (s mS1); zm = height of wind measurement (m); zh = the height of humidity measurement (m); d = zero plane displacement height in m = 2/3h; h = the crop height (m); zom = roughness length governing momentum transfer (m) = 0.123h; zoh = roughness length governing transfer of heat and vapour (m) = 0.1zom; uz = wind speed (m sS1) at zm; k = Karman’s constant = 0.41. For each VIT trapezoid vertices, Rn and G were taken as the specific fractions of the incoming solar radiation (Rs) as noted by Colaizzi et al. (2003b): Rn1, Rn2 and Rn3 as seven-tenths of Rs whereas Rn4 as half of Rs; G1 and G2 as one-tenth of the respective Rn; G3 and G4 as threetenths of the respective Rn. Table 6 represents an example of the calculation of the terms used in Eqs. (4)–(7). References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop Evapotranspiration. Food and Agriculture Organisation (FAO), Rome. Bureau of Meteorology, 2005. Automatic weather stations for agricultural and other applications. www.bom.gov.au/inside/services_policy/pub_ag/aws/aws.shtml (accessed December 2008). Clarke, T.R., 1997. An empirical approach for detecting crop water stress using multispectral airborne sensors. HortTechnology 7 (1), 9–16. Colaizzi, P.D., Barnes, E.M., Clarke, T.R., et al., 2003a. Estimating soil moisture under low frequency surface irrigation using crop water stress index. J. Irrig. Drain EASCE 129 (1), 27–35. Colaizzi, P.D., Barnes, E.M., Clarke, T.R., et al., 2003b. Water stress detection under high frequency sprinkler irrigation with water deficit index. J. Irrig. Drain EASCE 129 (1), 36–43. Ehler, W.L., 1973. Cotton leaf temperature as related to soil depletion and meteorological factors. Agron. J. 65 (3), 404–409. Gonzalez-Dugo, M.P., Moran, M.S., Mateos, L., Bryant, R., 2006. Canopy temperature variability as an indicator of crop water stress severity. Irrigation Sci. 24, 233– 240. Huete, A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25, 295–309. Idso, S.B., Baker, Donald, G., 1967. Relative importance of reradiation, convection, and transpiration in heat transfer from plants. Plant Physiol. 42, 631–640. Idso, S.B., Jackson, R.D., Pinter, P.J., Reginato, R.J., Hatfield, J.L., 1981. Normalizing the stress-degree-day parameter for environmental variability. Agric. Meteorol. 24, 45–55. Idso, S.B., 1982. Non-water-stressed baselines: a key to measuring and interpret ting plant water stress. Agric. Meteorol. 27, 59–70. Jackson, R.D., Idso, S.B., Reginato, R.J., Pinter, P.J, 1981. Canopy temperature as a crop water stress indicator. Water Resour. Res. 17 (4), 1133–1138. Jackson, R.D., 1982. Canopy temperature and crop water stress. Advances in Irrigation, vol. 1. D. Hillel. New York, Academic Press, pp. 43–85. Kjelgaard, J.F., Stockle, C.O., Evans, R.G., 1996. Accuracy of canopy temperature energy balance for determining daily evapotranspiration. Irrig. Sci. 16, 149–157.
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