Introduction

Water saving agriculture and sustainable use of water resources are extremely important for China because of the shortage of water resource and high yield pressure on farmland caused by huge population and developing economy. The over utilization of ground water in Northern China Plain (one of the main food production regions of China) causes serious water table decrease by as much as 1 m per year. After several decades of irrigation, the ground water table in this area has decreased about 30 m (Zhang et al. 2003). Therefore, it is necessary to detect water condition of crops for optimum water management and irrigation scheduling.

Irrigation scheduling based on measurement of soil water content or meteorological parameters for estimating evapotranspiraton, may be time consuming or rely on expensive equipments. Irrigation scheduling based on crop water status should be more advantageous since crops respond to both soil and aerial environments. With the development of infrared (IR) thermometry, it has been widely applied to measure canopy temperature by IR thermometers to detect water stress of crops. Based on the assumption that water becomes limiting, transpiration is reduced and crop temperature will be higher than air temperature because of the absorbed radiation, a number of scholars have studied the detection of crop water stress by canopy and leaf temperatures for the last 20 years (Jackson 1982; Idso and Clawson 1986).

Idso et al. (1981a) presented an empirical method for determining water stress of crops by estimating “non-water-stressed baselines”, which represents the lower limit of temperature of a particular crop canopy if transpiring at the potential rate. However, this empirical method does not account for net radiation and wind speed, and the baselines vary with crop species and seasons (Idso 1982; Hatfield et al. 1984; Burke et al. 1990; Nielsen 1994). The baseline has to be determined experimentally, which precludes its transfer to different climatic conditions. Using a simulation model, Stockle and Dugas (1992) pointed out that the empirical method provided late indication of irrigation needs, after some water stress has developed, which may limit its application for crops sensitive to water stress. Based on the energy balance, Jackson et al. (1981) presented a theoretical method for calculating CWSI by estimating net radiation, aerodynamic resistance, canopy temperature, air temperature, and vapor pressure deficit (VPD). This theoretical method offers an independent and direct measure of crop water status that can be used to supplement soil water measurements and/or crop water balance modeling either with crop growth or ET models to improve irrigation scheduling (Yazar et al. 1999). Although this approach specified how the upper and lower limits could be evaluated, more complex field measurement was required to get the parameter of aerodynamic resistance in order to determine minimum canopy resistance. Alves and Pereira (2000) proposed a new definition of non-water-stressed baseline theoretically based and driven by weather variables that can easily be measured and/or estimated, which allows measurements at any time of the day and whatever the weather conditions. Based on energy balance theory and field research, Qiu (1996) and Qiu et al. (1996a, b) developed a new approach to detect crop water stress by introducing an imitation leaf (a leaf without transpiration). This imitation leaf method proved to be a theoretically sound and practically simple way to estimate CWSI.

The objective of this research was to investigate widely the application of the imitation leaf method to detect water condition of winter wheat (Triticum aestivum L.) in Northern China Plain by field experiment in Luancheng, Hebei Province, the main production area of winter wheat in China.

Theory and methods

Idso and Clawson (1986) defined CWSI as: CWSI=(T c − T cl)/(T cu − T cl), where T c, T cl, and T cu is the canopy temperature, lower limiting canopy temperature, and upper limiting canopy temperature, respectively. The lower limiting canopy temperature is reached when the crop transpires without soil water shortage. On the other hand, the upper limiting canopy temperature occurs when plant transpiration is zero. Therefore, Qiu (1996) assumed that the temperature of imitation leaf (T p) is equivalent to the upper limiting canopy temperature (T cu). The CWSI by the imitation leaf method can be expressed as:

$$\hbox{CWSI} = \frac{{ T_{\rm c} - T_{\rm cl}}}{{ T_{\rm p} - T_{\rm cl}}}$$
(1)

Jackson et al. (1988) defined the lower bound of difference between the canopy and air temperature (T c−T a)ll by setting r cp=0 in Eq. 2. Assuming soil heat fluxes to be negligible, the lower limiting canopy temperature can be estimated by Eqs. 3, 4, and 5:

$$( T_{\rm c} - T_{\rm a})_{\rm ll} = \frac{{ r_{\rm a} ( R_{\rm n} - G)}}{{\rho C_{\rm p}}} \frac{{\gamma (1 + r_{\rm cp}/r_{\rm a})}}{{\Delta + \gamma (1 + r_{\rm cp}/r_{\rm a})}} - \frac{{e_{\rm a} ^ {*} - e_{\rm a}}}{{\Delta + \gamma (1 + r_{\rm cp}/r_{\rm a})}}$$
(2)
$$T_{\rm cl} = \frac{{R_{\rm n} (T_{\rm p} - T_{\rm a})}}{{R_{{\rm np}}}} \frac{{\gamma ^ *}}{{\Delta + \gamma ^ *}} - \frac{{e_{\rm a} ^{*} - e_{\rm a}}}{{\Delta + \gamma ^ *}} + T_{\rm a} $$
(3)
$$\gamma ^ * = \gamma \left[1 + \frac{{r_{\rm cp} \cdot R_{\rm np}}}{{\rho C_{\rm p} (T_{\rm p} - T_{\rm a})}}\right]$$
(4)
$$\Delta = \frac{{2503\exp {17.27T_{\rm a}}}/{{(T_{\rm a} + 237.3)}}}{{(T_{\rm a} + 237.3)^{2}}}$$
(5)

where T a is the air temperature (°C), r a the aerodynamic resistance (s m−1), R n the net radiation of canopy (W m−2), G the heat flux to soil (W  m−2), ρ the density of air (kg  m−3), C p the heat capacity of air (J  kg−1 °C−1), γ=6.6 kPa  °C−1, the psychrometric constant (Qiu et al. 1996b), e *a the saturated vapor pressure (kPa) at T c, e a the saturated air vapor pressure (kPa) at T a, Δ the slope of the saturated vapor pressure–temperature curve (kPa °C−1) (Allen et al. 1998), r cp the minimum canopy resistance (s m−1), R np the net radiation of the limitation leaf (W m−2), and T the average of the canopy and air temperature (°C). Therefore, the required parameters of the imitation leaf method are T c, T p, T a, R n, e *a , and e a. The aerodynamic resistance required by Jackson’s CWSI method is eliminated and accordingly the complexity reduced significantly .

Materials and experiment

A field experiment was conducted to verify the imitation leaf method during the winter wheat growing season in 2002–2003 at the Luancheng Agricultural Ecosystem Experimental Station, Chinese Academy of Sciences, in Hebei Province, the Northern China Plain (37°53′N, 114°40′E, 50.1 m above the sea level). The annual precipitation is 473 mm, concentrated in June–September. The dominant soil type is loam with average bulk density of 1.53 mg m−3. The field slope is less than 0.3%. The ground water table is 32 m below the ground surface with the mineral content of 0.5 g  L−1. The cultivar of winter wheat used was “4185”, which is the common winter wheat species in the local area and was sown on October 5, 2002 in rows 15 cm apart. There were six treatments with different irrigation schedules and each treatment had three/four replicates. One of the treatments is under rain-fed condition without irrigation I0. The other treatments were irrigated at different portions of the growing seasons with different irrigation amounts. The amount of water added to them was 60 mm (one-irrigation) I1, 120 mm (two-irrigation) I2, 180 mm (three-irrigation) I3, and 240 mm (four-irrigation) I4. There were 2 two-irrigation treatments I2a and I2b, which were irrigated at different portion of growing season. Twenty plots were designed randomly and each plot had an area of 40 m2 (4 m×10 m). Between two plots, a 2 m wide protect zone without irrigation was arranged to minimize the effects of two adjacent plots. Each plot was installed with a 2 m deep neutron access tube for soil moisture measurement. Manually flooding irrigation was applied with low-pressure plastic tube and a flow meter to control the quantity of irrigation to each plot.

Imitation leaves were made by coating thin glue layer in the middle of selected leaves. The glue layer obstructs stomata of leaves and eliminates transpiration, so the temperature of imitation leaf T p can substitute for the upper limiting canopy temperature T cu. The leaves selected as imitation leaves were the first or second leaf from the top, nonshadowed, with similar appearance. T p and T c were measured with IR thermometer (CT-3100N, Custom Ltd, Japan) with three replications at noon (12:30–13:30) on each clear day and we obtained 21 days datum. All the results illustrated in the next part are the mean values of each treatment during the season. T a and wind speed were obtained by Environment Meter (AHLT-100, Custom Ltd). For further analysis, photosynthesis rate and the stomata conductance were obtained by LI-6400 Portable Photosynthesis System (LI-COR Ltd, USA), the leaf water potential was obtained by leaf water potential meter (ZLZ-4, Lanzhou University) at the same time. Soil water content was measured with a neutron probe (IH-II) at 20 cm interval down to 2 m once a week. Soil water content of 0–20 cm was measured gravimetrically because neutron probe does not measure an accurate reading near the soil surface. Solar radiation and water vapor pressure were obtained from the meteorological station near the experimental site. Twenty plants from each plot were harvested randomly every ten days, the length and width of leaf, also the height of plant, was measured manually in order to estimate leaf area index (LAI). Plant samples were put into oven for 12 h with 80°C temperature to measure the dry biomass of winter wheat. The crop was harvested manually to obtain the yield of each treatment on June 10, 2003.

Results and discussion

Upper limiting canopy temperature T cu

The key point of the proposed method is the replacement of T cu in Jackson’s model by T p. In Jackson’s theoretical method (Jackson et al. 1988), T cu is estimated by:

$$T_{\rm cu} = \frac{{r_{\rm a} R_{\rm n}}}{{\rho C_{\rm p}}} + T_{\rm a} $$
(6)

where the aerodynamic resistance term is included, which has to be estimated by wind velocity (Monteith and Unsworth 1990). The measurement of wind velocity under field condition is not a difficult task, however, due to its fetch requirement, the field must be uniform and large enough (Jones 1992). Another advantage to use surface temperature related approach to replace wind velocity is the possibility for remote sensing application (Karnieli et al. 2001).

Figure 1 shows a comparison of the measured T p and estimated T cu by Eq. 6. In general, T p is linearly related with T cu with a regression equation of y=1.09x−3.84 and regression coefficient r=0.988, indicating a good agreement between these two parameters. However, Fig. 1 also shows that T p is slightly larger than the estimated T cu. These differences are also a reasonable result if we examine the properties of these two independent parameters. T cu is a parameter estimated by net radiation, air temperature, and aerodynamic resistance. T p is the temperature of a leaf directly measured by IR thermometer. The reason that T p is larger than T cu is probably due to that the stomata of leaf are blocked by glue layer and leads to leaf temperature rising. A slight large value of T p may result in a slight under estimation of CWSI, which will be discussed in later section.

Fig. 1
figure 1

Comparison of measured imitation leaf temperature (T p) and the upper limiting canopy temperature (T cu) estimated by Jackson’s method

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This result agrees well with our former research with sorghum crop, which indicated a relationship of y=1.07x−2.08 and regression coefficient r=1.00 (Qiu et al. 1996b). In our former research, the imitation leaf was made from green paper by selecting which had nearly the same color as the plant leaf and cutting the paper in the shape of plant leaf. These results indicate that the replacement of T cu by T p is reasonable even if the different types of imitation leaf.

Comparison with Jackson’s CWSI

As previously mentioned, imitation leaf method estimates CWSI by Eqs. 1, 3, 4, and 5, where T a, T p, and T c are measured values, r cp is estimated by minimum stomata resistance r s and LAI: r cp = r s/LAI, here r s=35.4 s  m−1 was the minimum value of 720 stomata resistance values obtained by LI-6400 Photosynthesis System. R n and R np can be estimated by the following equations:

$$R_{\rm n} = (1 - \alpha)R_{\rm s} + \Delta R_{\rm l} $$
(7)
$$\Delta R_{\rm l} = \left(0.4 + 0.6\frac{{R_{\rm s}}}{{R_{\rm so}}}\right)(R_{l \downarrow} - R_{l \uparrow})$$
(8)
$$R_{{\rm l} \downarrow} = \varepsilon _{\rm B} \sigma (T_{\rm a} + 273.2)^4 $$
(9)
$$\varepsilon _{\rm B} = (9.2E - 6)T_{\rm a} $$
(10)
$$R_{{\rm l} \uparrow} = \varepsilon \sigma T^4$$
(11)

where R s is the solar radiation (W m−2), ΔR l is the net long-wave radiation (W m−2) and Eq. 8 was recommended by Weiss (1982) and Burman et al .(1983), α the albedo, α=0.22, R so the clear-day solar radiation (W m−2), here R so=1,055.5, which was the maximum solar radiation value at noon during the whole experiment period, \(R_{{\rm l} \downarrow} \) the incoming long-wave radiation (W m−2), \(R_{{\rm l} \uparrow}\) the outgoing long-wave radiation (W m−2), εB the atmospheric emissivity, σ Stefan–Boltzman constant (J m−2 K−4 s−1), σ=5.675 E−8, ε the emissivity, ε=0.98, and T the canopy or imitation leaf temperature (K).

The aerodynamic resistance r a in Jackson’s method is calculated by Eq. 12:

$$r_{\rm a} = \frac{{4.72\{\ln [(Z - d)/Z_0]\} ^2}}{{1 + 0.54u}}$$
(12)

where Z is the reference height (m), d the displacement height (m), d=0.63h, h the height of crop (m), Z 0 the roughness length (m), Z 0=0.13h, u the wind speed at height Z (m s−1). Figure 2 demonstrates that the CWSI calculated by the imitation leaf method and Jackson’s method agrees with each other with a regression coefficient of 0.999. The profile clearly shows that CWSI estimated by Jackson’s method is slightly larger than CWSI calculated by the proposed method, which may be due to the measured T p being slightly larger than the estimated T cu. Because Jackson’s method is unquestionable in theory, the agreement between the results of two methods shows that the imitation leaf method can be applied to detect crop water condition.

Fig. 2
figure 2

Comparison of crop water stress index (CWSI) estimated by imitation leaf method and Jackson’s method

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Relations with soil water content

Figure 3 demonstrates the relationship between CWSI estimated by imitation leaf method and soil relative available water (RAW) under different soil water condition (here I1, I2, I3, and I4 means different irrigation treatments). RAW is defined as the ratio of the difference between soil volumetric water content and permanent wilting percentage and the difference between field capacity and permanent wilting percentage. CWSI increases linearly with decreasing soil water content, which shows that soil water content will limit the crop water condition to a certain extent. This result agrees with those of Qiu et al. (1996b, 2000), which also pointed that the use of temperature-related CWSI enables to predict plant water stress earlier than soil water-based method.

Fig. 3
figure 3

Relationship between CWSI estimated by imitation leaf method and soil relative available water (RAW)

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Relations with leaf water potential

As a further evaluation of the CWSI approach to quantify crop stress, leaf water potential (LWP) was plotted as a function of CWSI (Fig. 4). Some authors discussed the panicle effects on wheat temperature and CWSI versus LWP before Howell et al. (1986). Results of our experiment showed a linear correlation between CWSI and LWP. CWSI was increasing with the decreasing LWP when leaves were under water stress. The scattering around the regression line may arise from the atmospheric factors, such as air temperature, air humidity, irradiance, and so on, which may be removed by deleting atmospheric induced LWP from total LWP (Alok and Tripathi 1990). However, LWP can indicate crop water condition directly, and we can use CWSI estimated by imitation leaf method to detect crop water condition.

Fig. 4
figure 4

Relationship between CWSI and leaf water potential (LWP) under condition of water stress

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Relations with yield, harvest index, and water use efficiency

The yield, harvest index (HI) and water use efficiency (WUE) of winter wheat with six irrigation treatments plotted in relation to the mean CWSI values are shown in Figs. 5, 6, and 7, respectively. Here the values of yield, HI, and WUE are the average value of each treatment. The CWSI is the average value of CWSI over the growth stage of the head-filling period (Idso et al. 1981b). Because there was plenty of rainfall (171.4 mm) during the whole growing season of winter wheat in 2002–2003, the mean yields of each irrigation treatment have no significant difference. CWSI of each irrigation treatment range from 0.202 to 0.338, which shows that the winter wheat did not suffer serious water stress during the whole growing season. Therefore, the rain-fed treatment has the highest yield, HI, and WUE of 0.547 kg m−2, 0.469, and 2.321 kg  m−3, respectively, with the lowest CWSI of 0.202. Anyhow, the yield, HI, and WUE are all declined with increasing CWSI values, and the CWSI has exponential relation with the three indexes, and the regression coefficients between them are 0.862, 0.855, and 0.794, respectively, which indicate that the more serious the crop water stress is, the lower the yield, HI, and WUE of crop will gain.

Fig. 5
figure 5

Relation between CWSI and yield

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Fig. 6
figure 6

Relation between CWSI and harvest index (HI)

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Fig. 7
figure 7

Relation between CWSI and water use efficiency (WUE)

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Conclusions

We proposed a new method for estimating CWSI by introducing imitation leaf temperature T p. In this method, the upper limiting canopy temperature T cu is replaced by T p, and the aerodynamic resistance is omitted, which is the first advantage of this method. The included parameters in this method are imitation leaf temperature, canopy temperature, air temperature, air vapor, and solar radiation. It is not difficult to assess these data under field conditions, which is the method’s second advantage. The third advantage is that it is easily applicable in remote sensing because the required surface temperature can be easily measured.

The proposed method was conducted in a field experiment of winter wheat under different soil water conditions and the results were compared with Jackson’s method. Values of CWSI by imitation leaf method are in agreement with the Jackson’s CWSI. The regression coefficient between the two groups of CWSI is 0.999. Measured T p also agrees with T cu estimated by Jackson’s method with a regression coefficiency r=0.988. CWSI by the proposed method also has good relation with soil water content, leaf water potential, and so on. Therefore, T cu can be replaced by T p, and the required parameters for estimating CWSI also reduced evidently. However, the field experiment was conducted only in one place and on one cultivar; further experimentation and research should be conducted before wide application of the proposed method in North China Plain.