Comparison of the abilities of vegetation indices and photosynthetic parameters to detect heat stress in wheat

Agricultural and Forest Meteorology 265 (2019) 121–136

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Comparison of the abilities of vegetation indices and photosynthetic parameters to detect heat stress in wheat

T

Zhongsheng Cao, Xia Yao, Hongyan Liu, Bing Liu, Tao Cheng, Yongchao Tian, Weixing Cao, ⁎ Yan Zhu National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, PR China

A R T I C LE I N FO

A B S T R A C T

Keywords: Heat stress Detection Maximum efficiency of photosystem II (Fv/Fm) Vegetation indices (VIs) Photochemical reflectance index (PRI) Wheat (Triticum aestivum L.)

With the changing of the climate, the detection of heat stress as early as possible has become increasingly important for wheat (Triticum aestivum L.) production. Previous studies have demonstrated that photosynthetic parameters can serve as indicators of the stress conditions, and vegetation indices (VIs) provide the ability to non-destructively monitor photosynthetic parameters. However, it remains unclear whether VIs can be used to detect heat stress in a similar manner as the photosynthetic parameters. In addition, the optimal VIs for indicating heat stress and detecting the stress status are also currently unknown. In the present study, a heat stress experiment was designed with four temperature levels [T1, 17 °C/27 °C (Tmin/Tmax), T2 (25 °C/35 °C), T3 (29 °C/ 39 °C), and T4 (33 °C/43 °C)] and three treatment durations [three days (D1), six days (D2) and nine days (D3)]. Three photosynthetic parameters [leaf chlorophyll content (LCC), net photosynthesis rate (Pn), and maximum efficiency of photosystem II (Fv/Fm)] and 17 published VIs were selected to compare their sensitivity and assess their feasibility for detecting heat stress. The results showed that Fv/Fm was the most sensitive photosynthetic parameter to heat stress and had the ability to indicate the start and end of heat stress at the slight level or the early stage. The chlorophyll index-red edge (CIred-edge), normalized difference red edge index (NDRE) and photochemical reflectance index (PRI) were sensitive to heat stress owing to their close relationships with photosynthetic parameters. Among these three VIs, PRI displayed the highest sensitivity. Nevertheless, the sensitivity of PRI was less than that of Fv/Fm, and it failed to detect the beginning and end of heat stress lasting for three days. The ability of PRI to detect heat stress became similar to that of Fv/Fm when the duration of heat stress was increased to seven days. In conclusion, Fv/Fm is the optimum indicator for detecting early-stage heat stress, in which only the photosynthetic functions change. In contrast, PRI, a non-destructive indicator, works well to indicate relatively late-stage heat stress, in which the chemical and physical characteristics of leaves (e.g., chlorophyll content) are affected.

1. Introduction Wheat (Triticum aestivum L.), the staple food for approximately 60% of the world’s population, is a major cereal crop. Wheat is also an important crop in China, where it accounts for 21.3% of the total cultivated area and 20.9% of crop production. With the increases in population and food demand, wheat production has become increasingly important both in China and worldwide. Given the expected future trend of global climate warming, average temperature is expected to increase, bringing more extreme temperature events such as heat waves (Karl et al., 2015; Teixeira et al., 2013). Heat stress is one of the major

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abiotic adversities for plant growth. According to the literature, heat stress can result in the loss of wheat production and quality, especially during the reproductive period (Eyshi Rezaei et al., 2018, 2015; Talukder et al., 2014; Farooq et al., 2011). Thus, detecting heat stress as early as possible could help mitigate its deleterious effects on wheat production, which can be predicted by remote sense technology to help markets and governments to prepare for grain shortages (Liu et al., 2018a, 2018b; Xie et al., 2017). Photosynthesis, which is a key biophysical process in plants, is an efficient indicator of physiological function in vegetation. Photosynthesis is sensitive to temperature; higher temperature leads to

Corresponding author. E-mail address: [email protected] (Y. Zhu).

https://doi.org/10.1016/j.agrformet.2018.11.009 Received 16 July 2018; Received in revised form 8 November 2018; Accepted 12 November 2018 0168-1923/ © 2018 Elsevier B.V. All rights reserved.

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Table 1 Key details of the heat stress experiments in the environment-controlled phytotrons. Growth season

Location

Cultivar

Duration

Temperature (Tmin/Tmax)

Sample time

2013–2014

Rugao (120.33 °E, 32.23 °N)

V1 (Yangmai16) V2 (Xumai30)

D1 (3 days) D2 (6 days) D3 (9 days)

T1 T2 T3 T4

Anthesis, duration of treatment, three days after treatment, 6 DAA, 11 DAA, 16 DAA, 21 DAA, 26 DAA, 31 DAA

(17 °C/27 °C) (25 °C/35 °C) (29 °C/39 °C) (33 °C/43 °C)

DAA, days after anthesis.

2. Materials and methods

an obvious decrease in photosynthesis (Mathur et al., 2014; Wahid et al., 2007; Berry and Bjorkman, 1980). Thus, photosynthetic parameters are potential proxies for heat stress. The contents of photosynthetic pigments, chlorophyll fluorescence, and gas exchange rate are the most widely used parameters in the assessment of photosynthetic ability and have been related to temperature stress. For example, Jespersen et al. (2016) and Ristic et al. (2007) demonstrated that losses in chlorophyll content are strongly related to heat stress. Sharma et al. (2014); Wahid et al. (2007), and Lu and Zhang (2000) suggested that the maximum efficiency of photosystem II (Fv/Fm) and minimal fluorescence (F0) may reflect elevated temperature. Xue et al. (2016) and Feng et al. (2013) reported that the photosynthetic rate decreases under both low-and high-temperature conditions. Overall, past studies indicate that these three photosynthetic parameters are closely related to heat stress. Despite the usefulness of photosynthetic parameters for detecting heat stress, the determination of these parameters is time consuming, labor intensive, and relatively small scale, which limits their potential applications. For example, chlorophyll content is typically measured using a spectrophotometric method (Lichtenthaler, 1987), fluorescence parameters [e.g., maximum quantum efficiency of PSII photochemistry (Fv/Fm), and potential PSII (Fv/F0)] are determined with a pulsemodulated fluorometer (Feng et al., 2015), and photosynthetic parameters [e.g., net photosynthesis (Pn), stomatal conductance (gs), and tran-spiration rate (E)] are evaluated using the Li-6400XT system (Dwivedi et al., 2017). Remote sensing is an efficient alternative to noninvasively monitor plant growth parameters and stress conditions with vegetation indices (VIs) (Tong and He, 2017; Sims and Gamon, 2002). For decades, VIs have been applied to monitor photosynthetic parameters in numerous studies. These VIs include the modified chlorophyll absorption in reflectance index (MCARI), leaf chlorophyll index (LCI), chlorophyll index-red edge (CIred-edge), and modified simple ratio (mSR) for chlorophyll content mapping (Moharana and Dutta, 2016; Wu et al., 2009; Pu et al., 2008; Daughtry et al., 2000); the photochemical reflectance index (PRI) and double peak index (DPi) for fluorescence estimation (Zarco-Tejada et al., 2003; Gamon et al., 1992); the water balance indices (WABIs) for non-photochemical quenching (NPQ) quantification (Rapaport et al., 2015); and the fluorescence ratio indices (FRIs) for CO2 assimilation rate and stomatal conductance assessments (Dobrowski et al., 2005). VIs have also been applied in investigations of stress, including water stress and freezing injury (Wei et al., 2017; Sanches et al., 2014; Peñuelas et al., 1994). Therefore, VIs have the potential to be used to monitor photosynthetic status and indicate heat stress. However, few studies have attempted to identify which VI is the optimal for detecting heat stress as early as possible. In addition, few of the previous studies compared the sensitivity of photosynthetic parameters and optimal VIs under different heat stress stages and levels. Hence, the objectives of this study were as follows: (1) to select the optimal VIs for detecting heat stress based on their correlations with photosynthetic parameters, (2) to compare the sensitivity of the photosynthetic parameters and the optimal VIs under various heat stress conditions, and (3) to determine the optimum indicators for detecting heat stress among the photosynthetic parameters and optimal VIs.

2.1. Experimental design Two cultivars [Yangmai16 (V1) and Xumai30 (V2)] were used in this study. The heat stress treatment started at the stage of anthesis, which was defined as the point at which 50% of the spikelets in the middle position of the spike began to flower. Three treatment durations [three days (D1), six days (D2), and nine days (D3)] were used in the experiment, and four temperature levels [T1: 17 °C/27 °C (Tmin/Tmax), T2: 25 °C/35 °C, T3: 29 °C/39 °C, and T4: 33 °C/43 °C] were used for each treatment durations. The heat stress treatments were set up on the literatures of Liu et al., 2014 and Farooq et al., 2011. Among these four temperature levels, T1 represented the normal temperature, whereas the other temperature levels (T2, T3, and T4) were the heat stress treatments (Liu et al., 2016). Detailed information on the sampling time can be found in Table 1. Wheat plants were sowed in plastic pots at a density of 10 plants per pot. Prior to treatment, the plants were grown in a normal ambient environment. Once the treatment began, the plants were moved into phytotrons, where they were exposed to different levels of heat stress. During the treatment, the daily temperature dynamics followed the same pattern of ambient temperature (Fig. 1), and humidity and light were precisely controlled to simulate the ambient environment humidity and light status using the phytotrons control center and the supplement halogen lamp, respectively. After treatment, the plants were moved out of the phytotrons and grown under a normal ambient environment until harvest. To ensure that the growth process matched that of agricultural wheat plants, the following nutrients (N, 18.3 g m−2; P2O5, 10.2 g m−2; and K2O, 18.3 g m−2), were applied prior to sowing, and another nitrogen (N, 18.3 g m−2) was suppled during the jointing stage. 2.2. Data collection 2.2.1. Sample design The photosynthetic parameters and spectral reflectance were measured every day during the treatment period, three days after treatment, and every five days after 6 DAA (Table 1). Nine stems without obvious differences were selected for measurement and numbered. Next, the flag leaves of the selected stems were used for the measurement of photosynthetic parameters and spectral reflectance. Finally, the measured leaves were removed from the stems and frozen in liquid nitrogen and then stored at −40 °C for the determination of chlorophyll content. 2.2.2. Measurement of leaf photosynthetic parameters A Net photosynthetic rate (Pn) The net photosynthetic rate (Pn) was measured using a portable photosynthesis system (Li-6400XT, LI−COR Inc., Lincoln, Nebraska, USA) with a standard 2 × 3 cm chamber between 9:30 h and 11:00 h local time. During measurement, the chamber light intensity was set to 1000 μmol m−2 s−1, and the CO2 concentration was set as 380 μmol mol−1 with a constant flow rate of 500 μmol s−1 (Zhang et al., 2016). 122

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Fig. 1. Daily dynamics of temperature in the phytotrons during heat stress treatment and ambient temperature during the 2013–2014 growth season.

• Maximum photochemical efficiency of photosystem II (F /F v

2014) (Fig. 2).

m)

The maximum photochemical efficiency of photosystem II (Fv/Fm) was determined using a PAM-2500 pulse-modulated fluorometer (Walz, Effeltrich, Germany). The leaves were fully dark-adapted for 20 min before each test, and the minimal fluorescence (F0) and maximal fluorescence (Fm) were then determined. Fv/Fm was calculated according to a previously reported method (Gao et al., 2016; Kitajima and Butler, 1975).

2.2.3. Leaf spectral reflectance A portable field spectrometer (FieldSpec Pro FR, Analytical Spectral Devices, Boulder, Colorado, USA) was employed for the measurement of leaf reflectance spectra. The spectrometer has a wavelength range from 350 to 2500 nm and a spacing of 1 nm interpolated using the builtin software. In measurement, the leaf reflectance was measured by using a leaf clip which equipped with a halogen lamp. In the leaf clip, the spectrometer fiber was hold at 30° angle to the adaxial leaf surface. The three measurement positions in each leaf were set at the points located in the 1/3, 1/2, and 2/3 of leaf from the top, and three spectra were collected with a time interval of one second in each position. After measurement, all nine spectra were averaged to provide the leaf reflectance.

• Leaf chlorophyll content (LCC) The leaf chlorophyll content (LCC) was measured using a spectrophotometric method. For the measurement, 0.20 g of fresh leaf was cut into tiny pieces and extracted in 25 mL of 95% ethanol under dark conditions for 72 h. Subsequently, the absorbance of the supernatant was measured at wavelengths of 665 nm and 649 nm using a spectrophotometer (U2800, Hitachi High-Technologies Corporation, Tokyo, Japan). The LCC was then calculated according to a previously reported method, and the unit of LCC was mg g−1 (Gao et al., 2016; Nayek et al.,

2.3. Data analysis 2.3.1. Vegetation indices (VIs) Few reported VIs can directly detect heat stress. We hypothesize

Fig. 2. Relationships between the photosynthetic parameters and VIs: (a) LCC vs. CIred-edge, (b) Pn vs. NDRE, and (c) Fv/Fm vs. PRI. The red solid lines are the best-fit linear regressions, and the black dashed lines are the best-fit exponential regressions (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 123

Agricultural and Forest Meteorology 265 (2019) 121–136 Daughtry et al. (2000) Dash and Curran (2004) Guyot et al. (1988) Pu et al. (2008) Gitelson et al. (2006) Gamon et al. (1992) Zarco-Tejada et al. (2000) Zarco-Tejada et al. (2000) Zarco-Tejada et al. (2003) Dobrowski et al. (2005) Dobrowski et al. (2005) Sim & Gamon (2002) Chlorophyll Chlorophyll Chlorophyll Chlorophyll Chlorophyll Photosynthetic radiation use efficiency Chlorophyll fluorescence Chlorophyll fluorescence Steady-state chlorophyll a fluorescence CO2 assimilation CO2 assimilation Leaf pigment content

(R700−R670)−0.2×(R700−R550))×(R700/R670) (R754−R709)/(R709−R681) 700 + 40×((R670+R780)/2−R700)/(R740−R700) (R850−R710)/(R850+R680) (R750/R710)−1 (R531−R570)/(R531+R570) R750/R800 R6832/(R655×R691) (R688+R710)/R6972 R690/R600 R740/R800 (R750−R705)/ (R750+R705−2×R445) (R680−R430)/(R680+R430) (R678−R500)/R750 (R870−R1260)/(R870+R1260) (R790−R720)/(R790+R720) (R800+R550)/(R1660+R680)

that the VIs with close relationships to photosynthetic parameters can indirectly reflect heat sensitivity. In this study, we selected 17 existing VIs related to the chlorophyll content, fluorescence status, photosynthesis status, and stress factors. Details of the chosen VIs are summarized in Table 2.

Peñuelas et al. (1994) Merzlyak et al. (1999) Peñuelas et al. (1995) Barnes et al. (2000) Apan et al. (2004)

Reference Application Formula

2.3.2. Assessment of relationships between the VIs and photosynthetic parameters Linear and exponential functions were adopted to fit the relationships between the VIs and photosynthetic parameters, and the determination coefficient (R2) was utilized to assess the relationships. The optimal VIs (i.e., those most sensitive to heat stress) were determined as those with the highest R2. 2.3.3. Evaluation of the sensitivities of VIs and photosynthetic parameters to heat stress Analysis of variance (ANOVA) was used to examine the effects of heat treatment on the mean photosynthetic parameters and VIs using SPSS 20.0 statistical software (SPSS Inc., Chicago, Illinois, USA). In the examination, Duncan’s multiple range tests (p = 0.05) were applied to test the difference of photosynthetic parameters and VIs under the four temperature levels. To assess the sensitivity and stability of photosynthetic parameters and VIs under temperature levels, the metric coefficient of normalized root mean squared error (NRMSE) was constructed and calculated.

Modified chlorophyll absorption in reflectance index MERIS terrestrial chlorophyll index Red-edge position: linear interpolation method Leaf chlorophyll index Chlorophyll index-red edge Photochemical reflectance index 531/570 Simple ratio 750/800 Curvature optical index R6832/R655·R691 Double peak index Fluorescence ratio index 690/600 Fluorescence ratio index 740/800 Modified normalized difference 750/705

Normalized pigment chlorophyll ratio index Plant senescence reflectance index Structure insensitive pigment index Normalized difference red edge index Disease water stress index

MCARI MTCI REP-li LCI CIred-edge PRI SR750/800 R6832/R655·R691 DPi FRI690/600 FRI740/800 mND705

NPCI PSRI SIPI NDRE DWSI

Chlorophyll content Car/Chl ratio Car/Chl ratio Nitrogen and water stress Stress and disease

Vegetation index (VI) Abbreviation

Table 2 VIs related to chlorophyll content, leaf fluorescence, and vegetation status.

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NRMSE =

1 × n

n

∑i=1 (Si-SN )2/(max(S )-min(S ))

Where, Si is the photosynthetic parameters or related VIs under heat stress (T2, T3 and T4), SN is the mean value of photosynthetic parameters or related VIs under normal temperature (T1), max(S) and min (S) are the maximum and minimum photosynthetic parameters or related VIs in the whole dataset, respectively. 3. Results 3.1. Selection of optimal VIs for detecting heat stress Table 3 showed the R2 values for the relationships between VIs and photosynthetic parameters (LCC, Pn, and Fv/Fm) based on linear and exponential regressions. Generally, the linear relationships were stronger than the exponential relationships. Among the three photosynthetic parameters, LCC had the strongest correlation with VIs. The average R2 of all indices with LCC were 0.48 and 0.31 in linear and exponential regressions, respectively. The second strongest relationships were found for Pn, which had average R2 values of 0.39 and 0.36 for linear and exponential regression, respectively. Fv/Fm had the poorest correlations with VIs for both linear and exponential regression. The average R2 values of all indices with Fv/Fm were 0.34 and 0.33 for linear and exponential regression, respectively. The VIs most strongly related to the photosynthetic parameters differed among the photosynthetic parameters. With regard to LCC, the chlorophyll index-red edge (CIred-edge), normalized difference red edge index (NDRE), modified normalized difference 750/705 (mND705), MERIS terrestrial chlorophyll index (MTCI) and leaf chlorophyll index (LCI) exhibited the strongest correlations. The R2 values for these indices were higher than 0.65 in linear regression and higher than 0.34 in exponential regression. Among these six LCC-related VIs, CIred-edge had the highest R2 for both regressions. For Pn, the VIs with high R2 were the NDRE, CIred-edge, mND705, modified chlorophyll absorption reflectance index (MCARI) and MTCI, among which NDRE had the highest R2. In contrast to LCC and Pn, the VIs which strongly related to Fv/Fm were the photochemical reflectance index (PRI), mND705, NDRE, LCI, and double peak index (DPi). The VI with the highest R2 values was PRI, which had R2 values of 0.54 for both types of regressions. 124

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Table 3 Determination coefficients (R2) between VIs and photosynthetic parameters based on linear and exponential fittings.

The bold, highlighted values are the highest determination coefficients (R2) for each parameter. Table 4 Determination coefficients (R2) between the top performing VIs based on linear fittings.

3.2. Heat sensitivity of photosynthetic parameters and VIs

Additionally, we exhibited the relationships between the top performing VIs (CIred-edge, NDRE, mND705, MTCI, LCI, PRI and DPi) in Table 4. Except PRI, the other top performing VIs had higher correlations with each other as R2 higher than 0.90. Among them, CIred-edge and NDRE has the similar performance with the best R2 (0.99), however, PRI showed a different performance. The reason for CIred-edge and NDRE with higher correlation is that they have the similar band combination in the red edge and near-infrared regions, while PRI constructed only by two visible wavelengths. Overall, we concluded that CIred-edge, NDRE, and PRI were strongly related to LCC, Pn, and Fv/Fm, respectively, and they were selected as the optimal VIs with the potential to detect heat stress, though PRI had a relatively poor correlation compared with LCC and Pn among the photosynthetic parameters.

3.2.1. Dynamics of indicators under three-day stress (D1) The dynamics of indicators under heat stress in two varieties kept consistent. Figs. 3–5 show the dynamics of the three pairs of indicators (LCC and CIred-edge, Pn and NDRE, and Fv/Fm and PRI) under heat stress lasting for three days (D1). Generally, LCC and CIred-edge remained constant under the stress for three days, and the effect of heat stress on LCC and CIred-edge was not significant (Difference significance analysis marked with “a”, p = 0.05), the mean NRMSE in the period from 1 DAA to 6 DAA was less than 0.10 for both varieties (Table 5). Therefore, no significant difference was observed between the four temperature levels, which were consistent for the two varieties (Fig. 3). The changes of Pn and NDRE under heat stress lasting for three days are shown in Fig. 4. In contrast to LCC and CIred-edge, the variability of Pn under heat stress was higher, and the mean NRMSE in the displayed period was higher than 0.14 for two cultivars (Table 5). For V1 and V2, the value of Pn decreased under T3 and T4 when the heat stress began

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V2 (Difference significance analysis marked with “a”, p = 0.05), however, a significant decrease in PRI was only observed under T4 at 2 DAA and 3 DAA for V1 (Fig. 5). Overall, the photosynthetic parameters were more sensitive to heat stress than the VIs. Fv/Fm and Pn were the most sensitive indicators of those tested as the high variability; they had potential ability to indicate the start and end dates of three-day heat stress.

Table 5 NRMSE under the heat stress for three days (D1) and normal ambient environment for the photosynthetic parameters and VIs. Cultivar

V1

V2

Indicator

LCC Pn Fv/Fm CIred-edge NDRE PRI LCC Pn Fv/Fm CIred-edge NDRE PRI

Heat stress (D1)

Mean value

Normal ambient environment

1 DAA

2 DAA

3 DAA

4 DAA

5 DAA

6 DAA

0.09 0.12 0.22 0.06 0.07 0.08 0.09 0.11 0.24 0.08 0.09 0.13

0.07 0.14 0.28 0.06 0.07 0.13 0.09 0.13 0.29 0.07 0.08 0.16

0.09 0.15 0.27 0.03 0.03 0.07 0.09 0.28 0.25 0.11 0.11 0.11

0.07 0.18 0.21 0.07 0.08 0.12 0.10 0.24 0.27 0.11 0.11 0.18

0.06 0.12 0.15 0.10 0.10 0.18 0.11 0.15 0.22 0.12 0.09 0.18

0.12 0.10 0.22 0.09 0.09 0.16 0.09 0.10 0.24 0.13 0.14 0.18

0.08 0.14 0.23 0.07 0.07 0.12 0.10 0.17 0.25 0.10 0.10 0.16

3.2.2. Dynamics of indicators under stress for six days (D2) Figs. 6–8 show the dynamics of the three pairs of indicators (LCC and CIred-edge, Pn and NDRE, and Fv/Fm and PRI) under heat stress lasting for six days (D2). Because the performance of indicators before 3 DAA was described in the preceding section, the analysis in this section mainly focuses on the period from 4 to 9 DAA. Fig. 6 shows the dynamics of LCC and CIred-edge under the heat stress lasting for six days. As it displayed, the variability of LCC performed higher than that of CIred-edge from 4 to 9 DAA, especially in V1 (mean NRMSE = 0.21) (Table 6). With regard to V1, LCC and CIred-edge decreased under T4 at 3 and 4 DAA, respectively. For V2, LCC and CIrededge decreased under T4 at 4 and 6 DAA, respectively. Neither of the two indicators was affected by T3 before 8 DAA. In addition, LCC and CIrededge did not recover after the removal of stress (7 DAA). The dynamics of Pn and NDRE under heat stress lasting for six days are shown in Fig. 7. Generally, the sensitivity of Pn (mean NRMSE > 0.24) was higher than that of NDRE (mean NRMSE < 0.13) under heat stress (Table 6). For V1, T4 led to decreases in Pn and NDRE at 3 and 4 DAA, respectively. T3 resulted in the decrease of both Pn and NDRE at 4 DAA. T2 led to decreases in Pn and NDRE at 6 and 7 DAA, respectively. The dynamics of Pn and NDRE were different for V2. Under T4, Pn and NDRE decreased at 1 and 6 DAA, respectively. Under T3, Pn and NDRE decreased at 3 and 9 DAA, respectively. After decreasing under T3 and T4, Pn increased when the stress was removed. Fig. 8 shows the dynamics of Fv/Fm and PRI under heat stress lasting for six days (D2). Fv/Fm was more sensitive to heat stress than PRI, and

(1 DAA) and then increased when the stress was removed after 3 DAA. In contrast, NDRE remained constant during the stress with, and no significant difference was observed among the four temperature levels for both varieties (Difference significance analysis marked with “a”, p = 0.05), similar to LCC and CIred-edge (Fig. 4). The dynamics of Fv/Fm and PRI under heat stress lasting for three days are depicted in Fig. 5. The sensitivity of the two indicators under heat stress were different; Fv/Fm was more sensitive to heat stress (mean NRMSE > 0.23) (Table 5). The value of Fv/Fm decreased under stress and recovered when the stress was removed. For V1, Fv/Fm decreased under all stress levels (T2, T3, and T4) at 1 DAA. For V2, Fv/Fm decreased under T4 at 2 DAA and under T3 at 3 DAA. The effect of heat stress on PRI (mean NRMSE = 0.12/0.16) was less significant than that on Fv/Fm (mean NRMSE = 0.23/0.25) (Table 5); Additionally, PRI remained constant during the period of three-day heat stress period for

Fig. 3. Dynamics of LCC and CIred-edge under heat stress lasting for three days (D1). 126

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Fig. 4. Dynamics of Pn and NDRE under heat stress lasting for three days (D1).

was the most sensitive VI, although its sensitivity was less than those of Pn and Fv/Fm.

the mean NRSME was higher than those of others (Table 6), especially for V2. Despite the performance before 3 DAA, the performances of Fv/ Fm and PRI were similar at 4 DAA. With regard to the two cultivars, both T3 and T4 led to decreases in Fv/Fm and PRI at 4 DAA. Overall, PRI

Fig. 5. Dynamics of Fv/Fm and PRI under heat stress lasting for three days (D1). 127

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Fig. 6. Dynamics of LCC and CIred-edge under heat stress lasting for six days (D2).

and VIs under heat stress for V1. Among these six indicators, Fv/Fm was the most sensitive; it detected T2, T3 and T4 stress lasting for 1 day. Followed by Pn, which detected T3 and T4 stress lasting for 1 day, and T2 stress lasting for 3 days. PRI was the third sensitive indicator; it detected T4, T3 and T2 stress lasting for 2, 3 and 6 days. LCC was less sensitive than PRI; it detected T4, T3 and T2 stress lasting for 4, 5 and 7 days, respectively. CIred-edge and NDRE performed similar and showed least sensitivity among these six indicators; they detected T4, T3 and T2 stress lasting for 4, 4 and 6 days, respectively. The sensitivity of indicators to detect the end of heat stress was similar to the sensitivity to indicate the start of stress. Fv/Fm had the ability to indicate the end of T4, T3 and T2 stress lasting for three days (D1) at 4, 4 and 5 DAA, indicate the end of T2 stress lasting for six days (D2) at 7 DAA, and indicate the end of T2 stress lasting for nine days (D3) at 10 DAA. Pn was less sensitive than Fv/Fm in the detection of the stress ending; it indicated the end of T3 stress lasting for three days (D1) at 4 DAA, and indicated the end of T2 stress lasting for six days (D2) at 7 DAA. Followed by PRI; it indicated the end of T4 stress lasting for three (D1) and T2 stress lasting for six days (D2) at 4 and 8 DAA, respectively. LCC, CIred-edgeand NDRE performed similar and exhibited the least sensitive to indicate the end of heat stress. The sensitivity of six indicators for V2 was less than that for V1. Fig. 13 exhibits the heat sensitivity of all the six indicators under heat stress in V2. Similar to V1, Fv/Fm was also the most sensitive one among the six indicators; it detected T4, T3 and T2 stress lasting for 2, 3 and 7 days, respectively. Pn showed a relative higher sensitive in V2; it detected T4, T3 and T2 stress lasting for 1, 3 and 6 days. PRI was the most sensitive indicators among the three VIs; it detected T4, T3 and T2 stress lasting for 2, 3 and 6 days. LCC was less sensitive than PRI; it detected T4 and T3 stress lasting for 5 and 9 days, respectively. CIrededge and NDRE showed the least sensitivity among the all six indicators; they detected T4 and T3 stress lasting for 6 and 7 days. The sensitivity of Fv/Fm and Pn to detect the end of stress performed different to that in the indication of the stress start. Pn was the most sensitive indicator; it

Table 6 NRMSE under the heat stress for six days (D2) and normal ambient environment for the photosynthetic parameters and VIs. Cultivar

V1

V2

Indicator

LCC Pn Fv/Fm CIred-edge NDRE PRI LCC Pn Fv/Fm CIred-edge NDRE PRI

Heat stress

Mean value

Normal ambient environment

4 DAA

5 DAA

6 DAA

7 DAA

8 DAA

9 DAA

0.09 0.20 0.24 0.10 0.12 0.16 0.08 0.32 0.34 0.09 0.11 0.15

0.20 0.24 0.25 0.14 0.16 0.20 0.09 0.30 0.30 0.06 0.08 0.20

0.24 0.23 0.24 0.15 0.16 0.23 0.10 0.41 0.32 0.15 0.18 0.18

0.27 0.21 0.21 0.10 0.10 0.24 0.09 0.25 0.34 0.10 0.10 0.23

0.24 0.30 0.20 0.13 0.13 0.20 0.09 0.17 0.29 0.12 0.12 0.18

0.25 0.23 0.27 0.13 0.12 0.26 0.08 0.15 0.28 0.13 0.13 0.23

0.21 0.24 0.24 0.13 0.13 0.21 0.09 0.27 0.31 0.11 0.12 0.20

3.2.3. Dynamics of indicators under nine-day stress (D3) Figs. 9–11 show the dynamics of the three pairs of indicators (LCC and CIred-edge, Pn and NDRE, and Fv/Fm and PRI) under heat stress lasting for nine days (D3). The dynamics of the photosynthetic parameters and VIs became similar as the duration of heat stress increased from 7 to 12 DAA, with the decrease of difference for NRMSE (Table 7). Decreases in the indicators under stress were observed after 7 DAA for V1 and after 11 DAA for V2. Additionally, the difference between the dynamics of the photosynthetic parameters and VIs was reduced compared to under heat stress for three and six days, especially during the period after 7 DAA.

3.2.4. Heat sensitivity of photosynthetic parameters and VIs Fig. 12 exhibits the heat sensitivity of six photosynthetic parameters 128

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Fig. 7. Dynamics of Pn and NDRE under heat stress lasting for six days (D2).

Fig. 8. Dynamics of Fv/Fm and PRI under heat stress lasting for six days (D2). 129

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Fig. 9. Dynamics of LCC and CIred-edge under heat stress lasting for nine days (D3).

Fig. 10. Dynamics of Pn and NDRE under heat stress lasting for nine days (D3). 130

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Fig. 11. Dynamics of Fv/Fm and PRI under heat stress lasting for nine days (D3).

correlated to photosynthetic parameters to detect heat stress, 17 published VIs were evaluated in this study. Among them, CIred-edge, mND705, NDRE, and MTCI were strongly related to LCC, with CIred-edge having the strongest correlation. As detailed in Tables 3 and 4, all the above four VIs contained red-edge bands (i.e., 690–730 nm) and had high correlation with each other. Red-edge bands are located in the region between pigment absorption and leaf structural scattering, thus these bands are closely related to both leaf pigment concentration and structure (Curran et al., 1990; Collins, 1978), implying that the VIs with red-edge bands can reflect leaf chlorophyll signals (Delegido et al., 2011; Gitelson et al., 2005). In addition, the VIs with red-edge bands have the ability to reduce the saturation when compared with the VIs with red bands (Zarco-Tejada et al., 2003; Carter, 1994). In the past decade, numerous studies have provided evidence that VIs with rededge bands are sensitive to slight chlorophyll changes (Main et al., 2011; Wu et al., 2009; Dorigo et al., 2007; Maire et al., 2004;). Since the CIred-edge showed strongest correlatation to photosynthetic parameters among the four red-edge VIs in the present study, we selected CIred-edge as the most promising VI to detect heat stress in wheat. PRI, DPi, LCI, mND705, and NDRE were strongly related to Fv/Fm, with higher correlations observed for PRI and DPi. PRI is a most commonly used VI for monitoring the rapid changes in photosynthetic status based on the two visible bands at 531 and 570 nm (Magney et al., 2014; Thenot et al., 2002). As the 531 nm band is associated with the xanthophyll cycle, PRI has been widely used to evaluate changes in chlorophyll fluorescence and environmental conditions (Gamon et al., 1992; Magney et al., 2016). DPi is strongly related to steady-state chlorophyll fluorescence in plants (Zarco-Tejada et al., 2003). However, it collects the signals from the red-edge region, which is closely associated with the pigments related indices. Thus, DPi is potentially related to the red-edge VIs such as LCI, mND705, NDRE, and CIred-edge (Table 4). As CIred-edge is selected in this study, we recommend PRI as another potential indicator. There are few specialized VIs that are strongly related to Pn in previous studies similar to those related to LCC. Pn is apparently related

Table 7 NRMSE under the heat stress for nine days (D3) and normal ambient environment for the photosynthetic parameters and VIs. Cultivar

V1

V2

Indicator

LCC Pn Fv/Fm CIred-edge NDRE PRI LCC Pn Fv/Fm CIred-edge NDRE PRI

Heat stress

Mean value

Normal ambient environment

7 DAA

8 DAA

9 DAA

10 DAA

11 DAA

12 DAA

0.23 0.31 0.21 0.12 0.11 0.13 0.14 0.26 0.28 0.06 0.06 0.21

0.17 0.41 0.19 0.13 0.12 0.12 0.16 0.24 0.25 0.10 0.09 0.17

0.23 0.39 0.29 0.16 0.14 0.14 0.26 0.24 0.26 0.08 0.08 0.22

0.25 0.33 0.27 0.22 0.20 0.13 0.22 0.25 0.24 0.12 0.11 0.23

0.32 0.33 0.23 0.25 0.23 0.16 0.32 0.26 0.31 0.17 0.16 0.30

0.27 0.34 0.26 0.25 0.23 0.16 0.21 0.29 0.28 0.25 0.23 0.31

0.25 0.35 0.24 0.19 0.17 0.14 0.22 0.26 0.27 0.13 0.12 0.24

indicated the end of T4 stress lasting for three days (D1) at 6 DAA, indicated the end of T3 and T2 stress lasting for six days (D2) at 7 and 9 DAA, and indicated the end of T2 stress lasting for nine days (D3) at 10 DAA. Fv/Fm was less sensitive than Pn; it indicated the end of T3 stress lasting for three days (D1) and six days (D2) at 4 and 11 DAA, respectively. PRI and LCC detected the end of T2 and T4 stress lasting for six days at 8 DAA. CIred-edge and NDRE failed to detect the end of stress in each treatment. 4. Discussion 4.1. Potential of VIs to detect heat stress Photosynthetic parameters (LCC, Pn and Fv/Fm) have close relationships with heat stress. To select optimal VIs that can be strongly 131

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Fig. 12. Start and end of heat stress detected by indicators (photosynthetic parameters and VIs) for V1. (Note: symbol ‘×’ means the start of heat stress detected by indicators, symbol ‘●’ means the end of heat stress detected by indicators).

band at 531 nm, which is located in the center of the physiologically active wavelength, is closely related to the photosynthesis. Therefore, we conclude that CIred-edge, NDRE, and PRI are the most appropriate indicators to detect heat stress in wheat.

to the photosynthetic capacity and is affected by several parameters, including the chlorophyll content (Croft et al., 2016). Therefore, the VIs relevant to Pn are similar to those relevant to LCC because of their indirect linkage to chlorophyll. In present study, NDRE had the highest R2 in relation with Pn, with the performance similar to CIred-edge, thus could also be selected as a potential VI for detecting heat stress. Overall, this study demonstrated that the spectral wavelengths related to photosynthetic parameters are mainly in the red-edge and visible regions. Red-edge bands can reflect slight changes in chlorophyll content and have potential correlations with photosynthetic capacity (Zhao et al., 2003). The VIs with these bands are strongly related to both LCC and Pn and have the ability to detect heat stress. The visible

4.2. The reason of the photosynthetic parameters to detect heat stress Pn and Fv/Fm were sensitive to the heat stress, and they had the ability to detect the start of heat stress and the end of three daysduration stress. The reason performing the way may be that Fv/Fm and Pn are the indicators of photosynthetic capacity, which are sensitive to heat conditions (Mathur et al., 2014). Fv/Fm is the functional indicator 132

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Fig. 13. Start and end of heat stress detected by indicators (photosynthetic parameters and VIs) for V2. (Note: symbol ‘×’ means the start of heat stress detected by indicators, symbol ‘●’ means the end of heat stress detected by indicators).

et al., 2007; Salvucci and Vierling, 2001; Law and Crafts-Brandner, 1999). Previous studies reported that rubisco activity declined rapidly under moderately high temperature and recovered immediately when the stress factor was removed (Haldimann and Feller, 2005; Kim and Portis, 2005; Salvucci and Vierling, 2001; Feller et al., 1998). Therefore, Pn in crop plants is considered as a sensitive indicator to the heat stress. Leaf chlorophyll loss is one of the most common results in heat stress (Jespersen et al., 2016; Nankishore and Farrell, 2016). However, the response of chlorophyll to heat stress may be slower than photosynthesis, because the heat stress indirectly affects chlorophyll by impairing its synthesis and accelerating its degradation (Mathur et al.,

of Photosystem II (PSII), and the PSII is a vital component of the electron transport chain and one of the primary components damaged by heat stress, owing to the repair inhibition (Li et al., 2017; Allakhverdiev et al., 2008). Thus, it decreased rapidly under heat stress and recovered after removal of heat stress in our study, which is consistent with the results of published literatures (Chen et al., 2017; Haque et al., 2014). Pn is the result of the entire photosynthetic process, involving several steps including the electron transfer, photoenergy absorption and CO2 assimilation. For CO2 assimilation step, the Calvin cycle is the main process responsible, and ribulose-1, 5-bisphosphate carboxylase/oxygenase (rubisco) capacity is the primary limiting factor. Rubisco capacity is also sensitive to the high temperature (Farooq et al., 2011; Kurek 133

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the breed characteristics, with Xumai30 more tolerant to heat stress than Yangmai16. The present results would be extrapolated to other varieties and stress conditions in the future studies. When it comes to the scale of field with various platforms and sensors, we assume that the result would be similar, but the accuracy would be decreased. On canopy scale many factors might influence the reflectance and corresponding VIs, such as the type of sensors, reflectance acquisition techniques, canopy structure (orientations), background noise, sunlit and shaded leaf (Zhang et al., 2017; Garbulsky et al., 2011; Barton and North, 2001), which would cause VIs easily erratic and more uncertain. Whether the present results could be transplanted to the field studies remains to be validated in the ongoing work. With more quality satellites data and spectral meters available, our ability to monitor the heat stress in crop plants will be greatly enhanced in the near future.

2014), although it directly affects photosynthesis. Therefore, LCC decreased gradually with the expansion of heat stress duration and degree, which was slightly less sensitive to the heat stress than Pn and Fv/ Fm. 4.3. The reason of the VIs to detect heat stress VIs are calculated by the leaf reflectance of specific spectral bands that are strongly related to leaf physical and chemical characteristics, such as chlorophylls, carotenoids, anthocyanins, water, and structure (Feret et al., 2008; Jacquemoud and Baret, 1990). Because some leaf properties (e.g. chlorophyll and water contents) are sensitive to the stress, the VIs strongly related to these leaf properties also have the ability to indicate stresses (e.g. chlorophyll to heat stress and water contents to drought stress) (Ramoelo et al., 2015). CIred-edge and NDRE are closely related to the chlorophyll content, so they also have the ability to detect stress similar to LCC in a wide range of plants. Eitel et al. (2011) found that the NDRE was sensitive to the medium to high chlorophyll concentrations and efficient in detection of multiple plant stresses. Liu et al. (2018a, 2018b) reported that CIred-edge and NDRE based on the Sentinel-2 satellite images can detect the heavy metalinduced stress in rice. PRI is also sensitive to the chlorophyll content (Gitelson et al., 2017). Furthermore, its sensitivity to photosynthesis makes it more popular in plant stress detection than the VIs related to chlorophyll (e.g. high ozone concentrations, water limitation, flooding and heat wave) (Cremonese et al., 2017; Meroni et al., 2008; Suárez et al., 2008; Naumann et al., 2008). This is because the xanthophyll cycle and lightuse efficiency are associated with the photosynthesis changes on the stress conditions, which can be monitored by PRI in the short span time, yet the chlorophyll (Chl) content is relatively stable (Wong and Gamon, 2015; Gamon and Berry, 2012; Nichol et al., 2005; Evain et al., 2004). Thus, PRI performs more sensitive than CIred-edge and NDRE in the stress conditions. In terms of the sensitivity of the two kinds of six indicators, the photosynthetic parameters displayed much quicker responses than the vegetation indices, especially at the early stage or the light level stress. Fv/Fm is the most sensitive indicator, followed by Pn, although the measurement of photosynthetic parameters is a bit complexity, easily changing with the environmental conditions. PRI is ranked as the most sensitive vegetation index, although it was lagging as compared to the photosynthetic parameter. The characteristics of non-destructive, simple and fast measurement of the PRI constitute an important advantage over the physiological parameter, and will make it popular in detecting the stress, especially for detection of the severe level or the late stage of the stress. Yet it should be noted that such spectral technique normally needs additional information about the properties of crop environment-specific synthetic spectra, which is not always readily available.

5. Conclusion In this investigation, we assessed 17 published VIs as indicators for heat stress and compared them to three photosynthetic parameters (LCC, Pn, and Fv/Fm). The results showed that Fv/Fm and Pn were sensitive to heat stress and had the ability to indicate the start and end of heat stress lasting for three days. Thus, Fv/Fm and Pn can be effectively used to detect slight heat stress or early-stage heat stress. Meanwhile, PRI was the most sensitive VI among the three potential VIs (CIred edge, NDRE, and PRI) due to the slight sensitivity in the start of stress, yet it was less sensitive to heat stress compared to the photosynthetic parameters Fv/Fm and Pn, especially at the beginning of heat stress and the end of heat stress lasting for three days. Further, the sensitivity of PRI became similar to those of the photosynthetic parameters as the duration of heat stress was increased to six or nine days due to the major influence of chlorophyll content. This implies that PRI can be applied as a non-destructive method to detect moderate to severe heat stress or late-stage heat stress in practical production. Regarding the mechanism of heat stress detection based on photosynthetic parameters and VIs, the results of this study imply that the effects of heat stress on plant leaves mainly arise from changes in physiological function, such as changes in rubisco and PSII. In early-stage or less severe heat stress, it is difficult to monitor heat stress using VIs, thus photosynthetic parameters are the appropriate alternative. In contrast, severe heat stress damages the leaf chemical and physical characteristics, which are strongly associated with VIs. Therefore, PRI becomes more sensitive to severe heat stress. Funding sources This work was supported by the National Key Research and Development Plan of China (2016YFD0200700), the National Science Fundation for Distinguished Young Scholars (31725020), the National Natural Science Foundation of China (32101131), the National Natural Science Foundation of China (31671582), the Jiangsu Qinglan Project, the Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 project (B16026), the Qinghai Project of Transformation of Scientific and Technological Achievements (2018NK-126), and the Jiangsu province key technologies R&D program (BE2016375).

4.4. The robustness of VIs for heat stress detection Stress would cause series changes in function, structure and chemical/physical characteristics in crop plants. The ability of VIs for stress detection mainly depends on its stability or robustness. In this study, the reflectance was acquired at leaf scale with clip, a well-recognized methodology, many factors were also controlled in such a sealed environment, which facilitated obtaining of the pure signature and the stable VIs. These data were comprehensively analyzed based on the four temperature levels, three treatment durations, and two representative cultivars. Given that calibration occurred across different crop genotypes and various heat stress status, the obtained reflectance are expected to be sufficiently robust and stable to detect the heat stress in local situation. The differential sensitivity of two varieties indirectly produced a validation of the present result. The Xumai30 (V2) was less responsive with six indicators than the Yangmai16 (V1), which supports

Disclosure statement No potential conflict of interest was reported by the authors. Acknowledgements We are very grateful to Liying Tian, Xiudong Zou, Jun Li, Liujun Xiao, Hongting Ji and Xiao Zhang for the joint data collection. Finally, we acknowledge the anonymous reviewers who provided useful comments on this manuscript. 134

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