Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates

Agricultural Water Management 217 (2019) 356–373

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Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates

T

⁎

Salah E. El-Hendawya,b, , Nasser A. Al-Suhaibania, Salah Elsayedc, Wael M. Hassand,e, Yaser Hassan Dewira,f, Yahya Refaya, Kamel A. Abdellaa a

Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, 11451, Riyadh, Saudi Arabia Department of Agronomy, Faculty of Agriculture, Suez Canal University, 41522, Ismailia, Egypt Evaluation of Natural Resources Department, Environmental Studies and Research Institute, Sadat City University, Menoufia 32897, Egypt d Department of Agricultural Botany, Faculty of Agriculture, Suez Canal University, 41522, Ismailia, Egypt e Department of Biology, College of Science and Humanities at Quwayiah, Shaqra University, Saudi Arabia f Horticulture Department, Faculty of Agriculture, Kafrelsheikh University, Kafr El Sheikh, 33516, Egypt b c

A R T I C LE I N FO

A B S T R A C T

Keywords: Equivalent water thickness Estimated evapotranspiration Hyperspectral reflectance Leaf water potential Phenotyping Wavelength selection

Hyperspectral sensing technique can provide an exact and expeditious manner for effective management of deficit irrigation through assessment of changes in plant water status in a large scale and real-time. This hypothesis was tested in this study using the hyperspectral signatures of the canopy in the visible- (VIS), near(NIR), and shortwave-infrared (SWIR) to estimate and predict the leaf water status, measured here in terms of leaf water potential (LWP), relative water content (RWC), and equivalent water thickness (EWT), and grain yield (GY) of wheat cultivars exposed to 1.00, 0.75, and 0.50 of the estimated evapotranspiration (ETc). Results showed that the three parameters of leaf water status exhibited strong correlations with GY under 0.75 (except LWP) and 0.50 ETc treatments. This indicates that these parameters can be used as early indicators for management of deficit irrigation. Based on the relationships between these four phenotypic parameters and original canopy spectral reflectance within 350–2500 nm, the sensitive spectral wavelengths that exhibited strong correlations with all parameters existed mainly within the NIR and SWIR regions, with peak-wavelengths around 351, 518, and 687 nm in the VIS, 762, 974, 1100, and 1240 nm in the NIR, and 1392, 1515, 1930, and 2273 nm in the SWIR regions. These peak-wavelengths were used to build new two- and three-band normalized spectral reflectance indices (NDSIs). The NDSIs that combine NIR and VIS, NIR and NIR, SWIR and NIR, and SWIR and SWIR wavelengths were more effective for tracking changes in leaf water status and GY than those that combine only VIS wavelengths. The high fit between the observed and predicted values for phenotypic parameters based on twelve newly developed and published NDSIs indicates that the most recently developed NDSI models were more precise and accurate, and thus could be used for monitoring the changes in leaf water status and wheat production caused by deficit irrigation. The performance of partial least square regression (PLSR) based on either eleven wavelengths or different NDSIs as a predictive approach was the same and sometimes better than the individual NDSIs for assessment of phenotypic parameters. The results of spectral reflectance data and PLSR tools can serve as rapid and non-destructive alternative approaches for monitoring the water status and wheat production, and can be used to develop certain spectral indices for management of deficit irrigation in arid regions.

1. Introduction Water shortage currently plagues almost every country in arid and semiarid regions and is one of the key factors for stagnation and decrease of crop production in these regions. In addition, the global ⁎

climate change, which is synchronously accompanied by low and variable precipitation, high evaporation rates, and frequent incidences of drought, causing water shortage in these regions has already become the rule rather than the exception. Furthermore, most of cropping areas in these regions are irrigated and consumes about 75% of the total

Corresponding author at: Department of Plant Production, College of Food and Agriculture Sciences, King Saud University, 11451, Riyadh, Saudi Arabia. E-mail address: [email protected] (S.E. El-Hendawy).

https://doi.org/10.1016/j.agwat.2019.03.006 Received 27 May 2018; Received in revised form 4 December 2018; Accepted 2 March 2019 0378-3774/ © 2019 Elsevier B.V. All rights reserved.

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bands located in the NIR region near 970 and 1200 nm (Bowyer and Danson, 2004; Ceccato et al., 2002; Seelig et al., 2008; Wang et al., 2009). The canopy spectral reflectance in the NIR region is also strongly affected by several internal leaf structural factors such as leaf cuticles, intercellular air spaces, and the ratio between palisade mesophyll and spongy mesophyll that are strongly associated with the leaf water status. Although the VIS region (400–700 nm) does not have water bands, previous studies have shown that the wavelengths in this region could indirectly be used to estimate the plant water status through the effects of dehydration on the properties of the leaf pigments (Carter, 1991; Vescovo et al., 2012). Therefore, there is also a secondary effect of plant water status on canopy spectral reflectance in the NIR and VIS regions. Thus, many studies have demonstrated that various parameters related to plant water statuscould be assessed with hyperspectral instruments using different spectral reflectance indices (SRIs) based on the wavelengths of the three parts of the electromagnetic spectrum (VIS, NIR, and SWIR). For example, Rapaport et al. (2017) developed a new index, the water balance index (WABI, (R1500–R531/R1500+R531)), for monitoring the plant water status and detecting the irrigation scheduling in grapevine (Vitis vinifera) under field conditions. Several studies have also found that the wavelengths in the NIR region are appropriate to detect plant water status and the SRIs formulated based on NIR wavelengths such as the water index (WI = R970/R900) and different normalized water indices: NWI-1 = (R970 − R900)/(R970 + R900), NWI-2 = (R970 − R850)/(R970 + R850), NWI-3 = (R970 − R880)/ (R970 + R880), and NWI-4 = (R970 – R920)/(R970 + R920) are better for identifying genotypic differences in plant water content under waterlimited conditions (Babar et al., 2006; El-Hendawy et al., 2015; Gutierrez et al., 2010; Peñuelas et al., 1994; Prasad et al., 2007; Rischbeck et al., 2014). Zarco-Tejada et al. (2003) estimated the leaf water content in relation to leaf area (EWT) and dry matter content (fuel moisture content, FMC) using the simple ratio water index: SRWI = (R860/R1240). Interestingly, the normalized difference vegetation index (NDVI, (R900-R680)/(R900+R680)), which is one of the most widely used vegetation indices, showed significant correlation with LWP and leaf water content (Stimson et al., 2005). Yao et al. (2014) successfully estimated EWT in wheat under various water and nitrogen treatments by using a three-band index (R1429–R416–R1865)/ (R1429+R416+R1865). Gaulton et al. (2013) found strong correlations between leaf EWT (R2 = 0.80, root mean square error [RMSE] = 0.0069 g/cm2) and the normalized ratio between 1063 and 1545 nm wavelengths. Junttila et al. (2016) showed that the SRIs calculated from 690, 905, and 1550 nm showed significant correlations with leaf EWT. Importantly, although the strong water absorption bands located in SWIR are more sensitive to plant water status than the weak water absorption bands located in NIR, the SRIs that are used to assess the different parameters related to plant water status are effective when NIR and SWIR wavelengths are combined in order to overcome the sensitivity of SWIR to the other leaf parameters such as dry matter and internal structure (Ceccato et al., 2001, 2002; Chuvieco et al., 2002). In other words, the SRIs are efficient when measurement bands where water absorption is high and reference bands where the water absorption is weak are included. Although several SRIs have great potential for assessing different water-related parameters as aforementioned and can easily be calculated, these indices focus mostly on 2–3 wavelengths only. Importantly, there are different factors related to the conditions for the measurement the spectral reflectance of the canopy (i.e., crop phenological growth stages, cultivars, levels of treatments, and year) influencing the performance of the SRIs in the assessment of plant parameters. Additionally, most regression analyses used for assessment of plant parameters relies on a single SRI, and therefore very few SRIs are applied in published work, although hundreds of SRIs are available (Lobos and Poblete-Echeverría, 2017). Therefore, there are compelling reasons for integrating multiple SRIs as a single index for improving prediction analysis in the assessment of plant parameters. The advantages of such

available water supply. Thus, gradually shifting from full irrigation to deficit irrigation practices in order to achieve maximum crop production per unit of irrigation water applied rather than emphasizing on production per unit of area represents a plausible solution to deal with this issue (El-Hendawy et al., 2017a; Fereres and Soriano, 2007). Accurate estimation and real time detection of the responses of different plant parameters to deficit irrigation, particularly those associated with the plant water status, are increasingly required for effective water management in precision agriculture. The valuable information provided by the monitoring of plant water status can be used directly or indirectly for different purposes, such as assessment of drought indicators, understanding the relationships between plant and soil water, mapping and monitoring the conditions of plant biosphere processes, aiding in yield estimation, and making sound decisions related to irrigation scheduling, which involve deciding when and how much water should be applied (Gutierrez et al., 2010; Peñuelas et al., 1994; Stimson et al., 2005; Wang et al., 2015). Leaf water potential (LWP), relative water content (RWC), and equivalent water thickness (EWT) have been developed and widely used as indicators for monitoring plant water status. LWP is used as the standard parameter, while RWC is often used as a substitute plant parameter for detecting plant water status (Clevers et al., 2008; Elsayed et al., 2011; Kakani et al., 2007). RWC is expressed as a fraction of the water volume for the leaf at full turgidity and has a real physiological meaning, because it measures the amount of water available for leaf transpiration and reflects the physiological outcome of cellular water deficit (Chaves et al., 2003; Rossini et al., 2013). EWT is defined as the ratio between the volume of water and the leaf area (Danson et al., 1992; Yao et al., 2014). Therefore, gaining a comprehensive and better understanding of these plant water parameters will play an important role in managing deficit irrigation and sustaining crop production under such conditions. The ordinary methods for the measurement of these three plant water status parameters, that include measuring the area and fresh weight of the leaves and drying them in an oven, are practicable, very simple, and do not require special expertise. Nonetheless, they are generally time- and cost-inefficient, destructive and tedious, fail to fulfill the requirement of real-time evaluation, and are not feasible when the measurements are made on a large scale. Importantly, some methods provide information on the plant water status such as LWP solely on a single effective leaf. Such measurements are not only timeand cost-inefficient but also subject to errors and unrepresentative of the water status of the entire canopy. Consequently, quantitative and instantaneous methods for assessment of these plant parameters are needed to address the aforementioned drawbacks of the conventional measurement methods as well as to advance deficit irrigation regulation in wheat through precision agriculture practices. Spectral reflectance techniques have been demonstrated to be instantaneous, cheap, applicable for a large scale, and non-destructive alternative methods for integrative assessment of several phenotypic parameters under different environmental conditions (El-Hendawy et al., 2017a,b; Elsayed et al., 2017; Gitelson et al., 2003; Lobos et al., 2014; Rapaport et al., 2017; Sun et al., 2008). The ground-based hyperspectral reflectance technique, which detects the spectral reflectance of the canopy from visible- (VIS) to shortwave-infrared (SWIR), has been shown to have a great potential to detect even the slight variations and modifications in biophysical and biochemical characteristics of the canopy. Therefore, it is successfully used to track the changes in plant parameters related to plant water status. The possibility of detecting various water-related parameters through proximal remote sensing data derives from the fact that canopy reflectance in the near-infrared (NIR, 700–1300 nm) and the shortwave-infrared (SWIR, 1300–2500 nm) regions is strongly influenced by several internal leaf structural and water content of the canopy. The OeH bonds in the canopy water strongly absorbs solar radiation in the NIR and SWIR regions, with the strong water absorption bands found in the SWIR region and centered at approximately 1450, 1950, 2250, and 2500 nm and the weak absorption 357

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The daily reference evapotranspiration (ETo) was calculated by the Penman-Monteith modified equation (Allen et al., 1998) using daily meteorological data collected from a weather station located within 200 m of the field experimental site. The crop coefficients (Kc) for spring wheat recommended by FAO-56 (0.15, 1.10, and 0.30 for the initial, mild, and late stages, respectively) were adjusted to the conditions of the study location (Allen et al., 1998). The values of Kc must be adjusted when the wind velocity is less or greater than 2 m s−1 and the minimum relative humidity differ from 45%. Based on Eq. (1), the total amounts of irrigation water applied for 1.00 ETc were 571.0 and 565.0 mm ha−1 in first and second seasons, respectively. These amounts of irrigation water were reduced by 25.0% and 50.0% for the 0.75 and 0.50 ETc treatments, respectively. The irrigation water was applied using surface irrigation system. To deliver equal and consistent amounts of water to each experimental unit (subplot), the main pipe of the irrigation system was equipped with a water meter and distributed to the sub-main hoses at each unit and equipped with a manual control valve at each experimental unit.

analysis in the assessment of plant parameters are that (1) the plant parameters can be simultaneously assessed through different types of SRIs (e.g., simple ratio (SR), normalized spectral indices (NDSIs), perpendicular vegetation index (PVI), soil adjusted vegetation index (SAVI)), and (2) the plant parameters can be simultaneously assessed through a wide range of wavelengths from the three parts of the electromagnetic spectrum. Multivariate regression analyses, including partial least square regression (PLSR), are found to be suitable methods for dealing with a large number of SRIs as a single index in order to improve the prediction of plant parameters. Recently, PLSR has been widely used to estimate and predict different plant parameters. For instance, PLSR based on different normalized SRIs increased the accuracy estimations of grain yield under field conditions by decreasing RMSE and increasing the coefficients of determination (R2) (Elsayed et al., 2017). Strong and significant relationships were found between the observed and predicted values as well as the validation value of wheat grain yield (R2 = 0.87, RMSE = 413 kg ha−1) when PLSR based on different wavelengths (Sharabian et al., 2014). In general, hyperspectral measurements allow various multivariate analyses to deal with the whole spectrum wavelengths and various SRIs. The objectives of this study were to: (1) identify most wavelengths within the three regions of the spectrum (VIS, NIR, and SWIR) that were specifically sensitive to leaf water status (LWP, RWC, and EWT) and GY measured for different wheat cultivars under different levels of irrigation, (2) utilize these wavelengths for developing new specific normalized spectral reflectance indices (NDSIs), (3) examine the suitability of these new NDSIs and published SRIs for assessment of leaf water status and GY, (4) develop and validate prediction models for accurate estimation of leaf water status and GY based on various new and published NDSIs, and (5) compare the performance of PLSR based on either different wavelengths or NDSIs for estimating the leaf water status and GY in wheat. We propose that developing the new NDSIs will help in precise assessment and monitoring of plant water status in semiarid regions and will be useful for irrigation scheduling and maximizing the water productivity of wheat.

2.3. Experimental design and crop management The field experiments were laid out in a randomized complete block split-plot design, with three main plot irrigation treatments and three subplot cultivar treatments replicated three times. Each subplot was 4.0 m long and 1.5 m wide (6.0 m2 plot−1). The seeds of each cultivar were planted in ten rows per subplot at a seeding rate of 17 g m-2. All treatments were fertilized with 180 kg ha−1 of ammonium nitrate (33.5% N), 60 kg ha−1 of potassium chloride (60% K2O), and 90 kg ha−1 of calcium superphosphate (15.5% P2O5). Nitrogen fertilizer was applied at seeding, tillering, and booting stages in three equal doses. The entire amount of phosphorus and potassium were applied prior to seeding and at booting stage, respectively. Other field practices such as application of fungicides and herbicides were undertaken to control diseases and weed infestation, respectively. 2.4. Measurements of phenotypic parameters

2. Materials and methods Six fully-developed leaves from each subplot were collected randomly at the anthesis growth stage at 9:30–12:30 AM and immediately weighed for fresh weight (FW) determination. After measuring leaf water potential (LWP), using the Scholander pressure chamber (Scholander et al., 1965), and leaf area (LA), using a leaf area meter (LI 3100; LI-COR Inc., Lincoln, NE, USA), leaves were then rehydrated in deionised water in a dark cold room at 5 °C for 16 h until they were fully turgid. Following rehydration, each leaf was blotted and immediately weighed to obtain turgid weight (TW), and then oven-dried at 75 °C in a forced-air oven for 48 h to obtain dry weight (DW). Relative water content (RWC) and equivalent water thickness (EWT) were calculated using the following equations:

2.1. Plant materials and growing conditions This study was conducted on three different wheat cultivars (Pavon 76, Sakha 93, and Yecor Rojo) at the Dierab Experimental Research Station of the College of Food and Agricultural Sciences, King Saud University, Riyadh, Saudi Arabia, during two consecutive growing seasons of 2016/2017 and 2017/2018. Based on previous evaluation, the first two cultivars have been identified as drought-tolerant cultivars, while Yecor Rojo has been recognized as a drought-sensitive cultivar (Elshafei et al., 2013; El-Hendawy et al., 2015). The research station is located in the south west of Riyadh between latitudes 24°24′30′′ N 24°25′30′′ N and longitudes 46°39′0′′ E 46°39′30′′ (Fig. S1). The area is characterized by typical arid climate conditions, with the precipitation and temperature ranging from 5 to 28 mm and 9.9 to 35.2 °C, respectively. The soil type of the experimental area was sandy loam (8.5% clay, 15.4% silt, and 76.1% sand), with bulk density, field capacity, and wilting point of 1.51 g cm−3, 0.148, and 0.090 m3 m−3, respectively (El-Hendawy et al., 2017a).

RWC (%) = (FW – DW)/(TW – DW) × 100, EWT (g cm

−2

) = (FW – DW)/LA.

(2) (3)

After physiological maturity, five internal rows in each subplot, each 3 m long (2.25 m2 total area), were harvested and threshed, and the water content in the seed was adjusted to 15% to determine the total grain yield per hectare.

2.2. Irrigation treatments

2.5. Canopy hyperspectral reflectance measurements

Three irrigation treatments (1.00, 0.75, and 0.50 of the estimated crop evapotranspiration, ETc) were applied in this study. The amount of irrigation water applied, I, for the full irrigation treatment (1.00 ETc) was calculated according to the following equation:

Along with measurement of phenotypic parameters, canopy hyperspectral reflectance was measured using a portable ASD spectroradiometer (Field Spec FR 350–2500 nm, Analytical Spectral Devices Inc., Boulder, CO, USA). The spectrum of this instrument is characterized by 1.4 and 2.0 nm sampling intervals from 350 to 1050 nm and 1050 to 2500 nm, respectively. However, moving averages were

I = ETo × Kc.

(1) 358

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The R package “lattice” from the software R statistics version 3.0.2 (R foundation for statistical computing 2013) was used to produce the contour maps. Partial least square regression (PLSR) was performed with the Unscrambler X multivariate data analysis software version 10.2 (CAMO Software AS, Oslo). The PLSR is a technique that specifies a linear relationship between a set of independent variables (NDSIs and wavelengths) and target variables (LWP, RWC, EWT, and GY). It seeks the sensitive information from reflectance bands or NDSIs. Eleven wavelengths or sixty-six NDSIs were used as input variables in the PLSR. The optimum number of PLSR factors was identified based on the minimum RMSE for cross validation to avoid over-fitting or under-fitting problems. The quality of calibration and validation models was tested through RMSE and R2 both for calibration (RMSEcal. and R2cal.) and validation (RMSEval. and R2val.).

calculated automatically to achieve 1.0 nm width continuous bands. Measurements were taken within ± 2 h of solar noon under cloudless conditions using a fiber optic probe with a 25° full conical angle and 2.3 mm diameter. The probe was held vertically at approximately 80 cm above the canopy in a nadir position on an adjustable monopod pole to cover the three central rows. A spectralon white reference panel (Labsphere, Inc., North Sutton, NH) was used to calibrate the spectroradiometer and to generate reflected light percentages. The calibrations were made before and after reflectance measurements for each sub-plot. Four measurements were taken for each subplot on the three central rows. An average of 10 scans calculated automatically was used for each measurement, and the average of four measurements was considered a measured spectrum for a sub-plot. Based on the hyperspectral reflectance data, seven existing indices that are effective for assessing plant water status, and combined wavelengths from three parts of the spectrum (VIS, NIR, and SWIR regions) were calculated. These indices were the water index (WI = (R900/R970); Peñuelas et al. (1994)), the simple ratio water index (SRWI = (R860/R1240); Zarco-Tejada et al. (2003)), the moisture stress index (MSI = (R1600/R820); Hunt et al. (1989)), the normalized water index-3 (NWI-3 = (R970 − R880)/ (R970 + R880); Prasad et al. (2007)), the normalized difference water index-1240 (NDWI1240 = (R860 – R1240)/ (R860 + R1240); Gao (1996)), the normalized difference water index1640 (NDWI1640 = (R860 – R1640)/ (R860 + R1640); Chen et al. (2005)), and the three-band index (NDSI, 860, 1640, 2130); Yao et al., 2014). In this study, based on the relationship between the original spectral reflectance of the full wavelengths range (350–2500 nm) and the tested phenotypic parameters, eleven wavelengths were selected to create all possible combinations between pairs of wavelengths as normalized difference spectral indices (NDSIs) based on the following formula: NDSI = [Rλ1 – Rλ2]/[Rλ1 + Rλ2],

3. Results 3.1. Variation of phenotypic parameters between irrigation rates and cultivars The all phenotypic parameters of leaf water status [leaf water potential (LWP), relative water content (RWC) and equivalent water thickness (EWT)] and grain yield per hectare (GY) were significantly affected by irrigation rates, with the values of RWC and GY for 0.75 ETc treatment being similar to those of the full irrigation treatment (1.0 ETc). However, when averaged over the two seasons, severe water stress treatment (0.50 ETc) was found to have resulted in decreases in LWP of 35.5% and 78.0%, RWC of 13.8% and 22.4%, EWT of 35.6% and 52.4%, and GY of 19.7% and 31.4% when compared with the 0.75 and 1.00 ETc treatments, respectively (Table 1). Regardless of irrigation rates, the differences in all phenotypic parameters between Pavon 76 and Sakha 93 were not significant, with the drought-sensitive cultivar Yecora Rojo producing the lowest values for these parameters (Table 1). The interaction between irrigation rate and cultivar had a significant effect on all phenotypic parameters in both growing seasons. The differences in all phenotypic parameters between Pavon 76 and Sakha 93 appeared only under the severe water stress treatment (0.50 ETc), with the exception of EWT, which showed non-significant differences between both cultivars in the first season. When compared with these two cultivars, Yecora Rojo produced the lowest values for RWC and GY, even under the full irrigation treatment (1.00 ETc), and the values of both parameters for Pavon 76 under 0.50 ETc were competitive with those of Yecora Rojo under 1.00 ETc (Table 1).

(4)

where Rλ1 and Rλ2 are the spectral reflectance at the sensitive wavelengths λ1 and λ2, respectively.

2.6. Data analysis Data for phenotypic parameters (LWP, RWC, EWT, and GY) were tested using analysis of variance (ANOVA) appropriate for a randomized complete block split-plot design, with the irrigation rate and cultivar considered as the main factor and the split factor, respectively. Duncan’s test at the 95% probability level was used to compare the differences between the mean values of cultivars, irrigation rates, and their interactions. Pearson’s correlation coefficient matrix was used to determine the relationship between all phenotypic parameters for each irrigation rate as well as to determine the relationship between the original spectral reflectance of the full wavelength range and phenotypic parameters to select the sensitive wavelengths. Linear and nonlinear curve-fitting models were used to test the relationships between exiting and novel NDSIs (as independent variables) and the phenotypic parameters (as dependent variables). The relationships with the highest coefficients of determination (R2) were considered as the best fit relationships. Different statistical parameters were used to evaluate the fit of different models of the predicted and observed values. The fitting accuracy was visually assessed from a 1:1 scatter plot of the measured and predicted phenotypic values. The statistical parameters included the coefficient of determination (R2), the root mean square error (RMSE), the mean relative error (RE%), the Amemiya’s prediction criterion (PC), and the intercept and slope of the linear regression between the observed and predicted values of the phenotypic parameters. A model with highest values of R2 and slope and lowest values of RMSE, RE, and PC was considered as the model of the higher prediction accuracy. These statistical parameters were performed with XLSTAT statistical package (vers. 2017.4, Excel Add-ins soft SARL, New York, NY, USA).

3.2. Correlation of phenotypic parameters related to individual irrigation rate The only significant correlation found between phenotypic parameters under the full irrigation treatment (1.00 ETc) was a positive and strong correlation between GY and RWC, with a coefficient of 0.70 (Table 2). Under moderate (0.75 ETc) and severe water stress (0.50 ETc) treatments, all phenotypic parameters showed a stronger positive or negative correlation with each other, with the exception of the correlation between GY and LWP under the 0.75 ETc treatment. In addition, all phenotypic parameters showed a stronger relationship with each other under 0.50 ETc compared with 0.75 ETc (Table 2). 3.3. Correlation between spectral reflectance and phenotypic parameters The relationship between the original spectral reflectance of the full wavelength range (350 to 2500 nm) and the tested phenotypic parameters were analyzed using Pearson correlation coefficients and the pooled data of years, replications, irrigation rates, and cultivars (Fig. 1). Generally, the wavelengths in the near-infrared (NIR, 700–1300 nm) and shortwave-infrared (SWIR, 1300–2500 nm) regions exhibited 359

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Table 1 Effects of irrigation rate, cultivar, and their combination on leaf water potential (LWP), relative water content (RWC), equivalent water thickness (EWT), and grain yield parameters measured at the anthesis growth stage during two growing seasons. Irrigation rates

2016–2017

2017–2018

Cultivars Pavon 76

Sakha 93

Yecora Rojo

Mean

Pavon 76

Sakha 93

Yecora Rojo

Mean

LWP ((MPa) 1.00 ETc 0.75 ETc 0.50 ETc Mean

-0.56 e −1.63 d −2.02 c -1.40 A

−0.56 e −1.63 d −2.43 b -1.54 A

−0.59 e −1.93 c −2.93a -1.82 B

-0.57 C -1.73 B -2.46 A

−0.61 f −1.53 e −2.31 c -1.48 A

−0.55 f −1.52 e −2.67 b -1.58 A

−0.54 f −1.76 d −3.17 a -1.82 B

-0.57 C -1.60 B -2.72 A

RWC (%) 1.00 ETc 0.75 ETc 0.50 ETc Mean

79.5 a 74.2 b 67.1 c 73.6 A

79.6 a 73.1 b 58.6 d 70.4 A

69.2 c 60.5 d 50.9 e 60.2 B

76.1 A 69.3 AB 58.9 B

79.7 a 73.8 b 65.7 bc 73.1 A

80.3 a 73.1 b 60.1 d 71.2 A

71.2 b 59.0 de 54.3 e 61.5 B

77.1 A 68.6 AB 60.0 B

EWT (g cm2) 1.00 ETc 0.75 ETc 0.50 ETc Mean

0.056 a 0.044 b 0.032 c 0.044 A

0.061 a 0.044 b 0.029 c 0.045 A

0.054 a 0.034 c 0.020 d 0.036 B

0.057 A 0.041 B 0.027 C

0.050 ab 0.042 c 0.033 d 0.042 A

0.056 a 0.045 bc 0.025 e 0.042 A

0.050 ab 0.032 d 0.016 f 0.033 B

0.052 A 0.040 B 0.025 C

Grain yield (ton ha−1) 1.00 ETc 0.75 ETc 0.50 ETc Mean

8.47 ab 7.83 bc 6.71 de 7.67A

8.81 a 7.25 cd 5.71 f 7.25 A

6.93 cde 6.09 ef 3.92 g 5.65 B

8.07 A 7.06 AB 5.45 B

7.90 a 7.15 b 6.30 d 7.12 A

8.03 a 6.71 bc 5.79 e 6.85 A

6.60 cd 4.95 f 3.60 g 5.05 B

7.51 A 6.27 AB 5.23 B

Means in rows within irrigation rate as well as means in columns within cultivar followed by the same letter are not significantly different at the 0.05 level according to the Duncan’s test. Table 2 Pearson’s correlation matrix of leaf water status and grain yield parameters across three cultivars and two years (n = 18) under each irrigation rate. Parameters

LWP

RWC

EWT

GY

Leaf water potential (LWP) Relative water content (RWC) Equivalent water thickness (EWT) Grain yield per hectare (GY)

1.00 ETc 1.00 0.04 −0.14 0.04

1.00 0.25 0.70***

1.00 0.34

1.00

Leaf water potential (LWP) Relative water content (RWC) Equivalent water thickness (EWT) Grain yield per hectare (GY)

0.75 ETc 1.00 −0.74*** −0.57** −0.45 ns

1.00 0.81*** 0.85***

1.00 0.69**

1.00

Leaf water potential (LWP) Relative water content (RWC) Equivalent water thickness (EWT) Grain yield per hectare (GY)

0.50 ETc 1.00 −0.82*** −0.87*** −0.92***

1.00 0.80*** 0.84***

1.00 0.88***

1.00

Fig. 1. Correlation analysis between the original canopy spectral reflectance of the full wavelength range (350–2500 nm) and the parameters of leaf water potential (LWP), relative water content (RWC), equivalent water thickness (EWT), and grain yield (GY) per hectare based on pooled data of years, replications, water irrigation rates, and cultivars.

higher Pearson correlation coefficients with all phenotypic parameters than those in the visible (VIS, 400–700 nm) region. LWP was positively correlated with the wavelengths in the VIS and SWIR regions, while the other parameters (RWC, EWT, and GY) displayed opposite relations; the opposite held true for the wavelengths in the NIR region. The wavelengths in the three parts of the spectrum (VIS, NIR, and SWIR) exhibited higher correlation with RWC and GY than the LWP and EWT. The wavelength intervals from 708 to 734 nm in the NIR region, and from 1301 to 1388 nm in the SWIR were the only two regions that showed no significant correlation with all phenotypic parameters. The peaks with the strongest correlations were observed around 351, 518, and 687 nm in the VIS region, 762, 974, 1100, and 1240 nm in the NIR region, and 1392, 1515, 1930, and 2273 nm in the SWIR region (Fig. 1).

3.4. The relationships between wavelengths and phenotypic parameters determined using a contour map To further identify the sensitive band regions in the full wavelength range (350 to 2500 nm), the coefficients of determination (R2) of the relationships between phenotypic parameters and all normalized difference spectral indices (NDSIs), based on the combination of one wavelength on the horizontal axis (wavelength-1) and one wavelength on the vertical axis (wavelength-2), were plotted on contour maps, which is a two-dimensional representation of three-dimensional data, using the pooled data of years, replications, irrigation rates, and cultivars (n = 54) (Fig. 2). The relationships between phenotypic parameters and NDSIs were determined using the linear regression fits. Based on 360

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Fig. 2. Contour maps of coefficients of determination (R2) within the full wavelengths (350–2500 nm) between the normalized difference spectral indices (NDSIs) and the parameters of leaf water potential (LWP), relative water content (RWC), equivalent water thickness (EWT), and grain yield (GY) per hectare based on pooled data of years, replications, water irrigation rates, and cultivars.

regressions were the best models fitting the relationships between NDSIs and LWP and RWC, while the exponential regression and quadratic polynomial regressions best modeled the relationships between NDSIs and EWT. The three types of regression models (linear, quadratic polynomial, and exponential) were the best models providing the highest R2 for the relationships between NDSIs and GY (Table 3). Generally, most NDSIs estimated RWC and GY better than LWP and EWT. The NDSIs that incorporate VIS/VIS or SWIR/VIS were poorly correlated with all phenotypic parameters, while most NDSIs that incorporate NIR/VIS, NIR/NIR, or SWIR/NIR showed strongest relationships with all phenotypic parameters, with R2 values for these relationships ranging from 0.50 to 0.68, from 0.58 to 0.81, from 0.54 to 0.69, and from 0.59 to 0.81 for LWP, RWC, EWT, and GY, respectively (Table 3). In addition, these NDSIs were competitive with the published SRIs (No. 60-66 in Table 3) in estimating the phenotypic parameters, with the moisture stress index (MSI), normalized difference water index-1640 (NDWI1640), and NDSI (860, 1640, 2130) showed strongest relationships with all phenotypic parameters. The corresponding R2 for these relationships ranged from 0.65 to 0.66, 0.78 to 0.82, 0.65 to 0.68, and 0.73 to 0.74 for LWP, RWC, EWT, and GY, respectively. The water index (WI) and normalized water index-3 (NWI-3) were comparable to the MSI, NDWI1640, and NDSI (860, 1640, 2130) in estimating LWP and RWC (Table 3).

R2, wavelengths in the VIS region on the vertical axis showed significant relationships with phenotypic parameters when they combined only with wavelengths in the NIR region on the horizontal axis. Wavelengths in the NIR region on the vertical axis observed significant relationships with phenotypic parameters when they combined with wavelengths in the SWIR regions on the horizontal axis. The combination of wavelengths in the SWIR region on both the vertical and horizontal axes showed significant relationships with phenotypic parameters (Fig. 2). The R2 values for the relationships between phenotypic parameters and NDSIs were mostly greater than 0.60 when the NDSIs were based on the combination of wavelengths in the NIR and SWIR regions on the vertical axis and wavelengths in the SWIR region on the horizontal axis, or based on the combination of wavelengths in the VIS region on the vertical axis and wavelengths in the NIR region on the horizontal axis (yellow color in Fig. 2). The hotspot regions for R2 values larger than 0.80 were confined between the wavelength interval from 740 to 1240 nm on the vertical axis, and the wavelength intervals from 1300 to 1550 nm and 1900 to 2200 nm on the horizontal axis, as well as at around 2100 nm on the vertical axis and around 2300 nm on the horizontal axis (white color in Fig. 2). 3.5. Formulation of novel NDSIs to remotely assess phenotypic parameters Fifty-nine NDSIs were constructed in this study using all possible combinations of wavelengths, which were identified by the strongest peaks in Fig. 1 (NDSIs No. 1-59 in Table 3). The efficiency of these NDSIs in the estimation of phenotypic parameters was compared with seven published spectral reflectance indices (SRIs No. 60-66 in Table 3), which are sensitive to plant water status. The best models of regression and coefficients of determination (R2) for the relationships for all data (n = 54) between phenotypic parameters and these sixty-six spectral indices are summarized in Table 3. The linear and quadratic polynomial

3.6. Assessment of phenotypic parameters for each irrigation rate using a wide range of NDSIs Twelve different NDSIs (eight NDSIs constructed in this study and four NDSIs published in the literature) were selected from the previous 66 NDSIs to assess the phenotypic parameters individually for each irrigation rate. The relationships between these twelve NDSIs and phenotypic parameters are shown in Figs. 3–6. Coefficients of 361

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a The full name of the abbreviation of spectral reflectance indices from 60 to 65 explained in materials and methods section.

Table 3 The best models of regression and coefficients of determination (R2) for the relationships, for all the data (n = 54), between phenotypic parameters [leaf water potential (LWP), relative water content (RWC), equivalent water thickness (EWT), and grain yield (GY)] and different normalized difference spectral indices (NDSIs) constructed in this study (No. 1-59) and from literature (No. 6066). L, Q, and E represent linear, quadratic, and exponential fitting models, respectively. No.

SRIs

LWP

RWC

EWT

GY

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66

NDSI (518,351) NDSI (687,351) NDSI (762,351) NDSI (974,351) NDSI (1,100,351) NDSI (1,240,351) NDSI (1,392,351) NDSI (1,515,351) NDSI (1,930,351) NDSI (2,273,351) NDSI (687,518) NDSI (762,518) NDSI (974,518) NDSI (1,100,518) NDSI (1,240,518) NDSI (1,392,518) NDSI (1,515,518) NDSI (1,930,518) NDSI (2,273,518) NDSI (762,687) NDSI (974,687) NDSI (1,100,687) NDSI (1,240,687) NDSI (1,392,687) NDSI (1515,687) NDSI (1930,687) NDSI (2,273,687) NDSI (974,762) NDSI (1,100,762) NDSI (1,240,762) NDSI (1,392,762) NDSI (1,515,762) NDSI (1,930,762) NDSI (2,273,762) NDSI (1,100,974) NDSI (1,240,974) NDSI (1,392,974) NDSI (1,515,974) NDSI (1,930,974) NDSI (2,273,974) NDSI (12,401,100) NDSI (13,921,100) NDSI (15,151,100) NDSI (19,301,100) NDSI (22,731,100) NDSI (13,921,240) NDSI (15,151,240) NDSI (19,301,240) NDSI (22,731,240) NDSI (15,151,392) NDSI (19,301,392) NDSI (22,731,392) NDSI (19,301,515) NDSI (22,731,515) NDSI (22,731,930) NDSI (974,518,1392) NDSI(1100,351,1392) NDSI(762,518,1930) NDSI(1240,110012,401,100,762) WIa SRWI MSI NWI-3 NDWI1240 NDWI1640 NDSI (86,016,402,130)

0.09L 0.29Q 0.54L 0.50L 0.50Q 0.50Q 0.12Q 0.02Q 0.51L 0.04L 0.40Q 0.52L 0.49L 0.49Q 0.48Q 0.20Q 0.07L 0.39Q 0.05Q 0.46L 0.44L 0.45Q 0.44Q 0.32Q 0.27Q 0.05L 0.17Q 0.42L 0.40Q 0.52Q 0.67L 0.67Q 0.68Q 0.62Q 0.11Q 0.30Q 0.65L 0.65Q 0.66Q 0.59Q 0.39Q 0.64L 0.64Q 0.66Q 0.59Q 0.66L 0.65Q 0.65L 0.59Q 0.32Q 0.60L 0.25Q 0.57L 0.19Q 0.60L 0.61L 0.62L 0.62L 0.50Q 0.65Q 0.49Q 0.65Q 0.65Q 0.49Q 0.65Q 0.66Q

0.06L 0.31Q 0.72L 0.69L 0.69L 0.67L 0.23L 0.05L 0.48Q 0.07Q 0.37Q 0.63L 0.60L 0.60L 0.58L 0.26Q 0.09L 0.37Q 0.06Q 0.50Q 0.48Q 0.48Q 0.46L 0.33Q 0.30Q 0.10L 0.21Q 0.37Q 0.39Q 0.60Q 0.77L 0.81L 0.79Q 0.75L 0.19Q 0.44Q 0.78L 0.80L 0.78Q 0.73L 0.46Q 0.78L 0.80L 0.77Q 0.72L 0.78L 0.79L 0.76Q 0.71L 0.48L 0.68L 0.39L 0.62L 0.26L 0.59L 0.76L 0.78L 0.73L 0.58Q 0.70Q 0.63Q 0.78L 0.73Q 0.63Q 0.80Q 0.82Q

0.10L 0.26Q 0.62E 0.58E 0.59E 0.56E 0.15E 0.03L 0.44Q 0.06Q 0.35Q 0.59E 0.56E 0.56E 0.54 E 0.23Q 0.10L 0.36Q 0.10Q 0.47E 0.46E 0.46E 0.45E 0.30Q 0.25Q 0.05L 0.16Q 0.35E 0.35Q 0.50Q 0.69E 0.68E 0.68E 0.61Q 0.10Q 0.38E 0.68E 0.66E 0.67E 0.59Q 0.46E 0.69E 0.67E 0.67E 0.58Q 0.68E 0.65Q 0.66E 0.58Q 0.32Q 0.58E 0.24Q 0.56E 0.15Q 0.58Q 0.66E 0.68E 0.67E 0.54E 0.58Q 0.53E 0.65E 0.59Q 0.54E 0.66Q 0.68Q

0.16Q 0.45Q 0.77E 0.74L 0.76E 0.73L 0.31L 0.12L 0.47L 0.12Q 0.46Q 0.76E 0.73E 0.75E 0.70L 0.40Q 0.20L 0.34Q 0.11Q 0.65E 0.64E 0.64E 0.63E 0.48Q 0.44Q 0.05L 0.30Q 0.36E 0.27L 0.58Q 0.74L 0.75L 0.81Q 0.73Q 0.09Q 0.41Q 0.74L 0.74L 0.80Q 0.71Q 0.59 E 0.76L 0.76L 0.81Q 0.72Q 0.75L 0.74L 0.80Q 0.71Q 0.38L 0.76L 0.37Q 0.73L 0.29Q 0.72L 0.78L 0.79L 0.81Q 0.62Q 0.62Q 0.56Q 0.74Q 0.65Q 0.56Q 0.74Q 0.73Q

determination (R2) values for these relationships depended on irrigation rate and phenotypic parameters. All selected NDSIs (constructed and published NDSIs) failed to assess LWP and EWT under the full irrigation treatment (1.00 ETc). All published NDSIs (MSI, NWI-3, NDWI1640, and NDSI (860, 1640, 2130)) and two constructed NDSIs (NDSI (1240, 687), and NDSI (2273, 1240)) also failed to assess GY under the same irrigation rate. Except NDSI (1240, 687) and NWI-3, the other NDSIs showed a moderate to strong correlation with RWC under the 1.00 ETc treatment (R2 = 0.51 – 0.70) (Fig. 4). Under severe water stress (0.50 ETc), most selected NDSIs were highly correlated with LWP (R2 = 0.74 – 0.82) and GY (R2 = 0.73 – 0.89) (Figs. 3 and 6) but showed weak and moderate correlations with RWC (R2 = 0.36 – 0.69) and EWT (R2 = 0.33 – 0.67), with the exception of NWI-3 and NDSI (860, 1640, 2130)), which showed a strong relationship (Figs. 4 and 5). Under the moderate irrigation rate (0.75 ETc), most selected NDSIs failed to assess EWT; while most correlations associated with the other parameters (LWP, RWC, and GY) showed moderate to strong correlations (Figs. 3–6). 3.7. Validation of predictive models for phenotypic parameters based on different NDSIs The twelve NDSIs, which were used to assess the phenotypic parameters for each irrigation rate, were also used to predict the measured phenotypic parameters. The data of NDSIs of the second year were used to predict the measured phenotypic parameters of the first year. Table 4 shows various statistical parameters for determining the accuracy of the validation of predictive models between the observed and predicted values of the phenotypic parameter (see materials and methods). A model with highest values of R2 and slope and lowest values of RMSE, RE, and PC displayed the beast prediction accuracy. The models that fulfilled most criteria of the six statistical parameters were selected for accurate prediction of each phenotypic parameter and presented on a 1:1 scatter plot line (Fig. 7). In general, based on the criteria of six parameters, different models of the twelve NDSIs provided a more accurate estimation of RWC and GY than of LWP and EWT. Among the twelve NDSIs, five confirmed the most criteria of the accuracies of the models for each phenotypic parameter (Fig. 7 and Table 4). The models based on NDSI No. 39 (NDSI (1930, 974)), No. 44 (NDSI (1930, 1100)), and No. 58 (NDSI (762, 518, 1930)) provided accurate estimation of all phenotypic parameters (No. of NDSIs listed in Table 3). The four phenotypic parameters can also be estimated effectively from the prediction models based on the NDSI No. 56 (NDSI (974, 518, 1392)), with the exception of LWP. The LWP, RWC, and GY can also be estimated effectively from the prediction models based on NDSIs No. 65 (NDWI1640), No. 32 (NDSI (1515, 762)), and No. 23 (NDSI (1240, 687)), respectively. The models based on the NDSI No. 66 (NDSI (860, 1640, 2130)) provided accurate estimation for only LWP and EWT (Table 4 and Fig. 7). Furthermore, the three-band NDSIs, such as NDSIs No. 56 and No. 58, provided accurate estimation of LWP and RWC similar to the two-band NDSIs, but provided more accurate estimation of EWT and GY than the two-band NDSIs. Importantly, some NDSIs formulated in this study such as NDSIs No. 39, 44, 56, and 58 provided more accurate estimation of the measured phenotypic parameters than the published NDSIs such as NDSIs No. 62, 63, and 65 (Table 4). 3.8. Prediction of phenotypic parameters based on wavelengths and NDSIs using partial least squares regression (PLSR) with full cross-validation The eleven wavelengths selected from Fig. 1 and sixty-six NDSIs listed in Table 3 as independent data were also used to predict the measured phenotypic parameters using the PLSR model. The data of spectral reflectance of eleven wavelengths or the data of sixty-six NDSIs

The bold values indicate significant relationships at 0.05, 0.01, or 0.001.

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Fig. 3. Relationships between leaf water potential and selected new and published normalized difference spectral indices (NDSIs) for 1.00 ETc (triangle), 0.75 ETc (square), and 0.50 ETc (circle) treatments. Data correspond to two years, three cultivars, and three replications (n = 18) for each irrigation rate. *, **, ***Significant at the 0.05, 0.01, and 0.001 probability levels, respectively, and ns: not significant.

of RWC and GY than of LWP and EWT as such as the individual NDSI. In addition, the performance of PLSR model was the same and sometimes better than some individual NDSIs for assessment of phenotypic parameters. Table 5 summarizes the statistical parameters of the calibration and validation results of the PLSR models for predicting phenotypic

of the second year were used to predict the measured phenotypic parameters of the first year using the pooled data of years, replications, irrigation rates, and cultivars. The equation, R2 and RMSE of the linear validations between the observed and predicted values of all phenotypic parameters are shown in Fig. 8. In general, different models of the spectral reflectance or NDSIs provided also a more accurate estimation 363

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Fig. 4. Relationships between leaf relative water content and selected new and published normalized difference spectral indices (NDSIs) for 1.00 ETc (triangle), 0.75 ETc (square), and 0.50 ETc (circle) treatments. Data correspond to two years, three cultivars, and three replications (n = 18) for each irrigation rate. *, **, ***Significant at the 0.05, 0.01, and 0.001 probability levels, respectively, and ns: not significant.

wavelengths, these principles of accurate model were observed in 7, 4, and 7 factors of the PLSR model for LWP, RWC, and EWT, respectively, in the 2nd season, as well as in 5 and 4 factors for RWC and EWT for 0.75 ETc. Based on all 66-NDSIs, these principles were found in 4 and 6 factors for RWC and GY in the 1st season, 7 and 7 factors for LWP and EWT in the 2nd season, 7 and 5 factors for LWP and RWC for 0.75 ETc, and 7 and 6 factors for LWP and GY for 0.50 ETc, respectively, in both the calibration and validation models (Table 5). Based on either wavelengths or NDSIs, the PLSR model showed greater modeling and

parameters based on eleven wavelengths selected from Fig. 1 and sixtysix NDSIs listed in Table 3 (Fifty-nine NDSIs formulated from the eleven wavelengths and seven published NDSIs). The PLSR models were determined for each season across irrigation rates and cultivars as well as for each irrigation rate across two seasons and cultivars (Table 5). Generally, increasing the number factors of PLSR models tended to decrease RMSE and increase R2 and slope of regression in both calibration and validation models. An accurate model should have a low RMSE value with high values for R2 and slope. Based on all 14364

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Fig. 5. Relationships between leaf equivalent water thickness and selected new and published normalized difference spectral indices (NDSIs) for 1.00 ETc (triangle), 0.75 ETc (square), and 0.50 ETc (circle) treatments. Data correspond to two years, three cultivars, and three replications (n = 18) for each irrigation rate. *, **, ***Significant at the 0.05, 0.01, and 0.001 probability levels, respectively, and ns: not significant.

the same treatment when based on NDSIs. The validation sets were poorer than the calibration sets for EWT under 0.75 and 0.50 ETc when the PLSR model was based on wavelengths and NDSIs, as well as for RWC under 0.50 ETc and LWP and EWT in the 1st season when the PLSR model was based on NDSIs, but still showed significant relationships between the measured and predicted values (R2 ranged from 0.28 to 0.46, Table 5).

prediction accuracy for phenotypic parameters in the 2nd season than in the 1st season as well as for 0.75 and 0.50 ETc than 1.00 ETc. Generally, the R2 and slope of regression in the calibration sets were higher than those in the validation sets; the opposite held true for RMSE. Interestingly, the PLSR model failed to predict the LWP, EWT, and GY in both calibration and validation sets under the 1.00 ETc treatment when based on wavelengths, as well as LWP and EWT under 365

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Fig. 6. Relationships between grain yield and selected new and published normalized difference spectral indices (NDSIs) for 1.00 ETc (triangle), 0.75 ETc (square), and 0.50 ETc (circle) treatments. Data correspond to two years, three cultivars, and three replications (n = 18) for each irrigation rate. *, **, ***Significant at the 0.05, 0.01, and 0.001 probability levels, respectively, and ns: not significant.

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Table 4 Validation statistics of predictive models for phenotypic parameters based on twelve NDSIs selected from the previous sixty-six NDSIs presented in Table 3. R2, RMSE, RE, and PC represent coefficient of determination, root mean square error, mean relative error (%), and Amemiya’s prediction criterion, respectively. The numbers reflect the NDSIs presented in Table 3. NDSIs No.

R2

Leaf water potential (LWP) 5 0.57* 23 0.61** 32 0.69** 39 0.71*** 44 0.71*** 49 0.67** 56 0.67** 58 0.69** 62 0.68** 63 0.68** 65 0.70** 66 0.72***

R2

RMSE

RE

PC

intercept

Slope

NDSIs No.

RMSE

RE

PC

intercept

Slope

0.621 0.591 0.525 0.504 0.506 0.544 0.545 0.527 0.534 0.535 0.513 0.500

52.9 45.0 45.6 36.5 37.3 44.3 41.5 39.1 45.8 45.3 45.1 43.8

0.505 0.457 0.360 0.332 0.335 0.387 0.388 0.363 0.373 0.374 0.345 0.327

−0.762 −1.401 −0.320 −0.338 −0.313 −0.509 −0.575 −0.549 −0.343 −0.377 −0.350 −0.339

1.499 1.837 1.246 1.232 1.218 1.380 1.367 1.341 1.290 1.319 1.307 1.315

Relative water content (RWC) 5 0.74*** 4.835 23 0.66** 5.541 *** 32 0.77 4.534 *** 39 0.81 4.165 44 0.80*** 4.245 49 0.71*** 5.153 56 0.79*** 4.366 58 0.78*** 4.447 *** 62 0.73 4.899 ** 63 0.65 5.597 *** 65 0.75 4.790 66 0.75*** 4.755

5.8 6.6 5.1 4.6 4.8 6.0 5.2 5.4 5.3 6.4 5.4 5.2

0.300 0.394 0.264 0.223 0.232 0.341 0.245 0.254 0.308 0.402 0.295 0.290

−16.523 −42.885 −1.095 −0.683 0.131 −2.199 −9.639 −8.321 0.424 −1.048 0.025 0.740

1.252 1.655 1.019 1.020 1.008 1.034 1.155 1.137 0.992 1.009 0.994 0.980

15.0 17.4 15.5 15.6 15.4 16.8 14.4 14.3 17.3 16.6 14.7 14.7

0.383 0.365 0.336 0.305 0.312 0.414 0.306 0.278 0.396 0.399 0.315 0.287

−0.018 −0.038 −0.007 −0.006 −0.006 −0.009 −0.012 −0.013 −0.007 −0.011 −0.009 −0.010

1.380 1.888 1.093 1.084 1.072 1.144 1.234 1.249 1.079 1.173 1.127 1.123

grain yield (GY) 5 0.88*** 23 0.88*** 32 0.86*** 39 0.91*** 44 0.91*** 49 0.82*** 56 0.92*** 58 0.94*** 62 0.84*** 63 0.76*** 65 0.83*** 66 0.81***

6.4 6.5 6.9 5.9 5.8 8.1 5.0 4.5 6.5 8.0 6.9 7.7

0.141 0.137 0.168 0.108 0.108 0.208 0.097 0.065 0.191 0.274 0.199 0.218

−1.991 −4.338 −1.058 −0.521 −0.407 −1.193 −1.689 −1.302 −1.056 −1.606 −0.913 −0.846

1.218 1.587 1.072 1.004 0.986 1.087 1.180 1.127 1.061 1.137 1.036 1.022

Equivalent water thickness (EWT) 0.0076 5 0.67** 23 0.69** 0.0074 *** 32 0.71 0.0071 *** 39 0.74 0.0068 44 0.73*** 0.0069 49 0.64** 0.0079 56 0.74*** 0.0068 58 0.76*** 0.0065 ** 62 0.66 0.0077 ** 63 0.66 0.0078 *** 65 0.73 0.0069 66 0.75*** 0.0066

0.485 0.479 0.530 0.425 0.426 0.590 0.402 0.330 0.565 0.677 0.577 0.604

Bold numbers indicate more accurate estimation of measured parameters and fulfill the best values of the six statistical evaluation parameters. * P < 0.05. ** P < 0.01. *** P < 0.001.

4. Discussion

and was accompanied by parallel reductions in LWP, RWC, and EWT (Table 1). This result was also supported by the strong positive and negative correlations between GY and the three plant water relation parameters under moderate (0.75 ETc) (except for LWP) and severe (0.50 ETc) water stress treatments (Table 2). Close relationships between GY and these plant water relation parameters on one side, and the strong relationships between each other on the other side suggest that these three plant water status parameters could be used to understand the mechanisms of plant response and adaption to deficit irrigation stress and optimize crop production through right irrigation scheduling.

4.1. Relationship between plant water status and crop production Crop productivity under deficit irrigation is closely related to plant water status, and the latter status is directly influenced by soil water conditions. As expected, increasing deficit irrigation stress negatively affected the plant water relations, which in turn reduced leaf expansion and elongation, leaf initiation rates, and dry matter accumulation, ultimately resulting in significant reduction in final grain yield. Therefore, the assessment of plant water status through monitoring the status of leaf water potential (LWP), relative water content (RWC), and equivalent water thickness (EWT) could provide important insights for enhancing crop productivity under deficit irrigation stress by taking the right decisions of the irrigation scheduling, such as when and how much water should be applied. RWC is an immediate response to deficit irrigation at the canopy level, and thus is widely used for describing the water stress levels (Chaves et al., 2003; Chen et al., 2016). Changes in LWP, which provides a good indication of the degree of tissue and cell hydration, are synchronously accompanied by the changes in the soil water potential next to the plant roots (Bellvert et al., 2016; Elsayed et al., 2011; Gutierrez et al., 2010). Drought stress affects EWT before any symptoms appear on the canopy. Therefore, detecting the changes in EWT under deficit irrigation stress provides an early-warning signal for water deficit in the soil next to the plant roots (Ihuoma and Madramootoo, 2017; Zarco-Tejada et al., 2003). This indicates that these three plant water relation parameters could serve as key indicators for providing information about the actual water stress level, and thus accurate detection of these parameters could potentially minimize crop irreversible physiological damage and yield loss under deficit irrigation conditions. As observed in this study, final grain yield (GY) per hectare gradually decreased with decreasing irrigation rates,

4.2. Performance of spectral reflectance for assessment of plant water status and grain yield The relative importance of these three plant water status parameters in improving irrigation water management depends on monitoring their status at frequent times in order to determine irrigation thresholds and implementation of suitable irrigation scheduling. Fortunately, various changes in plant water status, which can be induced by deficit irrigation stress, generate variability in the canopy spectral reflectance in the range of 400–2500 nm (El-Hendawy et al., 2017a). Therefore, many studies have shown significant correlations between different water relation parameters and spectral information of the canopy at different wavelengths, which offers the possibility of assessing plant water status in a non-destructive and expeditious manner using the spectroradiometer (El-Hendawy et al., 2017a; Elsayed et al., 2017; Erdle et al., 2011, 2013). In this study, with the exception of the wavelength intervals from 708 to 734 nm and 1301 to 1388 nm, all other wavelengths in the three parts of the spectrum showed moderate to strong significant correlations with the three plant water relation parameters and GY (r = ± 0.50 – ± 0.84) with some clear peaks at around 351, 518, and 367

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Fig. 7. Scatter plots and linear relationships between observed and predicted values of the parameters of leaf water potential (LWP), relative water content (RWC), equivalent water thickness (EWT), and grain yield (GY) per hectare based on different normalized difference spectral indices (NDSIs) plotted on a 1:1 line. The NDSIs selected based on various statistical parameters are listed in Table 4. The numbers (No.) of NDSIs are presented in Table 3. The data of the NDSIs of the second year were used to predict the measured parameters of the first year.

have shown that the wavebands centered at 540, 610, 630, 680, 760, 970, 1100, 1200, 1240 1400, 1450, 1730, 1930, 2100, 2250, and 2500 nm provided insights for estimating different parameters related to plant water status under different irrigation treatments and for different crops (Cheng et al., 2010; Curran et al., 2001; Clevers et al., 2008; Elsayed et al., 2017; Yao et al., 2014). Datt (1999) reported significant negative correlations between the wavelengths intervals of 1120–1870 nm and 1980–2440 nm and EWT. Kriston-Vizi et al. (2008) reported moderate correlations between the plant water status of mandarin and peach canopies, expressed by LWP, and reflectance at green (490–580 nm) and red (580–760 nm) regions. Sun et al. (2008)

687 nm in the visible region (VIS), 762, 974, 1100, and 1240 nm in the near-infrared region (NIR), and 1392, 1515, 1930, and 2273 nm in the shortwave-infrared region (SWIR) (Fig. 1). These results reveal that these eleven peaks may have some specific roles in the eco-physiological process that is directly and indirectly associated with the plant water status. Therefore, building different spectral reflectance indices (SRIs) based on these wavelengths could be used effectively to assess the leaf water relation parameters and grain yield. Interestingly, a series of typical wavelengths have been identified in literature at the reflection peaks, valleys, and inflection points of the canopy spectral reflectance from VIS to SWIR of the spectrum ranges. Previous studies 368

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Fig. 8. Scatter plots and linear relationships between observed and predicted values of the parameters of leaf water potential (LWP), relative water content (RWC), equivalent water thickness (EWT) and grain yield (GY) per hectare based on the data of spectral reflectance of eleven wavelengths or the data of sixty-six normalized difference spectral indices (NDSIs) plotted on 1:1 line. The data of spectral reflectance or spectral indices of the second year across all treatments were used to predict the measured parameters of the first year.

at 1400, 1516, 1870, 2225, and 2273 nm exhibited strong relationships with leaf water content under drought stress. All the wavelengths listed above, other published wavelengths not mentioned here, or the wavelengths discovered in this study indicate that the effects of leaf water status on spectral reflectance may include both direct and indirect effects. The direct effects, which are manifested by the magnitude in spectral reflectance in the SWIR region, are related to the plant water status as well as to leaf biochemical properties that are independent of

reported that the wavelengths in the SWIR region (i.e., 1502, 1391, and 1656 nm) were also found to be more sensitive to changes in RWC in olive plants exposed to different levels of water stress than other wavelengths in the NIR region. Zhang et al. (2012) also found significant correlations (r > 0.60) between leaf water content and the wavelength intervals of 553–556 nm, 689–720 nm, 755–842 nm, 950–970 nm, 1013–1034 nm, and 1055–1075 nm under different water stress levels. In grass (Poa pratensis), Bayat et al. (2016) found that the wavelengths 369

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Table 5 Model summary for predicting phenotypic parameters [leaf water potential (LWP), relative water content (RWC), equivalent water thickness (EWT), and grain yield (GY)] based on eleven wavelengths and sixty-six normalized spectral reflectance indices (NDSIs) using partial least square regression (PLSR) model for two growing seasons and each irrigation rate across two seasons. Seasons and Treat.

Parameters

PLSR factors

Calibration R²

1st season

2nd season

1.00 ETc

0.75 ETc

0.50 ETc

1st season

2nd season

1.00 ETc

0.75 ETc

0.50 ETc

RMSE

cal.

cal.

Cross-validation Slope

cal.

R²

val.

RMSE

val.

Slope

val.

LWP RWC EWT GY LWP RWC EWT GY LWP RWC EWT GY LWP RWC EWT GY LWP RWC EWT GY

Estimation models based on eleven wavelengths 2 0.62*** 0.50 2 0.81*** 4.06 2 0.61*** 0.008 2 0.77*** 0.71 7 0.88*** 0.32 4 0.84*** 3.64 *** 0.004 7 0.90 *** 2 0.93 0.37 2 0.15 0.08 2 0.64** 3.02 2 0.04 0.009 2 0.25 0.74 2 0.65** 0.10 *** 5 0.99 0.47 *** 4 0.85 0.002 *** 2 0.88 0.27 2 0.81*** 0.17 2 0.69** 3.44 2 0.62** 0.004 2 0.86*** 0.44

0.61 0.81 0.61 0.77 0.88 0.84 0.90 0.93 0.15 0.64 0.04 0.35 0.65 0.99 0.85 0.88 0.81 0.69 0.62 0.86

0.61*** 0.79*** 0.61*** 0.76*** 0.82*** 0.73*** 0.76*** 0.91*** 0.20 0.54** 0.26 0.20 0.55** 0.85*** 0.42* 0.85*** 0.76*** 0.58** 0.46* 0.80***

0.55 4.40 0.009 0.78 0.46 4.82 0.006 0.42 0.11 2.63 0.011 0.87 0.12 2.79 0.005 0.34 0.20 4.23 0.005 0.55

0.56 0.77 0.55 0.71 0.84 0.79 0.83 0.93 0.21 0.55 0.15 0.24 0.51 0.86 0.57 0.80 0.77 0.59 0.53 0.83

LWP RWC EWT GY LWP RWC EWT GY LWP RWC EWT GY LWP RWC EWT GY LWP RWC EWT GY

Estimation models based on sixty-six NDSIs 2 0.64** 0.49 4 0.91*** 2.80 2 0.63** 0.008 6 0.93*** 0.37 7 0.89*** 0.30 *** 3 0.83 3.75 *** 7 0.92 0.004 *** 2 0.93 0.34 2 0.12 0.046 4 0.76*** 2.47 2 0.23 0.005 3 0.84*** 1.18 7 0.86*** 0.06 *** 5 0.84 2.72 ** 4 0.63 0.004 4 0.78*** 0.45 7 0.95*** 0.09 2 0.67** 3.55 2 0.71*** 0.004 *** 6 0.96 0.24

0.64 0.91 0.63 0.94 0.89 0.83 0.92 0.94 0.12 0.76 0.23 0.85 0.86 0.84 0.63 0.78 0.95 0.67 0.71 0.96

0.44* 0.84*** 0.44* 0.80*** 0.78*** 0.73*** 0.82*** 0.92*** 0.29 0.56** 0.01 0.54** 0.69** 0.73*** 0.36* 0.52** 0.81*** 0.33* 0.28* 0.65**

0.59 3.90 0.010 0.66 0.44 4.82 0.006 0.37 0.060 3.54 0.006 2.79 0.010 3.81 0.005 0.70 0.18 5.34 0.006 0.75

0.55 0.82 0.53 0.80 0.83 0.77 0.89 0.92 0.22 0.60 0.04 0.65 0.79 0.84 0.50 0.75 0.93 0.45 0.45 0.83

The NDSIs based on VIS-vs.-VIS wavelengths failed to track the changes in these parameters. These findings also reinforce the direct and indirect effects of the state of water hydration in the leaf on spectral reflectance features, as well as suggest that it is advantageous to construct the NDSIs based on combined information from the three parts of the spectrum (VIS, NIR, and SWIR) to assess plant water relations and crop productivity under a wide range of water irrigation rates. Furthermore, these findings also indicate that to build new NDSIs for monitoring plant water relations, the NDSIs should be established using bands not sensitive to plant water content. The absorption of light by water in tissues at these bands is very weak or do not occur, which makes them suitable for use as reference bands. These bands should be combined with bands sensitive to plant water content and the absorption of light by water in tissues at these bands is moderate or high. Data from literature allow us to conclude that the NDSIs formulated using SWIR combined with NIR, combining two wavelengths from the NIR regions, but the energy of one of them is not absorbed by water in tissues, or using NIR combined with VIS are useful for monitoring the plant water relations for different crops under different levels of water stress (Bayat et al., 2016; Ceccato et al., 2002; Colombo et al., 2008; Elsayed et al., 2011; Gao, 1996; Gutierrez et al., 2010; Ollinger, 2011). Elsayed et al.

leaf water status such as sugar, cellulose, or lignin. The indirect effects, which are manifested by changes in spectral reflectance in the VIS and NIR regions, are not explained by the spectral properties of plant water status but associated with other vegetation properties such as leaf pigment concentration, cell wall–air interfaces, leaf anatomy, and the leaf thickness and structure, which are indirect influenced by the state of hydration in the leaf. Therefore, it is feasible to use different wavelengths from the three parts of the spectrum (VIS, NIR, and SWIR) and incorporate it in the different SRIs to assess changes in plant water relation parameters and grain yield under different levels of irrigation rates. The contour map (Fig. 2), which shows the best coefficients of determination (R2) between phenotypic parameters and normalized difference spectral indices (NDSIs) based on linear regression, and the data in Table 3, which shows the best models of regression and R2 for the relationships between the tested parameters and 55 NDSIs constructed from the possible combinations of eleven wavelength peaks and 7 published NDSIs, reinforce these findings and also show that the NDSIs based on NIR-vs.-VIS, NIR-vs.-NIR, SWIR-vs.-NIR, and SWIR-vs.-SWIR wavelengths showed significant relationships (R2 values ranging from 0.50 to 0.81) with the three plant water relation parameters and GY.

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and GY being more accurate than those of LWP and EWT. At least five out of the 12 NDSI models were selected for each parameter with very high accuracy because they fulfilled most criteria of the six validation statistics (Table 4 and Fig. 7). The new NDSIs constructed in this study such as NDSIs No. 32, 39, 44, and 49 as well as the published SRIs such as SRIs No. 62 and 65, which showed very good prediction accuracy with the observed values of all phenotypic parameters, combined most relevant SWIR wavelengths with the most relevant NIR wavelengths (Table 4). The three-band indices, such as NDSIs No. 56, 58, and 66, also combined SWIR wavelengths together with NIR and VIS wavelengths. This indicates that most NDSIs composed of the NIR and SWIR bands could be used for indirect management of irrigation scheduling through detecting the changes in plant water status. The three water relation parameters tested in this study are always used to detect the water status of plants. This is because the sensitivity of the NDSIs to changes in plant water status depends on the selection of the wavelengths whose energy are weakly and strongly absorbed by water, which found always with the NIR and SWIR wavelengths, respectively. The wavelengths within the SWIR region are less sensitive to noise caused by the leaf internal structure, penetrate less far into the canopy, and are more sensitive to changes in plant water status; the opposite is true for the wavelengths within the NIR region (Eitel et al., 2006; Mariotto et al., 2013; Rapaport et al., 2017). As a result, the models derived from this study are simple, applicable, and conforms to the principle of remote sensing in management of irrigation scheduling. The results of this study also showed that the tested phenotypic parameters can also be accurately estimated from the prediction models based on the NDSIs that incorporate NIR-VIS regions such as NDSI No. 5 (NDSI (1100, 351)) or only NIR wavelengths such as NDSI No. 63 (NWI3 = R970–R880)/(R970 + R880). These results indicate significant responses of leaf structure, photosynthetic efficiency, and photosynthetic pigments to deficit irrigation stress. Therefore, it is likely that a cheaper NIR–VIS broadband sensor may be suitable also for detecting vegetation water status under a range of irrigation water levels. This assumption is supported by Becker and Schmidhalter (2017); Elsayed et al. (2011); Gutierrez et al. (2010); Marino et al. (2014); Prasad et al. (2007); Rapaport et al. (2017), and Ruthenkolk et al. (2002), who reported that the NIR-based indices and NIR–VIS based indices are sufficient to detect the parameters of plant water status such as LWP and RWC, and are sufficient to explain a large proportion of the variability in GY. The NIR wavelengths at 970 and 1100 nm penetrate deeper into the canopy and successfully detect the direct changes in the internal leaf structure caused by water deficit, and therefore are also suitable for detecting changes in plant water status. However, the other wavelengths at 880 and 351 nm are used as a reference because their energy is not absorbed by water.

(2011) also assessed LWP of wheat under drought stress using the SRI (R100/R1100). Strong correlations between the SRI based on the ratio between the water index (WI) and the normalized difference vegetation index (NDVI) and RWC, as well as between NWI-3 and LWP of wheat have been reported (Gutierrez et al., 2010; Kakani et al., 2007). Winterhalter et al. (2011) successfully identified strong relationships between the water status of wheat and SRIs based on VIS/NIR regions. Ruthenkolk et al. (2002) also reported that although the NDSIs based only on the SWIR regions delivered favorable relationships with LWP, the NDSI based on the NIR/VIS regions were found to be more related to LWP. Yao et al. (2014) successfully estimated EWT in wheat under various water regimes using a three-band index (R1429–R416–R1865)/ (R1429+R416+R1865). No consistent relationship was found between the photochemical reflectance index (PRI), which is based only on the VIS, and the water potential of olive plants because PRI was strongly affected by canopy structure and soil background (Suárez et al., 2008). Different factors affect the measured canopy spectral reflectance, affecting the performance of SRIs for tracking the changes in the measured parameters under different conditions. For instance, some SRIs performed the best for tracking the changes in the measured parameters under drought stress conditions but failed to track these changes under well-watered conditions and vice versa. Garriga et al. (2017) found that various SRIs showed the strongest predictive power for assessment of the measured parameters when the data of spectral reflectance for full irrigation and water stress were combined together as compared with the results obtained for individual treatments. On the contrary, El-Hendawy et al. (2017a) reported that various SRIs failed to track the changes in transpiration rate (E) when the data of spectral reflectance of different irrigation rates were pooled together, whereas they successfully tracked these changes under each irrigation rate. Elsayed et al. (2011) also found that the changes in LWP of wheat can reliably be tracked under either well-watered or water stress conditions. The results of this study showed that most selected NDSIs showed moderate to strong relationships with RWC under the full irrigation rate (R2 = 0.51–0.70), although they failed to assess the LWP and EWT, which suggest that the relationships between the NDSIs and both LWP and EWT were independent of RWC under full irrigation conditions (Figs. 3–5). However, half of the NDSIs (six out of 12 NDSIs) showed moderate relationships with GY under full irrigation conditions (R2 = 0.41–0.65, Fig. 6). Although the LWP was tracked effectively by the NDSIs under both moderate (0.75 ETc) and severe (0.50 ETc) water stress conditions similar to RWC and GY, these NDSIs showed moderate to strong relationships with EWT under severe water stress conditions only (R2 = 0.43–0.70, Fig. 5). These results indicate that major changes in EWT did not occur until the plants were exposed to highly water stress conditions and therefore the analyzed NDSIs are not able to detect light and moderate changes in EWT under full and moderate irrigation conditions. Furthermore, EWT may be less related to spectral reflectance than to RWC and sometimes LWP. The reason for this may be that although the EWT provides information about canopy water content such as the RWC and LWP, it is very sensitive to changes in the leaf area of the plant. Severe water stress is sufficient to notably decrease the plant leaf area. Thus, strong responses of leaf area to severe water stress may also have strong impacts on the canopy spectral reflectance. Therefore, the results of this study suggest that EWT does not directly provide information about water status as such as LWP and RWC, because it is dependent of the leaf area of the plant.

4.4. Evaluation of the partial least square regression (PLSR) model Although several SRIs have great potential for detecting the changes in different phenotypic parameters of plants under different environmental conditions, most common SRIs with simple ratios and normalized or combined formulas focus on 2–3 wavelengths only. This makes it difficult to build a unified spectral index to cover most associated bands related to the crop biophysical and biochemical parameters as well as to deal with potentially confounding factors. Several researchers have used PLSR to improve the prediction accuracy of phenotypic parameters of plants by using several and different sensitive wavelengths or SRIs (Colombo et al., 2008; Elsayed et al., 2017; Gislum et al., 2004; Jin et al., 2013; Li et al., 2014). For instance, Colombo et al. (2008) examined the performance of different SRIs for estimation of leaf EWT using the model of PLSR. The results of this study showed that leaf EWT could be successfully estimated through different SRIs using the PLSR model, with the mean relative errors between the predicted and the measured values around 19%. Jin et al. (2013) also reported that the PLSR model has great potential similar to the three-band

4.3. Validation of predictive models The three water relation parameters (LWP, RWC, and EWT) and GY were estimated from the developed predictive models based on the twelve NDSIs that are used for tracking changes in these parameters under each irrigation rate. Interestingly, all of these twelve NDSI models provided very good prediction accuracy of all parameters as indicated by the six validation statistics, with the prediction of RWC 371

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indices for estimating the leaf water content of winter wheat under water stress conditions using the entire spectral dataset, with R2 value of 0.74 and RMSE value of 9.82%. Elsayed et al. (2017) also found that the PLSR model based on different NDSIs improved the estimation accuracy of leaf and canopy RWC of wheat by decreasing the RMSE value and increasing the coefficients of determination. Garriga et al. (2017) compared different regression algorithms including PLSR with different SRIs for the estimation of different agronomic parameters of wheat under full irrigation and water stress conditions; the results indicated that the PLSR model was similar to or better than the individual SRIs for estimation of GY. Similarly, the results of this study found that the PLSR model based on either the data of spectral reflectance of eleven wavelengths selected from Fig. 1 or the data of sixty-six NDSIs listed in Table 3 was similar to or better than most individual NDSIs for estimation of water relation parameters and GY (Fig. 8). PLSR was better than the NDSI for estimation of LWP and EWT in the 2nd year and LWP under 0.75 and 0.50 ETc treatments, especially in the validation datasets (Table 5). This further confirms that the PLSR model includes a wide range of sensitive wavelengths compared to the method of SRIs.

reflectance of leaves. Am. J. Bot. 78, 916–924. Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., Grégoire, J.-M., 2001. Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sens. Environ. 77, 22–33. https://doi.org/10.1016/S0034-4257(01)00191-2. Ceccato, P., Gobron, N., Flasse, S., Pinty, B., Tarantola, S., 2002. Designing a spectral index to estimate vegetation water content from remote sensing data. Part 1 theoretical approach. Remote Sens. Environ. 82, 188–197. https://doi.org/10.1016/ S0034-4257 0100191-2. Chaves, M.M., Maroco, J.P., Pereira, J.S., 2003. Understanding plant responses to drought—from genes to the whole plant. Funct. Plant Biol. 30, 239–264. https://doi. org/10.1071/FP02076. Chen, D., Huang, J., Jackson, T.J., 2005. Vegetation water content estimation for corn and soybeans using spectral indices from MODIS near- and short-wave infrared bands. Remote Sens. Environ. 98, 225–236. Chen, D., Wang, S., Cao, B., Cao, D., Leng, G., Li, H., Yin, L., Shan, L., Deng, X., 2016. Genotypic variation in growth and physiological response to drought stress and rewatering reveals the critical role of recovery in drought adaptation in maize seedlings. Front. Plant Sci. 6, 1–15. Cheng, T., Rivard, B., Sánchez-Azofeifa, A., 2010. Spectroscopic determination of leaf water content using continuous wavelet analysis. Remote Sens. Environ. 115 (2), 659–670. Chuvieco, E., Riaño, D., Aguado, I., Cocero, D., 2002. Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment. Int. J. Remote Sens. 23, 2145–2162. Clevers, J.G.P.W., Kooistra, L., Schaepman, M.E., 2008. Using spectral information from the NIR water absorption features for the retrieval of canopy water content. Int. J. Appl. Earth Obs. Geoinf. 10, 388–397. Colombo, R., Meroni, M., Marchesi, A., Busetto, L., Rossini, M., Giardino, C., Panigada, C., 2008. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling. Remote Sens. Environ. 112, 1820–1834. Curran, P.J., Dungan, J.L., Peterson, D.L., 2001. Estimating the foliar biochemical concentration of leaves with reflectance spectrometry testing the Kokaly and Clark methodologies. Remote Sens. Environ. 76, 349–359. Danson, F.M., Steven, M.D., Malthus, T.J., Clark, J.A., 1992. High-spectral resolution data for determining leaf water content. Int. J. Remote Sens. 13, 461–470. Datt, B., 1999. Remote sensing of water content in eucalyptus leaves. Aust. J. Bot. 47, 909–923. Eitel, J.U.H., Gessler, P.E., Smith, A.M.S., Robberecht, R., 2006. Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. For. Ecol. Manage. 229, 170–182. El-Hendawy, S., Al-Suhaibani, N., Salem, A.E.A., Ur Rehman, S., Schmidhalter, U., 2015. Spectral reflectance indices as a rapid and nondestructive phenotyping tool for estimating different morphophysiological traits of contrasting spring wheat germplasms under arid conditions. Turk. J. Agric. For. 39, 572–587. https://doi.org/10.3906/tar1406-164. El-Hendawy, S., Al-Suhaibani, N., Hassan, W., Tahir, M., Schmidhalter, U., 2017a. Hyperspectral reflectance sensing to assess the growth and photosynthetic properties of wheat cultivars exposed to different irrigation rates in an irrigated arid region. PLoS One 12 (8), e0183262. https://doi.org/10.1371/journal.pone.0183262. El-Hendawy, S., Hassan, W., Al-Suhaibani, N., Schmidhalter, U., 2017b. Spectral assessment of drought tolerance indices and grain yield in advanced spring wheat lines grown under full and limited water irrigation. Agric. Water Manage. 182, 1–12. Elsayed, S., Mistele, B., Schmidhalter, U., 2011. Can changes in leaf water potential be assessed spectrally? Funct. Plant Biol. 38, 523–533. Elsayed, S., Elhewity, M., Ibrahim, H.H., Dewir, Y.H., Migdadi, H.M., Schmidhalter, U., 2017. Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes. Agric. Water Manage 189, 98–110. Elshafei, A., Saleh, M., Al-Doss, A.A., Moustafa, K.A., Al-Qurainy, F.H., Barakat, M.N., 2013. Identification of new SRAP markers linked to leaf chlorophyll content, flag leaf senescence and cell membrane stability traits in wheat under water-stressed condition. Aust. J. Crop Sci. 7, 887–893. Erdle, K., Mistele, B., Schmidhalter, U., 2011. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res. 124, 74–84. https://doi.org/10.1016/j.fcr.2011.06.007. Erdle, K., Mistele, B., Schmidhalter, U., 2013. Spectral high-throughput assessments of phenotypic differences in biomass and nitrogen partitioning during grain filling of wheat under high yielding Western European conditions. Field Crops Res. 141, 16–26. https://doi.org/10.1016/j.fcr.2012.10.018. Fereres, F., Soriano, M.A., 2007. Deficit irrigation for reducing agricultural water use. J. Exp. Bot. 58 (2), 147–159. Gao, B., 1996. NDWI- a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens. Environ. 58, 257–266. Garriga, M., Romero-Bravo, S., Estrada, F., Escobar, A., Matus, I.A., del Pozo, A., Astudillo, C.A., Lobos, G.A., 2017. Assessing wheat traits by spectral reflectance: do we really need to focus on predicted trait-values or directly identify the elite genotypes group? Front. Plant Sci. 8, 280. https://doi.org/10.3389/fpls.2017.00280. Gaulton, R., Danson, F., Ramirez, F., Gunawan, O., 2013. The potential of dual- avelength laser scanning for estimating vegetation moisture content. Remote Sens. Environ. 132, 32–39. https://doi.org/10.1016/j.rse.2013.01.001. Gislum, R., Micklander, E., Nielsen, J.P., 2004. Quantification of nitrogen concentration in perennial ryegrass and red fescue using near-infrared reflectance spectroscopy (NIRS) and chemometrics. Field Crops Res. 88, 269–277. Gitelson, A., Gritz, Y., Merzlyak, M., 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160, 271–282.

5. Conclusion In this study, close relationships between GY and three parameters of plant water relations (LWP, RWC, and EWT) under moderate and severe water stress conditions suggested that accurate and real-time estimation of these three plant water status parameters could be useful for irrigation scheduling and maximizing the water productivity of wheat. The observed significant relationships between plant water status parameters and the spectral information of the canopy at different wavelengths identified a series of novel wavelengths and, therefore, indicate that it is possible to assess changes in plant water status in a non-destructive and expeditious manner using the spectroradiometer. In this study, Fifty-nine novel NDSIs were also developed and the NIR/VIS, NIR/NIR, SWIR/NIR, and SWIR/SWIR based indices were most effective for tracking changes in leaf water status and GY. Different phenotypic parameters can be estimated from the prediction models based on a series novel NDSIs with very high accuracy. Acknowledgments The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. (RG-1435-032), and the Researchers Support & Services Unit (RSSU) for their technical support. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.agwat.2019.03.006. References Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration. Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage. Paper No. 56, FAO, Rome, Italy 300. Babar, M.A., van Ginkel, M., Klatt, A.R., Prasad, B., Reynolds, M.P., 2006. The potential of using spectral reflectance indices to estimate yield in wheat grown under reduced irrigation. Euphytica 150, 155–172. Bayat, B., van der Tol, C., Verhoef, W., 2016. Remote sensing of grass response to drought stress using spectroscopic techniques and canopy reflectance model inversion. Remote Sens. 8, 557–581. https://doi.org/10.3390/rs8070557. Becker, E., Schmidhalter, U., 2017. Evaluation of yield and drought using active and passive spectral sensing systems at the reproductive stage in wheat. Front. Plant Sci. 8, 379. https://doi.org/10.3389/fpls.2017.00379. Bellvert, J., Marsal, J., Girona, J., Gonzalez-Dugo, V., Fereres, E., Ustin, S.L., ZarcoTejada, P.J., 2016. Airborne thermal imagery to detect the seasonal evolution of crop water status in peach, nectarine and saturn peach orchards. Remote Sens. 8, 39. Bowyer, P., Danson, F.M., 2004. Sensitivity of remotely sensed spectral reflectance to variation in live fuel moisture content. Remote Sens. Environ. 92, 297–308. Carter, G.A., 1991. Primary and secondary effects of water content on the spectral

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Agricultural Water Management 217 (2019) 356–373

S.E. El-Hendawy, et al.

Busetto, L., Migliavacca, M., Amaducci, S., Colombo, R., 2013. Assessing canopy PRI from airborne imagery to map water stress in maize. ISPRS J. Photogramm. Remote Sens. 86, 168–177. Ruthenkolk, F., Gutser, R., Schmidhalter, U., 2002. Development of a noncontacting method for the determination of the plant water status. In: Horst, W.J., Schenk, M.K., Bürkert, A., Claassen, N., Flessa, H., Frommer, W.B., Goldbach, H., Olfs, H.W., Römheld, V., Sattelmacher, B., Schmidhalter, U., Schubert, S., Wirén, N.V., Wittenmayer, L. (Eds.), Plant Nutrition. Food Security and Sustainability of AgroEcosystems. Kluwer Academic Publishers, Dordrecht, The Netherlands, pp. 392–393. Scholander, P.F., Hammel, H.J., Bradstreet, A., Hemmingsen, E.A., 1965. Sap pressure in vascular plants. Science 148, 339–346. https://doi.org/10.1126/science.148.3668. 339. Seelig, H.D., Hoehn, A., Stodieck, L.S., Klaus, D.M., Adams III, W.W., Emery, W.J., 2008. Relations of remote sensing leaf water indices to leaf water thickness in cowpea, bean, and sugar beet plants. Remote Sens. Environ. 112 (1), 445–455. https://doi. org/10.1016/j.rse.2007.05.002. Sharabian, V.R., Noguchi, N., Ishi, K., 2014. Significant wavelengths for prediction of winter wheat growth status and grain yield using multivariate analysis. Eng. Agric. Environ. Food. 7 (1), 14–21. Stimson, H.C., Breshears, D.D., Ustin, S.L., Kefauver, S.C., 2005. Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sens. Environ. 96, 108–118. https://doi.org/10.1016/j.rse. 2004.12.007. Suárez, L., Zarco-Tejada, P.J., Sepulcre-Cantó, G., Pérez-Priego, O., Miller, J.R., JiménezMuñoz, J.C., Sobrino, J., 2008. Assessing canopy PRI for water stress detection with diurnal airborne imagery. Remote Sens. Environ. 112, 560–575. https://doi.org/10. 1016/j.rse.2007.05.009. Sun, P., Grignetti, A., Liu, S., Casacchia, R., Salvatori, R., Pietrini, F., Loreto, F., Centritto, M., 2008. Associated changes in physiological parameters and spectral reflectance indices in olive (Olea europaea L.) leaves in response to different levels of water stress. Inter. J. Remote Sens 29, 1725–1743. https://doi.org/10.1080/ 01431160701373754. Vescovo, L., Wohlfahrt, G., Balzarolo, M., Pilloni, S., Sottocornola, M., Rodeghiero, M., Gianelle, D., 2012. New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass. Inter. J. Remote Sens. 33, 2178–2195. Wang, J., Xu, R., Yang, S., 2009. Estimation of plant water content by spectral absorption features centered at 1,450 nm and 1,940 nm regions. Environ. Monit. Assess. 157, 459–469. https://doi.org/10.1007/s10661-008-0548-3. Wang, X., Zhao, C., Guo, N., Li, Y., Jian, S., Yu, K., 2015. Determining the canopy water stress for spring wheat using canopy hyperspectral reflectance data in loess plateau semiarid regions. Spectrosc. Lett. 48, 492–498. Winterhalter, L., Mistele, B., Jampatong, S., Schmidhalter, U., 2011. High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. Eur. J. Agron. 35, 22–32. Yao, X., Jia, W., Si, H., Guo, Z., Tian, Y., Liu, X., et al., 2014. Monitoring leaf equivalent water thickness based on hyperspectrum in wheat under different water and nitrogen treatments. PLoS One 9, 1–11. Zarco-Tejada, P.J., Rueda, C.A., Ustin, S.L., 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sens. Environ. 85, 109–124. Zhang, L., Zhou, Z., Zhang, G., Meng, Y., Chen, B., Wang, Y., 2012. Monitoring the leaf water content and specific leaf weight of cotton (Gossypium hirsutum L.) in saline soil using leaf spectral reflectance. Eur. J. Agron. 41, 103–117.

Gutierrez, M., Reynolds, M.P., Raun, W.R., Stone, M.L., Klatt, A.R., 2010. Spectral water indices for assessing yield in elite bread wheat genotypes in well irrigated, water stressed, and high temperature conditions. Crop Sci. 50, 197–214. Hunt, J., Ramond, E., Rock, B.N., 1989. Detection in changes in leaf water content using Near and mid infrared reflectance. Remote Sens. Environ. 30, 45–54. Ihuoma, S.O., Madramootoo, C.A., 2017. Recent advances in crop water stress detection. Comp. Elect. Agric. 141, 267–275. https://doi.org/10.1016/j.compag.2017.07.026. Jin, X., Xu, X., Song, X., Li, Z., Wang, J., Guo, W., 2013. Estimation of leaf water content in winter wheat using grey relational analysis-partial least squares modeling with hyperspectral data. Agron. J. 105, 1385–1392. Junttila, S., Vastaranta, M., Liang, X., Kaartinen, H., Kukko, A., Kaasalainen, S., Holopainen, M., Hyyppä, H., Hyyppä, J., 2016. Measuring leaf water content with dual-wavelength intensity data from terrestrial laser scanners. Remote Sens. (Basel) 9, 8. https://doi.org/10.3390/ rs9010008. Kakani, V.G., Reddy, K.R., Zhao, D., 2007. Deriving a simple spectral reflectance ratio to determine cotton leaf water potential. J. New Seeds 8, 11–27. https://doi.org/10. 1300/J153v08n03/02. Kriston-Vizi, J., Umeda, M., Miyamoto, K., 2008. Assessment of water status of mandarin and peach canopies using visible multispectral imagery. Biosys. Engin. 100, 338–345. https://doi.org/10.1016/j.biosystemseng.2008.04.001. Li, F., Mistele, B., Hu, Y., Chen, X., Schmidhalter, U., 2014. Reflectance estimation of canopy nitrogen content in winter wheat using optimised hyperspectral spectral indices and partial least squares regression. Eur. J. Agron. 52, 198–209. https://doi. org/10.1016/j.eja.2013.09.006. Lobos, G.A., Poblete-Echeverría, C., 2017. Spectral knowledge (SKUTALCA): software for exploratory analysis of high-resolution spectral reflectance data. Front. Plant Sci. 7, 1996. https://doi.org/10.3389/fpls.2016.01996. Lobos, G.A., Matus, I., Rodriguez, A., Romero-Bravo, S., Araus, J.L., Pozo, A.D., 2014. Wheat genotypic variability in grain yield and carbon isotope discrimination under Mediterranean conditions assessed by spectral reflectance. J. Integr. Plant Biol. 56, 470–479. Marino, G., Pallozzi, E., Cocozza, C., Tognetti, R., Giovannelli, A., Cantini, C., Centritto, M., 2014. Assessing gas exchange, sap flow and water relations using tree canopy spectral reflectance indices in irrigated and rainfed Olea europaea L. Environ. Exp. Bot. 99, 43–52. https://doi.org/10.1016/j.envexpbot.2013.10.008. Mariotto, I., Thenkabail, P.S., Huete, A., Slonecker, E.T., Platonov, A., 2013. Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission. Remote Sens. Environ. 139, 291–305. Ollinger, S.V., 2011. Sources of variability in canopy reflectance and the convergent properties of plants. New Phytolog. 189, 375–394. https://doi.org/10.1111/j.14698137.2010.03536.x. Peñuelas, J., Gamon, J.A., Fredeen, A.L., Merino, J., Field, C.B., 1994. Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves. Remote Sens. Environ. 48, 135–146. Prasad, B., Carver, B.F., Stone, M.L., Babar, M.A., Raun, W.R., Klatt, A.R., 2007. Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions. Crop Sci. 47, 1426–1440. https://doi.org/10.2135/ cropsci2006.07.0492. Rapaport, T., Hochberg, U., Cochavi, A., Karnieli, A., Rachmilevitch, S., 2017. The potential of the spectral ‘water balance index’ (WABI) for crop irrigation scheduling. New Phytolog. 216 (3), 741–757. https://doi.org/10.1111/nph.14718. Rischbeck, P., Baresel, P., Elsayed, S., Mistele, B., Schmidhalter, U., 2014. Development of a diurnal dehydration index for spring barley phenotyping. Funct. Plant Biol. 41, 12. https://doi.org/10.1071/FP14069. Rossini, M., Fava, F., Cogliati, S., Meroni, M., Marchesi, A., Panigada, C., Giardino, C.,

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