The experiments were carried out on wheat seedlings (Triticum aestivum L.). Eleven cultivars of wheat were used in the study: Lutescens 62, Strubes Schlesischer Grannen, Alen’kaya Uimonskaya, Khludovka, Paradis, Saratovskaya 29, Wachtel, Solo, Kantegirskaya 89, Sibirskaya 12, Naxos (hereinafter, cultivars C1–C11, respectively). Wheat seeds were provided by Federal Research Center N.I. Vavilov All-Russian Institute of Plant Genetic Resources (VIR).

Plants were grown in 1.2 L pots (9 plants per pot, peat soil Peter Peat Agro Black, Peter Peat, Moscow, Russia) under controlled conditions (air temperature 24 °С, relative humidity 50% and a 16 h photoperiod) in a vegetation room. The plants were illuminated using cool white fluorescent lamps L36W/640 (Osram, Munich, Germany); the light intensity was 200 μmol m⁻² s⁻¹.

Reflectance and ChlF parameters for all experimental groups were assessed at the age of 2 weeks ( Figure 1 ). In the first set of experiments, soil drought stress conditions were applied; for this purpose, irrigation of plants in the experimental group (drought-stressed group) was stopped at the age of 2 weeks. In the control group, irrigation (every 2 days) continued; soil moisture (calculated as R W C   = F W   −   D W F W * 100 % ) was at least 70%. The dynamics of water content in the soil and for the control and drought-stressed groups is shown in Supplementary Figure 1 in Supplementary Files .

Morphometric (length, fresh and dry weight of shoots and roots) and ChlF parameters of plants were measured at the age of 4 weeks to assess tolerance to drought. Drought tolerance index ( D T I   = D W d r o u g h t D W c o n t r o l * 100 % ) and residual levels of photosynthetic activity parameters, expressed in % of control ( P a r a m e t e r r e s i d   = P a r a m e t e r d r o u g h t P a r a m e t e r c o n t r o l * 100 % ) were used as tolerance indicators. In the second set of experiments, 4-week-old plants grown under conditions of sufficient water availability were subjected to gradual heating of leaves (heat-stressed group) fixed on a hot plate (Microstat-30/80, KB Technom, Yekaterinburg, Russia).

The hot plate temperature increased every 5 minutes from 25 to 55°C in increments of 5°C; the duration of treatment at 55°C was 10 minutes. Infrared images of wheat leaves were acquired using a testo 885 thermal imager (Testo, Lenzkirch, Germany) every 60 s to assess the temperature of wheat leaves and the hot plate. Image analysis was performed using IRSoft software (Testo, Lenzkirch, Germany); regions of interest (ROIs) were placed on wheat leaves and on the hot plate around the leaves.

The tolerance of the photosynthetic apparatus was used as an indicator of wheat tolerance to high-temperature stress. For this purpose, ChlF parameters were recorded using the PAM imaging method simultaneously with heating. The tolerance was expressed quantitatively in two parameters: the residual level of the quantum yield of photochemical reactions of photosystem II (Φ _PSII), calculated as the ratio of the final level of Φ _PSII at 55°C to the initial level at 25°C, expressed as a percentage ( Ф PSII  r e s i d   = Ф PSII ( 55 ° С ) Ф PSII ( 25 ° С ) * 100 % ), as well as the hot plate temperature at which Φ _PSII decreased below the initial control value of 25°C (t_dec).

The length, fresh and dry weight of the roots, shoots and whole 4-week-old plants were used as morphometric parameters of wheat seedlings. Shoots and roots of wheat seedlings were weighed separately using the analytical balance (Explorer EX125D, Ohaus, Parsippany, NJ, USA), then placed in the drying oven for 3 hours at 90°С; after that they were weighed again. Weight was assessed integrally for a pot (9 plants) and calculated for an individual plant. The length of shoots and roots was measured individually for each plant.

Recording of photosynthetic activity parameters was carried out using a system based on the PAM fluorometry method (Open FluorCam FC 800-O/1010-S, Photon Systems Instruments, Drásov, Czech Republic). Wheat plants were subjected to dark adaptation for 15 minutes, after which the dark (F₀) and maximum (F_m) fluorescence yields were determined. Actinic light (AL, cool white light, 200 µmol m⁻² s⁻¹) was then turned on, and the current (F) and maximum (F_m’) fluorescence yields were determined every 30 s for 15 minutes.

The parameters F_m and F_m′ were measured using a saturation pulse (cold white light, 4000 µmol m⁻² s⁻¹, 800 ms duration, 6500 K). Based on the recorded values, parameters of photosynthetic activity such as the quantum yield of photochemical reactions of photosystem II (Φ _PSII) and non-photochemical quenching of chlorophyll fluorescence (NPQ) were calculated by a program integrated into the recording system, and their dynamics, induced by a change in the illumination mode, were analyzed. Φ _PSII and NPQ values were calculated using the equations: Φ _PSII = (F_m′ − F)/F_m′ and NPQ = (F_m − F_m′)/F_m′ (Maxwell and Johnson, 2000), where F_m is the maximum fluorescence yield of chlorophyll after dark adaptation, F_m′ is the maximum fluorescence yield under lighting, F is the current fluorescence level under lighting.

The following quantitative characteristics of the light-induced dynamics of Φ _PSII and NPQ were analyzed ( Figure 2 ): maximum quantum yield of photochemical reactions of photosystem II, determined after dark adaptation (F_v/F_m), steady-state level of photosystem II quantum yield under AL (Φ _PSIIef), time for the Φ _PSII value to reach ½ Φ _PSIIef after switching on AL (t_1/2(Φ _PSIIef)), maximum value of non-photochemical fluorescence quenching after switching on AL (NPQ_max), time for the NPQ value to reach NPQ_max after switching on AL (t(NPQ_max)), and steady-state NPQ level under AL (NPQ_s) (Sherstneva et al., 2021).

In experiments with gradual heating of leaves, plants were also adapted to darkness within 15 minutes. Then the initial parameters (F₀ and F_m) were determined, AL was turned on, and ChlF parameters were recorded every 60 s for 40 minutes.

Hyperspectral images of plants were obtained using the Specim IQ hyperspectral camera (Specim, Spectral Imaging Ltd., Oulu, Finland). Images of wheat leaves were separated from the background, then ROIs were placed (each ROI included the whole aboveground part of all plants in the pot above 3 cm from the soil); one reflectance spectrum was integrally obtained from each ROI. The resulting reflectance spectra of shoots in the range of 400-800 nm in steps of 3 nm were normalized by the reflectance spectrum of the white standard. Normalized difference indices (NDIs) were also calculated for each combination of wavelengths according to the equation:

where I λ 1 and I λ 2 are the intensities of the reflectance at the wavelengths λ 1 and λ 2 , respectively.

NDIs were presented as heat maps.

Statistical processing of the results was carried out using GraphPad Prism (GraphPad Software Inc., San Diego, CA, USA) and Microsoft Excel (Microsoft Corporation, Redmond, WA, USA). The results are presented as average curves with standard errors of the mean (SEM), average values with SEM, heat maps of parameters, as well as spectra and heat maps of Pearson correlation coefficients (r). The probability value (p-value) was used to assess the statistical significance of the result; p< 0.05 was considered significant. The normality of data distribution was assessed using the Kolmogorov–Smirnov test. The one-way analysis of variance (ANOVA) followed by Tukey’s test was used.

At the age of 2 weeks, 4 pots of 9 plants for each cultivar were assessed in each experimental group (control, drought-stressed, heat-stressed). Weight was assessed integrally for a pot (9 plants) and calculated for an individual plant (n = 4). Shoot and root length were measured for individual plants. The chlorophyll fluorescence parameters were registered for 5 plants per pot (n = 20 for 2-week-old plants; n = 10 for 4-week-old plants in each experimental group).

An important part of this work was the study of the relationship between reflectance parameters recorded using HS sensors and ChlF parameters recorded by PAM fluorometry. An obvious advantage of multi- and hyperspectral phenotyping methods is their high throughput and ability to perform measurements over wide spatial scales. Non-invasively obtained canopy reflectance parameters are widely used to evaluate the structural and biochemical characteristics of wheat (Al-Tamimi et al., 2022; Skendžić et al., 2023; Zhang et al., 2023). The PAM fluorometry method, which directly provides information on the activity of photosynthesis, a key physiological process that determines productivity, has both spatial and temporal limitations. In this regard, studies aimed at assessing the photosynthetic activity of plants based on reflectance parameters, as well as the search for such parameters correlating with ChlF parameters, have become widespread (Sukhova and Sukhov, 2018; Pérez-Bueno et al., 2019; Fu et al., 2022).

In our work, a number of ChlF parameters were determined and classified into two groups. The first group (stationary parameters) includes such parameters as F_v/F_m, Φ _PSIIef, and NPQ_s which reflect the functioning of PSA in a state adapted to darkness or light. The second group (transient parameters) includes the parameters t_1/2(Φ _PSIIef), NPQ_max, and t(NPQ_max) which reflect transient processes when actinic light is switched on after dark adaptation. The relationship of the recorded ChlF parameters with the reflectance spectra of wheat leaves and NDIs, calculated from all combinations of recorded wavelengths in the range of 400-800 nm, was analyzed. Three wavelength ranges, demonstrating a strong relationship with PAM parameters, were identified ( Figure 5 ): Blue (B, 420-490 nm), Red (R, 640-690 nm) and FR-NIR (710-800 nm).

F_v/F_m and Φ _PSIIef (included in the group of stationary parameters) negatively correlated with the B and R bands of the VIS spectral range. It is known that leaf reflectance in VIS depends primarily on the pigment composition of the green parts of plants (Li et al., 2014; Liu et al., 2022). It can be assumed that the content of chlorophyll, which has absorption maxima in the B and R spectral bands, determines the relationship of these spectral bands with stationary ChlF parameters. The relationship between F_v/F_m and Φ _PSIIef and chlorophyll content is a well-known fact (Kalaji et al., 2017).

Reflectance in the FR-NIR range was associated with ChlF parameters reflecting transient processes caused by the dark-light transition (NPQ_max, t(NPQ_max), t_1/2(Φ _PSIIef)). The rate of change in the Φ _PSII and NPQ parameters when actinic light is switched on is determined by the rate of achieving a balance between the production of NADPH and ATP in the light-dependent photosynthetic reactions and their consumption in the reactions of the Calvin-Benson cycle (Baker, 2008). The activity of the Calvin-Benson cycle reactions is largely determined by the availability of the substrate – CO₂ (Baker, 2008; Flexas et al., 2008; Zhu et al., 2010; Tholen and Zhu, 2011). In this regard, it is important to note that the optical properties of the green parts of plants in the FR-NIR range, characterized by a high reflectance, are determined by the structural features of plant tissues (Ollinger, 2011). In particular, the intensity of NIR light reflection positively correlates with the volume of intercellular air spaces (Ustin and Jacquemoud, 2020), including at wavelengths in the range we studied (Slaton et al., 2001). The looser mesophyll may contribute to the greater possible rate of influx of CO₂ consumed in cells in the Calvin-Benson cycle.

Spectral indices (NDIs), calculated for the entire studied spectral range, showed a more complex relationship with the parameters of photosynthetic activity. Two significant areas were identified on the heat maps of NDIs correlations against ChlF parameters: area for λ₁ in R band and area for λ₁ in FR-NIR band. The highest NDIs correlations in the R range were found for Φ _PSIIef and NPQ_s. It is worth noting that these parameters characterize the activity of photosynthetic processes in a state adapted to light. Strong relationship of NDIs in the indicated range was also for t_1/2(Φ _PSIIef) and t(NPQ_max); however, the size of this region was significantly smaller. The second wide area on the NDIs heat map with high correlation coefficients (FR-NIR) appeared predominantly for the parameters t_1/2(Φ _PSIIef), NPQ_max and t(NPQ_max) which characterize transient processes in the photosynthetic apparatus caused by the switching on the actinic light. At the same time, stationary parameters correlated with NDIs in a much narrower range. This effect for NDIs fits into the nature of the relationship between the reflectance spectrum and the ChlF parameters, described above; in this case, R and FR-NIR can serve as measuring wavelength ranges to characterize stationary and transient processes in the photosynthetic apparatus.

The search for the relationship between NDIs in the studied wavelength range and ChlF parameters of wheat has been described in a number of works. In particular, NDVI (Normalized difference vegetation index) was significantly positively correlated with F_v/F_m in wheat under optimal conditions (El-Hendawy et al., 2019). The NDVI and NDRE (Red edge NDVI) indices showed a positive relationship with F_v/F_m in unstressed leaves of aspen and cherry trees (Peng et al., 2017). The work of Sukhova and Sukhov (Sukhova and Sukhov, 2020) showed that the light-induced changes in the PRIs (ΔPRIs) can be used to assess a number of parameters of PSI and PSII activity.

During the search for predictors of biomass accumulation, a number of parameters determined by HS and PAM methods in young plants demonstrated a relationship with the biomass of older plants. Considering the spectral characteristics, we can highlight the R range (650-670 nm), which showed a significant negative correlation with DW of 4-week-old seedlings ( Figure 7 ). When examining the predictive potential of spectral indices, two regions on the NDIs heat map with a significant correlation against the DW of 4-week-old seedlings were found: R and FR-NIR ranges ( Figure 7 ). It is worth noting that the identified ranges of statistically significant correlations between NDIs and biomass accumulation include such widely used spectral indices as GNDVI (Green normalized difference vegetation index, (R₇₈₀–R₅₅₀)/(R₇₈₀+R₅₅₀) (Aparicio et al., 2000), and RNDVI (Red normalized difference vegetation index, (R₇₈₀−R₆₇₀)/(R₇₈₀+R₆₇₀) (Raun et al., 2001). GNDVI and RNDVI, recorded using a spectrometer with high spectral resolution, showed a high positive correlation with yield under full irrigation conditions (El-Hendawy et al., 2017). NDVI (λ₁ 774, λ₂ 656) recorded by a multispectral sensor was positively correlated with the yield of irrigated wheat; moreover, the yield significantly correlated with the aboveground biomass (Thapa et al., 2019). Grain yield and biomass were also positively correlated with GNDVI and RNDVI, starting from the tillering stage, under different irrigation and nitrogen levels (S. Pradhan et al., 2012). These data are consistent with the results of our work, which indicate a significant positive relationship between NDIs in FR-NIR region and biomass accumulation in wheat plants.

Correlation analysis of the relationship between potential fluorescent predictors and biomass accumulation revealed a significant correlation with stationary ChlF parameters (Φ _PSIIef and NPQ_s), as well as with transient parameters of light-induced ChlF dynamics [t_1/2(Φ _PSIIef) and t(NPQ_max)]. Our previous work (Sherstneva et al., 2021), carried out using other cultivars, also showed the potential of Φ _PSIIef and t_1/2(Φ _PSIIef) for predicting seedling biomass. At the same time, t_1/2(Φ _PSIIef) maintained a significant correlation with biomass accumulation with increasing prediction period.

It is worth noting that the identified HS and ChlF predictors are related to each other. The presence of such a connection suggests a generic physiological basis for the predictive potential of the fluorescent and spectral parameters used. In particular, the R range of the correlation spectrum with DW lies in the region of high correlation of the reflectance spectrum with Φ _PSIIef of plants of the same age ( Figure 5 ). This relationship is consistent with the high positive correlation between these ChlF parameters and biomass accumulation 2 weeks after the ChlF recording ( Table 5 ). Φ _PSIIef as a stationary parameter of photosynthetic activity in the light-adapted state reflects the efficiency of using the energy of absorbed light by photosystems and is linearly related to the rate of CO₂ assimilation (Leipner et al., 1999; Maxwell and Johnson, 2000; Baker, 2008). These processes directly influence the synthesis of organic matter and the rate of biomass accumulation (Kruger and Volin, 2006; Ferguson et al., 2021). The parameter t_1/2(Φ _PSIIef), which characterizes the rate at which maximum quantum efficiency is achieved after actinic light is switched on, depends on the rate of activation of the Calvin-Benson cycle (Baker, 2008), which is regulated, in particular, by CO₂ availability. The greater availability of CO₂ determines the intensification of the production process, leading to more intensive accumulation of biomass. In the case of NDIs heat maps, the significant R region of correlation with biomass accumulation coincided to a greater extent with that for heat maps of NDIs correlations against stationary parameters of ChlF (Φ _PSIIef, NPQ_s). On the other hand, the significant region for FR-NIR range corresponded predominantly to heat maps of NDIs correlations against the transient ChlF parameters [t_1/2(Φ _PSIIef), t(NPQ_max)] ( Figure 5 ). In particular, as noted earlier, such a relationship between the ChlF parameters which characterize the rate of optimization of the dark reactions activity regulated by CO₂ availability, and HS parameters in the FR-NIR range may be due to the structural characteristics of the leaves, in particular, the volume of intercellular air spaces (Ustin and Jacquemoud, 2020), promoting more intense gas exchange and, as a result, greater availability of CO₂.

The drought tolerance index (reflecting the percentage ratio between the DW of experimental and control plants) was used as the main criterion for tolerance of wheat seedlings to water deficiency. Additional tolerance criteria (residual levels of photosynthetic activity parameters in plants affected by drought, also expressed as a percentage of control levels) were also used. A statistically significant correlation between residual Φ _PSIIef and the drought tolerance index was found ( Table 3 ), which is consistent with the data of earlier work, which showed the relationship between the drought tolerance index and residual Φ _PSIIef 2 weeks after stopping irrigation (Sherstneva et al., 2021). An approach to assessing drought tolerance based on the use of several criteria worked well in the search for fluorescent predictors of tolerance in wheat seedlings (Sherstneva et al., 2021) and made it possible to evaluate drought-induced suppression of physiological processes in plants. However, despite the wide range of wavelengths and high spectral resolution of the data obtained in our work, which provides high information content of the method, no strong relationship between HS and drought tolerance parameters was identified ( Figures 8 , 9 ). This effect persisted for both reflectance and NDIs. Analysis of the predictive potential of ChlF parameters of 2-week-old seedlings also did not reveal their connection with tolerance to 14-day drought.

At the moment, there is a sufficient number of works devoted to the prediction of economically important traits of wheat under conditions of water deficiency (Aparicio et al., 2000; Bandyopadhyay et al., 2014; Tattaris et al., 2016; Becker and Schmidhalter, 2017; El-Hendawy et al., 2017; Condorelli et al., 2018; Thapa et al., 2019). In this case, HS parameters covering a wide range of wavelengths are used. In particular, water and other indices that take into account the reflectance in a longer wavelengths region than the range recorded in our work have shown their efficiency (Bandyopadhyay et al., 2014; Becker and Schmidhalter, 2017; El-Hendawy et al., 2017). Such indices may show a higher and more significant correlation with plant yield under water deficit conditions compared to indices in the range up to 800 nm (Becker and Schmidhalter, 2017; El-Hendawy et al., 2017). In addition, there are works showing the potential of NDVI for predicting biomass accumulation (Tattaris et al., 2016; El-Hendawy et al., 2017; Condorelli et al., 2018) and crop yield (Aparicio et al., 2000; Tattaris et al., 2016; Becker and Schmidhalter, 2017; El-Hendawy et al., 2017; Thapa et al., 2019) under conditions of water deficiency. At the same time, only a few studies used spectral predictors recorded under control conditions. In particular, the work of Condorelli et al. (Condorelli et al., 2018) showed the potential of NDVI recorded before the irrigation stop to predict wheat biomass at the terminal stage of drought. In another work, some vegetation and water indices recorded under full irrigation correlated with the yield index and a number of drought tolerance parameters of spring wheat (El-Hendawy et al., 2017).

The next step was to assess the relationship between HS predictors and the tolerance of wheat seedlings to elevated temperatures. A fairly wide spectral band that significantly correlated with tolerance to heating two weeks after HS imaging was discovered ( Figure 10 ). This range partially coincided with that for biomass accumulation under control conditions ( Figure 7 ). Heat maps of correlations of NDIs with tolerance also showed partial overlap of significant regions with a heat map of correlations against biomass accumulation in the R band. The direction of the correlation for biomass accumulation and heat tolerance was opposite.

It is also important to note that ChlF parameters which have a statistically significant correlation with heat tolerance ( Table 7 ), also correlated with productivity. As in the case of spectral characteristics, the correlation of these parameters with biomass and heat tolerance had the opposite trend. This result is consistent with the fact that heat tolerance was negatively correlated with biomass accumulation (r = –0.7, p = 0.02).

The discovered patterns can apparently be due to a number of causes. It is known that during heat stress, reactive oxygen species (ROS), the concentration of which increases when exposed to elevated temperatures, play a significant role in plant damage, including inactivation of photosynthesis and damage to PSA in plants (Fortunato et al., 2023). The main sites for the production of excess ROS under heat stress are the photosynthetic electron transport chain and the Calvin-Benson cycle (Allakhverdiev et al., 2008). It can be assumed that the level of ROS increases more significantly under heat stress in plants with high activity of photosynthetic processes, which ensure high productivity, compared to plants with a lower level of photosynthetic activity. A greater increase in ROS can have a greater negative effect on the PSA and, as a result, on the biomass accumulation.