Feasibility of Low-Cost Thermal Imaging for Monitoring Water Stress in Young and Mature Sweet Cherry Trees

: Infrared thermography has been introduced as an a ﬀ ordable tool for plant water status monitoring, especially in regions where water availability is the main limiting factor in agricultural production. This paper outlines the potential applications of low-cost thermal imaging devices to evaluate the water status of young and mature sweet cherry trees ( Prunus avium L.) submitted to water stress. Two treatments per plot were assayed: (i) a control treatment irrigated to ensure non-limiting soil water conditions; and (ii) a water-stress treatment. The seasonal evolution of the temperature of the canopy (Tc) and the di ﬀ erence between Tc and air temperature ( ∆ T) were compared and three thermal indices were calculated: crop water stress index (CWSI), degrees above control treatment (DAC) and degrees above non-water-stressed baseline (DANS). Midday stem water potential ( Ψ stem) was used as the reference indicator of water stress and linear relationships of Tc, ∆ T, CWSI, DAC and DANS with Ψ stem were discussed in order to assess their sensitivity to quantify water stress. CWSI and DANS exhibited strong relationships with Ψ stem and two regression lines to young and mature trees were found. The promising results obtained highlight that using low-cost infrared thermal devices can be used to determine the plant water status in sweet cherry trees. stress indicators. The results of this study could help improve sweet cherry cultivation, as well as other Prunus fruit trees with similar phenology and water stress behavior such as extra early plum trees, and not only in areas where water is scarce, but in regions where water availability is not currently a problem and sweet cherry trees are mainly rainfed, to assess the tree water status. P.J.B.-R.; P.J.B.-R., F.S.-V., R.D.; resources, F.S.-V., R.D.; data curation, P.J.B.-R. V.B.; P.J.B.-R.; writing—review R.T.-S.; visualization, P.J.B.-R. V.B.; P.J.B.-R., R.T.-S.;


Introduction
Irrigated agriculture is the largest consumer of fresh water, accounting for 70% of worldwide water use [1]. In this sense, water availability in arid and semi-arid regions is the main factor limiting agricultural production. These regions are subjected to water constraints and are particularly vulnerable to climate change. As a direct result, it is expected that there will be an increase in the mean air temperature with severe drought events occurring during the high evapotranspiration demand periods, accompanied by an irregular rainfall pattern during the wet periods [2].
In addition, Spain-the largest fresh fruit producer in the European Union-has been experiencing severe water supply issues in recent decades, caused mainly by a structural imbalance between water resources and demand [3]. With regards to sweet cherry (Prunus avium L.) production, Spain is the seventh-largest producer of cherries in the world and the second-largest producer in Europe [4]. The application of water-saving strategies to this crop, such as deficit irrigation (DI) procedures, should be a priority for their production in areas with water supply issues. Sweet cherry has been

Treatments
Plot 1: the young sweet cherry trees were irrigated to satisfy the full crop water requirements from the beginning of the irrigation season until July 5 2019. From that date, two irrigation treatments were imposed: (i) a control, YCTL, irrigated daily at 115% of the crop water requirements (ETc) to guarantee the trees were under non-limiting soil water conditions; and (ii) severe deficit irrigation, YS, in which the trees were submitted to two drought cycles that reached a midday stem water potential (Ψstem) of −1.6 MPa and −2.2 MPa in the first and second drought cycle, respectively. After each drought period, a recovery period was applied in which YS trees were irrigated until their Ψstem values reached similar values to the YCTL trees.
Plot 2: In the orchard of mature sweet cherry trees, we applied two irrigation treatments: (i) a control, MCTL, irrigated daily at 110% ETc during all irrigation season to maintain the trees under non-limiting soil water conditions; and (ii) a regulated deficit irrigation, MS, irrigated at 100% of ETc during pre-harvest and the first days of flower differentiation (from April until the end of June) and 55% of ETc post-harvest, from the end of June to November (see Blanco et al. [26] for details). The irrigation doses for both Plot 1 and Plot 2 were calculated using the methodology proposed by Allen et al. [27]: ETc = ET 0 × Kc × Kr, where ET 0 is reference evapotranspiration, Kc is a crop-specific coefficient for sweet cherry reported by Marsal [28], and Kr is a factor of localization related to the percentage of ground covered by the crop [29].
Treatments were distributed according to a completely randomized block design in both Plot 1 and Plot 2. In Plot 1, each treatment consisted of three replicates and each replicate had a row of four trees. The two central trees (6 per treatment) were used to measure stem water potential and canopy temperature. In Plot 2, each treatment had three blocks and each replicate consisted of seven adjacent trees. The measurements were taken in the two central trees per replicate, with the other trees serving as guard trees.

Field Data
Meteorological variables were collected by two weather stations of the Agricultural Information System of Murcia (CA52 for Plot 1 and JU42 for Plot 2; SIAM, http://siam.imida.es/). Daily reference crop evapotranspiration (ET 0 ) was estimated using the Penman-Monteith equation and daily mean air vapour pressure deficit (VPD) using air temperature and relative humidity data [27]. Additionally, in Plot 1, three microclimate sensors (ATMOS-14, METER Group Inc., Pullman, WA, USA) were installed. The ATMOS-14 sensors were connected to a datalogger (CR1000 with AM16/32B multiplexer, Campbell Scientific Ltd., Logan, UT, USA), programmed to take measurements every 30 s and report mean values every 10 min.
In both experiments, every 2-5 days in Plot 1 and 10-15 days in Plot 2, midday stem water potential (Ψstem) was measured at solar noon (12:00 to 13:00 UT) with a Scholander-type pressure bomb (mod. SF-PRES-70, SolFranc Tecnologías, S.L., 43480 Tarragona Spain) following the recommendations of McCutchan and Shackel [30]. Ψstem was measured in 2 mature leaves per replicate (6 leaves per treatment). The mature and healthy leaves, close to the trunk, were enclosed in small black plastic bags and covered with aluminium foil for 2 h before the measurement.
The canopy temperature (Tc) was measured at the same time as Ψstem with a low-cost thermal camera (ThermalCam Flir One, Flir Systems, Wilsonville, OR, USA) connected to a smartphone. Two images per replicate (n = 6) were taken at 1.5 m from the sunny side of the trees in order to identify the highest differences between irrigation treatments, according to Costa et al. [31] and Jones [13]. The camera uses a thermal sensor with a spectral range of 8-14 µm and 80 × 60 pixels, and a visible-light sensor of 1440 × 1080 pixels with ±2% precision. The emissivity, ε, was set at matt (ε = 0.95), as suggested by Stoll and Jones [32] and Costa et al. [31]. The images were analyzed using the Flir Tools application (Flir One, Flir Systems, Wilsonville, OR, USA). The Tc average of four sunny areas was selected within the same image (24 areas per treatment; Figure 1). The distance of the camera from the canopy, the background temperature, relative humidity and air temperature were used as input to discard the effect of reflection by the object's surface and the radiation emitted by the object's surroundings, according to the methodology proposed by Gómez-Bellot [33] and García-Tejero [22].
Three thermal indices were calculated to mitigate the effect of meteorological variables: (i) The difference between the canopy and air temperature (∆T); (ii) crop water stress index (CWSI), calculated following the recommendation by Jackson et al. [19]; and (iii) the degree above control treatment (DAC) and degree above non-water-stressed baseline (DANS) were calculated according to Taghvaeian et al. [15]: where Tc is the canopy temperature; Tair is the air temperature at the moment of the measurement; T S is the canopy temperature of the water-stress treatment; T CTL is the canopy temperature of the control treatment; ∆T wet and ∆T dry are the differences between canopy and air temperature when the crop has the stomata fully transpiring and fully closed, respectively. According to Idso et al. [18] ∆T wet was calculated from non-water-stress baselines (NWSB; ∆T wet = a + b·VPD). As stated by Jones [34], NWSB was obtained by spraying a thin layer of water on leaves 15 to 30 s before images were taken and ∆T dry was estimated by covering two leaves with a layer of petroleum-jelly (Vaseline) on both sides, blocking all transpiration flows. In this regard, several authors do not empirically calculate ∆T dry , and they work with a value set to 5 • C [22,35,36]. Consequently, with the aim of testing whether ∆T dry can always be taken as 5 • C or should be measured every day, CWSI was calculated from the two different methods depending on ∆T dry .
ΔTdry, and they work with a value set to 5 °C [22,35,36]. Consequently, with the aim of testing whether ΔTdry can always be taken as 5 °C or should be measured every day, CWSI was calculated from the two different methods depending on ΔTdry.

Statistical Analysis
Data were analyzed using statistical software Statgraphics Centurion XVI (StatPoint Technologies Inc., The Plains, VA, USA) and IBM SPSS Statistics (SPSS Inc., 24.0 Statistical package; Chicago, IL, USA). Statistically significant differences among treatments and water stress indicators were determined using analysis of variance (ANOVA) with a significance level of p < 0.05. Linear and

Statistical Analysis
Data were analyzed using statistical software Statgraphics Centurion XVI (StatPoint Technologies Inc., The Plains, VA, USA) and IBM SPSS Statistics (SPSS Inc., 24.0 Statistical package; Chicago, IL, USA). Statistically significant differences among treatments and water stress indicators were determined using analysis of variance (ANOVA) with a significance level of p < 0.05. Linear and nonlinear regression analysis among water indicators were determined using Sigmaplot Plus for Windows v.12.5 (Systat Software, San Jose, CA, USA).

Environmental Conditions
Environmental conditions at both locations during the experimental period were characteristic of areas with a Mediterranean climate ( Table 1). All climatic parameters showed a similar trend with values that increased during spring and early summer and dropped in autumn. Mean temperatures in Plot 1 were generally 3 • C higher than Plot 2. This could be due to the lower daily minimum temperatures recorded in Plot 2 compared to Plot 1. The highest differences in VPD values were recorded during early summer (July) when VPD values in Plot 1 were double those measured in Plot 2. The highest difference between both experimental sites occurred in late summer. In late August a considerable decline of both air temperature and ET0 occurred in Plot 2, while in Plot 1 the decrease in both parameters was observed in late September.

Midday Stem Water Potential
Midday stem water potential, Ψstem, accurately reflected the tree water status in both young and mature sweet cherry trees ( Figure 2). Ψstem has been reported as a sensitive water stress indicator in mature sweet cherry trees [6,8]; however, there is scarce information about the use of this indicator in young sweet cherry trees, for which pre-dawn stem water potential and midday leaf water potential have been reported as robust water status indicators [37,38].
Appl. Sci. 2020, 10, x FOR PEER REVIEW 7 of 18 Figure 2. Seasonal evolution of the midday stem water potential (Ψstem) in young (a) and mature (b) sweet cherry trees during the study period. Each point corresponds to the mean ± standard error of the mean for six measurements per treatment. Asterisks indicate statistically significant differences between treatments by ANOVA (p < 0.05). CTL and S correspond to control and deficit irrigation treatment for young (Y) and mature (M) sweet cherry trees, respectively. FI is full irrigation period, D is drought period and R is recovery period in young sweet cherry trees (Plot 1), and 100% and 55% are the percentages of crop water requirements (ETc) applied to mature sweet cherry trees (Plot 2).
The mean Ψstem measured in young and mature CTL trees was between −0.5 and −0.7 MPa, values typical of well-watered trees. These differences in water potential of control trees were due to changes in the climatic demand. Regarding the water stress treatments, the lowest Ψstem values were measured in young trees which were submitted to two drought and recovery cycles, with minimum values that fell below −1.7 and −2.1 MPa for the first and second cycle, respectively. After irrigation was resumed, recovery of Ψstem in young sweet cherry trees was rapid in both cycles. The Ψstem Figure 2. Seasonal evolution of the midday stem water potential (Ψstem) in young (a) and mature (b) sweet cherry trees during the study period. Each point corresponds to the mean ± standard error of the mean for six measurements per treatment. Asterisks indicate statistically significant differences between treatments by ANOVA (p < 0.05). CTL and S correspond to control and deficit irrigation treatment for young (Y) and mature (M) sweet cherry trees, respectively. FI is full irrigation period, D is drought period and R is recovery period in young sweet cherry trees (Plot 1), and 100% and 55% are the percentages of crop water requirements (ETc) applied to mature sweet cherry trees (Plot 2). The mean Ψstem measured in young and mature CTL trees was between −0.5 and −0.7 MPa, values typical of well-watered trees. These differences in water potential of control trees were due to changes in the climatic demand. Regarding the water stress treatments, the lowest Ψstem values were measured in young trees which were submitted to two drought and recovery cycles, with minimum values that fell below −1.7 and −2.1 MPa for the first and second cycle, respectively. After irrigation was resumed, recovery of Ψstem in young sweet cherry trees was rapid in both cycles. The Ψstem values measured in the young trees showed that they were submitted to severe water stress. During the first drought period, Ψstem in young sweet cherry trees continuously declined from values similar to those of CTL trees down to −1.7 MPa in 16 days, and needed eight days of full irrigation to exhibit similar values to CTL trees. During the second drought cycle, a steeper drop of Ψstem was observed, and the minimum value reached −2.1 MPa (Figure 2a). Ψstem values measured were lower than those reported by Higgs et al. [39] for unirrigated young sweet cherry trees.
In the mature trees (Figure 2b), deficit irrigation trees resulted in Ψstem values that remained above −1.5 MPa, which could be considered a mild-severe water stress that would not compromise the tree's yield the following year [5,40]. Water stress in mature trees resulted in different rates depending on the evaporative demand. Thus, in mid-August (DOY 229, 230), as a result of several rainy episodes in Plot 2, the ET 0 decreased from 6 mm day −1 to 3 mm day −1 and consequently, mature trees exhibited higher Ψstem values. Similarly, at the end of the season, the evaporative demand decreased and the trees of the deficit treatment resulted in Ψstem values similar to those measured in control trees.

Canopy Temperature
The pattern of Tc was in accordance with the evolution of Ψstem in young sweet cherry trees ( Figure 3); however, in mature trees it was not possible to differentiate between the control and water-stressed trees at the end of the season (September, DOY 270 onwards) using the temperature of the canopy, when the air temperature significantly decreased from 24 to 13 • C (Figure 3a,b).
Appl. Sci. 2020, 10, x FOR PEER REVIEW 8 of 18 Figure 3. Seasonal evolution of the canopy temperature (Tc) and the difference between canopy and air temperature (ΔT) in young (a,c) and mature (b,d) sweet cherry trees during the study period. Each point corresponds to the mean ± standard error of the mean for six images per treatment. CTL and S correspond to control and deficit irrigation treatment for young (Y) and mature (M) sweet cherry trees, respectively. Asterisks indicate statistically significant differences between treatments by ANOVA (p < 0.05). FI is full irrigation period, D is drought period and R is recovery period in young sweet cherry trees (Plot 1), and 100% and 55% are the percentages of ETc applied in mature sweet cherry trees (Plot 2).
As expected, young and mature control trees had lower values of canopy temperature minus air temperature than water-stressed trees during the period of water restriction (Figure 3c,d). Regarding the control trees, it was observed that mature control trees had a canopy temperature on average 2.5 °C below the temperature of the air, while in the same period the young trees had a temperature of the canopy only 1 °C below the air temperature. This difference in ∆T of control trees depended on their age, according to Taghvaeian et al. [15], who related the influence of leaf area on the temperature of the plants. Thus, mature trees with greater canopy volume exhibited lower canopy temperatures  . Seasonal evolution of the canopy temperature (Tc) and the difference between canopy and air temperature (∆T) in young (a,c) and mature (b,d) sweet cherry trees during the study period. Each point corresponds to the mean ± standard error of the mean for six images per treatment. CTL and S correspond to control and deficit irrigation treatment for young (Y) and mature (M) sweet cherry trees, respectively. Asterisks indicate statistically significant differences between treatments by ANOVA (p < 0.05). FI is full irrigation period, D is drought period and R is recovery period in young sweet cherry trees (Plot 1), and 100% and 55% are the percentages of ETc applied in mature sweet cherry trees (Plot 2).
As expected, young and mature control trees had lower values of canopy temperature minus air temperature than water-stressed trees during the period of water restriction (Figure 3c,d). Regarding the control trees, it was observed that mature control trees had a canopy temperature on average 2.5 • C below the temperature of the air, while in the same period the young trees had a temperature of the canopy only 1 • C below the air temperature. This difference in ∆T of control trees depended on their age, according to Taghvaeian et al. [15], who related the influence of leaf area on the temperature of the plants. Thus, mature trees with greater canopy volume exhibited lower canopy temperatures than young trees with lower canopy volume.
The maximum ∆T was measured on DOY 239 in stressed young trees (3.5 • C), which was the day with the lowest Ψstem (−2.1 MPa, Figure 2a). The difference in canopy temperature between stressed and control young trees was higher than 4 • C on that day. These results indicated a smaller difference than that reported by Ballester et al. [41] and Wang and Gartung [42] in non-irrigated citrus (∆T = 5.0 • C) and peach trees (∆T = 6.5 • C) under similar values of Ψstem (<−2.0 MPa). Similarly, the maximum difference of ∆T observed between water-stressed and control mature trees was 4.4 • C (DOY 204, Figure 3d). The difference of 4.4 • C between treatments was mainly due to the contribution of the control trees (∆T MCTL = −3.1 • C) rather than the high value of the temperature of the canopy of water-stressed trees above the air temperature (∆T MS = 1.3 • C). These values of canopy temperatures that were lower than the air temperature in control sweet cherry trees are similar to those reported in almond [43] and peach trees [42], but are contrary to those recorded for orange trees [44]. This difference with citrus trees might be due to the stomatal closure of citrus trees at midday, which increases the leaf temperature even though the tree has no soil water restrictions, while in well-watered Prunus trees this does not occur [45,46].
Data from control and water-stressed trees were pooled to determine the upper (non-transpiration) and lower (non-water-stress) baselines for the mature and young sweet trees ( Figure 4). All the obtained equations for the non-water-stress baselines showed a strong linear relationship between VPD and canopy temperature of sunny leaves ( Table 2). Regardless of the different location and age of trees, the non-water-stress baseline did not differ among them, and fitted in the linear regression: ∆T = 3.87 − 2.62·VPD (R 2 = 0.91). Mature trees overestimated ∆T by 1 • C compared to young trees for the lowest VPD value (1 kPa), and underestimated by 1.3 • C for the highest value (4 kPa). The non-transpiration baseline obtained for both young and mature trees achieved 6 • C, a similar value to that reported in peach trees under semiarid climate conditions by Paltineanu et al. [47] and 1 • C above the stated value of 5 • C reported by Jones et al. [35].
Appl. Sci. 2020, 10, x FOR PEER REVIEW 9 of 18 between VPD and canopy temperature of sunny leaves ( Table 2). Regardless of the different location and age of trees, the non-water-stress baseline did not differ among them, and fitted in the linear regression: ∆T = 3.87 − 2.62·VPD (R 2 = 0.91). Mature trees overestimated ∆T by 1 °C compared to young trees for the lowest VPD value (1 kPa), and underestimated by 1.3 °C for the highest value (4 kPa). The non-transpiration baseline obtained for both young and mature trees achieved 6 °C, a similar value to that reported in peach trees under semiarid climate conditions by Paltineanu et al. [47] and 1 °C above the stated value of 5 °C reported by Jones et al. [35].

Crop Water Stress Index and Degrees above Non-Stress
CWSI was calculated based on the methodology proposed by Idso et al. [19], which uses a water stress baseline of 5 • C, and with the baselines we obtained from our measures in non-transpiring leaves ( Figure 5). In accordance with the results obtained, both methodologies showed similar results; however, the method of Idso et al. [19] led to slightly higher CWSI maximum values. Each point corresponds to the mean ± standard error of the mean for six images per treatment. Asterisks indicate statistically significant differences between treatments by ANOVA (p < 0.05). CTL and S correspond to control and deficit irrigation treatment for young (Y) and mature (M) sweet cherry trees, respectively. FI is full irrigation period, D is drought period and R is recovery period in young sweet cherry trees (Plot 1), and 100% and 55% are the percentages of ETc applied in mature sweet cherry trees (Plot 2).
In general, the control treatment in both young and mature trees exhibited CWSI values significantly lower than those of water-stressed trees. The CWSI values of control trees ranged from −0.05 to 0.35 ( Figure 5). Negative CWSI values were measured on days of low evaporative demand and high Ψstem (−0.5 MPa, Figure 2), and have been related to increased transpiration in almond trees [48]. The water-stressed treatment exhibited CWSI values that achieved 0.75 and 0.65 for young and mature sweet cherry trees, respectively, calculated with the upper baseline of 6 °C (Figure 5a,b). These CWSI values obtained in water-stressed trees were similar to those reported in nectarine trees [49] but are lower than those described in almond trees [48,50], which reached values close to 1 on dates with similar values of ∆T (4.0 °C). When the evolutions throughout the experiment of CWSI and ∆T were compared, a trend that CWSI values presented sharper peaks and troughs and greater oscillations than the evolution of ∆T values was observed, particularly in young trees. However, CWSI as a water stress indicator showed significant differences between treatments on the same days Each point corresponds to the mean ± standard error of the mean for six images per treatment. Asterisks indicate statistically significant differences between treatments by ANOVA (p < 0.05). CTL and S correspond to control and deficit irrigation treatment for young (Y) and mature (M) sweet cherry trees, respectively. FI is full irrigation period, D is drought period and R is recovery period in young sweet cherry trees (Plot 1), and 100% and 55% are the percentages of ETc applied in mature sweet cherry trees (Plot 2).
In general, the control treatment in both young and mature trees exhibited CWSI values significantly lower than those of water-stressed trees. The CWSI values of control trees ranged from −0.05 to 0.35 ( Figure 5). Negative CWSI values were measured on days of low evaporative demand and high Ψstem (−0.5 MPa, Figure 2), and have been related to increased transpiration in almond trees [48]. The water -stressed treatment exhibited CWSI values that achieved 0.75 and 0.65 for young and mature sweet cherry trees, respectively, calculated with the upper baseline of 6 • C (Figure 5a,b). These CWSI values obtained in water-stressed trees were similar to those reported in nectarine trees [49] but are lower than those described in almond trees [48,50], which reached values close to 1 on dates with similar values of ∆T (4.0 • C). When the evolutions throughout the experiment of CWSI and ∆T were compared, a trend that CWSI values presented sharper peaks and troughs and greater oscillations than the evolution of ∆T values was observed, particularly in young trees. However, CWSI as a water stress indicator showed significant differences between treatments on the same days that ∆T showed differences, and the absolute minimum and maximum values occurred on the same days in both water stress indicators.
The DANS index followed the same pattern of significance as the CWSI, with significant differences between treatments on the same dates. The DANS values of young and mature water-stressed trees ranged from slightly below 0.0 • C when they were irrigated as control trees to over 8 • C at the time with the highest difference (DOY 236 and 207 for young and mature trees, respectively; Figure 6). Contrary to CWSI, the DANS index exhibited higher values in mature trees than young trees (Figure 6c,d), despite the young trees being submitted to greater water stress. Regarding the DAC index, in young trees the seasonal evolution was barely higher than results obtained by ∆T; on the other hand, in mature trees, the DAC index resulted in values which achieved a 4.4 • C difference between control and water-stressed trees, while on the same dates ∆T did not achieve values higher than 2.0 • C (Figure 6a,b).
Appl. Sci. 2020, 10, x FOR PEER REVIEW 11 of 18 between control and water-stressed trees, while on the same dates ∆T did not achieve values higher than 2.0 °C (Figure 6a,b). Figure 6. Seasonal evolution of degrees above control (a,b) and non-stressed (c,d) in young (a,c) and mature (b,d) sweet cherry trees during the study period. Each point corresponds to the mean ± standard error of the mean for six images per treatment. Asterisks indicate statistically significant differences between treatments by ANOVA (p < 0.05). CTL and S correspond to control and deficit irrigation treatment for young (Y) and mature (M) sweet cherry trees, respectively. FI is full irrigation period, D is drought period and R is recovery period in young sweet cherry trees (Plot 1), and 100% and 55% are the percentages of ETc applied in mature sweet cherry trees (Plot 2).
A linear relationship between the thermal indicators and Ψstem was calculated. The Tc showed a non-linear relationship with Ψstem ( Figure 7a). As expected, higher Tc values were related to trees submitted to water stress (MS and YS). Although the coefficient of correlation obtained between Ψstem and Tc for all the trees exhibited a strong relationship (r = 0.73), Tc as a water stress indicator showed important limitations. Thus, the second-grade polynomial relationship obtained showed two different relationships. At first, Tc increased linearly as Ψstem fell from −0.5 MPa to a threshold value close to −1.0 MPa, which corresponded to 33 °C. From that value onwards, Ψstem values below −1.0 MPa were not related to higher values of Tc. It was observed that Tc did not exceed values above 36 °C regardless of the intensity of the water deficit applied. Consequently, within the Tc range between 33 and 36 °C, similar values were measured in CTL trees on a hot day of high evaporative demand (Ψstem = −0.8 MPa) and in sweet cherry trees under severe water stress (Ψstem = −2.0 MPa).  control (a,b) and non-stressed (c,d) in young (a,c) and mature (b,d) sweet cherry trees during the study period. Each point corresponds to the mean ± standard error of the mean for six images per treatment. Asterisks indicate statistically significant differences between treatments by ANOVA (p < 0.05). CTL and S correspond to control and deficit irrigation treatment for young (Y) and mature (M) sweet cherry trees, respectively. FI is full irrigation period, D is drought period and R is recovery period in young sweet cherry trees (Plot 1), and 100% and 55% are the percentages of ETc applied in mature sweet cherry trees (Plot 2).
A linear relationship between the thermal indicators and Ψstem was calculated. The Tc showed a non-linear relationship with Ψstem ( Figure 7a). As expected, higher Tc values were related to trees submitted to water stress (MS and YS). Although the coefficient of correlation obtained between Ψstem and Tc for all the trees exhibited a strong relationship (r = 0.73), Tc as a water stress indicator showed important limitations. Thus, the second-grade polynomial relationship obtained showed two different relationships. At first, Tc increased linearly as Ψstem fell from −0.5 MPa to a threshold value close to −1.0 MPa, which corresponded to 33 • C. From that value onwards, Ψstem values below −1.0 MPa were not related to higher values of Tc. It was observed that Tc did not exceed values above 36 • C regardless of the intensity of the water deficit applied. Consequently, within the Tc range between 33 and 36 • C, similar values were measured in CTL trees on a hot day of high evaporative demand (Ψstem = −0.8 MPa) and in sweet cherry trees under severe water stress (Ψstem = −2.0 MPa). Therefore, while it is known that in sweet cherry trees water deficit induces stomatal closure and increases leaf temperature [6,8], it is also well known that Tc is highly dependent on tree density, canopy architecture, tree phenological stage and environmental conditions [14,51]. In light of this, the use of absolute values of Tc cannot be recommended as a water stress indicator. The ∆T exhibited a linear relationship with Ψstem (Figure 7b). The negative ∆T values obtained by CTL trees (young and mature) were related to Ψstem values below −0.8 MPa, which corresponded to trees under non-limiting soil water conditions. In sweet cherry trees under post-harvest deficit irrigation, −1.5 MPa is generally considered a threshold value for irrigation management and higher values have been reported not to negatively affect the yield in the following year and reduce excessive vegetative growth [5]. In this sense, 1.6 °C has been suggested as the ∆T corresponding value to −1.5 MPa. The relationship between Ψsteam and ∆T was significantly different in young and mature trees. The weaker relationship found in mature trees is due to the fact that MS trees did not reach Ψstem values below −1.3 MPa (Figure 2b). The consistent relationship found in the young sweet cherry trees (r = 0.91) was similar to that reported in peach trees by Wang and Gartung [42] and higher than that reported in almond trees by García-Tejero et al. [52] with similar ∆T maximum values at 3.6 °C and Ψstem values below −2.0 MPa. According to the results obtained, ∆T was less dependent than Tc on weather conditions, clearly identified control and stressed trees, and did not show any inflexion point in its relationship with Ψstem. Consequently, these advantages of ∆T over Tc highlight its utility as a water stress indicator.
Similar to ΔT, CWSI showed a strong linear relationship with Ψstem ( Figure 8). Young and mature trees resulted in high correlation coefficients (r = 0.89 and 0.88, respectively). These results are similar to those reported in sweet cherry trees by Köksal et al. [53] on the relationship between CWSI and leaf water potential. The correlation between Ψstem and CWSI was identified as CWSI = −0.44 Ψstem = −0.17 in young sweet cherry trees and as CWSI = −0.86 Ψstem = −0.36 in mature sweet cherry trees. Regarding the different regression lines found in young and mature trees, Oberhuber et al. [54] reported that young trees have a greater capacity to extract water from water reserves in their organs (water storage tissues) than mature trees, and quickly transport it through the plant with the aim of sustaining leaf transpiration. Mature trees require a larger amount of water for their transpiration process because they have a greater leaf area, release more water to the atmosphere and have a proportionally smaller water reserve. Consequently, the mechanism used by mature trees to face water stress does not only consist of recruiting water from the water storage tissues but to promote The ∆T exhibited a linear relationship with Ψstem (Figure 7b). The negative ∆T values obtained by CTL trees (young and mature) were related to Ψstem values below −0.8 MPa, which corresponded to trees under non-limiting soil water conditions. In sweet cherry trees under post-harvest deficit irrigation, −1.5 MPa is generally considered a threshold value for irrigation management and higher values have been reported not to negatively affect the yield in the following year and reduce excessive vegetative growth [5]. In this sense, 1.6 • C has been suggested as the ∆T corresponding value to −1.5 MPa. The relationship between Ψsteam and ∆T was significantly different in young and mature trees. The weaker relationship found in mature trees is due to the fact that MS trees did not reach Ψstem values below −1.3 MPa (Figure 2b). The consistent relationship found in the young sweet cherry trees (r = 0.91) was similar to that reported in peach trees by Wang and Gartung [42] and higher than that reported in almond trees by García-Tejero et al. [52] with similar ∆T maximum values at 3.6 • C and Ψstem values below −2.0 MPa. According to the results obtained, ∆T was less dependent than Tc on weather conditions, clearly identified control and stressed trees, and did not show any inflexion point in its relationship with Ψstem. Consequently, these advantages of ∆T over Tc highlight its utility as a water stress indicator.
Similar to ∆T, CWSI showed a strong linear relationship with Ψstem ( Figure 8). Young and mature trees resulted in high correlation coefficients (r = 0.89 and 0.88, respectively). These results are similar to those reported in sweet cherry trees by Köksal et al. [53] on the relationship between CWSI and leaf water potential. The correlation between Ψstem and CWSI was identified as CWSI = −0.44 Ψstem = −0.17 in young sweet cherry trees and as CWSI = −0.86 Ψstem = −0.36 in mature sweet cherry trees. Regarding the different regression lines found in young and mature trees, Oberhuber et al. [54] reported that young trees have a greater capacity to extract water from water reserves in their organs (water storage tissues) than mature trees, and quickly transport it through the plant with the aim of sustaining leaf transpiration. Mature trees require a larger amount of water for their transpiration process because they have a greater leaf area, release more water to the atmosphere and have a proportionally smaller water reserve. Consequently, the mechanism used by mature trees to face water stress does not only consist of recruiting water from the water storage tissues but to promote stomatal closure. Stomatal closure avoids plant water release, decreases tree transpiration, and leads to an increase in leaf temperature [8]. These increments in leaf temperature of mature sweet cherry trees are referred to against the same baselines for young sweet cherry trees (Figure 4). Therefore, for a similar value of Ψstem, mature trees exhibit higher CWSI values. However, despite the difference in results for young and mature trees, it can be stated for both of them that CWSI values lower than 0.2 match with Ψstem values of well-watered trees. Similar to CWSI, when DAC and DANS indices were compared to Ψstem, young and mature trees, they showed significantly different linear regressions (Figure 9a,b). In the case of the DAC index, young trees were more closely related to Ψstem than mature trees (r = 0.9 and 0.76, respectively), with maximum values of 4.5 °C. In the case of the DANS index, mature and young trees were closely related (r = 0.84), and the maximum value (8.6 °C) was found in mature trees at −0.95 MPa. As expected according to the results reported by Taghvaeian et al. [15], DAC and DANS were strongly related to CWSI, especially DANS (Figure 9c,d).
In general, water stress indicators derived from thermal imaging evaluated in the present work were not sensitive enough to detect slight plant water stress in sweet cherry trees, due to Tc strongly depending on both stomatal conductance and transpiration rate, which are physiological processes that are less sensitive than other plant water indicators such as micrometric fluctuation of the different plant organs (trunk, branch, fruit, etc.) [8,11,55]. This limitation has been observed in all indices and has been reported in several fruit trees such as apple, citrus and nectarine [56][57][58]. This is because water status indicators based on leaf temperature when the soil water deficit is not moderate or severe are highly dependent on weather conditions. However, when trees were submitted to moderate water stress, CWSI, ΔT and DANS were robust water indicators able to assess young and mature sweet cherry tree water statuses. Similar to CWSI, when DAC and DANS indices were compared to Ψstem, young and mature trees, they showed significantly different linear regressions (Figure 9a,b). In the case of the DAC index, young trees were more closely related to Ψstem than mature trees (r = 0.9 and 0.76, respectively), with maximum values of 4.5 • C. In the case of the DANS index, mature and young trees were closely related (r = 0.84), and the maximum value (8.6 • C) was found in mature trees at −0.95 MPa. As expected according to the results reported by Taghvaeian et al. [15], DAC and DANS were strongly related to CWSI, especially DANS (Figure 9c,d).
In general, water stress indicators derived from thermal imaging evaluated in the present work were not sensitive enough to detect slight plant water stress in sweet cherry trees, due to Tc strongly depending on both stomatal conductance and transpiration rate, which are physiological processes that are less sensitive than other plant water indicators such as micrometric fluctuation of the different plant organs (trunk, branch, fruit, etc.) [8,11,55]. This limitation has been observed in all indices and has been reported in several fruit trees such as apple, citrus and nectarine [56][57][58]. This is because water status indicators based on leaf temperature when the soil water deficit is not moderate or severe are highly dependent on weather conditions. However, when trees were submitted to moderate water stress, CWSI, ∆T and DANS were robust water indicators able to assess young and mature sweet cherry tree water statuses.

Conclusions
The use of thermal imaging obtained from low-cost devices provided reliable data which were used to obtain the thermal indicator Tc and to calculate the thermal indices ∆T, CWSI, DAC and DANS to assess the response of young and mature sweet cherry trees submitted to water stress. Our results revealed that Tc was highly dependent on weather conditions, while the thermal indices mitigated this dependency, so the use of Tc in water stress detection is not recommended. ∆T was highly influenced by VPD, and when upper and lower baselines were obtained there were no differences found either between young and mature sweet cherry trees or between plots, which supports the use of the proposed baselines. CWSI and DANS were strongly related to Ψstem and were calculated on the basis of the experimental non-water-stress baseline and water stress baseline, over arange of VPD values between 1 and 4 kPa. The DANS index differentiated between irrigation treatments as well as CWSI, despite being much easier to calculate than CWSI, and exhibited a strong relationship with Ψstem. These results indicate that the DANS index is a promising thermal index which can be used in fruit tree water assessment. It must also be added that CWSI and DANS resulted in different regression lines with Ψstem, depending on the plot studied. These differences might not be solely attributable to the different age of the trees but also to the different soil and weather conditions of each plot. When thermal indices were compared with Ψstem, it was observed that, under non-limiting soil water conditions (values below −0.7 MPa), all indices were highly influenced by climatic conditions. Moreover, despite thermal indices being a non-invasive and fast means with which to assess tree water status, Tc strongly depends on crop transpiration rate. This is a limiting factor in the interpretation of thermography data for the early detection of water stress, so in phenological stages when even slight water stress must be avoided, its use should be coupled with other water status indicators. However, when deficit irrigation was applied, CWSI and DANS could be considered reliable water stress indicators. The results of this study could help improve sweet cherry cultivation, as well as other Prunus fruit trees with similar phenology and water stress

Conclusions
The use of thermal imaging obtained from low-cost devices provided reliable data which were used to obtain the thermal indicator Tc and to calculate the thermal indices ∆T, CWSI, DAC and DANS to assess the response of young and mature sweet cherry trees submitted to water stress. Our results revealed that Tc was highly dependent on weather conditions, while the thermal indices mitigated this dependency, so the use of Tc in water stress detection is not recommended. ∆T was highly influenced by VPD, and when upper and lower baselines were obtained there were no differences found either between young and mature sweet cherry trees or between plots, which supports the use of the proposed baselines. CWSI and DANS were strongly related to Ψstem and were calculated on the basis of the experimental non-water-stress baseline and water stress baseline, over arange of VPD values between 1 and 4 kPa. The DANS index differentiated between irrigation treatments as well as CWSI, despite being much easier to calculate than CWSI, and exhibited a strong relationship with Ψstem. These results indicate that the DANS index is a promising thermal index which can be used in fruit tree water assessment. It must also be added that CWSI and DANS resulted in different regression lines with Ψstem, depending on the plot studied. These differences might not be solely attributable to the different age of the trees but also to the different soil and weather conditions of each plot. When thermal indices were compared with Ψstem, it was observed that, under non-limiting soil water conditions (values below −0.7 MPa), all indices were highly influenced by climatic conditions. Moreover, despite thermal indices being a non-invasive and fast means with which to assess tree water status, Tc strongly depends on crop transpiration rate. This is a limiting factor in the interpretation of thermography data for the early detection of water stress, so in phenological stages when even slight water stress must be avoided, its use should be coupled with other water status indicators. However, when deficit irrigation was applied, CWSI and DANS could be considered reliable water