Physiological features of Potato (Solanum tuberosum L. Var. Diacol Capiro) canopy reflectance changes under different levels of water stress

Physiological features of Potato (Solanum tuberosum L. Var. Diacol Capiro) canopy reflectance changes under different levels of water stress | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Physiological features of Potato ( Solanum tuberosum L. Var. Diacol Capiro) canopy reflectance changes under different levels of water stress Fabio Ernesto Martinez Maldonado, Angela María Castaño Marín, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6580783/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The analysis of the plant's spectral signature makes it possible to identify specific metabolic responses through variations in reflectivity, measured from high-resolution spectral images, at both canopy and vegetation levels. In potato, reflectance variations and their relationship with photosynthetic behavior under water stress have been scarcely studied, particularly regarding stress assessments based on xylem water potential. In this study, the relationships between xylem water potential, changes in net photosynthesis, stomatal conductance, and transpiration, as well as variations in spectral response in the visible region considering specific bands and vegetation indices, were analyzed. Spectral measurements of light reflectance in the VIS region at canopy level, water potential, and leaf gas exchange parameters were performed at tuber differentiation and maximum tuberization phenological stages under three intensities of water stress (light, moderate and severe). Under moderate and severe water stresses, increases in canopy reflectance were observed from 530 to 570 nm, 660 – 670 nm and around 700 nm. The 400R/690R and 450R/550R ratios showed the strongest association and dependence with gas exchange variables under different water stress levels, in both growth stages. These results contribute to the monitoring of photosynthetic performance and the detection of water stress events in potato plants. Agricultural Engineering Horticulture Biophysics canopy reflectance water stress leaf gas exchange vegetation indices Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Under water stress conditions a reduction in chlorophyll content, leaf area, premature leaf senescence, and stunted growth occur, changing leaf spectral reflectance response [ 1 , 2 ]. In this sense, spectral reflectance measurements at different levels (leaf, plant, and canopy) have been widely employed to assess water status in plants [ 3 – 5 ], mainly by sensing the reflected radiation in the visible (VIS, 400–700 nm), the near-infrared (NIR, 700–1200 nm) and short-wave infrared (SWIR, 1300–2500 nm) regions of the electromagnetic spectrum. These regions are correlated with leaf pigment concentration, cell structure, and water content, respectively [ 6 ].Reflectance data are used to calculate vegetation indices (VIs), derived from the combination of several bands, within the visible and NIR spectral regions [ 7 , 8 ].Some indices, based on the visible band reflectance, are sensitive to a decrease in the plant (leaf) water content [ 3 , 9 , 10 ]. For example, the Photochemical Reflectance Index (PRI) is an indicator of the de-epoxidation state of the xanthophyll pigments related to photosynthetic processes [ 11 ]. It is calculated using the reference band located at 570 and the 530 nm band where xanthophyll pigment absorption occurs. The PRI index has shown a direct relation to photosynthesis rate [ 9 , 12 , 13 ], chlorophyll fluorescence, and non-photochemical quenching [ 9 , 10 , 12 ]. Potato is a drought-sensitive species [ 14 – 19 ]. Assessment of plant water status has mainly focused on methods such as leaf water potential and relative water content (RWC), which are time-consuming and require destructive sampling. Although, the approaches based on reflectance responses have become a fundamental non-destructive tool in diagnosing plant water/physiological status [ 3 ], in potato, it has not been developed adequately. In general, spectral approaches in potato have been used to identify N rate [ 20 , 21 ], water content, and to estimate the proportion of ground covered by potato canopy [ 22 ]. However, there is no available information about reflectance variations and their relationship with water stress (Stricto sensu) and physiological status. [ 23 ] used indices like Crop Water Stress Index (CWSI), Moisture Stress Index (MSI), Photochemical Reflectance Index (PRI), and spectral emissivity, in potato variety Cilena (Solanum tuberosum L. cv. Cilena) demonstrating thus that water stress detection is feasible using indices depicting leaf temperature, leaf water content and spectral emissivity.Romero et al. (2017) [ 24 ] compared the physiological and spectral traits between Diacol Capiro and Perla Negra genotypes, under two drought levels. They found that spectral information was correlated with physiological variables such as foliar area, total water content, relative growth rate of potato tubers, leaf area ratio, and leaf area index. These current approaches associate spectral responses with soil water deficit or days after suspension of irrigation, but not with plant water stress, particularly when assessed through xylem water potential. This does not allow a clear definition of the actual intensity of stress and its influence on the reflectance variations at the canopy level. To contribute to the quantification and monitoring of water stress in potato plants, this work aims to study the relationship between physiological and spectral responses to water stress. Additionally, to find correlations of spectral reflectance indices with xylem water potential and photosynthetic performance. The following questions are addressed in this study: (1) What is the link between canopy-level reflectance and plant physiological status under water stress? and (2) which vegetation indices could be useful to detect water stress in potato plants? 2. Methodology 2.1 Plant Material and Experimental design The experiment was carried out in the AGROSAVIA (Corporación Colombiana de Investigación Agropecuaria) Tibaitatá research center, Colombia (4º 41’ 25.7064’’ N, 74º 12’ 08.23’’ W) at greenhouse conditions. To determine the effect of water stress on spectral responses and gas exchange of potato ( Solanum tuberosum L. “Diacol Capiro”) plants, a randomized complete block design distributed in 3 blocks, each one with 7 treatments was established. Each treatment was the combination of one stress level (light, moderate and severe) and two plant growth stages: tuber differentiation (TD) and maximum tuberization (MT), plus a control group (well hydrated plants). Potato plants were sown in a loam soil that was kept at field capacity (soil water potential did not decline below 0.033 MPa) by drip irrigation from sowing until each growth stage was reached. At each growth stage, the stress treatments were induced by suspending irrigation until reaching each level of stress. The water stress levels were determined based on [ 25 , 26 ] and using the xylem water potential (ψw) measured in the terminal leaflet of the 3rd or 4th fully expanded leaf. Control plants (well-watered plants) had a water potential ranging from 0 to -0.49 MPa, the light water stress treatment corresponded to a ψ w range of -0.50 to -0.59MPa, moderate water stress has a ψ w between − 0.60 to -0.89 MPa and severe water stress was defined as ψ w lower than − 0.90 MPa. 2.2 Leaf-level measurements Once potato plants reached the tuber differentiation and maximum tuberization growth stages (at ninth and thirteenth week after sowing, respectively), daily xylem water potential (ψ w ), net photosynthesis (A), stomatal conductance (g s ), and transpiration (E) measurements were carried out from stress induction until each stress level was reached. Pre-dawn xylem water potential ( ψ w ) was determined, always between 4:00 am and 7:00 am [ 27 , 28 ] using a Scholander type pressure chamber, pump-up model (PMS Instrument Company, Oregon, USA). For this purpose, the terminal leaflet from the third or fourth developed leaves was removed and measured within two minutes after being removed from the plant. Control plants were evaluated as well. Gas exchange was recorded using an infrared gas analyzer IRGA LICOR 6800 IRGA system (LI-COR Biosciences, Lincoln, Nebraska USA) between 9:00 a.m. and 11:00 a.m., using a CO 2 concentration of 400 µmol m − 2 s − 1 . 2.3 Spectral Reflectance Measurements Spectral images were taken at 3m above the plant’s canopy level with the camera looking downwards. The image acquisition campaigns were done at around the same hour of the day using a camera with 520×696 pixels and 128 spectral bands in the 400–1000 nm range (710-VP Surface Optics Corporation). A Spectralon reflectance white panel was used on each image to convert the hyperspectral intensity images to reflectance. To segment the white spectralon panel from the spectral image, the average of the red, green, blue, and NIR bands was computed and divided by the maximum intensity. The Spectralon reflectance panel was segmented from the image using a threshold above 0.5. The reflectance of each spectral imagery was computed using: $$\:\rho\:\left(x,y,\lambda\:\right)=\frac{I\left(x,y,\lambda\:\right){\rho\:}_{S}\left(\lambda\:\right)}{Is\left(\lambda\:\right)}$$ where \(\:\rho\:\left(x,y,\lambda\:\right)\) is the reflectance image at pixel coordinates \(\:x,y\) and waveband \(\:\lambda\:\) , \(\:I\left(x,y,\lambda\:\right)\) is the raw intensity image at pixel coordinates \(\:x,y,\) and waveband \(\:\lambda\:\) , \(\:{\rho\:}_{S}\left(\lambda\:\right)\) the known reflectance of the Spectralon panel at \(\:\lambda\:\) wavelength (0.99 at visible and NIR ranges) and \(\:Is\left(\lambda\:\right)\) the mean intensity of the Spectralon panel at waveband \(\:\lambda\:\) . To segment the canopy from its background, the Soil-Adjusted Vegetation Index (SAVI) was used following the methodology reported by Duarte-Carvajalino et al. (2021) [ 29 ] Two image campaigns on tubers differentiation and maximum tuberization growth stages were performed, acquiring images starting from the water supply suspension to each level of stress (Light, Moderate, Severe). The 710-VP camera records spectral information in the full wavelength range of 400–1000 nm, however only the 400–760 nm range was used for the study. Simple and multiple linear regressions between xylem water potential ( ψ w ), gas exchange variables (A n , E and g s ) and reflectance responses in terms of specific bands and vegetation indices (Table 1 ) were performed. Pearson correlation coefficient (r) and coefficient of determination (R 2 ) were used to explore the significant relationships between all parameters and indices. 3. Results 3.1 Spectral behavior In general, under water stress conditions, the decrease in water potential induces changes in the optical properties of the potato canopy, which are observed as increases in reflectance across the visible spectrum. A xylem water potential lesser than -0.7 MPa caused a more evident increase in reflectance, mainly in the wavelength ranges of 520–560 nm and 580–750 nm (Figure 1). The most noticeable changes were observed mainly in the regions surrounding green (520–570 nm), red (650–660 nm) and red edge (beyond 700–750 nm) during the maximum tuberization phase and at all intensities of water stress (Figure 1). However, under moderate and severe stress, the increases in reflectance around 640 – 660 nm (red region) are more easily appreciable. The increase in reflectance in the 520 – 570 nm region (green region), was more evident, mainly during maximum tuberization. In the region between 700 – 750 nm (region around red edge), the curves corresponding to more negative water potentials showed a greater orientation towards the vertical, indicating a higher slope (Figure 1). In the violet-blue region (400 – 500 nm), the changes in reflectance caused by more negative water potentials are mainly noticeable in the tuber differentiation phase during moderate and severe water stress. In the maximum tuberization phase, changes in the violet-blue region caused by variations in water potential are not clearly observed. 3.2. Relationship between ψ w , An, g s , E and the main absorption bands in the visible spectrum In general, the 550 nm, and 670 nm bands behaved similarly for the TD and MT growth stages (correlation coefficients >0.6). The 710 nm band showed the greatest variation associated with changes in water potential (Figure 2). The relationship between the xylem water potential and the main pigment absorption bands indicates that the reflectance increases in a non-linear way with the progressive decrease in the water potential of the xylem. In other words, the more water stress, the greater the increase in reflectance in the main pigment absorption bands. The same behavior was observed in the relationship between net photosynthesis (An) and the main pigment absorption bands reflectance. Reflectance increases with the decrease in the photosynthetic rate. The response in both growth phases was similar in the 550 nm and 710 nm bands, with their determination and correlation coefficients (>0.5 and >0.7, respectively) being higher compared to those of the 440 nm and 670 nm bands. The greatest variation in the reflectance response to changes in A n is observed mainly in the 710 nm band in both growth phases. The trend of increasing reflectance of the main pigment absorption bands associated with a decrease in stomatal conductance (g s ) was observed in both growth phases in the 550 and 710 nm bands. However, the largest reflectance variations for both phases were most evident in the 710 nm band. Red edge and red were important bands for DT and MT, respectively, and again, the results indicate that the green region is more sensitive to changes in stomatal closure. Even though there is a trend of increasing reflectance in non-linear way with the progressive decrease in transpiration in the 710 nm band, the responses are generally different for both growth phases. During tuber differentiation, all the examined bands showed Pearson’s correlation and determination coefficient values greater than 0.5 and 0.7, respectively. However, the 710 nm band showed a greater association with changes in E, with r and R² values of 0.87 and 0.76, respectively. In contrast, during maximum tuberization, the 710 nm band showed low values for the adjustment coefficients. In this phase, the greatest association of reflectance with changes in E was observed in the 550 nm and 670 nm bands. These bands exhibited similar behavior in both growth phases, considering the values of the adjustment coefficients r and R 2 (Figure 3). 3.3. Relationship between vegetation indices and ψ w , An, g s and E Table 2 shows the correlation coefficients (r) and coefficients of determination (R²) for simple and multiple linear regression models between different indices and gas exchange variables ( ψ w , An, g s, and E). Dark gray represents higher R² values (>0.75), while light gray represents R² values between 0.5 and 0.75. In Maximum tuberization (MT), the highest positive correlations were observed for relationships between mRESR and ψ w ; PRI and ψ w ; SRPI and g s ; TVI and ψ w , An; NPQI and An, g s , E; BGI2 and An, g s ; BGI and ψ w , An, g s and E; BRI and An, g s , E. For tuber differentiation, only the relationships between BRI and ψw, and BGI2 and An showed r and R² values greater than 0.75. Even though most indices are not strongly explained by variations in the photosynthetic parameters during the two evaluated growth stages, the BRI and BGI2 indices generally show higher correlation and determination values (>0.75), or at least greater than 0.5 for all photosynthetic parameters and across both growth stages. The other indices generally showed weaker relationships, with r and R 2 <= 0.49. In general, the 400R/690R and 450R/R550R ratios exhibited correlations with the xylem water potential (Figure 4). The correlation coefficients in both growth stages were higher than 0.8, indicating a strong association between the variables. However, the behavior of BRI and BGI differs between the two crop growth stages due to the correlation slopes having opposite signs. In the 400R/690R, at tuber differentiation phase, an increase of index value over 1.5, indicates the beginning of more negative water potentials with an approximate threshold of -0.6 Mpa. In contrast, for the maximum tuberization phase, index values below 1.5 indicate water stress potentials (<-0.6 Mpa). Regarding the 450R/550R ratio, an index threshold of 0.6 (higher or lower) indicates the beginning of more negative water potentials (Figure 4). The BRI and BGI2 indices not only exhibited a strong correlation with net photosynthesis (An) in both growth stages (DT and MT), but also a high coefficient of determination (R² > 0.8). Therefore, these indices appear to be suitable for assessing the impact of leaf water potential on photosynthesis (Figure 5). Again, the behavior of BRI and BGI differs between the two crop growth stages. In tuber differentiation, a decrease in photosynthesis is matched by an increase in the value of the indices. In contrast, during maximum tuberization, the positive correlation indicates that low values of photosynthesis correspond to low values of the indices. The lowest correlation was observed between the 400 R /690 R and 450 R /R550 R ratios and the stomatal conductance during the tuber differentiation (TD) phase. In contrast, the 400 R /690 R ratio presented the highest correlation and determination coefficients (0.9 and 0.89, respectively) in maximum tuberization (MT). Like the An-BRI/BGI relationship, the behavior of both indices differs between the two crop growth stages due to the correlation slopes having opposite signs (Figure 6). The same behavior was observed for the correlations between transpiration and the ratios 400 R /690 R and 450 R /R550 R , again the relation 400 R /690 R presented the highest coefficients of correlation and determination (0.9 and 0.89, respectively) in maximum tuberization, which is expected considering the close relationship between the variables E and g s . Again, a negative correlation between the indices and transpiration is observed during the DT phase while a positive correlation is observed during the MT phase (Figure 7). 4. Discussion Data analysis in this study indicated that progressive water stress caused changes in photosynthesis and in the optical properties of potato canopy. The spectral curves showed some slight variations in the regions around 550 nm and 710 nm, but distinguishing these differences from well-hydrated plants is not easy. Under moderate and severe water stress events, the impact on reflectance is much more evident. Although increases in reflectance are observed in the violet-blue region (400–500 nm), considerable increases in the canopy reflectance of stressed plants are mainly observed in the region from 530 nm to 570 nm, red (660 nm – 670 nm) and red edge around 700 nm. Typically, absorption in the red range is high due to the action of both chlorophyll-a and -b. On the other hand, the sensitivity of absorption and reflectance in the green and red edge spectral regions is higher than in the blue and red regions of the spectrum [ 37 ]. However, changes in the reflectance of the absorption bands at 550 and 710 nm were related to changes in the gas exchange process under water stress conditions. The decrease in water potential, An, g s and E was related to an increase in reflectance at 710 and 550 nm, indicating that reflectance in these bands could serve as an indirect reading of possible stomatal limitations for water and CO 2 at different levels of water stress. Under light or moderate drought stress, stomata close rapidly, resulting in a decrease in stomatal conductance, transpiration, and net photosynthesis [ 38 , 39 ]. The functional relationships between the BRI and BGI2 vegetation indices and gas exchange parameters followed third-order polynomials, demonstrating the non-linear nature of these relationships. Nonlinear relationships were also found by [ 36 ] for BRI and BGI indices in vine. Changes in the 400R/690R and 450R/R550R ratios were more evident under more negative water potential during severe water stress events. The correlation results during the tuber differentiation phase (DT) indicate a trend of increasing 400R/690R and 450R/550R ratio values as the water potential becomes more negative due to intensified water stress. This is because, even when the reflectance at 690 nm and 550 nm increases due to the lower absorption in these bands, the increase in the 400–450 nm region is much greater, which can be seen in Fig. 1 . This increase in reflectance values in the blue-violet region, inverts the sense of the correlations during DT, generating a negative association between the variables. During maximum tuberization (MT), the process of chlorophyll breakdown (during complete leaf dehydration) may cause a corresponding decreasing 400R/690R and 450R/550R ratios, since chlorophyll contributes mainly to the 650 nm absorption band [ 10 , 40 – 42 ] and the higher sensitivity of the 550 nm band with chlorophyll contents. As it can be seen in Fig. 1 , unlike tuber differentiation, during the maximum tuberization phase, there are not such evident increases in the 400–450 nm region, which maintains a positive correlation between the variables. The blue/green/red ratio indices included blue/green indices (BGI1 = 400R/R550R; BGI2 = 450R/R550R) and blue/red indices (BRI1 = 400R/690R; BRI2 = 450R/690R). These indices were initially proposed and used by [ 36 , 43 ] for monitoring the physiological condition of Vitis vinifera L. and for the remote detection of water stress in a citrus orchard, finding that BGI1 index (blue/green ratio) showed the highest correlation for both g s (R 2 = 0.62) and Ψ w (R2 = 0.49), suggesting it could serve as a good estimator of water stress. In this study, both the blue/green and the blue/red indices showed correlations greater than 0.7 and R 2 greater than 0.75 with the xylem water potential, stomatal conductance (g s ), net photosynthesis (A n ), transpiration (E), which postulates them as good indicators of water stress in potato. These results are consistent with the correlations found between gas exchange parameters and the specific absorption bands at 550 nm, 670 nm, and 710 nm (section 3.2), where the association and dependence of these spectral regions on variations in xylem water potential, stomatal conductance (gₛ), net photosynthesis (Aₙ), and transpiration (E) are evident. 5. Conclusions Although reflectance changes in the visible spectrum naturally reflect non-stomatal limitations, the results of this study demonstrated an association and dependence between different portions of the visible range and variables that characteristically indicate stomatal-type limitations, such as decreased transpiration and stomatal closure. Therefore, spectral analysis in the visible range could serve as an indirect indicator of stomatal limitations to photosynthesis during stress events. The BRI and BGI indices, with ratios of 400R/690R and 450R/550R, respectively, exhibited the strongest association and dependence with gas exchange variables under different levels of water stress and in both growth stages. The results demonstrate the potential of the 550 nm and 710 nm bands, as well as the BRI and BGI indices, for detecting water stress in potato and reflecting both stomatal and non-stomatal limitations to photosynthesis under water-limited conditions. Declarations Author Contributions: Conceptualization, F.E.M.-M.; methodology, F.E.M.-M., J.M.D.C., A.M.C.-M., G.A.G.-V. and E.A.S.A.; software, G.A.G.-V. and J.M.D.C.; formal analysis, F.E.M.-M.; investigation, F.E.M.-M., A.M.C.-M., J.M.D.C., E.A.S.A.; G.A.G.-V. and J.A.M.B; resources, A.M.C.-M.; data curation, F.E.M.-M. and J.M.D.C.; writing—original draft, F.E.M.-M.; writing—review and editing, F.E.M.-M., J.M.D.C., A.M.C.-M., J.A.M.B., G.A.G.-V. and E.A.S.A.; supervision, A.M.C.-M.; project administration, A.M.C.M.; funding acquisition, A.M.C.-M. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Fondo de Ciencia, Tecnología e Innovación del Sistema General de Regalías, administered by the Fondo Nacional de Financiación para Ciencia, Tecnología e Innovación—Francisco José de Caldas, Programa Colombia BIO, Gobernación de Cundinamarca and Ministerio de Ciencia, Tecnología e Innovación (MINCIENCIAS) funding number 66153, and Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA) funding number TV19 1000911. Acknowledgments: This work is part of a larger project in Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA) named Sistema de Información Agroclimática del cultivo de la papa en la región de Cundinamarca, Colombia (SIAP).We thank Óscar Dubán Ocampo Páez, for their contribution in the equipment installation process. References Maimaitiyiming M, Ghulam A, Bozzolo A, et al (2017) Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy. 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J Plant Physiol 148:501–508. https://doi.org/10.1016/S0176-1617(96)80285-9 Haboudane D, Miller JR, Pattey E, et al (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sens Environ 90:337–352. https://doi.org/10.1016/j.rse.2003.12.013 Barnes JD, Balaguer L, Manrique E, et al (1992) A reappraisal of the use of DMSO for the extraction and determination of chlorophylls a and b in lichens and higher plants. Environ Exp Bot 32:85–100. https://doi.org/10.1016/0098-8472(92)90034-Y Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81:337–354. https://doi.org/10.1016/S0034-4257(02)00010-X Peñuelas J, Filella I (1995) Reflectance assessment of mite effects on apple trees. Int J Remote Sens 16:2727–2733. https://doi.org/10.1080/01431169508954588 Zarco-Tejada PJ, Berjón A, López-Lozano R, et al (2005) Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens Environ 99:271–287. https://doi.org/10.1016/j.rse.2005.09.002 Gitelson A (2011) Non-destructive estimation of foliar pigment (chlorophylls, carotenoids and anthocyanins) contents: Espousing a semi-analytical three-band model. Hyperspectral Remote Sens Veg 141–166 Li W, Zhang S, Shan L (2007) Responsibility of non-stomatal limitations for the reduction of photosynthesis-response of photosynthesis and antioxidant enzyme characteristics in alfalfa (Medicago sativa L.) seedlings to water stress and rehydration. Front Agric China 1:255–264. https://doi.org/10.1007/s11703-007-0044-5 Mafakheri A, Siosemardeh A, Bahramnejad B, et al (2010) Effect of drought stress on yield, proline and chlorophyll contents in three chickpea cultivars. Aust J Crop Sci 4:580–585 Dobrowski SZ, Pushnik JC, Zarco-Tejada PJ, Ustin SL (2005) Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale. Remote Sens Environ 97:403–414. https://doi.org/10.1016/j.rse.2005.05.006 Seelig HD, Hoehn A, Stodieck LS, et al (2008) The assessment of leaf water content using leaf reflectance ratios in the visible, near-, and short-wave-infrared. Int J Remote Sens 29:3701–3713. https://doi.org/10.1080/01431160701772500 Suárez L, Zarco-Tejada PJ, González-Dugo V, et al (2012) The photochemical reflectance index (PRI) as a water stress indicator in peach orchards from remote sensing imagery. Acta Hortic 962:363–370. https://doi.org/10.17660/actahortic.2012.962.50 Zarco-Tejada PJ, González-Dugo V, Berni JAJ (2012) Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens Environ 117:322–337. https://doi.org/10.1016/j.rse.2011.10.007 Tables Tables 1 and 2 are available in the Supplementary Files section Additional Declarations The authors declare no competing interests. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6580783","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451268482,"identity":"f840c221-e5ca-46aa-89fc-92ea29734ffc","order_by":0,"name":"Fabio Ernesto Martinez Maldonado","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYBAC9gYwJQHEzAegYowNeLXwgNUlgLSwJZCkBcw0IM5hPOzdiZ8rf1jk8fOf+fjx6x4be/4ZyY0fGGpscGvhObtZ8kyCRLFkw9nN0jLP0hJn3EhslmA4loZTi71E7gbJhgSJxA0HezdISxw4nMBw5mCDBGPDYdy2yL/d/BOkZf9hnse/gVrs5c8cbP6BV4sE7zaILWw8bJIfDhxm3HC8sQ2/LTy52ywb0iQSZ5xhM7NmOJCWuBGoxSIBj1942M9uvtlgU5fY33/48c0fB2zs5Q6zP77xAU+IoQBmHhgrgTgNwGj/QazKUTAKRsEoGFEAAJrnWRMpR576AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1244-5897","institution":"Agrosavia","correspondingAuthor":true,"prefix":"","firstName":"Fabio","middleName":"Ernesto Martinez","lastName":"Maldonado","suffix":""},{"id":451268612,"identity":"95f90399-62a7-4f27-a41c-e1ea0ef07dc4","order_by":1,"name":"Angela María Castaño Marín","email":"","orcid":"","institution":"Agrosavia","correspondingAuthor":false,"prefix":"","firstName":"Angela","middleName":"María Castaño","lastName":"Marín","suffix":""},{"id":451268656,"identity":"f404a5a9-39d2-4e00-8c29-13f3d9eeddb1","order_by":2,"name":"Julio Martin Duarte Carvajalino","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Julio","middleName":"Martin Duarte","lastName":"Carvajalino","suffix":""},{"id":451268728,"identity":"d45d3a52-1e9e-4ae2-bad7-59d5ccb54d7c","order_by":3,"name":"Elías Alexander Silva Arero","email":"","orcid":"","institution":"DISAN","correspondingAuthor":false,"prefix":"","firstName":"Elías","middleName":"Alexander Silva","lastName":"Arero","suffix":""},{"id":451268729,"identity":"c5c951c5-31cd-4722-8a5f-1673fd39820f","order_by":4,"name":"Gerardo Antonio Goez Vinasco","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gerardo","middleName":"Antonio Goez","lastName":"Vinasco","suffix":""},{"id":451268730,"identity":"b083af4b-592e-4039-8062-431f56a27bb6","order_by":5,"name":"Jose Alfredo Molina Varón","email":"","orcid":"","institution":"Agrosavia","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Alfredo Molina","lastName":"Varón","suffix":""}],"badges":[],"createdAt":"2025-05-02 20:37:08","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6580783/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6580783/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82070059,"identity":"328d18de-2749-4c65-a897-b5766bd12005","added_by":"auto","created_at":"2025-05-06 13:09:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":229815,"visible":true,"origin":"","legend":"\u003cp\u003ePotato canopy reflectance measured in two growth stages TD (tuber differentiation, left) and MT (Maximum tuberization, right) for well-watered plants and light, moderate and severe stress levels on the 400 to 760 nm wavelengths.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6580783/v1/967e4d7ec24254c93ba46f01.png"},{"id":82070084,"identity":"6833274e-15a2-4c5b-b5a8-e27ac9e66c35","added_by":"auto","created_at":"2025-05-06 13:09:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36378,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between xylem water potential and reflectance in the blue (440 nm), green (550 nm), red (670 nm), and red edge (710 nm) bands of the spectrum in potato leaves for two growth stages TD (tuber differentiation) and MT (Maximum tuberization). Well-watered plants have hydric potential in the 0 to -0.49 Mpa range, light water stress range -0.5 to -0.59; moderate water stress range -0.6 to -0.89 MPa, severe water stress range \u0026lt; -0.9 MPa.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6580783/v1/cd5ab4528264b13569a4ac2c.png"},{"id":82070079,"identity":"847184c7-00f7-49f6-a377-c078786eae2c","added_by":"auto","created_at":"2025-05-06 13:09:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":225528,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships between net photosynthesis (A\u003csub\u003en\u003c/sub\u003e), stomatal conductance (g\u003csub\u003es\u003c/sub\u003e), transpiration (E) and reflectance in the blue (440 nm), green (550 nm), red (670 nm), and red edge (710 nm) bands of the spectrum in potato leaves for two growth stages TD (tuber differentiation) and MT (Maximum tuberization). Determination coefficients (R\u003csup\u003e2\u003c/sup\u003e) are inserted inside graphs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6580783/v1/5401b91b67a03fd514e400ec.png"},{"id":82070046,"identity":"7b10ba19-95bb-41a4-8c63-7c5b91478fb7","added_by":"auto","created_at":"2025-05-06 13:09:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38154,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between Blue Red Index (BRI) (A, B), Blue Green Index (BGI) (C, D), and Leaf xylem water potential for two growth stages TD (tuber differentiation) and MT (Maximum tuberization). Solid lines represent the relationship between variables. Equations and determination coefficients (R\u003csup\u003e2\u003c/sup\u003e) are inserted inside graphs. Well-watered plants have hydric potential in the 0 to -0.49 Mpa range, light water stress (LWS) range -0.5 to -0.59; moderate water stress (MWS) range -0.6 to -0.89 MPa, severe water stress (SWS) range \u0026lt; -0.9 MPa. Dotted lines indicate stress levels thresholds.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6580783/v1/62eede2879d179c60b894557.png"},{"id":82070100,"identity":"b194dd41-caa6-4763-b5a8-e1b67cb5916c","added_by":"auto","created_at":"2025-05-06 13:09:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":37400,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between Blue Red Index (BRI) (A, B), Blue Green Index (BGI) (C, D), and Net Photosynthesis (An) for two growth stages TD (tuber differentiation) and MT (Maximum tuberization). Solid lines represent the relationship between variables. Equations and determination coefficients (R\u003csup\u003e2\u003c/sup\u003e) are inserted inside graphs.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6580783/v1/586f53bea636d3eb81fa9541.png"},{"id":82070089,"identity":"cb8da548-292e-4b3c-992a-8140286e2db3","added_by":"auto","created_at":"2025-05-06 13:09:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":58124,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between Blue Red Index (BRI) (A, B), Blue Green Index (BGI) (C, D), and Stomatal Conductance (g\u003csub\u003es\u003c/sub\u003e) for both growth stages TD (tuber differentiation) and MT (Maximum tuberization). Solid lines represent the regression relationships between the variables. Equations and determination coefficients (R\u003csup\u003e2\u003c/sup\u003e) are inserted inside graphs.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6580783/v1/e02c92de90c94500f18e4aeb.png"},{"id":82070050,"identity":"e889ac3e-59d4-4ac5-96b4-d587ef713f5f","added_by":"auto","created_at":"2025-05-06 13:09:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":60468,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between Blue Red Index (BRI) (A, B), Blue Green Index (BGI) (C, D), and Transpiration (E) for both growth stages TD (tuber differentiation) and MT (Maximum tuberization). Solid lines represent the linear relationship between variables. Equations and determination coefficients (R\u003csup\u003e2\u003c/sup\u003e) are inserted inside graphs.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6580783/v1/9b8f7137db54db42f7dff1fc.png"},{"id":82070849,"identity":"06c3c738-2460-4abf-9a00-69029b870c5b","added_by":"auto","created_at":"2025-05-06 13:17:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1033895,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6580783/v1/ba7e9184-250e-4d35-9402-cc92689a85be.pdf"},{"id":82070054,"identity":"0dc3d962-af24-4c0f-b35b-a82edaca27cf","added_by":"auto","created_at":"2025-05-06 13:09:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":67050,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6580783/v1/b20ae136cc61ce094018545c.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePhysiological features of Potato (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSolanum tuberosum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e L. Var. Diacol Capiro) canopy reflectance changes under different levels of water stress\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUnder water stress conditions a reduction in chlorophyll content, leaf area, premature leaf senescence, and stunted growth occur, changing leaf spectral reflectance response [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In this sense, spectral reflectance measurements at different levels (leaf, plant, and canopy) have been widely employed to assess water status in plants [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], mainly by sensing the reflected radiation in the visible (VIS, 400\u0026ndash;700 nm), the near-infrared (NIR, 700\u0026ndash;1200 nm) and short-wave infrared (SWIR, 1300\u0026ndash;2500 nm) regions of the electromagnetic spectrum. These regions are correlated with leaf pigment concentration, cell structure, and water content, respectively [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].Reflectance data are used to calculate vegetation indices (VIs), derived from the combination of several bands, within the visible and NIR spectral regions [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].Some indices, based on the visible band reflectance, are sensitive to a decrease in the plant (leaf) water content [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. For example, the Photochemical Reflectance Index (PRI) is an indicator of the de-epoxidation state of the xanthophyll pigments related to photosynthetic processes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It is calculated using the reference band located at 570 and the 530 nm band where xanthophyll pigment absorption occurs. The PRI index has shown a direct relation to photosynthesis rate [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], chlorophyll fluorescence, and non-photochemical quenching [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePotato is a drought-sensitive species [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Assessment of plant water status has mainly focused on methods such as leaf water potential and relative water content (RWC), which are time-consuming and require destructive sampling. Although, the approaches based on reflectance responses have become a fundamental non-destructive tool in diagnosing plant water/physiological status [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], in potato, it has not been developed adequately. In general, spectral approaches in potato have been used to identify N rate [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], water content, and to estimate the proportion of ground covered by potato canopy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, there is no available information about reflectance variations and their relationship with water stress (Stricto sensu) and physiological status. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] used indices like Crop Water Stress Index (CWSI), Moisture Stress Index (MSI), Photochemical Reflectance Index (PRI), and spectral emissivity, in potato variety Cilena (Solanum tuberosum L. cv. Cilena) demonstrating thus that water stress detection is feasible using indices depicting leaf temperature, leaf water content and spectral emissivity.Romero et al. (2017) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] compared the physiological and spectral traits between Diacol Capiro and Perla Negra genotypes, under two drought levels. They found that spectral information was correlated with physiological variables such as foliar area, total water content, relative growth rate of potato tubers, leaf area ratio, and leaf area index. These current approaches associate spectral responses with soil water deficit or days after suspension of irrigation, but not with plant water stress, particularly when assessed through xylem water potential. This does not allow a clear definition of the actual intensity of stress and its influence on the reflectance variations at the canopy level.\u003c/p\u003e \u003cp\u003eTo contribute to the quantification and monitoring of water stress in potato plants, this work aims to study the relationship between physiological and spectral responses to water stress. Additionally, to find correlations of spectral reflectance indices with xylem water potential and photosynthetic performance. The following questions are addressed in this study: (1) What is the link between canopy-level reflectance and plant physiological status under water stress? and (2) which vegetation indices could be useful to detect water stress in potato plants?\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Plant Material and Experimental design\u003c/h2\u003e\n \u003cp\u003eThe experiment was carried out in the AGROSAVIA (Corporaci\u0026oacute;n Colombiana de Investigaci\u0026oacute;n Agropecuaria) Tibaitat\u0026aacute; research center, Colombia (4\u0026ordm; 41\u0026rsquo; 25.7064\u0026rsquo;\u0026rsquo; N, 74\u0026ordm; 12\u0026rsquo; 08.23\u0026rsquo;\u0026rsquo; W) at greenhouse conditions. To determine the effect of water stress on spectral responses and gas exchange of potato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L. \u0026ldquo;Diacol Capiro\u0026rdquo;) plants, a randomized complete block design distributed in 3 blocks, each one with 7 treatments was established. Each treatment was the combination of one stress level (light, moderate and severe) and two plant growth stages: tuber differentiation (TD) and maximum tuberization (MT), plus a control group (well hydrated plants). Potato plants were sown in a loam soil that was kept at field capacity (soil water potential did not decline below 0.033 MPa) by drip irrigation from sowing until each growth stage was reached. At each growth stage, the stress treatments were induced by suspending irrigation until reaching each level of stress. The water stress levels were determined based on [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] and using the xylem water potential (\u0026psi;w) measured in the terminal leaflet of the 3rd or 4th fully expanded leaf. Control plants (well-watered plants) had a water potential ranging from 0 to -0.49 MPa, the light water stress treatment corresponded to a \u0026psi;\u003csub\u003ew\u003c/sub\u003e range of -0.50 to -0.59MPa, moderate water stress has a \u0026psi;\u003csub\u003ew\u003c/sub\u003e between \u0026minus;\u0026thinsp;0.60 to -0.89 MPa and severe water stress was defined as \u0026psi;\u003csub\u003ew\u003c/sub\u003e lower than \u0026minus;\u0026thinsp;0.90 MPa.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Leaf-level measurements\u003c/h2\u003e\n \u003cp\u003eOnce potato plants reached the tuber differentiation and maximum tuberization growth stages (at ninth and thirteenth week after sowing, respectively), daily xylem water potential (\u0026psi;\u003csub\u003ew\u003c/sub\u003e), net photosynthesis (A), stomatal conductance (g\u003csub\u003es\u003c/sub\u003e), and transpiration (E) measurements were carried out from stress induction until each stress level was reached. Pre-dawn xylem water potential (\u003cem\u003e\u0026psi;\u003c/em\u003e\u003csub\u003e\u003cem\u003ew\u003c/em\u003e\u003c/sub\u003e) was determined, always between 4:00 am and 7:00 am [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] using a Scholander type pressure chamber, pump-up model (PMS Instrument Company, Oregon, USA). For this purpose, the terminal leaflet from the third or fourth developed leaves was removed and measured within two minutes after being removed from the plant. Control plants were evaluated as well. Gas exchange was recorded using an infrared gas analyzer IRGA LICOR 6800 IRGA system (LI-COR Biosciences, Lincoln, Nebraska USA) between 9:00 a.m. and 11:00 a.m., using a CO\u003csub\u003e2\u003c/sub\u003e concentration of 400 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Spectral Reflectance Measurements\u003c/h2\u003e\n \u003cp\u003eSpectral images were taken at 3m above the plant\u0026rsquo;s canopy level with the camera looking downwards. The image acquisition campaigns were done at around the same hour of the day using a camera with 520\u0026times;696 pixels and 128 spectral bands in the 400\u0026ndash;1000 nm range (710-VP Surface Optics Corporation). A Spectralon reflectance white panel was used on each image to convert the hyperspectral intensity images to reflectance. To segment the white spectralon panel from the spectral image, the average of the red, green, blue, and NIR bands was computed and divided by the maximum intensity. The Spectralon reflectance panel was segmented from the image using a threshold above 0.5.\u003c/p\u003e\n \u003cp\u003eThe reflectance of each spectral imagery was computed using:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\rho\\:\\left(x,y,\\lambda\\:\\right)=\\frac{I\\left(x,y,\\lambda\\:\\right){\\rho\\:}_{S}\\left(\\lambda\\:\\right)}{Is\\left(\\lambda\\:\\right)}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\rho\\:\\left(x,y,\\lambda\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e is the reflectance image at pixel coordinates \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x,y\\)\u003c/span\u003e\u003c/span\u003e and waveband \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I\\left(x,y,\\lambda\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e is the raw intensity image at pixel coordinates \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x,y,\\)\u003c/span\u003e\u003c/span\u003e and waveband \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{S}\\left(\\lambda\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e the known reflectance of the Spectralon panel at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e wavelength (0.99 at visible and NIR ranges) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Is\\left(\\lambda\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e the mean intensity of the Spectralon panel at waveband \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\lambda\\:\\)\u003c/span\u003e\u003c/span\u003e. To segment the canopy from its background, the Soil-Adjusted Vegetation Index (SAVI) was used following the methodology reported by Duarte-Carvajalino et al. (2021) [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] Two image campaigns on tubers differentiation and maximum tuberization growth stages were performed, acquiring images starting from the water supply suspension to each level of stress (Light, Moderate, Severe). The 710-VP camera records spectral information in the full wavelength range of 400\u0026ndash;1000 nm, however only the 400\u0026ndash;760 nm range was used for the study. Simple and multiple linear regressions between xylem water potential (\u003cem\u003e\u0026psi;\u003c/em\u003e\u003csub\u003e\u003cem\u003ew\u003c/em\u003e\u003c/sub\u003e), gas exchange variables (A\u003csub\u003en\u003c/sub\u003e, E and g\u003csub\u003es\u003c/sub\u003e) and reflectance responses in terms of specific bands and vegetation indices (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) were performed. Pearson correlation coefficient (r) and coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) were used to explore the significant relationships between all parameters and indices.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003e3.1\u0026nbsp;\u003c/em\u003e\u003cem\u003eSpectral behavior\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In general, under water stress conditions, the decrease in water potential induces changes in the optical properties of the potato canopy, which are observed as increases in reflectance across the visible spectrum. A xylem water potential lesser than -0.7 MPa caused a more evident increase in reflectance, mainly in the wavelength ranges of 520\u0026ndash;560 nm and 580\u0026ndash;750 nm (Figure 1). The most noticeable changes were observed mainly in the regions surrounding green (520\u0026ndash;570 nm), red (650\u0026ndash;660 nm) and red edge (beyond 700\u0026ndash;750 nm) during the maximum tuberization phase and at all intensities of water stress (Figure 1). However, under moderate and severe stress, the increases in reflectance around 640 \u0026ndash; 660 nm (red region) are more easily appreciable. The increase in reflectance in the 520 \u0026ndash; 570 nm region (green region), was more evident, mainly during maximum tuberization. In the region between 700 \u0026ndash; 750 nm (region around red edge), the curves corresponding to more negative water potentials showed a greater orientation towards the vertical, indicating a higher slope (Figure 1). In the violet-blue region (400 \u0026ndash; 500 nm), the changes in reflectance caused by more negative water potentials are mainly noticeable in the tuber differentiation phase during moderate and severe water stress. In the maximum tuberization phase, changes in the violet-blue region caused by variations in water potential are not clearly observed.\u003c/p\u003e\n\u003cp\u003e3.2. \u003cem\u003e\u0026nbsp;Relationship between\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026psi;\u003c/em\u003e\u003cem\u003e\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e\u003cem\u003e, An, g\u003csub\u003es\u003c/sub\u003e, E and the main absorption bands in the visible spectrum\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In general, the 550 nm, and 670 nm bands behaved similarly for the TD and MT growth stages (correlation coefficients \u0026gt;0.6). The 710 nm band showed the greatest variation associated with changes in water potential (Figure 2). The relationship between the xylem water potential and the main pigment absorption bands indicates that the reflectance increases in a non-linear way with the progressive decrease in the water potential of the xylem. In other words, the more water stress, the greater the increase in reflectance in the main pigment absorption bands.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The same behavior was observed in the relationship between net photosynthesis (An) and the main pigment absorption bands reflectance. Reflectance increases with the decrease in the photosynthetic rate. \u0026nbsp;The response in both growth phases was similar in the 550 nm and 710 nm bands, with their determination and correlation coefficients (\u0026gt;0.5 and \u0026gt;0.7, respectively) being higher compared to those of the 440 nm and 670 nm bands. The greatest variation in the reflectance response to changes in A\u003csub\u003en\u003c/sub\u003e is observed mainly in the 710 nm band in both growth phases. The trend of increasing reflectance of the main pigment absorption bands associated with a decrease in stomatal conductance (g\u003csub\u003es\u003c/sub\u003e) was observed in both growth phases in the 550 and 710 nm bands. However, the largest reflectance variations for both phases were most evident in the 710 nm band. \u0026nbsp;Red edge and red were important bands for DT and MT, respectively, and again, the results indicate that the green region is more sensitive to changes in stomatal closure. \u0026nbsp;Even though there is a trend of increasing reflectance in non-linear way with the progressive decrease in transpiration in the 710 nm band, the responses are generally different for both growth phases. \u0026nbsp;During tuber differentiation, all the examined bands showed Pearson\u0026rsquo;s correlation and determination coefficient values greater than 0.5 and 0.7, respectively. \u0026nbsp;However, the 710 nm band showed a greater association with changes in E, with r and R\u0026sup2; values of 0.87 and 0.76, respectively. In contrast, during maximum tuberization, the 710 nm band showed low values for the adjustment coefficients. In this phase, the greatest association of reflectance with changes in E was observed in the 550 nm and 670 nm bands. These bands exhibited similar behavior in both growth phases, considering the values of the adjustment coefficients r and R\u003csup\u003e2\u003c/sup\u003e (Figure 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.3. \u003cem\u003e\u0026nbsp;Relationship between vegetation indices and\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026psi;\u003c/em\u003e\u003cem\u003e\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e\u003cem\u003e, An, g\u003csub\u003es\u003c/sub\u003e and E\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2 shows the correlation coefficients (r) and coefficients of determination (R\u0026sup2;) for simple and multiple linear regression models between different indices and gas exchange variables (\u003cem\u003e\u0026psi;\u003c/em\u003e\u003cem\u003e\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e, An, g\u003csub\u003es,\u003c/sub\u003e and E). \u0026nbsp;Dark gray represents higher R\u0026sup2; values (\u0026gt;0.75), while light gray represents R\u0026sup2; values between 0.5 and 0.75. In Maximum tuberization (MT), the highest positive correlations were observed for relationships between mRESR and \u003cem\u003e\u0026psi;\u003c/em\u003e\u003cem\u003e\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e; PRI and \u003cem\u003e\u0026psi;\u003c/em\u003e\u003cem\u003e\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e; SRPI and g\u003csub\u003es\u003c/sub\u003e; TVI and \u003cem\u003e\u0026psi;\u003c/em\u003e\u003cem\u003e\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e, An; NPQI and An, g\u003csub\u003es\u003c/sub\u003e, E; BGI2 and An, g\u003csub\u003es\u003c/sub\u003e; BGI and \u003cem\u003e\u0026psi;\u003c/em\u003e\u003cem\u003e\u003csub\u003ew\u003c/sub\u003e\u003c/em\u003e, An, g\u003csub\u003es\u003c/sub\u003e and E; BRI and An, g\u003csub\u003es\u003c/sub\u003e, E. \u0026nbsp;For tuber differentiation, only the relationships between BRI and \u0026psi;w, and BGI2 and An showed r and R\u0026sup2; values greater than 0.75. \u0026nbsp;Even though most indices are not strongly explained by variations in the photosynthetic parameters during the two evaluated growth stages, the BRI and BGI2 indices generally show higher correlation and determination values (\u0026gt;0.75), or at least greater than 0.5 for all photosynthetic parameters and across both growth stages. The other indices generally showed weaker relationships, with r and R\u003csup\u003e2\u003c/sup\u003e \u0026lt;= 0.49.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn general, the 400R/690R and 450R/R550R ratios exhibited correlations with the xylem water potential (Figure 4). The correlation coefficients in both growth stages were higher than 0.8, indicating a strong association between the variables. \u0026nbsp;However, the behavior of BRI and BGI differs between the two crop growth stages due to the correlation slopes having opposite signs. In the 400R/690R, at tuber differentiation phase, an increase of index value over 1.5, indicates the beginning of more negative water potentials with an approximate threshold of -0.6 Mpa. In contrast, for the maximum tuberization phase, index values below 1.5 indicate water stress potentials (\u0026lt;-0.6 Mpa). Regarding the 450R/550R ratio, an index threshold of 0.6 (higher or lower) indicates the beginning of more negative water potentials (Figure 4).\u003c/p\u003e\n\u003cp\u003eThe BRI and BGI2 indices not only exhibited a strong correlation with net photosynthesis (An) in both growth stages (DT and MT), but also a high coefficient of determination (R\u0026sup2; \u0026gt; 0.8). \u0026nbsp;Therefore, these indices appear to be suitable for assessing the impact of leaf water potential on photosynthesis (Figure 5). \u0026nbsp;Again, the behavior of BRI and BGI differs between the two crop growth stages. In tuber differentiation, a decrease in photosynthesis is matched by an increase in the value of the indices. In contrast, during maximum tuberization, the positive correlation indicates that low values of photosynthesis correspond to low values of the indices.\u003c/p\u003e\n\u003cp\u003eThe lowest correlation was observed between the 400\u003csub\u003eR\u003c/sub\u003e/690\u003csub\u003eR\u003c/sub\u003e and 450\u003csub\u003eR\u003c/sub\u003e/R550\u003csub\u003eR\u003c/sub\u003e ratios and the stomatal conductance during the tuber differentiation (TD) phase. In contrast, the 400\u003csub\u003eR\u003c/sub\u003e/690\u003csub\u003eR\u003c/sub\u003e ratio presented the highest correlation and determination coefficients (0.9 and 0.89, respectively) in maximum tuberization (MT). \u0026nbsp;Like the An-BRI/BGI relationship, the behavior of both indices differs between the two crop growth stages due to the correlation slopes having opposite signs (Figure 6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe same behavior was observed for the correlations between transpiration and the ratios 400\u003csub\u003eR\u003c/sub\u003e/690\u003csub\u003eR\u003c/sub\u003e and 450\u003csub\u003eR\u003c/sub\u003e/R550\u003csub\u003eR\u003c/sub\u003e, again the relation 400\u003csub\u003eR\u003c/sub\u003e/690\u003csub\u003eR\u003c/sub\u003e presented the highest coefficients of correlation and determination (0.9 and 0.89, respectively) in maximum tuberization, which is expected considering the close relationship between the variables E and g\u003csub\u003es\u003c/sub\u003e.\u003csub\u003e\u0026nbsp;\u003c/sub\u003eAgain, a negative correlation between the indices and transpiration is observed during the DT phase while a positive correlation is observed during the MT phase (Figure 7).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eData analysis in this study indicated that progressive water stress caused changes in photosynthesis and in the optical properties of potato canopy. The spectral curves showed some slight variations in the regions around 550 nm and 710 nm, but distinguishing these differences from well-hydrated plants is not easy. Under moderate and severe water stress events, the impact on reflectance is much more evident. Although increases in reflectance are observed in the violet-blue region (400\u0026ndash;500 nm), considerable increases in the canopy reflectance of stressed plants are mainly observed in the region from 530 nm to 570 nm, red (660 nm \u0026ndash; 670 nm) and red edge around 700 nm. Typically, absorption in the red range is high due to the action of both chlorophyll-a and -b. On the other hand, the sensitivity of absorption and reflectance in the green and red edge spectral regions is higher than in the blue and red regions of the spectrum [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, changes in the reflectance of the absorption bands at 550 and 710 nm were related to changes in the gas exchange process under water stress conditions. The decrease in water potential, An, g\u003csub\u003es\u003c/sub\u003e and E was related to an increase in reflectance at 710 and 550 nm, indicating that reflectance in these bands could serve as an indirect reading of possible stomatal limitations for water and CO\u003csub\u003e2\u003c/sub\u003e at different levels of water stress. Under light or moderate drought stress, stomata close rapidly, resulting in a decrease in stomatal conductance, transpiration, and net photosynthesis [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe functional relationships between the BRI and BGI2 vegetation indices and gas exchange parameters followed third-order polynomials, demonstrating the non-linear nature of these relationships. Nonlinear relationships were also found by [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] for BRI and BGI indices in vine. Changes in the 400R/690R and 450R/R550R ratios were more evident under more negative water potential during severe water stress events. The correlation results during the tuber differentiation phase (DT) indicate a trend of increasing 400R/690R and 450R/550R ratio values as the water potential becomes more negative due to intensified water stress. This is because, even when the reflectance at 690 nm and 550 nm increases due to the lower absorption in these bands, the increase in the 400\u0026ndash;450 nm region is much greater, which can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThis increase in reflectance values in the blue-violet region, inverts the sense of the correlations during DT, generating a negative association between the variables. During maximum tuberization (MT), the process of chlorophyll breakdown (during complete leaf dehydration) may cause a corresponding decreasing 400R/690R and 450R/550R ratios, since chlorophyll contributes mainly to the 650 nm absorption band [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and the higher sensitivity of the 550 nm band with chlorophyll contents. As it can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, unlike tuber differentiation, during the maximum tuberization phase, there are not such evident increases in the 400\u0026ndash;450 nm region, which maintains a positive correlation between the variables.\u003c/p\u003e \u003cp\u003eThe blue/green/red ratio indices included blue/green indices (BGI1\u0026thinsp;=\u0026thinsp;400R/R550R; BGI2\u0026thinsp;=\u0026thinsp;450R/R550R) and blue/red indices (BRI1\u0026thinsp;=\u0026thinsp;400R/690R; BRI2\u0026thinsp;=\u0026thinsp;450R/690R). These indices were initially proposed and used by [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] for monitoring the physiological condition of \u003cem\u003eVitis vinifera\u003c/em\u003e L. and for the remote detection of water stress in a citrus orchard, finding that BGI1 index (blue/green ratio) showed the highest correlation for both g\u003csub\u003es\u003c/sub\u003e (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.62) and Ψ\u003csub\u003ew\u003c/sub\u003e (R2\u0026thinsp;=\u0026thinsp;0.49), suggesting it could serve as a good estimator of water stress. In this study, both the blue/green and the blue/red indices showed correlations greater than 0.7 and R\u003csup\u003e2\u003c/sup\u003e greater than 0.75 with the xylem water potential, stomatal conductance (g\u003csub\u003es\u003c/sub\u003e), net photosynthesis (A\u003csub\u003en\u003c/sub\u003e), transpiration (E), which postulates them as good indicators of water stress in potato. These results are consistent with the correlations found between gas exchange parameters and the specific absorption bands at 550 nm, 670 nm, and 710 nm (section 3.2), where the association and dependence of these spectral regions on variations in xylem water potential, stomatal conductance (gₛ), net photosynthesis (Aₙ), and transpiration (E) are evident.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eAlthough reflectance changes in the visible spectrum naturally reflect non-stomatal limitations, the results of this study demonstrated an association and dependence between different portions of the visible range and variables that characteristically indicate stomatal-type limitations, such as decreased transpiration and stomatal closure. Therefore, spectral analysis in the visible range could serve as an indirect indicator of stomatal limitations to photosynthesis during stress events.\u003c/p\u003e \u003cp\u003eThe BRI and BGI indices, with ratios of 400R/690R and 450R/550R, respectively, exhibited the strongest association and dependence with gas exchange variables under different levels of water stress and in both growth stages. The results demonstrate the potential of the 550 nm and 710 nm bands, as well as the BRI and BGI indices, for detecting water stress in potato and reflecting both stomatal and non-stomatal limitations to photosynthesis under water-limited conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, F.E.M.-M.; methodology, F.E.M.-M., J.M.D.C.,\u003c/p\u003e\n\u003cp\u003eA.M.C.-M., G.A.G.-V. and E.A.S.A.; software, G.A.G.-V. and J.M.D.C.; formal analysis, F.E.M.-M.; investigation, F.E.M.-M., A.M.C.-M., J.M.D.C., E.A.S.A.; G.A.G.-V. and J.A.M.B; resources, A.M.C.-M.; data curation, F.E.M.-M. and J.M.D.C.; writing—original draft, F.E.M.-M.; writing—review and editing, F.E.M.-M., J.M.D.C., A.M.C.-M., J.A.M.B., G.A.G.-V. and E.A.S.A.; supervision, A.M.C.-M.; project administration, A.M.C.M.; funding acquisition, A.M.C.-M. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by the Fondo de Ciencia, Tecnología e Innovación del Sistema General de Regalías, administered by the Fondo Nacional de Financiación para Ciencia, Tecnología e Innovación—Francisco José de Caldas, Programa Colombia BIO, Gobernación de Cundinamarca and Ministerio de Ciencia, Tecnología e Innovación (MINCIENCIAS) funding number 66153, and Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA) funding number TV19 1000911.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThis work is part of a larger project in Corporación Colombiana de Investigación Agropecuaria (AGROSAVIA) named Sistema de Información Agroclimática del cultivo de la papa en la región de Cundinamarca, Colombia (SIAP).We thank Óscar Dubán Ocampo Páez, for their contribution in the equipment installation process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMaimaitiyiming M, Ghulam A, Bozzolo A, et al (2017) Early Detection of Plant Physiological Responses to Different Levels of Water Stress Using Reflectance Spectroscopy. 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Remote Sens Environ 117:322\u0026ndash;337. https://doi.org/10.1016/j.rse.2011.10.007\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Agrosavia","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"canopy reflectance, water stress, leaf gas exchange, vegetation indices","lastPublishedDoi":"10.21203/rs.3.rs-6580783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6580783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe analysis of the plant's spectral signature makes it possible to identify specific metabolic responses through variations in reflectivity, measured from high-resolution spectral images, at both canopy and vegetation levels. In potato, reflectance variations and their relationship with photosynthetic behavior under water stress have been scarcely studied, particularly regarding stress assessments based on xylem water potential. In this study, the relationships between xylem water potential, changes in net photosynthesis, stomatal conductance, and transpiration, as well as variations in spectral response in the visible region considering specific bands and vegetation indices, were analyzed. Spectral measurements of light reflectance in the VIS region at canopy level, water potential, and leaf gas exchange parameters were performed at tuber differentiation and maximum tuberization phenological stages under three intensities of water stress (light, moderate and severe). Under moderate and severe water stresses, increases in canopy reflectance were observed from 530 to 570 nm, 660 – 670 nm and around 700 nm. The 400R/690R and 450R/550R ratios showed the strongest association and dependence with gas exchange variables under different water stress levels, in both growth stages. These results contribute to the monitoring of photosynthetic performance and the detection of water stress events in potato plants.\u003c/p\u003e","manuscriptTitle":"Physiological features of Potato (Solanum tuberosum L. Var. Diacol Capiro) canopy reflectance changes under different levels of water stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 13:08:43","doi":"10.21203/rs.3.rs-6580783/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ad86dc7-ee35-431a-b323-0ce02ae0fb04","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48013157,"name":"Agricultural Engineering"},{"id":48013158,"name":"Horticulture"},{"id":48013159,"name":"Biophysics"}],"tags":[],"updatedAt":"2025-05-06T13:08:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 13:08:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6580783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6580783","identity":"rs-6580783","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}