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Research Article | Open Access

Practical Considerations and Limitations of Using Leaf and Canopy Temperature Measurements as a Stomatal Conductance Proxy: Sensitivity across Environmental Conditions, Scale, and Sample Size

Ismael K. Mayanja1Christine H. Diepenbrock2Vincent Vadez3Tong Lei2Brian N. Bailey2( )
Department of Biological Systems Engineering, University of California, Davis, Davis, CA, USA
Department of Plant Sciences, University of California, Davis, Davis, CA, USA
French National Research Institute for Sustainable Development (IRD), UMR DIADE, University of Montpellier, Montpellier, France
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Abstract

Stomatal conductance (gs) is a crucial component of plant physiology, as it links plant productivity and water loss through transpiration. Estimating gs indirectly through leaf temperature (Tl) measurement is common for reducing the high labor cost associated with direct gs measurement. However, the relationship between observed Tl and gs can be notably affected by local environmental conditions, canopy structure, measurement scale, sample size, and gs itself. To better understand and quantify the variation in the relationship between Tl measurements to gs, this study analyzed the sensitivity of Tl to gs using a high-resolution three-dimensional model that resolves interactions between microclimate and canopy structure. The model was used to simulate the sensitivity of Tl to gs across different environmental conditions, aggregation scales (point measurement, infrared thermometer, and thermographic image), and sample sizes. Results showed that leaf-level sensitivity of Tl to gs was highest under conditions of high net radiation flux, high vapor pressure deficit, and low boundary layer conductance. The study findings also highlighted the trade-off between measurement scale and sample size to maximize sensitivity. Smaller scale measurements (e.g., thermocouple) provided maximal sensitivity because they allow for exclusion of shaded leaves and the ground, which have low sensitivity. However, large sample sizes (up to 50 to 75) may be needed to differentiate genotypes. Larger-scale measurements (e.g., thermal camera) reduced sample size requirements but include low-sensitivity elements in the measurement. This work provides a means of estimating leaf-level sensitivity and offers quantitative guidance for balancing scale and sample size issues.

References

1

Brodribb TJ, Holbrook MA, Zwieniecki NM, Palma B. Leaf hydraulic capacity in ferns, conifers and angiosperms: Impacts on photosynthetic maxima. New Phytol. 2005;165(3): 839–846.

2

Buckley TN. How do stomata respond to water status? New Phytol. 2019;224(1): 21–36.

3

Sinclair TR, Tanner CB, Bennett JM. Water-use efficiency in crop production. Bioscience. 1984;34(1): 36–40.

4

Messina CD, Sinclair TR, Hammer GL, Curan D, Thompson J, Oler Z, Gho C, Cooper M. Limited-transpiration trait may increase maize drought tolerance in the us corn belt. Agron. J. 2015;107(6): 1978–1986.

5
Raymundo R, Mclean G, Sexton-Bowser S, Morris GP. Cropmodeling suggests limited transpiration would increase yieldof sorghum across drought-prone regions of the United States.bioRxiv. 2023. https://doi.org/10.1101/2023.06.27.546776.
6

Flexas J, Medrano H. Drought-inhibition of photosynthesis in C3 plants: Stomatal and non-stomatal limitations revisited. Ann. Bot. 2002;89(2):183–189.

7

Kumar R, Solankey SS, Singh M. Breeding for drought tolerance in vegetables. Veget Sci. 2012;39(1):1–15.

8

Hummel M, Hallahan BF, Brychkova G, Ramirez-Villegas J, Guwela V, Chataika B, Curley PC. Reduction in nutritional quality and growing area suitability of common bean under climate change induced drought stress in africa. Sci. Rep. 2018;8(1):16187.

9

Cooper M, Technow F, Messina C, Gho C, Totir LR. Use of crop growth models with whole-genome prediction: Application to a maize multienvironment trial. Crop. Sci. 2016;56(5):2141–2156.

10

Jones HG. Irrigation scheduling: Advantages and pitfalls of plant-based methods. J. Exp. Bot. 2004;55(407):2427–2436.

11
Clark RN, Brauer DK. Overview of ogallala aquifer program. Paper presented at: 5th National Decennial Irrigation Conference Proceedings; 2010 December 5–8; Phoenix, Arizona, USA.
12
Pietragalla J, Pask A. In: Pask A, Pietragalla J, Mullan D, Reynolds M, editors. Stomatal conductance. Physiological breeding Ⅱ: A field guide to wheat phenotyping. México: CIMMYT; 2012. p. 15–17.
13

Pallas JE Jr, Michel BE, Harris DG. Photosynthesis, transpiration, leaf temperature, and stomatal activity of cotton plants under varying water potentials. Plant Physiol. 1967;42(1):76–88.

14

Jackson RD, Kustas WP, Choudhury BJ. A reexamination of the crop water stress index. Irrig Sci. 1988;9(4):309–317.

15

Idso SB. Non-water-stressed baselines: A key to measuring and interpreting plant water stress. Agric Meteorol. 1982;27(1–2):59–70.

16

Jackson RD, Idso SB, Reginato RJ, Pinter PJ Jr. Canopy temperature as a crop water stress indicator. Water Resour. Res. 1981;17(4):1133–1138.

17

Jones HG. Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agric. For. Meteorol. 1999;95(3):139–149.

18

Grant OM, Tronina L, Jones HG, Chaves MM. Exploring thermal imaging variables for the detection of stress responses in grapevine under different irrigation regimes. J. Exp. Bot. 2007;58(4):815–825.

19

Poirier-Pocovi M, Bailey BN. Sensitivity analysis of four crop water stress indices to ambient environmental conditions and stomatal conductance. Sci. Hortic. 2020;259: Article 108825.

20

Vialet-Chabrand S, Lawson T. Thermography methods to assess stomatal behaviour in a dynamic environment. J. Exp. Bot. 2020;71(7):2329–2338.

21
Figliola RS, Beasley DE. Theory and design for mechanical measurements. New Jersey, USA: John Wiley & Sons; 2020.
22

Bailey BN, Stoll R, Pardyjak ER, Miller NE. A new three-dimensional energy balance model for complex plant canopy geometries: Model development and improved validation strategies. Agric. For. Meteorol. 2016;218–219:146–160.

23
Campbell GS, Norman JM. An introduction to environmental biophysics. New York, USA: Springer-Verlag; 1998.
24
Dauzat J, Franck N, Rapidel B, Luquet D, Vaast P. Simulation of ecophysiological processes on 3d virtual stands with the ARCHIMED simulation platform. Paper presented at: 2006 Second International Symposium on Plant Growth Modeling and Applications; 2006 Nov 13–17; Beijing, China.
25

Hemmerling R, Kniemeyer O, Lanwert D, Kurth W, Buck-Sorlin G. The rule-based language xl and the modelling environment groimp illustrated with simulated tree competition. Funct. Plant Biol. 2008;35(10):739–750.

26

Albasha R, Fournier C, Pradal C, Chelle M, Prieto JA, Louarn G, Simonneau T, Lebon E. HydroShoot: A functional-structural plant model for simulating hydraulic structure, gas and energy exchange dynamics of complex plant canopies under water deficit—Application to grapevine (Vitis vinifera). In Silico Plants. 2019;1(1):diz007.

27

Wang Y, Kallel A, Yang X, Regaieg O, Lauret N, Guilleux J, Chavanon E, Gastellu-Etchegorry J-P. DART-lux: An unbiased and rapid Monte Carlo radiative transfer method for simulating remote sensing images. Remote Sens. Environ. 2022;274: Article 112973.

28

Qi J, Xie D, Yin T, Yan G, Gastellu-Etchegorry J-P, Li L, Zhang W, Xihan M, Norford LK. LESS: LargE-scale remote sensing data and image simulation framework over heterogeneous 3D scenes. Remote Sens. Environ. 2019;221:695–706.

29

Bailey BN. Helios: A scalable 3D plant and environmental biophysical modeling framework. Front. Plant Sci. 2019;10:1185.

30

Bailey BN. A reverse ray-tracing method for modelling the net radiative flux in leaf-resolving plant canopy simulations. Ecol. Model. 2018;368:233–245.

31
Suffern K. Ray tracing from the ground up. Boca Raton, USA: CRC Press; 2016.
32

Bouchet S, Olatoye MO, Marla SR, Perumal R, Tesso T, Yu J, Tuinstra M, Morris GP. Increased power to dissect adaptive traits in global sorghum diversity using a nested association mapping population. Genetics. 2017;206(2):573–585.

33

Bailey BN, Kent ER. On the resolution requirements for accurately representing interactions between plant canopy structure and function in three-dimensional leaf-resolving models. In Silico Plants. 2021;3(2):diab023.

34
Hsu J. Multiple comparisons: Theory and methods. Boca Raton, USA: CRC Press; 1996.
35

Irmak S, Mutiibwa D, Irmak A, Arkebauer TJ, Weiss A, Martin DL, Eisenhauer DE. On the scaling up leaf stomatal resistance to canopy resistance using photosynthetic photon flux density. Agric. For. Meteorol. 2008;148(6–7):1034–1044.

36

Ding R, Kang S, Du T, Hao X, Zhang Y. Scaling up stomatal conductance from leaf to canopy using a dual-leaf model for estimating crop evapotranspiration. PLOS ONE. 2014;9(4): Article e95584.

37

Buckley TN, Mott KA. Modelling stomatal conductance in response to environmental factors. Plant Cell Environ. 2013;36(9):1691–1699.

38
Kothari CR. Research methodology: Methods and techniques. New Delhi, India: New Age International; 2004.
39

Woods HA, Saudreau M, Pincebourde S. Structure is more important than physiology for estimating intracanopy distributions of leaf temperatures. Ecol. Evol. 2018;8(10):5206–5218.

40

Baguley T. Understanding statistical power in the context of applied research. Appl. Ergon. 2004;35(2):73–80.

41

Vining RC, Blad BL. Estimation of sensible heat flux from remotely sensed canopy temperatures. J Geophys Res Atmos. 1992;97(D17):18951–18954.

42

Meron M, Tsipris J, Orlov V, Alchanatis V, Cohen Y. Crop water stress mapping for site-specific irrigation by thermal imagery and artificial reference surfaces. Precis Agric. 2010;11(2):148–162.

43

Poblete T, Ortega-Farías S, Ryu D. Automatic coregistration algorithm to remove canopy shaded pixels in UAV-borne thermal images to improve the estimation of crop water stress index of a drip-irrigated cabernet sauvignon vineyard. Sensors. 2018;18(2):397.

44

Zhang L, Niu Y, Zhang H, Han W, Li G, Tang J, Peng X. Maize canopy temperature extracted from UAV thermal and RGB imagery and its application in water stress monitoring. Front. Plant Sci. 2019;10:1270.

45

Jarvis PG, McNaughton KG. Stomatal control of transpiration: Scaling up from leaf to region. Adv Ecol Res. 1986;15:1–49.

46

Meinzer FC. Stomatal control of transpiration. Trends Ecol. Evol. 1993;8(8):289–294.

47

Jones HG. Use of thermography for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces. Plant Cell Environ. 1999;22(9):1043–1055.

48

Van Der Straeten, Chaerle L, Sharkov G, Lambers H, Van Montagu. Salicylic acid enhances the activity of the alternative pathway of respiration in tobacco leaves and induces thermogenicity. Planta. 1995;196:412–419.

49

Deery DM, Greg J, Rebetzke JA, Jimenez-Berni RA, James AG, Condon WD, Bovill P, Hutchinson J, Scarrow RD, Furbank RT. Methodology for high-throughput field phenotyping of canopy temperature using airborne thermography. Front. Plant Sci. 2016;7:1808.

50

Jones HG, Sirault XRR. Scaling of thermal images at different spatial resolution: The mixed pixel problem. Agronomy. 2014;4(3):380–396.

51

Ponce de León MA, Bailey BN. A 3D model for simulating spatial and temporal fluctuations in grape berry temperature. Agric. For. Meteorol. 2021;306: Article 108431.

52
Yol E, Toker C, Uzun B. Traits for phenotyping. In: Kumar J, Pratap A, Kumar S, editors. Phenomics in crop plants: Trends, options and limitations. New Delhi, India: Springer; 2015. p. 11–26.
53

Prashar A, Jones HG. Infra-red thermography as a high-throughput tool for field phenotyping. Agronomy. 2014;4(3):397–417.

54

Prata AJ. A new long-wave formula for estimating downward clear-sky radiation at the surface. Q. J. R. Meteorol. Soc. 1996;122(533):1127–1151.

55

Viswanadham Y. The relationship between total precipitable water and surface dew point. J Appl Meteorol Climatol. 1981;20(1):3–8.

56

Buckley TN, Turnbull TL, Adams MA. Simple models for stomatal conductance derived from a process model: Cross-validation against sap flux data. Plant Cell Environ. 2012;35(9):1647–1662.

57
Press WH, Teukolsky SA, Vetterling WT, Flannery BP. Numerical recipes 3rd edition: The art of scientific computing. New York, USA: Cambridge University Press; 2007.
58

Kustas WP, Norman JM. Evaluation of soil and vegetation heat flux predictions using a simple two-source model with radiometric temperatures for partial canopy cover. Agric. For. Meteorol. 1999;94(1):13–29.

59
J. A Dahlberg. Classifying the genetic diversity of sorghum: A revised classification of sorghum of California, USA; and DT Rosenow, formerly Agricultural Research and Extension Center–Texas A&M University, USA. In: Achieving sustainable cultivation of sorghum. Cambridge, UK: Burleigh Dodds Science Publishing; 2018. Vol. 1, p. 23–86.
Plant Phenomics
Article number: 0169
Cite this article:
Mayanja IK, Diepenbrock CH, Vadez V, et al. Practical Considerations and Limitations of Using Leaf and Canopy Temperature Measurements as a Stomatal Conductance Proxy: Sensitivity across Environmental Conditions, Scale, and Sample Size. Plant Phenomics, 2024, 6: 0169. https://doi.org/10.34133/plantphenomics.0169

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Received: 15 December 2023
Accepted: 13 March 2024
Published: 15 April 2024
© 2024 Ismael K. Mayanja et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).

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