Reviewer: Goldsmith GR7
Measurement unit: K (Kelvin); Measurement scale: leaf or canopy; Equipment costs: €€-€€€; Running costs: €-€€€; Installation effort: medium; Maintenance effort: medium; Knowledge need: medium; Measurement mode: manual or data logger
The temperature of plant tissues is determined both by environmental conditions (e.g. De Boeck et al., 2016) and by plant thermal traits that determine heat storage and fluxes (e.g. Michaletz et al., 2015); see protocol 5.6 Leaf thermal traits). As such, leaf temperatures can vary widely between environments, but also between plants growing under the same conditions and even within the canopy of a single plant (Leuzinger & Körner, 2007). Leaf temperatures directly impact metabolic rates and are coupled to the water cycle, thus affecting growth and fluxes of CO2 and H2O (Michaletz, 2018). Furthermore, leaf temperature is also important regarding interactions between plants and ectotherm herbivores (Caillon et al., 2014). Because leaf temperatures can deviate substantially from air temperatures (differences of > 30 K have been observed in extreme cases, (cf. Neuner & Buchner, 2012), it is imperative to measure leaf temperatures to avoid erroneous conclusions that may result from use of ambient air temperatures in analyses. This is especially relevant for studies of plant and ecosystem responses to projected changes in climate, hydrology, and land use, as well as other global-change agents. Changes in microclimate (e.g. air temperature, humidity, wind, soil moisture, radiation) together with plant thermal traits (see protocol 5.6 Leaf thermal traits) will determine whether leaf temperature variation will be weakened or strengthened relative to ambient levels (helping maintain leaves near metabolic optima). Under global change, high variation can lead to more frequent deviations from optima and higher incidence of temperature stress, or could also lift temperatures closer to the optimum in cold climates (e.g. Marchand et al., 2005). The measurement of leaf and canopy temperatures is therefore essential in helping to predict responses of plants to environmental change.
5.5.1 What and how to measure?
Most investigators are interested in measuring the leaf operating temperature, which is the temperature at which leaf physiological processes occur. Leaf operating temperatures can be determined through direct or indirect methods. Direct methods require either contact with the leaf (thermocouples), or are achieved remotely via non-contact thermal sensors (infrared thermometers or cameras). In either case, direct methods involve in situ measurement of leaves on intact live plants. The main indirect method involves measurement of stable isotopes of oxygen (δ18O) in plant tissue. This indirect method requires destructive sampling of plant tissues that are later processed and measured in the laboratory.
Thermocouples consist of a soldered bead connecting two wires that are each composed of a different metal alloy. As a result of the thermoelectric effect, the thermocouple bead will produce a voltage that is a nonlinear function of temperature. Calibration relationships allow conversion between thermocouple bead temperature and measured voltages. Thermocouples attached to an in situ leaf will thus measure the leaf temperature after a period of equilibration (e.g. Hall et al., 2014).
An infrared thermometer uses radiation theory to estimate skin (surface) temperature. Based on the emissivity of the object in question and the measurement of a portion of the emitted radiation, the object’s temperature is derived in accordance with the Stefan–Boltzmann law. The thermometer usually consists of a filter that permits infrared radiation to pass through and a thermopile detector, yielding two voltages from which the object’s surface temperature can be established. These detectors average the radiation fluxes over a certain angular field of view, and do not provide point measurements of temperature unless placed very close to the object in question.
Infrared cameras permit imaging and point measurements across a field of view. The images produced by such cameras are not of the visible portion of the spectrum (c. 0.4–0.7 µm), but instead of the thermal/infrared portion (cameras often capture the 7–14 µm band). The measurement principle is comparable to that of infrared thermometers, where a lens made of a material that is transparent to infrared radiation (such as germanium) is used to focus radiation on an infrared-sensitive detector array. A complete image is formed that contains surface temperature information at the pixel scale. Pixel values are a nonlinear function of radiation intensity. Various calibration relationships (assuming different object and atmospheric properties) can then be employed to convert pixel values to temperature (Aubrecht et al., 2016). These calibration relationships are more sophisticated than typically used for infrared thermometers. Images are often displayed in pseudo-colour to highlight temperature differences. Imaging software then allows the selection of regions of interest, such as single leaves in an image of a canopy, depending on the available resolution (e.g. Jerbi et al., 2015).
Finally, indirect measurements of leaf temperature can be achieved through the stable isotopes of oxygen (δ18O) of leaf tissue (see protocol 5.13 Stable isotopes of water for inferring plant function). These isotopes have different molecular masses, so that evaporation (i.e. phase change from liquid to vapour) of water molecules with a different number of neutrons requires different amounts of energy. The δ18O of leaf water is thus strongly correlated with the temperature of the leaf (though the primary temperature control is on source water, which the method accounts for). As such, δ18O has long been used for past climate reconstruction, but the method can also be applied to infer photosynthetically-weighted leaf temperatures over a period of time (e.g. the growing season) in existing vegetation (Helliker & Richter, 2008).
In the following, we focus on the advantages and disadvantages of these four methods.
Where to start
Aubrecht et al. (2016), Chelle (2005), Costa et al. (2013), Jones & Vaughan (2010), Jones et al. (2009)
Installation, field operation, maintenance, interpretation
a) Thermocouples are widely used for measuring leaf temperature, but to obtain accurate measurements there are several points that should be considered. Since thermocouple measurements are made in situ on leaves of intact live plants, it is important that the thermocouple does not affect any of the climate variables or thermal properties that control leaf temperature, such as solar radiation, leaf angle, or boundary layer development (see protocol 5.6 Leaf thermal traits). Care should be taken to avoid touching the leaf, moving the leaf out of its resting position, or shading the leaf, as these will influence the temperature of the leaf. Leaf temperature is generally measured on the abaxial side (bottom) of the leaf to avoid shading of solar radiation on the top of the leaf. It is also important to recognise that temperature measured with a thermocouple represents the temperature of the thermocouple bead and not necessarily the temperature of the leaf. Thus, care must be taken to ensure that the thermocouple temperature is in equilibrium with that of the leaf. This can be achieved using a fine-gauge bare wire thermocouple that will respond rapidly to temperature fluctuations (i.e. the thermocouple time constant should be smaller than that of the leaf). The thermocouple bead must also be maintained in direct contact with the leaf surface, which can be achieved in a number of ways. For short-term measurements, the thermocouple can simply be held against the leaf surface, but efforts should be made to prevent heat conduction from one’s hand down the thermocouple wires to the thermocouple bead. This can be accomplished by wearing an insulated glove or constructing a thermocouple handle from a fine wire or paper clip. Leaf thermocouple clamps may also be constructed (Slot et al., 2016), although these should be small and lightweight so as to not interfere with the thermal boundary layer or leaf angle. The thermocouple may also be threaded into the leaf (Hanson & Sharkey, 2001), although this may cause xylem embolisms that can reduce stomatal conductance and transpirational cooling. Finally, thermocouples may also be affixed to the leaf surface using porous tape (e.g. 3 M Transpore surgical tape; Slot et al., 2016) that permits gas exchange and transpirational cooling between the leaf and atmosphere. Thermocouples affixed with porous tape were found to agree closely with infrared sensor measurements (Slot et al., 2016).
Thermocouples have some advantages over other temperature measurement methods. For example, they measure temperature over a relatively small leaf area, and are thus suitable for measurements of individual leaves (including spatial variation within leaves or leaflets). Thermocouples measure leaf temperature via conduction from the leaf into the thermocouple bead and are thus immune to the view and emissivity issues that adversely affect non-contact sensors that rely on thermal radiation (infrared sensors and cameras). For this reason, thermocouples can also be used to measure leaf temperatures within the interiors of crowns and canopies, which are difficult if not impossible to measure using non-contact methods. Finally, thermocouples are relatively simple devices and are therefore durable in outdoor weather conditions. The main disadvantages of thermocouples are that they cannot measure average temperatures over large areas and must maintain good contact between the thermocouple bead and the leaf. The latter may be especially challenging when the application requires long-term measurements in outdoor weather conditions.
b) Infrared thermometers are a relatively cost-efficient method for remote in situ measurement of leaf or canopy temperatures, with the cost per sensor amounting to a fraction (< 5%) of the cost of an infrared camera (Martínez et al., 2017). As such, they can be considered the bronze standard in (non-contact) thermography. Infrared thermometers give instantaneous temperature values that, depending on the view angle of the sensor (usually between 10 and 90 °) and the distance to the object, permit the determination of the average temperature of individual leaves or full canopies. Aligning a regular camera with the sensor can aid in making sure the correct area or object is monitored (Martínez et al., 2017). Measurements can be made by hand, in which case the variability of microenvironmental variables such as wind speed should be taken into account by taking multiple measurements spread over time. This is facilitated by the high speed of each measurement, with no equilibration required (in contrast to thermocouple readings). Scanning of larger areas can be achieved by attaching the sensor to an unmanned aerial vehicle (UAV), although data interpretation may be challenging when dealing with open canopies and/or vegetation with low ground cover, as well as temporal variation in microenvironmental conditions (cf. Martínez et al., 2017). For continuous monitoring, for example in the temperature control of infrared heating arrays (De Boeck et al., 2017), sensors can be installed on a support. Usually, measurements are taken perpendicular to the surface of interest. When measuring at an angle, the area will be elliptical and thus more difficult to control. The main disadvantage of infrared thermometers is that they provide a single value of temperature averaged over their entire field of view. This means that measurements are generally only possible for leaves situated on the exterior of a crown or canopy, and that a view that includes non-leaf bodies such as branches, stems, or the ground can lead to erroneous leaf temperature estimates. This can also mask important temperature variation in heterogeneous crowns and canopies.
c) Infrared cameras also perform remote in situ measurement of leaf or canopy temperatures, and are the gold standard of (non-contact) thermography. They have developed rapidly over the past two decades, helped by modern commercial applications such as home energy auditing. Although still expensive, infrared cameras with relatively high resolution (640 × 480 pixels) have seen their prices drop by 50% or more during the past ten years. The main advantage of infrared cameras is that they capture a lot of information within each image, which allows comparison of different objects (e.g. species, phenotypes) under the same ambient conditions. Of course, the radiation environment may differ significantly within the same image (Morecroft & Roberts, 1999). For example, the mean irradiance (and thus temperature) will be greatest for sun-leaves, while leaves deeper within the canopy will be primarily exposed to diffuse radiation scattered by other leaves and the soil (Jones et al., 2009). The orientation of the camera relative to the sun directly affects the readings, as alignment with the solar angle will yield a larger fraction of sun-lit leaves, and hence a higher surface temperature. This is described by the bidirectional reflectance distribution function (Liang, 2005). Depending on the research question, the camera can thus be used aligned with the solar angle at the time of measurement (taking care not to self-shade), or at a fixed angle (e.g. 45 °). If readings are taken at more than one angle, both the mean canopy and leaf temperature components can be calculated, as well as the sunlit and shaded leaf temperature components (Jia et al., 2003). Another factor to keep in mind regarding the camera angle is that a greater proportion of bare soil is captured when the angle is closer to being perpendicular to the soil surface. It is possible to use infrared cameras attached to UAVs (Martínez et al., 2017), although altitudinal corrections need to be applied to account for atmospheric radiation (Jones & Vaughan, 2010). As a general rule, the camera distance from objects in a comparison should be as similar as possible to avoid complications due to distance-induced bias (Faye et al., 2016). Because emissivity plays such an important role in the derivation of surface temperature, it should ideally be determined for the canopy under study (see protocol 5.6 Leaf thermal traits). For very distant vegetation, it is more appropriate to use blackbody values (Aubrecht et al., 2016). Infrared cameras can be suspended in one place and used to take automated measurements, so that a time series is created. The large amount of data thus recorded can be rendered manageable through batch-processing via image analysis software, although finer details (e.g. specific leaves in a vegetation) usually require researcher input as plant growth induces changes in their location within the image and/or changes in the radiation environment. Finally, calibrated temperatures can be affected by several factors, such as relative humidity of the air and sensor temperature, causing spatial or temporal variation (discussed in Aubrecht et al., 2016).
d) δ18O of cellulose can be used to estimate a photosynthetically-weighted leaf temperature over the period when the carbon in the cellulose was assimilated (Helliker & Richter, 2008). This is an indirect method that requires destructive sampling and laboratory measurement of plant tissues. Leaf water δ18O is influenced by source water δ18O and transpiration, both of which vary with climate variables (relative humidity and air temperature, VPD; Bögelein et al., 2017; Kahmen et al., 2011). During photosynthesis, transpiration will yield a leaf water δ18O that is in part dependent on leaf temperature. The leaf water δ18O is then incorporated into the sugars produced by photosynthesis, which ultimately become incorporated into the cellulose in plant tissue (Gessler et al., 2014). Thus, the δ18O of cellulose reflects the temperature integrated across the plant’s total leaf area and through the time period when source carbon was assimilated. Unlike point measurements, δ18O estimates provide an integrated measure of variation in leaf biophysics (energy balance and biochemistry) because they quantify the “effective temperature” driving rates of leaf-level metabolism and physiology. Thus, this method provides a more time-integrated measure of the average temperature at which net photosynthesis is most productive – i.e. an average temperature of leaf metabolism. Relative to thermocouples and IR sensors, the method is simple and low-cost, making δ18O estimates of leaf temperature especially useful for global change studies that span macroecological scales of time and space.
Leaf temperature is estimated by solving a model originally developed for estimating the δ18O of plant tissue (Barbour & Farquhar, 2000). This requires data for the cellulosic δ18O of the tissue of interest, the δ18O of plant source water, air temperature, and relative humidity. Depending on the tissue source (e.g. leaf v. tree-ring cellulose), additional fractionation must be accounted for due to exchange of oxygen isotopes among different water and carbohydrate pools (see protocol 5.13 Stable isotopes of water for inferring plant function). Leaf cellulose may yield more accurate estimates of photosynthetically-weighted leaf temperature than wood cellulose, because it has a shorter phloem path length over which post-photosynthetic oxygen exchange may occur. The δ18O of source water can either be directly measured for the plant-available water, or estimated from models for δ18O of precipitation (e.g. Bowen et al., 2017). Air temperature and relative humidity should be measured on-site at a fine temporal resolution (at least hourly) so that 24 h and daytime averages can be calculated. Ideally, these should be weighted by gross primary production (GPP), but if temporal estimates of GPP are unavailable, these can be resolved for 24 h and daytime using irradiance or photosynthetically active radiation (PAR) data.
Different plant tissues can be used to estimate photosynthetically-weighted leaf temperatures corresponding to different time periods. Shorter-term estimates may be obtained using leaf cellulose (Flanagan & Farquhar, 2014), which will integrate over the time that the leaf formed and the cellulose was synthesised. Longer-term estimates can be obtained from annual growth rings from branches or increment cores of woody plants. For example, annual growth rings can be homogenised to give long-term average leaf temperatures (Helliker & Richter, 2008; Song et al., 2011), while individual rings can be analysed in order to characterise inter- or intra-annual variation.
Preparation of the plant tissue and measurements of isotopic composition are described in protocol 5.13 Stable isotopes of water for inferring plant function)
5.5.2 Special cases, emerging issues, and challenges
In agronomy, there is much interest in using measurements of leaf temperature as a method to control precision irrigation as one image can reveal where soil water is deficient and where it is not (e.g. Padhi et al., 2012). Irrigation can thus be applied before visual stress is perceivable (Gerhards et al., 2016), which helps to avoid production losses. It should be noted that anisohydric species such as soybean and wheat are less suitable for such strategies due to the poor correlation between stomatal conductance (and therefore leaf temperature) and soil water status (cf. Costa et al., 2013). In such cases, leaf water potential is a much better (but less practical) indicator of soil water deficit. Apart from being an indicator of drought stress, increased leaf temperature can also be indicative of several plant diseases (e.g. the tobacco mosaic virus) before visual symptoms are apparent, as the infection changes water use and/or movement (Chaerle et al., 2004; Oerke et al., 2011).
A concept with various applications in ecology is the thermal time constant τ (s). This is a composite leaf trait that quantifies the thermal stability of a leaf, i.e. how rapidly leaf temperature responds to temporal variation in microclimate. It can be measured indirectly via leaf thermal traits (see protocol 5.6 Leaf thermal traits and Michaletz et al., 2015, 2016) or directly through periodic thermal forcing of a leaf or via step-changes in temperatures followed by curve-fitting through the ensuing time series data (Jones, 2014). Large time constants dampen leaf temperature variation relative to ambient and may help maintain leaves near metabolic optima (Michaletz et al., 2015, 2016), while small time constants result in stronger atmospheric coupling. The thermal time constant thus plays a fundamental role in water relations and net carbon gain of a leaf. It governs temporal variation of leaf temperature, which in turn influences instantaneous carbon assimilation rates (Berry & Bjorkman, 1980; Yamori et al., 2014) and ultimately time-integrated net carbon gain (Michaletz et al., 2015, 2016) and ecosystem carbon fluxes.
Many Earth system models make use of air temperatures, even though air and leaf temperatures may strongly differ. If models fail to correctly derive tissue temperatures, this may lead to significant error in outputs. For example, while models simulating heat impacts on rice production have accounted for heat dissipation through transpiration (van Oort et al., 2014), models widely applied for wheat have used simpler approaches such as air temperature thresholds (Alderman et al., 2014; Neukam et al., 2016). Moreover, most models are highly sensitive to input temperatures (Bassu et al., 2014; Neukam et al., 2016), meaning that more accurate consideration of leaf and canopy temperatures is of prime importance in correctly predicting climate-change impacts on plant functioning. Direct use of leaf temperatures in addition to air temperatures is likely to improve such predictions and can elucidate the exact role of temperature (Levis, 2014; Eyshi Rezaei et al., 2015). To that end, model routines need to be systematically evaluated and adapted (Webber et al., 2017) as these are currently often based on empirical data relating to air temperatures, while they lack information on how these translate into leaf and tissue temperatures (Neukam et al., 2016). More widespread monitoring of leaf temperatures, spurred by improvements in measurement technology and lowering of prices, will likely help move the focus from air to leaf temperatures in modelling.
Theory, significance, and large datasets
Aubrecht et al. (2016), Gessler et al. (2014), Helliker & Richter (2008), Michaletz et al. (2016)
More on methods and existing protocols
Costa et al. (2013), Doughty et al. (2011), Ehleringer (1981), Leigh et al. (2012), Pérez-Harguindeguy et al. (2013), Shiklomanov et al. (2016)
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Authors: Michaletz ST1,2,3, Blonder B4,5, De Boeck HJ6
Reviewer: Goldsmith GR7
1 Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, USA
2 Biosphere 2 and Department of Ecology & Evolutionary Biology, University of Arizona, Tucson, USA
3 Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, Canada
4 Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK
5 School of Life Sciences, Arizona State University, Tempe, USA
6 Centre of Excellence PLECO (Plants and Ecosystems), Department of Biology, Universiteit Antwerpen, Wilrijk, Belgium
7 Schmid College of Science and Technology, Chapman University, Orange, USA