Leaf temperature measurement techniques

Lomas, J.; Schlesinger, E.; Israeli, A.

doi:10.1007/bf00184783

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…Two ways are possible: direct or indirect measurement. Direct measurement consists of putting small sensors on each organ for radiation (Gutschick et al ., 1985) or temperature (Lomas et al ., 1971) (Fig. 5) or near each organ for air features (Grantz & Vaughn, 1999; Wang et al ., 1999).…”

Section: What Is Phylloclimate?mentioning

confidence: 99%

Phylloclimate or the climate perceived by individual plant organs: What is it? How to model it? What for?

Chelle

2005

New Phytologist

157

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SummaryThis review introduces the emergence of a new research topic, phylloclimate , located at the crossroads between ecophysiology and canopy microclimate research. Phylloclimate corresponds to the physical environment actually perceived by each individual aerial organ of a plant population, and is described by physical variables such as spectral irradiance, temperature, on-leaf water and features of around-organ air (wind speed, temperature, humidity, etc.). Knowing the actual climate in which plant organs grow may enable advances in the understanding of plant-environment interactions, as knowing surface temperature instead of air temperature enabled advances in the study of canopy development. Characterizing phylloclimate variables, using experimental work or modeling, raises many questions such as the choice of suitable space-and time-scale as well as the ability to individualize plant organs within a canopy. This is of particular importance when aiming to link phylloclimate and functionstructure plant models. Finally, recent trends and challenging questions in phylloclimate research are discussed, as well as the possible applications of phylloclimate results.New Phytologist (2005) 166 : 781-790 © New Phytologist (2005

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Section: What Is Phylloclimate?mentioning

confidence: 99%

Phylloclimate or the climate perceived by individual plant organs: What is it? How to model it? What for?

Chelle

2005

New Phytologist

157

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“…The air must therefore have been heated by foliage considerably warmer than itself. However, this conclusion has been disputed by Lomas et al [1971], and the difficulty in obtaining representative measurements is probably responsible for the uncertainty. If the model is rerun with the stomatal resistance increased by a factor of !00, but with all else the same, the maximum foliage temperature reaches a value 5.5 K warmer (12.0 K warmer for at = 1), which is an example of 'foliage fever.'…”

Section: Diagnose Tr From the Foliage Energy Budget Equation (32) By mentioning

confidence: 99%

Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation

Deardorff¹

1978

J. Geophys. Res

1,546

800

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An efficient time‐dependent equation for predicting ground surface temperature devised by Bhumralkar (1975) and Blackadar (1976) is tested against a 12‐layer soil model and compared with five other approximate methods in current use. It is found to be generally superior if diurnal forcing is present and very much superior to the use of the insulated surface assumption. An analogous method of predicting ground surface moisture content is presented which allows the surface to become moist quickly during rainfall or to become drier than the bulk soil while evaporation occurs. These improved methods are not of much relevance unless the main influences of a vegetation layer are included. An efficient one‐layer foliage parameterization is therefore developed that extends continuously from the case of no shielding of the ground by vegetation to complete shielding. It includes influences of both ground and foliage albedos and emissivities, net leaf area index, stomatal resistance, retained water on the foliage, and several other considerations. When it is tested against data for wheat measured by Penman and Long (1960), it appears quite adequate despite the many simplifying assumptions. The parameterization predicts that errors of up to a factor of 2 in evapotranspiration can be incurred by ignoring the presence of a vegetation layer.

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“…Frequency of radiation reflected by plant leaves (8970–9890 nm) is shifted towards longer wavelengths in respect to solar radiation (300–2000 nm) reaching their surface (Peñuelas and Filella 1998 ; IRT_protocol 2015 ). Thus, sun-exposed leaves’ temperature could rapidly rise by as much as 10 °C relative to shaded leaves (Ansari and Loomis 1959 ; Lomas et al 1971 ; SEOS 2015 ). Our data similarly indicated that the sun-exposed mines’ temperature exceeded air temperature by 10 °C (even 20 °C in a single measurement) within minutes.…”

Section: Discussionmentioning

confidence: 99%

Towards understanding Cameraria ohridella (Lepidoptera: Gracillariidae) development: effects of microhabitat variability in naturally growing horse-chestnut tree canopy

et al. 2021

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Dwelling intensity of horse-chestnut miner (Cameraria ohridella) larvae in various leaves insolation and temperature was measured to determine whether this pest’s development follows a predictable pattern or depends more on local microenvironment conditions. Mines growing on leaves of mature host plants (Aesculus hippocastanum L.) in their natural conditions were photographed for two consecutive generations of the pest and in two separated vegetation periods. Apart from meteorological data obtained from the nearest station, the temperature of intact and mined parts of sun-exposed and shaded leaf blades was measured at various daytimes throughout the experiment. Obtained sets of digital data were analysed and combined to model mine area growth as a function of degree-days sum by adopting of Verhulst logistic equation. We showed the predictive potential of our model based on experimental data, and it may be useful in the scheduling of pest control measures in natural conditions. Our analyses also revealed that despite significant differences in microenvironment conditions depending on mines’ insolation, the horse-chestnut miner larvae could partially compensate for them and complete their development at similar endpoints expressed as the cumulative sum of degree-days. We conclude that computer-aided analysis of photographic documentation of leaf-miner larval growth followed by mathematical modelling offers a noninvasive, reliable, and inexpensive alternative for monitoring local leaf-miners populations.

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Leaf temperature measurement techniques

Cited by 10 publications

References 4 publications

Phylloclimate or the climate perceived by individual plant organs: What is it? How to model it? What for?

Phylloclimate or the climate perceived by individual plant organs: What is it? How to model it? What for?

Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation

Towards understanding Cameraria ohridella (Lepidoptera: Gracillariidae) development: effects of microhabitat variability in naturally growing horse-chestnut tree canopy

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