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Remote Sens. 2014, 6(10), 9775-9801; doi:10.3390/rs6109775

Diurnal Dynamics of Wheat Evapotranspiration Derived from Ground-Based Thermal Imagery

1
Institute of Crop Science and Plant Breeding, Kiel University, Hermann-Rodewald-Str. 9, 24118 Kiel, Germany
2
Institute of Meteorology and Geophysics, University of Köln, Pohligstrasse 3, 50969 Cologne, Germany
3
Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany
4
Forschungszentrum Jülich GmbH, Institute of Bio- and Geosciences, IGB-2: Plant Sciences, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
*
Author to whom correspondence should be addressed.
Received: 14 February 2014 / Revised: 20 September 2014 / Accepted: 24 September 2014 / Published: 14 October 2014
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Abstract

The latent heat flux, one of the key components of the surface energy balance, can be inferred from remotely sensed thermal infrared data. However, discrepancies between modeled and observed evapotranspiration are large. Thermal cameras might provide a suitable tool for model evaluation under variable atmospheric conditions. Here, we evaluate the results from the Penman-Monteith, surface energy balance and Bowen ratio approaches, which estimate the diurnal course of latent heat fluxes at a ripe winter wheat stand using measured and modeled temperatures. Under overcast conditions, the models perform similarly, and radiometric image temperatures are linearly correlated with the inverted aerodynamic temperature. During clear sky conditions, the temperature of the wheat ear layer could be used to predict daytime turbulent fluxes (root mean squared error and mean absolute error: 20–35 W∙m2, r2: 0.76–0.88), whereas spatially-averaged temperatures caused underestimation of pre-noon and overestimation of afternoon fluxes. Errors are dependent on the models’ ability to simulate diurnal hysteresis effects and are largest during intermittent clouds, due to the discrepancy between the timing of image capture and the time needed for the leaf-air-temperature gradient to adapt to changes in solar radiation. During such periods, we suggest using modeled surface temperatures for temporal upscaling and the validation of image data. View Full-Text
Keywords: thermal imaging; evapotranspiration modeling; image analysis; surface temperature; canopy conductance to water vapor thermal imaging; evapotranspiration modeling; image analysis; surface temperature; canopy conductance to water vapor
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ahrends, H.E.; Haseneder-Lind, R.; Schween, J.H.; Crewell, S.; Stadler, A.; Rascher, U. Diurnal Dynamics of Wheat Evapotranspiration Derived from Ground-Based Thermal Imagery. Remote Sens. 2014, 6, 9775-9801.

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