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∙m−2
: 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.
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