An optimized surface aerodynamic temperature approach to estimate maize sensible heat flux and evapotranspiration

Abstract

Typical errors for single-source sensible heat flux estimation models range from 15-40% for vegetated surfaces, with 10 to 20% errors introduced by atmosphere stability functions and remaining errors due to the quality of the input data and assumptions regarding the surface aerodynamic temperature (To). This paper describes the development and evaluation of an optimized To model, which serves as key input to estimate and improve sensible heat fluxes for maize fields. The best resulting optimized linear model included four variables: air temperature, surface temperature, fractional vegetation cover, and a proposed new variable that accounts for wind speed and the relative angle of attack between wind direction and crop row orientation. During the 2017 to 2019 maize growing seasons, on two different fields located at a Research Farm near Greeley, Colorado, USA, field data were collected. Micrometeorological data were measured in situ at the height of 3.30 m above the ground surface. Nadir-looking stationary infrared thermometers measured surface temperature. Multispectral surface reflectance data were collected on-site weekly using a handheld multispectral radiometer. Net radiation and soil heat fluxes were acquired at approximately ¼ and ½ of the field's length. Sensible heat fluxes were measured with two sets of Large Aperture Scintillometers to calibrate the To model. A one-way Analysis of Variance was performed to model To for different ranges of maize leaf area index values. A local sensitivity analysis was performed to identify the most influential explanatory variables in the To optimization model. The sensitivity analysis indicated that uncertainty on air temperature and surface temperature measurements were responsible for the most variability in sensible heat flux estimates when using the developed optimized To model. Overall, results indicated that the optimized To model improved the estimation of maize sensible heat fluxes by 31%; which resulted in an improvement on latent heat flux (or evapotranspiration) by 9% compared to a non-optimized linear To model. The interactions between the crop row layout and wind direction promote more turbulent mixing for heat transfer mechanisms.