This paper describes two approaches for estimating sensible heat flux, using surface renewal and similarity concepts. One approach depends on a temperature structure function parameter and is valid in the inertial sublayer. The other approach depends on the temperature standard deviation and operates when measurements are made above the canopy top, either in the roughness or inertial sublayer. The approaches were tested over grass, rangeland grass, wheat, grape vineyard, and nectarine and olive orchards. It is shown that the free convection limit expression for the standard deviation method holds for slightly unstable conditions. When surface homogeneity and fetch requirements are not fully met in the field, the results show that the equations based on surface renewal principles are more robust and accurate than equations exclusively based on similarity backgrounds. It is likely that the two methods are less sensitive to site-specific adjustment of the similarity relationships unless the canopy is rather heterogeneous. Under unstable conditions, the free convection limit equation, which depends on the temperature standard deviation, can provide online sensible heat flux estimates using affordable battery-powered data logger with temperature data as the only input. The approach performed well when measuring above the canopy in the roughness and inertial sublayers, thus suggesting that the method is useful for long-term monitoring over growing vegetation.