Canopy temperature (CT) estimates from drone-based uncooled thermal cameras are prone to confounding effects, which affects the interpretability of CT estimates. Experimental sources of variance, such as genotypes and experimental treatments blend with confounding sources of variance such as thermal drift, spatial field trends, and effects related to viewing geometry. Nevertheless, CT is gaining popularity to characterize crop performance and crop water use, and as a proxy measurement of stomatal conductance and transpiration. Drone-based thermography was therefore proposed to measure CT in agricultural experiments. For a meaningful interpretation of CT, confounding sources of variance must be considered. In this study, the multi-view approach was applied to examine the variance components of CT on 99 flights with a drone-based thermal camera. Flights were conducted on two variety testing field trials of winter wheat over two years with contrasting meteorological conditions in the temperate climate of Switzerland. It was demonstrated how experimental sources of variance can be disentangled from confounding sources of variance and on average more than 96.5 ​% of the initial variance could be explained with experimental and confounding sources combined. Not considering confounding sources led to erroneous conclusions about phenotypic correlations of CT with traits such as yield, plant height, fractional canopy cover, and multispectral indices. Based on extensive and diverse data, this study provides comprehensive insights into the manifold sources of variance in CT measurements, which supports the planning and interpretation of drone-based CT screenings in variety testing, breeding, and research.