Research Article | Open Access

Volume 2025 |Article ID 100046 | https://doi.org/10.1016/j.plaphe.2025.100046

Analysis of variance and its sources in UAV-based multi-view thermal imaging of wheat plots

Simon Treier ,^1,2 Lukas Roth,² Andreas Hund,² Helge Aasen,³ Lilia Levy H€aner,¹ Nicolas Vuille-dit-Bille,¹ Achim Walter,² and Juan M. Herrera¹

¹Cultivation Techniques and Varieties in Arable Farming Group, Agroscope, Route de Duillier 60, Nyon, 1260, Switzerland
²ETH Zürich, Institute of Agricultural Sciences, Universitatstrasse 2, Zürich, 8092, Switzerland
³Earth Observation of Agroecosystems Team, Agroecology and Environment Division, Agroscope, Reckenholzstrasse 191, Zürich, 8046, Switzerland

Abstract

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.