Capturing Fine-Scale Variability in Dryland Evapotranspiration Through Multi-Scale Thermal Image Analysis

Accurate estimation of evapotranspiration (ET) in drylands is critically dependent on capturing fine-scale spatial variability, yet current thermal remote sensing approaches face significant scaling limitations. While satellite-based thermal imagery provides broad coverage for ET estimation, its coarse resolution fails to capture the heterogeneous vegetation patterns characteristic of dryland ecosystems, leading to systematic biases in ET estimates. The non-linear relationship between land surface temperature (LST) and ET means that coarse-resolution LST measurements cannot simply be averaged to estimate ecosystem-scale ET. Instead, the underlying spatial variance in LST must be properly accounted for when scaling between observations at different resolutions. Here, we demonstrate an approach using very high resolution (VHR) UAV-derived thermal imagery (0.3-m resolution) combined with multi-scale satellite observations (up to 90-m resolution) to develop scaling relationships between LST variance and spatial resolution. We show how these relationships vary with vegetation composition and seasonal dynamics in a dryland ecosystem over one year. By modeling how LST variance changes across scales, we can better estimate ET from coarser thermal imagery while preserving the influence of fine-scale heterogeneity. Our results indicate that vegetation pattern and phenological stage significantly influence scaling behavior, allowing us to identify optimal measurement resolutions for different ecosystem conditions. This approach reduces uncertainty in ET estimates from satellite thermal imagery by incorporating the effects of sub-pixel spatial variability revealed by VHR observations. The scaling relationships we develop provide a framework for improving regional ET estimates in drylands while accounting for their characteristic fine-scale vegetation patterns.