UAV‐Based Land Surface Temperatures and Vegetation Indices Explain and Predict Spatial Patterns of Soil Water Isotopes in a Tropical Dry Forest

The spatial variation of soil water isotopes (SWI)—representing the baseline for investigating
root water uptake (RWU) depths with water stable isotope techniques—has rarely been investigated. Here, we
use spatial SWI depth profile sampling in combination with unmanned aerial vehicle (UAV) based land surface
temperature estimates and vegetation indices (VI) in order to improving process understanding of the
relationships between the spatial variability of soil water content and soil water isotope patterns with canopy
status, represented in the form of VI. We carried out a spatial sampling of 10 SWI depth profiles in a tropical dry
forest. UAV data were collected and analyzed to obtain detailed characterization of soil temperature and canopy
status. We then performed a statistical analysis between the VI and land surface temperatures with soil water
content and SWI values at different spatial resolutions (3 cm–5 m). Best relationships were used for generating
soil water isoscapes for the entire study area. Results suggest that soil water content and SWI values are strongly
mediated by canopy parameters (VI). Various VI correlate strongly with soil water content and SWI values
across all depths. SWI at the surface depend on land surface temperature (R2 of 0.66 for δ18O and 0.64 for δ2H).
Strongest overall correlations were found at a spatial resolution of 0.5 m. We speculate that this might be the
ideal resolution for spatially characterizing SWI patterns and investigate RWU in tropical dry forest
environments. Supporting spatial analyses of SWI with UAV‐based approaches might be a future avenue for
improving the spatial representation and credibility of such studies.