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\begin{document}

\title{UAV-based land surface temperatures and vegetation indices explain and
predict spatial patterns of soil water isotopes in a tropical dry forest}



\author[1]{Beyer Matthias}%
\author[1]{Alberto Iraheta}%
\author[1]{Malkin Gerchow}%
\author[1]{Katrin Kühnhammer}%
\author[2]{Ana Claudia Callau-Beyer}%
\author[3]{Paul Koeniger}%
\author[4]{David Dubbert}%
\author[4]{Maren Dubbert}%
\author[5]{Ricardo Sánchez-Murillo}%
\author[6]{Christian Birkel}%
\affil[1]{Institute of Geoecology, Technische Universität Braunschweig}%
\affil[2]{Institute of Horticultural Production Systems, Leibniz Universität Hannover}%
\affil[3]{BGR}%
\affil[4]{Isotope Biogeochemistry and Gas Fluxes, Landscape Functioning (ZALF)}%
\affil[5]{Department of Earth and Environmental Sciences, University of Texas at Arlington}%
\affil[6]{University of Costa Rica}%


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  \date{May 27, 2024}


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\begin{abstract}
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 soil water content and isotope patterns with
canopy status.

We carried out a spatial sampling of ten 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 to 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 (R\selectlanguage{ngerman}² of 0.65 for \selectlanguage{greek}δ\selectlanguage{english}18O and 0.57 for
\selectlanguage{greek}δ\selectlanguage{english}2H). 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. Supporting
spatial analyses of SWI with UAV-based approaches might be a future
avenue for improving the spatial representation and credibility of such
studies.%
\end{abstract}%



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