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\begin{document}

\title{Global prediction of soil saturated hydraulic conductivity using random
forest in a Covariate-based Geo Transfer Functions (CoGTF) framework}



\author[1]{Surya Gupta}%
\author[2]{Tomislav Hengl}%
\author[3]{Peter Lehmann}%
\author[1]{Sara Bonetti}%
\author[1]{Andreas Papritz}%
\author[1]{Dani Or}%
\affil[1]{Soil and Terrestrial Environmental Physics, Department of Environmental Systems Science, ETH Zurich, Switzerland}%
\affil[2]{Envirometrix Ltd., Wageningen, the Netherlands}%
\affil[3]{Soil and Terrestrial Environmental Physics,Department of Environmental Systems Science, ETH Zurich, Switzerland}%


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  \date{June 09, 2024}


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\begin{abstract}
The saturated hydraulic conductivity (Ksat) is a key soil hydraulic
parameter for representing infiltration and drainage in Earth system and
land surface models. For large scale applications, Ksat is often
estimated from pedotransfer functions (PTFs) based on easy-to-measure
soil properties like soil texture and bulk density. The reliance of PTFs
on data from uniform arable lands and omission of soil structure limits
the applicability of texture-based predictions of Ksat in vegetated
lands. A method to harness technological advances in machine learning
and availability of remotely sensed surrogate information to derive a
new global Ksat map at 1 km resolution using terrain, climate,
vegetation, and soil covariates is proposed. For model training and
testing, global compilation of 6,814 georeferenced Ksat measurements
from the literature across the globe were used. The accuracy assessment
results based on model cross-validations with re-fitting show a
concordance correlation coefficient of 0.79 and root mean square error
of 0.72 (in log10Ksat given in cm/day). The generated maps of Ksat
represent spatial patterns of the vegetation-induced soil structure
formation and clay mineralogy, more distinctly than previous global maps
of Ksat such as computed with Rosetta 3 pedotransfer function. The
validation of the model indicates that Ksat could be more accurately
modeled using covariate-based Geo Transfer Functions (CoGTFs) that
harness spatially distributed surface and climate attributes, compared
to pedotransfer functions that rely only on soil information.%
\end{abstract}%



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