  This paper introduces a modular processing chain to derive global
high-resolution maps of leaf traits. In particular, we present global maps at
500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen
and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The
processing chain exploits machine learning techniques along with optical remote
sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of
in-situ measured leaf traits. The chain first uses random forests regression
with surrogates to fill gaps in the database ($> 45 \% $ of missing entries)
and maximize the global representativeness of the trait dataset. Along with the
estimated global maps of leaf traits, we provide associated uncertainty
estimates derived from the regression models. The process chain is modular, and
can easily accommodate new traits, data streams (traits databases and remote
sensing data), and methods. The machine learning techniques applied allow
attribution of information gain to data input and thus provide the opportunity
to understand trait-environment relationships at the plant and ecosystem
scales.
