  While soil moisture information is essential for a wide range of hydrologic
and climate applications, spatially-continuous soil moisture data is only
available from satellite observations or model simulations. Here we present a
global, long-term dataset of soil moisture generated from in-situ measurements
using machine learning, SoMo.ml. We train a Long Short-Term Memory (LSTM) model
to extrapolate daily soil moisture dynamics in space and in time, based on
in-situ data collected from more than 1,000 stations across the globe. SoMo.ml
provides multi-layer soil moisture data (0-10 cm, 10-30 cm, and 30-50 cm) at
0.25{\deg} spatial and daily temporal resolution over the period 2000-2019. The
performance of the resulting dataset is evaluated through cross validation and
inter-comparison with existing soil moisture datasets. SoMo.ml performs
especially well in terms of temporal dynamics, making it particularly useful
for applications requiring time-varying soil moisture, such as anomaly
detection and memory analyses. SoMo.ml complements the existing suite of
modelled and satellite-based datasets given its independent and novel
derivation, to support large-scale hydrological, meteorological, and ecological
analyses.
