  Machine learning (ML) has emerged as a novel tool for generating large-scale
land surface data in recent years. ML can learn the relationship between input
and target, e.g. meteorological variables and in-situ soil moisture, and then
estimate soil moisture across space and time, independently of prior
physics-based knowledge. Here we develop a high-resolution (0.1{\deg}) daily
soil moisture dataset in Europe (SoMo.ml-EU) using Long Short-Term Memory
trained with in-situ measurements. The resulting dataset covers three vertical
layers and the period 2003-2020. Compared to its previous version with a lower
spatial resolution (0.25{\deg}), it shows a closer agreement with independent
in-situ data in terms of temporal variation, demonstrating the enhanced
usefulness of in-situ observations when processed jointly with high-resolution
meteorological data. Regional comparison with other gridded datasets also
demonstrates the ability of SoMo.ml-EU in describing the variability of soil
moisture, including drought conditions. As a result, our new dataset will
benefit regional studies requiring high-resolution observation-based soil
moisture, such as hydrological and agricultural analyses. The SoMo.ml-EU is
available at figshare.
