Global soil moisture data derived through machine learning trained with in-situ measurements

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 derived through machine learning trained with in-situ measurements, 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 (0-10cm, 10-30cm, and 30-50cm) at 0.25 degrees 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 distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses

Automated summary

SoMo.ml is a global, long-term dataset of soil moisture derived through machine learning, providing multi-layer soil moisture data at high spatial and temporal resolution. It performs well in capturing temporal dynamics and is particularly useful for applications requiring time-varying soil moisture information. This dataset complements existing modelled and satellite-based datasets, supporting large-scale hydrological, meteorological, and ecological analyses.