The International Soil Moisture Network (ISMN): A global hub for in situ observations serving earth system science

Soil moisture is a critical component of the Earth’s hydrological cycle, influencing weather, climate, agriculture, and ecosystems. In situ soil moisture measurements are indispensable for validating satellite observations, calibrating hydrological and land surface models, and advancing our understanding of regional and global water cycles. Unlike remote sensing, in situ measurements provide direct observations of soil moisture variability across temporal and spatial scales, offering a benchmark for numerous environmental applications.

The International Soil Moisture Network (ISMN) serves as a vital repository of harmonized in situ soil moisture data collected from diverse networks worldwide. Since its inception, the ISMN has integrated measurements from over 80 networks with more than 3000 stations at various depths, standardizing and curating them to ensure accessibility and comparability. Beyond offering comprehensive in situ soil moisture data, ISMN disseminates additional environmental variables, including soil temperature, snow depth, snow water equivalent, precipitation, air temperature, surface temperature and soil water potential if they are available from our data providers. ISMN’s quality control framework addresses inconsistencies and errors, enabling researchers and practitioners to confidently utilize its datasets for applications ranging from hydrological modeling to climate change studies. ISMN’s free data access (https://ismn.earth) has fostered global collaboration and supported hundreds of studies in Earth system science.

Ongoing efforts are concentrated on expanding the database by incorporating additional stations and networks from institutional or governmental sources. Further resources are directed towards fortifying the operational system and improve usability to better serve our users. ISMN further contributes to the data-to-value chain on international initiatives like WMO, FAO and GCOS. One example is the contribution to WMO’s yearly Global State of the Water Resources report.  To enhance data quality, ISMN is researching AI-based methods for detecting anomalies such as spikes, dips, and plateaus, showing promising initial results.