PYthon Sentinel-1 soil-Moisture Mapping Toolbox (PYSMM) This package acts as an interface to Google Earth Engine for the estimation of surface soil moisture based on Copernicus Sentinel-1 intensity data. It is meant as a supplement to the following publication: Greifeneder, F., C. Notarnicola, W. Wagner. A machine learning based approach for global surface soil moisture estimations with Google Earth Engine. The estimation of soil moisture is based on a Gradient Boosting Trees Regression machine learning approach. The model training was performed based on in-situ data from the International Soil Moisture Network (ISMN). PYSMM all processing steps for spatial and temporal mapping of surface soil moisture are fully executed online on GEE - none of the input data-sets needs to be downloaded. Acknowledgements: This work was partially funded by the Horizon 2020 project “Ecopotential – Improving Future Ecosystem Benefits through Earth Observation, which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement n° 641762) and the European Fund for Regional Development project “DPS4ESLAB”.