Global long-term daily 1km surface soil moisture dataset with physics-informed machine learning (GSSM1km)

Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Global Surface Soil Moisture (GSSM1km) provides surface soil moisture (0-5 cm) at 1 km spatial and daily temporal resolution over the period 2000-2020. The performance of the GSSM1km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1km in testing set is 0.05 cm3/cm3, and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1km product can support the investigation of large-scale climate extremes and long-term trend analyses. Due to the whole dataset for the global scale is too big (779GB) to deposit at once, we uploaded the data in the Netherlands to figshare. For other areas, the data is stored in Google Earth Engine (https://code.earthengine.google.com/?asset=users/qianrswaterr/GlobalSSM1km0509), and we provide codes to download our data (https://code.earthengine.google.com/4b577bb83981e1ac43fd77127cfbdb4a). Due to the dataset is exported from Google Earth Engine, the bandNames can’t display in ArcGIS, the band is displayed as band1, band2,…. Just in case other softwares also can't display, I put the bandNames in the csv file “bandNames2000-2020”.