###### Abstract  
Surface water is the most important resource and environmental factor in maintaining human survival and ecosystem stability; therefore, timely accurate information on dynamic surface water is urgently needed. However, the existing water datasets fall short of the current needs of the various organizations and disciplines due to the limitations of optical sensors in dynamic water mapping. The advancement of the cloud-based Google Earth Engine (GEE) platform and free-sharing Sentinel-1 imagery makes it possible to map the dynamics of a surface water body with high spatial-temporal resolution on a large scale. This study first establishes a water extraction method oriented towards Sentinel-1 Synthetic Aperture Radar (SAR) data based on the statistics of a large number of samples of land-cover types. An unprecedented high spatial-temporal water body dataset in China (HSWDC) with monthly temporal and 10-m spatial resolution using the Sentinel-1 data from 2016 to 2018 is developed in this study. The HSWDC is validated by 14,070 random samples across China. A high classification accuracy (overall accuracy = 0.93, kappa coefficient = 0.86) is achieved. The HSWDC is highly consistent with the Global Surface Water Explorer dataset and water levels from satellite altimetry. In addition to the good performance of detecting frozen water and small water bodies, the HSWDC can also classify various water cover/uses, which are obtained from its high spatial-temporal resolution. The HSWDC dataset can provide more detailed information on surface water bodies in China and has good application potential for developing high-resolution wetland maps. China water dynamics; Sentinel-1; Google Earth Engine; time series satellite images 2020  
a 30-m resolution for circa-2000. A global water mask based on MODIS archives is produced by [PERSON] et al. [1]. The aforementioned studies produced static water maps and can meet some applications, but the demand for information on the spatial and temporal changes of inland water bodies and their long-term evolution is still growing [6]. Therefore, many authors have already attempted to map dynamic changes of water bodies [7; 8; 9; 10; 11; 12; 13]. These datasets provide information on the extent of the water bodies at daily to monthly intervals and cover a limited geographic area. [PERSON] et al. [11] produced an excellent Global Surface Water Explorer (GSWE) dataset with 30-m spatial resolution and a monthly time interval. It comes from the entire multi-temporal orthorectified Landsat 5, 7 and 8 archive spanning the past 32 years and shows the spatial and temporal variability of global surface water and its long-term changes. However, due to the limitations of optical data, the above efforts for monitoring the water body dynamics are still in need of further improvement. For example, GSWE still cannot form regular periodic monitoring of water body dynamics. The dynamic water dataset formed by time series interpolation is also affected by the model itself. Synthetic Aperture Radar (SAR) data have the advantage of being unaffected by clouds, so they can be used to regularly monitor surface water [14; 15]. Yet, due to the difficulty of SAR data acquisition at large scale and the complexity of data processing, in the past, water body monitoring and mapping using SAR data has mainly focused on individual bodies of water or specific small areas [16; 17; 18]. With the free distribution of Sentinel-1 data, as well as the release and application of the Google Earth Engine (GEE) platform, it is possible to carry out dynamic monitoring and mapping of water bodies on a large scale. At the same time, Sentinel-1 provides data on 10-m resolution, which can produce more accurate water maps that include many small water bodies that are missed by the aforementioned surface water data products.