Surface Soil Moisture (SM) plays a key role in the Earth water cycle and many hydrological processes (Koster et al. 2004), it is essential for accurate weather forecasting (Drusch et al. 2007, De Rosnay et al. 2013, Rodriguez-Fernandez et al. 2019) and agriculture management (Guerif et al. 2000). SM was also identified as one of the 50 “Essential Climate Variables” (ECVs) by the Global Climate Observing System (GCOS) in the context of the United Nations Framework Convention on Climate Change (UNFCCC) (GCOS 2015). Long time series of ECVs are crucial to monitor the Earth’s climate evolution, and this is the goal of initiatives such as the European Space Agency’s Climate Change Initiative (ESA CCI, https://climate.esa.int/en/). The ESA SM CCI dataset (Gruber et al. 2019) provides time series for the 1979-2021 period in a 25 km resolution grid using scatterometers and passive microwave sensors. Based on extensive feedback from the user communities of SM products, a strong need for higher spatial resolutions SM data was identified (Dorigo et al. 2018, Peng et al. 2020). This includes climate applications such as assessment of climate change impacts at regional level. Soil moisture can also be estimated at high spatial resolutions using Synthetic Aperture Radars such as Sentinel 1 (S1). Even if Sentinel high resolution time series are still short for climate applications (Sentinel 1A was launched in 2014 and Sentinel 1B in 2016), it is worth to evaluate the interest of such data set in the context of the ESA CCI as an additional SM high resolution data set and also for comparison with high resolution SM datasets that could be obtained by the downscaling of coarser resolution sensors. In this contribution, SM maps at 1 km resolution produced using the S²MP (Sentinel-1/2 Soil Moisture Product) algorithm (El Hajj et al. 2017) are presented. The maps cover six 100 x 100 km2 regions over the Southwest and South East of France, Tunisia, North America, Spain and Australia. The S²MP algorithm is based on a neural network approach and exploits the synergic use of S1 and Sentinel 2 (S2). Backscattering coefficients and incidence angles from S1 as well as NDVI from S2 are used as input data. In the framework of this study, the algorithm was also adapted to use NDVI from Sentinel-3 (S3) instead of S2. Both S1+S2 and S1+S3 1 km SM maps are compared to other high resolution SM data sets such as the SM and Soil Water Index (SWI) computed from S1 for the Copernicus Global Land Service and the SMAP + S1 product. The S1+S2 and S1+S3 SM maps are in very good agreement in terms of correlation (R > 0.9), bias (< 0.05 m3.m-3) and standard deviation of the difference (STDD < 0.025 m3.m-3) over the 6 regions of study. They also are well correlated (R ~ 0.6-0.7) with the Copernicus products over croplands and herbaceous vegetation land cover classes. However, the results are more mitigated over Tunisia and when the maps are compared to those of SMAP + S1. The correlation decreases significantly for mixed land cover pixels. All the high-resolution products were also evaluated against in situ measurements along with coarse scale SM data sets (SMAP, SMOS, ESA CCI). The coarse resolution SM products show better correlation than the high resolution products except for the Copernicus SWI. However, the high resolution data sets, in particular the S2SM product, show a lower STDD and bias than coarse resolution data sets.