<p>Surface Soil Moisture (SM) plays a key role in the Earth water cycle and many hydrological processes (Koster 2004), it is essential for accurate weather forecasting (Rodriguez-Fernandez 2019) and agriculture management (Guerif 2000). SM was also identified as one of the 50 &#8220;Essential Climate Variables&#8221; (ECVs) by the Global Climate Observing System (GCOS). Long time series of ECVs are crucial to monitor the Earth&#8217;s climate evolution, and developing them is the goal of initiatives such as the European Space Agency&#8217;s Climate Change Initiative (ESA CCI).</p><p>The ESA SM CCI product (Gruber 2019) provides global time series for the 1979-2021 period at 25-km resolution using scatterometers and passive microwave sensors. Based on extensive feedbacks from the user communities of SM products, a strong need for higher spatial resolutions SM data was identified (Dorigo 2018, Peng 2020). This also includes climate applications such as assessment of climate change impacts at regional level.</p><p>SM can be estimated at high spatial resolution using Synthetic Aperture Radars such as Sentinel-1 (S1). Several high resolution (HR) S1 SM data sets exist such as the products from the Copernicus Global Land Service and the one using the S&#178;MP (Sentinel-1/2 Soil Moisture Product) algorithm (El Hajj 2017). Despite the actual short temporal coverage of such data, it is worth to evaluate them in the context of the ESA CCI as potential future HR SM long time series, and also as benchmarking references for HR SM data sets that could be obtained by the downscaling of coarser resolution sensors.</p><p>In this context, the S&#178;MP algorithm, which was originally designed to retrieve SM at a plot level, was adapted to compute SM maps at 1-km resolution over six 100-km^&#178; regions in the Southwest and Southeast of France, Tunisia, North America, Spain and Australia. The S&#178;MP algorithm is based on a neural network approach using backscattering coefficients and incidence angles from S1, and either NDVI from Sentinel-2 (S2) or that of Sentinel-3 (S3), as input data.</p><p>Both S1+S2 and S1+S3 1-km SM maps are compared to HR SM data from the SMAP+S1 product and the Copernicus SM and Soil Water Index (SWI) data sets. The S1+S2 and S1+S3 SM maps are in very good agreement in terms of correlation (R > 0.9), bias (< 0.05 m^<em>3</em>.m^<em>-3</em>) and standard deviation of the difference (STDD < 0.025 m^<em>3</em>.m^<em>-3</em>) over the 6 regions of study. They also are well correlated (R ~ 0.6-0.7) with the Copernicus products over homogeneous pixels containing croplands and herbaceous vegetation. However, the results are more mitigated over Tunisia and mixed land cover pixels as well as when the maps are compared to those of SMAP+S1.</p><p>The high resolution products are also evaluated against in-situ measurements along with coarse scale SM data sets (SMAP, SMOS, ESA CCI). In general, the coarse resolution SM products show better correlation than the HR ones. However, the HR products, in particular S&#178;MP, show lower STDD and bias.</p>