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Measurements of the surface soil moisture (SM) content are important for a\udwide range of applications. Among them, operational hydrology and numerical\udweather prediction, for instance, need SM information in\udnear-real-time (NRT), typically not later than 3 h after sensing. The\udEuropean Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite\udis the first mission specifically designed to measure SM from\udspace. The ESA Level 2 SM retrieval algorithm is based on a detailed\udgeophysical modelling and cannot provide SM in NRT. This paper presents the\udnew ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in\udNRT. The NN inputs are SMOS brightness temperatures for horizontal and\udvertical polarizations and incidence angles from 30 to 45°. In\udaddition, the NN uses surface soil temperature from the European Centre for\udMedium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The\udNN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is\udsomewhat narrower (∼ 915 km) than that of the L2 SM dataset (∼ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of\ud< 0.05 m3 m−3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m−3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM\udproduct but it has the advantage of being available in less than 3.5 h\udafter sensing, complying with NRT requirements. The new product is processed\udat ECMWF and it is distributed by ESA and via the European Organisation for the\udExploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast)

SMOS near-real-time soil moisture product: processor overview and first validation results