Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Hydrol. Earth Syst. Sci., 21, 5201-5216, 2017
https://doi.org/10.5194/hess-21-5201-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
17 Oct 2017
SMOS near-real-time soil moisture product: processor overview and first validation results
Nemesio J. Rodríguez-Fernández1,2, Joaquin Muñoz Sabater1, Philippe Richaume2, Patricia de Rosnay1, Yann H. Kerr2, Clement Albergel1,3, Matthias Drusch4, and Susanne Mecklenburg5 1European Centre for Medium-Range Weather Forecasts, Shinfield Road, Reading, RG2 9AX, UK
2CESBIO, Université de Toulouse, CNES, CNRS, IRD, 18 av. Edouard Belin, bpi 2801, 31401 Toulouse, France
3CNRM – UMR3589, Météo-France/CNRS, Toulouse, France
4European Space Agency, ESTEC, Noordwijk, the Netherlands
5European Space Agency, ESRIN, Frascati, Italy
Abstract. Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat 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 < 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 product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

Citation: Rodríguez-Fernández, N. J., Muñoz Sabater, J., Richaume, P., de Rosnay, P., Kerr, Y. H., Albergel, C., Drusch, M., and Mecklenburg, S.: SMOS near-real-time soil moisture product: processor overview and first validation results, Hydrol. Earth Syst. Sci., 21, 5201-5216, https://doi.org/10.5194/hess-21-5201-2017, 2017.
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Short summary
The new SMOS satellite near-real-time (NRT) soil moisture (SM) product based on a neural network is presented. The NRT SM product has been evaluated with respect to the SMOS Level 2 product and against a large number of in situ measurements showing performances similar to those of the Level 2 product but it is available in less than 3.5 h after sensing. The new product is distributed by the European Space Agency and the European Organisation for the Exploitation of Meteorological Satellites.
The new SMOS satellite near-real-time (NRT) soil moisture (SM) product based on a neural network...
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