Abstract
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).
Highlights
Surface soil moisture (SM) represents less than 0.001 % of the global freshwater budget by volume but it plays an important role in the water, carbon and energy cycles (Lahoz and De Lannoy, 2014)
The HV angle-binned Tb values have been collocated with European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) forecasts for the soil temperature and snow cover and they have been collocated with version 620 Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2) SM data (Kerr et al, 2012) in the 1 June 2010 to 30 June 2012 period
When the trained neural network (NN) was applied to the test subset and the NN output was compared to the SMOS L2 SM, the Pearson correlation R was 0.86, the standard deviation of the difference (STDD) was 0.068 m3 m−3 and the root mean square error or difference (RMSE) was 0.068 m3 m−3, which implies that there was not a significant bias between both SM datasets
Summary
Surface soil moisture (SM) represents less than 0.001 % of the global freshwater budget by volume but it plays an important role in the water, carbon and energy cycles (Lahoz and De Lannoy, 2014). In addition to operational hydrology applications, operational numerical weather prediction benefits from remotely sensed SM data assimilation Meteorological agencies such as the European Centre for Medium-Range Weather Forecasts (ECMWF) and the United Kingdom Met Office assimilate ASCAT surface SM into their operational numerical weather prediction models (de Rosnay et al, 2013; Dharssi et al, 2011). With 6 years of SMOS measurements available, statistical algorithms can be exploited to provide faster retrievals and neural networks (NNs) have shown to be a promising technique to generate a SM dataset from SMOS Tb measurements (Rodríguez-Fernández et al, 2015) Based on the latter, a NN processing chain to provide SMOS SM in NRT has been implemented by the ESA.
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