Correlation methods
; Forecasting
; Moisture
; Soil moisture
; Soil surveys
; Soils
; Temperature
; European centre for medium-range weather forecasts
; Horizontal and vertical polarizations
; Numerical weather prediction
; Operational hydrologies
; Pearson correlation coefficients
; Soil Moisture and Ocean Salinity (SMOS)
; Surface soil temperatures
; Us climate reference networks
; Weather forecasting
; algorithm
; brightness temperature
; in situ measurement
; satellite data
; SMOS
; soil moisture
; soil temperature
; North America
英文摘要:
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).
European Centre for Medium-Range Weather Forecasts, Shinfield Road, Reading, United Kingdom; CESBIO, Université de Toulouse, CNES, CNRS, IRD, 18 av. Edouard Belin, bpi 2801, Toulouse, France; CNRM - UMR3589, Météo-France/CNRS, Toulouse, France; European Space Agency, ESTEC, Noordwijk, Netherlands; European Space Agency, ESRIN, Frascati, Italy
Recommended Citation:
Rodríguez-Fernández N,J,, Muñoz Sabater J,et al. SMOS near-real-time soil moisture product: Processor overview and first validation results[J]. Hydrology and Earth System Sciences,2017-01-01,21(10)