Articles | Volume 21, issue 10
https://doi.org/10.5194/hess-21-5201-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-5201-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
17 Oct 2017
Research article |
| 17 Oct 2017
SMOS near-real-time soil moisture product: processor overview and first validation results
Nemesio J. Rodríguez-Fernández
CORRESPONDING AUTHOR
European Centre for Medium-Range Weather Forecasts, Shinfield Road, Reading, RG2 9AX, UK
CESBIO, Université de Toulouse, CNES, CNRS, IRD, 18 av. Edouard Belin, bpi 2801, 31401 Toulouse, France
Joaquin Muñoz Sabater
European Centre for Medium-Range Weather Forecasts, Shinfield Road, Reading, RG2 9AX, UK
Philippe Richaume
CESBIO, Université de Toulouse, CNES, CNRS, IRD, 18 av. Edouard Belin, bpi 2801, 31401 Toulouse, France
Patricia de Rosnay
European Centre for Medium-Range Weather Forecasts, Shinfield Road, Reading, RG2 9AX, UK
Yann H. Kerr
CESBIO, Université de Toulouse, CNES, CNRS, IRD, 18 av. Edouard Belin, bpi 2801, 31401 Toulouse, France
Clement Albergel
European Centre for Medium-Range Weather Forecasts, Shinfield Road, Reading, RG2 9AX, UK
CNRM – UMR3589, Météo-France/CNRS, Toulouse, France
Matthias Drusch
European Space Agency, ESTEC, Noordwijk, the Netherlands
Susanne Mecklenburg
European Space Agency, ESRIN, Frascati, Italy
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33 citations as recorded by crossref.
- LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the Contiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables C. Albergel et al. 10.3390/rs10101627
- An Improved Coupled Routing and Excess Storage (CREST) Distributed Hydrological Model and Its Verification in Ganjiang River Basin, China G. Kan et al. 10.3390/w9110904
- Prediction of desert locust breeding areas using machine learning methods and SMOS (MIR_SMNRT2) Near Real Time product D. Gómez et al. 10.1016/j.jaridenv.2021.104599
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- A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP S. Sadri et al. 10.1016/j.rse.2020.111864
- Using machine learning to predict fire‐ignition occurrences from lightning forecasts R. Coughlan et al. 10.1002/met.1973
- The Met Office Operational Soil Moisture Analysis System B. Gómez et al. 10.3390/rs12223691
- Intercomparison of recent microwave satellite soil moisture products on European ecoregions A. Mazzariello et al. 10.1016/j.jhydrol.2023.130311
- A new soil moisture index driven from an adapted long-term temperature-vegetation scatter plot using MODIS data F. Mohseni & M. Mokhtarzade 10.1016/j.jhydrol.2019.124420
- Assessment of Bottom-Up Satellite Precipitation Products on River Streamflow Estimations in the Peruvian Pacific Drainage J. Qquenta et al. 10.3390/rs16010011
- Evaluation of SMOS, SMAP, ASCAT and Sentinel-1 Soil Moisture Products at Sites in Southwestern France M. El Hajj et al. 10.3390/rs10040569
- A Long-term Consistent Artificial Intelligence and Remote Sensing-based Soil Moisture Dataset O. Skulovich & P. Gentine 10.1038/s41597-023-02053-x
- Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review G. Balsamo et al. 10.3390/rs10122038
- Assimilation of near-real time data products into models of an urban basin J. Leach et al. 10.1016/j.jhydrol.2018.05.064
- Soil Moisture and Sea Surface Salinity Derived from Satellite-Borne Sensors J. Boutin et al. 10.1007/s10712-023-09798-5
- Global downscaling of remotely sensed soil moisture using neural networks S. Alemohammad et al. 10.5194/hess-22-5341-2018
- Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan . Rahman et al. 10.3390/rs11172040
- A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements J. Peng et al. 10.1016/j.rse.2020.112162
- The Impact of SMOS Soil Moisture Data Assimilation within the Operational Global Flood Awareness System (GloFAS) C. Baugh et al. 10.3390/rs12091490
- SMOS brightness temperature forward modelling and long term monitoring at ECMWF P. de Rosnay et al. 10.1016/j.rse.2019.111424
- The International Soil Moisture Network: serving Earth system science for over a decade W. Dorigo et al. 10.5194/hess-25-5749-2021
- Performance of SMOS Soil Moisture Products Over Core Validation Sites A. Colliander et al. 10.1109/LGRS.2023.3272878
- SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact N. Rodríguez-Fernández et al. 10.3390/rs11111334
- Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations L. Ge et al. 10.3390/rs10091327
- Soil moisture estimates at 1 km resolution making a synergistic use of Sentinel data R. Madelon et al. 10.5194/hess-27-1221-2023
- Integration of microwave satellite soil moisture products in the contextual surface temperature-vegetation index models for spatially continuous evapotranspiration estimation W. Zhu et al. 10.1016/j.isprsjprs.2023.08.004
- Deep learning in environmental remote sensing: Achievements and challenges Q. Yuan et al. 10.1016/j.rse.2020.111716
- Toward the Removal of Model Dependency in Soil Moisture Climate Data Records by Using an $L$-Band Scaling Reference R. Madelon et al. 10.1109/JSTARS.2021.3137008
- Assessment and Error Analysis of Satellite Soil Moisture Products Over the Third Pole J. Zeng et al. 10.1109/TGRS.2021.3116078
- Evaluation of an Adaptive Soil Moisture Bias Correction Approach in the ECMWF Land Data Assimilation System D. Fairbairn et al. 10.3390/rs16030493
- Estimating Retrieval Errors From Neural Network Inversion Schemes—Application to the Retrieval of Temperature Profiles From IASI F. Aires & V. Pellet 10.1109/TGRS.2020.3026944
- From SMOS Soil Moisture to 3-hour Precipitation Estimates at 0.1° Resolution in Africa T. Pellarin et al. 10.3390/rs14030746
2 citations as recorded by crossref.
Discussed (final revised paper)
Latest update: 07 May 2024
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...
