Articles | Volume 20, issue 12
https://doi.org/10.5194/hess-20-4895-2016
© Author(s) 2016. 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-20-4895-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model
Gabriëlle J. M. De Lannoy
KU Leuven, Department of Earth and Environmental Sciences, Heverlee, Belgium
Rolf H. Reichle
NASA Goddard Space Flight Center, Global Modeling and Assimilation Office, Greenbelt, Maryland, USA
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94 citations as recorded by crossref.
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- Comparison of different assimilation methodologies of groundwater levels to improve predictions of root zone soil moisture with an integrated terrestrial system model H. Zhang et al. 10.1016/j.advwatres.2017.11.003
- Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model H. Zhao et al. 10.3390/s23052620
- Comparing Seven Variants of the Ensemble Kalman Filter: How Many Synthetic Experiments Are Needed? J. Keller et al. 10.1029/2018WR023374
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- Challenges and benefits of quantifying irrigation through the assimilation of Sentinel-1 backscatter observations into Noah-MP S. Modanesi et al. 10.5194/hess-26-4685-2022
- Performance analysis of regional AquaCrop (v6.1) biomass and surface soil moisture simulations using satellite and in situ observations S. de Roos et al. 10.5194/gmd-14-7309-2021
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- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- More severe drought detected by the assimilation of brightness temperature and terrestrial water storage anomalies in Texas during 2010–2013 W. Chen et al. 10.1016/j.jhydrol.2021.126802
- Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model A. Patil et al. 10.3389/frwa.2021.729592
- Modelling the passive microwave signature from land surfaces: A review of recent results and application to the L-band SMOS & SMAP soil moisture retrieval algorithms J. Wigneron et al. 10.1016/j.rse.2017.01.024
- Assimilation of SMAP and ASCAT soil moisture retrievals into the JULES land surface model using the Local Ensemble Transform Kalman Filter E. Seo et al. 10.1016/j.rse.2020.112222
- Assimilation of SMAP Brightness Temperature Observations in the GEOS Land–Atmosphere Data Assimilation System R. Reichle et al. 10.1109/JSTARS.2021.3118595
- Merging active and passive microwave observations in soil moisture data assimilation J. Kolassa et al. 10.1016/j.rse.2017.01.015
- SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives J. Wigneron et al. 10.1016/j.rse.2020.112238
- Assimilation of Passive L-band Microwave Brightness Temperatures in the Canadian Land Data Assimilation System: Impacts on Short-Range Warm Season Numerical Weather Prediction M. Carrera et al. 10.1175/JHM-D-18-0133.1
- Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations E. Cooper et al. 10.5194/hess-25-2445-2021
- Assessing the Feasibility of Satellite‐Based Thresholds for Hydrologically Driven Landsliding M. Thomas et al. 10.1029/2019WR025577
- Mapping Surface Heat Fluxes by Assimilating SMAP Soil Moisture and GOES Land Surface Temperature Data Y. Lu et al. 10.1002/2017WR021415
- The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0 E. Pinnington et al. 10.5194/gmd-13-55-2020
- Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
- Evaluation of State and Bias Estimates for Assimilation of SMOS Retrievals Into Conceptual Rainfall-Runoff Models V. Pauwels et al. 10.3389/frwa.2020.00004
- Improved groundwater table and L-band brightness temperature estimates for Northern Hemisphere peatlands using new model physics and SMOS observations in a global data assimilation framework M. Bechtold et al. 10.1016/j.rse.2020.111805
- SMOS brightness temperature assimilation into the Community Land Model D. Rains et al. 10.5194/hess-21-5929-2017
- Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation K. Mohammed et al. 10.3390/w15020321
- Monitoring Soil Moisture Drought over Northern High Latitudes from Space J. Blyverket et al. 10.3390/rs11101200
- The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning K. Fang et al. 10.1109/TGRS.2018.2872131
- Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons L. Karthikeyan et al. 10.1016/j.advwatres.2017.09.010
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- Preliminary Assimilation of Satellite Derived Land Surface Temperature from SEVIRI in the Surface Scheme of the AROME-France Model M. Sassi et al. 10.16993/tellusa.48
- Reappraisal of the roughness effect parameterization schemes for L-band radiometry over bare soil B. Peng et al. 10.1016/j.rse.2017.07.006
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- Estimation of Snow Mass Information via Assimilation of C-Band Synthetic Aperture Radar Backscatter Observations Into an Advanced Land Surface Model J. Park et al. 10.1109/JSTARS.2021.3133513
- Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm O. Kozhushko et al. 10.23939/mmc2022.02.203
- The impact of multi-sensor land data assimilation on river discharge estimation W. Wu et al. 10.1016/j.rse.2022.113138
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- Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: the Murray–Darling basin in Australia as a test case R. Hostache et al. 10.5194/hess-24-4793-2020
- Leveraging Soil Moisture Assimilation in Permafrost Affected Regions A. Pradhan et al. 10.3390/rs15061532
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- Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China L. Jiang et al. 10.1007/s13351-020-0033-7
- Validation practices for satellite soil moisture retrievals: What are (the) errors? A. Gruber et al. 10.1016/j.rse.2020.111806
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- A Dielectric Mixing Model Accounting for Soil Organic Matter C. Park et al. 10.2136/vzj2019.04.0036
- Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface Model Z. Zhang et al. 10.3390/rs14102405
- Improving Soil Moisture and Surface Turbulent Heat Flux Estimates by Assimilation of SMAP Brightness Temperatures or Soil Moisture Retrievals and GOES Land Surface Temperature Retrievals Y. Lu et al. 10.1175/JHM-D-19-0130.1
- SHui, an EU-Chinese cooperative project to optimize soil and water management in agricultural areas in the XXI century J. Gómez et al. 10.1016/j.iswcr.2020.01.001
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93 citations as recorded by crossref.
- The SMOS-Derived Soil Water EXtent and equivalent layer thickness facilitate determination of soil water resources B. Usowicz et al. 10.1038/s41598-020-75475-x
- Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China A. Nadeem et al. 10.3390/rs14040982
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- A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. 10.3389/fsci.2023.1190191
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- Merging active and passive microwave observations in soil moisture data assimilation J. Kolassa et al. 10.1016/j.rse.2017.01.015
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- Monitoring Soil Moisture Drought over Northern High Latitudes from Space J. Blyverket et al. 10.3390/rs11101200
- The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of Deep Learning K. Fang et al. 10.1109/TGRS.2018.2872131
- Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons L. Karthikeyan et al. 10.1016/j.advwatres.2017.09.010
- A Novel Fusion Method for Generating Surface Soil Moisture Data With High Accuracy, High Spatial Resolution, and High Spatio‐Temporal Continuity S. Huang et al. 10.1029/2021WR030827
- Prediction of Active Microwave Backscatter Over Snow-Covered Terrain Across Western Colorado Using a Land Surface Model and Support Vector Machine Regression J. Park et al. 10.1109/JSTARS.2021.3053945
- Preliminary Assimilation of Satellite Derived Land Surface Temperature from SEVIRI in the Surface Scheme of the AROME-France Model M. Sassi et al. 10.16993/tellusa.48
- Reappraisal of the roughness effect parameterization schemes for L-band radiometry over bare soil B. Peng et al. 10.1016/j.rse.2017.07.006
- Retrieving accurate soil moisture over the Tibetan Plateau using multi-source remote sensing data assimilation with simultaneous state and parameter estimations W. Chen et al. 10.1175/JHM-D-20-0298.1
- Improvement of the soil-atmosphere interactions and subsequent heavy precipitation modelling by enhanced initialization using remotely sensed 1 km soil moisture information S. Helgert & S. Khodayar 10.1016/j.rse.2020.111812
- Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication G. De Lannoy et al. 10.3389/frwa.2022.981745
- Estimation of Snow Mass Information via Assimilation of C-Band Synthetic Aperture Radar Backscatter Observations Into an Advanced Land Surface Model J. Park et al. 10.1109/JSTARS.2021.3133513
- Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm O. Kozhushko et al. 10.23939/mmc2022.02.203
- The impact of multi-sensor land data assimilation on river discharge estimation W. Wu et al. 10.1016/j.rse.2022.113138
- Benefits and pitfalls of irrigation timing and water amounts derived from satellite soil moisture L. Zappa et al. 10.1016/j.agwat.2024.108773
- Assimilation of Soil Moisture and Ocean Salinity (SMOS) brightness temperature into a large-scale distributed conceptual hydrological model to improve soil moisture predictions: the Murray–Darling basin in Australia as a test case R. Hostache et al. 10.5194/hess-24-4793-2020
- Leveraging Soil Moisture Assimilation in Permafrost Affected Regions A. Pradhan et al. 10.3390/rs15061532
- Evaluation of GEOS-Simulated L-Band Microwave Brightness Temperature Using Aquarius Observations over Non-Frozen Land across North America J. Park et al. 10.3390/rs12183098
- Fusion of In-Situ Soil Moisture and Land Surface Model Estimates Using Localized Ensemble Optimum Interpolation over China L. Jiang et al. 10.1007/s13351-020-0033-7
- Validation practices for satellite soil moisture retrievals: What are (the) errors? A. Gruber et al. 10.1016/j.rse.2020.111806
- Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring H. Zhou et al. 10.3390/rs14133187
- A Dielectric Mixing Model Accounting for Soil Organic Matter C. Park et al. 10.2136/vzj2019.04.0036
- Effect of Assimilating SMAP Soil Moisture on CO2 and CH4 Fluxes through Direct Insertion in a Land Surface Model Z. Zhang et al. 10.3390/rs14102405
- Improving Soil Moisture and Surface Turbulent Heat Flux Estimates by Assimilation of SMAP Brightness Temperatures or Soil Moisture Retrievals and GOES Land Surface Temperature Retrievals Y. Lu et al. 10.1175/JHM-D-19-0130.1
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Latest update: 20 Nov 2024
Short summary
The SMOS mission provides various various products to estimate soil moisture. This paper evaluates the performance of assimilating either Level-1-based multi-angle brightness temperature (Tb) observations, Level-1-based single-angle Tb observations, or Level 2 soil moisture retrievals, into the NASA Catchment land surface model.
The SMOS mission provides various various products to estimate soil moisture. This paper...