Articles | Volume 25, issue 3
https://doi.org/10.5194/hess-25-1617-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-1617-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2021
Research article |
| 31 Mar 2021
| 31 Mar 2021
Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data
Ewan Pinnington
CORRESPONDING AUTHOR
National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
Javier Amezcua
National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
Elizabeth Cooper
UK Centre for Ecology and Hydrology, Wallingford, UK
Simon Dadson
UK Centre for Ecology and Hydrology, Wallingford, UK
School of Geography and the Environment, University of Oxford, Oxford, UK
Rich Ellis
UK Centre for Ecology and Hydrology, Wallingford, UK
Jian Peng
Department of Remote Sensing, Helmholtz Centre for Environmental Research – UFZ, Permoserstraße 15, 04318 Leipzig, Germany
Remote Sensing Centre for Earth System Research, Leipzig University, 04103 Leipzig, Germany
Emma Robinson
UK Centre for Ecology and Hydrology, Wallingford, UK
Ross Morrison
UK Centre for Ecology and Hydrology, Wallingford, UK
Simon Osborne
Met Office Field Site, Cardington Airfield, Shortstown, Bedford, UK
Tristan Quaife
National Centre for Earth Observation, Department of Meteorology, University of Reading, Reading, UK
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Cited
20 citations as recorded by crossref.
- Comparative Analysis of Machine and Deep Learning Models for Soil Properties Prediction from Hyperspectral Visual Band D. Datta et al. 10.3390/environments10050077
- COSMOS-UK: national soil moisture and hydrometeorology data for environmental science research H. Cooper et al. 10.5194/essd-13-1737-2021
- Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe X. Shan et al. 10.1016/j.rse.2024.114167
- On the Uncertainty Induced by Pedotransfer Functions in Terrestrial Biosphere Modeling A. Paschalis et al. 10.1029/2021WR031871
- Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product? E. Jalilvand et al. 10.1029/2022WR033342
- Thermal Hydraulic Disaggregation of SMAP Soil Moisture Over the Continental United States P. Liu et al. 10.1109/JSTARS.2022.3165644
- Validation of Satellite Soil Moisture Products by Sparsification of Ground Observations L. Hao et al. 10.1109/JSTARS.2024.3362833
- Potential of Mapping Global Soil Texture Type From SMAP Soil Moisture Product: A Pilot Study L. Zhao et al. 10.1109/TGRS.2021.3119667
- 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
- Bias correction of satellite soil moisture through data assimilation J. Qin et al. 10.1016/j.jhydrol.2022.127947
- Spectral Indices as a Tool to Assess the Moisture Status of Forest Habitats A. Młynarczyk et al. 10.3390/rs14174267
- A regional coupled approach to water cycle prediction during winter 2013/14 in the United Kingdom H. Lewis & S. Dadson 10.1002/hyp.14438
- Local-scale evaluation of the simulated interactions between energy, water and vegetation in ISBA, ORCHIDEE and a diagnostic model J. De Pue et al. 10.5194/bg-19-4361-2022
- Automatic Regionalization of Model Parameters for Hydrological Models M. Feigl et al. 10.1029/2022WR031966
- Modeling Soil Water Content and Crop-Growth Metrics in a Wheat Field in the North China Plain Using RZWQM2 K. Du et al. 10.3390/agronomy11061245
- Modeling natural forage dependent livestock production in arid and semi-arid regions: analysis of seasonal soil moisture variability and environmental factors S. Kassa et al. 10.1007/s40808-024-01973-w
- Accounting for the spatial range of soil properties in pedotransfer functions S. Wang et al. 10.1016/j.geoderma.2023.116411
- Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios M. Buechel et al. 10.1038/s43247-021-00334-0
- Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model H. Zhao et al. 10.3390/s23052620
- Modelling the impact of forest management and CO2-fertilisation on growth and demography in a Sitka spruce plantation A. Argles et al. 10.1038/s41598-023-39810-2
20 citations as recorded by crossref.
- Comparative Analysis of Machine and Deep Learning Models for Soil Properties Prediction from Hyperspectral Visual Band D. Datta et al. 10.3390/environments10050077
- COSMOS-UK: national soil moisture and hydrometeorology data for environmental science research H. Cooper et al. 10.5194/essd-13-1737-2021
- Assimilating ASCAT normalized backscatter and slope into the land surface model ISBA-A-gs using a Deep Neural Network as the observation operator: Case studies at ISMN stations in western Europe X. Shan et al. 10.1016/j.rse.2024.114167
- On the Uncertainty Induced by Pedotransfer Functions in Terrestrial Biosphere Modeling A. Paschalis et al. 10.1029/2021WR031871
- Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product? E. Jalilvand et al. 10.1029/2022WR033342
- Thermal Hydraulic Disaggregation of SMAP Soil Moisture Over the Continental United States P. Liu et al. 10.1109/JSTARS.2022.3165644
- Validation of Satellite Soil Moisture Products by Sparsification of Ground Observations L. Hao et al. 10.1109/JSTARS.2024.3362833
- Potential of Mapping Global Soil Texture Type From SMAP Soil Moisture Product: A Pilot Study L. Zhao et al. 10.1109/TGRS.2021.3119667
- 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
- Bias correction of satellite soil moisture through data assimilation J. Qin et al. 10.1016/j.jhydrol.2022.127947
- Spectral Indices as a Tool to Assess the Moisture Status of Forest Habitats A. Młynarczyk et al. 10.3390/rs14174267
- A regional coupled approach to water cycle prediction during winter 2013/14 in the United Kingdom H. Lewis & S. Dadson 10.1002/hyp.14438
- Local-scale evaluation of the simulated interactions between energy, water and vegetation in ISBA, ORCHIDEE and a diagnostic model J. De Pue et al. 10.5194/bg-19-4361-2022
- Automatic Regionalization of Model Parameters for Hydrological Models M. Feigl et al. 10.1029/2022WR031966
- Modeling Soil Water Content and Crop-Growth Metrics in a Wheat Field in the North China Plain Using RZWQM2 K. Du et al. 10.3390/agronomy11061245
- Modeling natural forage dependent livestock production in arid and semi-arid regions: analysis of seasonal soil moisture variability and environmental factors S. Kassa et al. 10.1007/s40808-024-01973-w
- Accounting for the spatial range of soil properties in pedotransfer functions S. Wang et al. 10.1016/j.geoderma.2023.116411
- Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios M. Buechel et al. 10.1038/s43247-021-00334-0
- Retrieving Soil Physical Properties by Assimilating SMAP Brightness Temperature Observations into the Community Land Model H. Zhao et al. 10.3390/s23052620
- Modelling the impact of forest management and CO2-fertilisation on growth and demography in a Sitka spruce plantation A. Argles et al. 10.1038/s41598-023-39810-2
Latest update: 29 Jun 2024
Short summary
Land surface models are important tools for translating meteorological forecasts and reanalyses into real-world impacts at the Earth's surface. We show that the hydrological predictions, in particular soil moisture, of these models can be improved by combining them with satellite observations from the NASA SMAP mission to update uncertain parameters. We find a 22 % reduction in error at a network of in situ soil moisture sensors after combining model predictions with satellite observations.
Land surface models are important tools for translating meteorological forecasts and reanalyses...
