Articles | Volume 22, issue 4
https://doi.org/10.5194/hess-22-2255-2018
© Author(s) 2018. 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-22-2255-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Apr 2018
Research article |
| 13 Apr 2018
| 13 Apr 2018
Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values
Coleen D. U. Carranza
CORRESPONDING AUTHOR
Soil Physics and Land Management Group, Wageningen University, Wageningen, the Netherlands
Martine J. van der Ploeg
Soil Physics and Land Management Group, Wageningen University, Wageningen, the Netherlands
Paul J. J. F. Torfs
Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen, the Netherlands
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Cited
16 citations as recorded by crossref.
- Biophysical constraints on evapotranspiration partitioning for a conductance-based two source energy balance model J. Bu et al. 10.1016/j.jhydrol.2021.127179
- Towards interpreting machine learning models for predicting soil moisture droughts F. Huang et al. 10.1088/1748-9326/acdbe0
- Short-Term Hydrological Forecast Using Artificial Neural Network Models with Different Combinations and Spatial Representations of Hydrometeorological Inputs R. Jougla & R. Leconte 10.3390/w14040552
- Responsive characteristics of soil water regimes to rainfall events in a boreal larch forest in China: Dynamic processes and decoupling effects Z. Xu et al. 10.1016/j.geoderma.2023.116741
- Relationships among soil moisture at various depths under diverse climate, land cover and soil texture N. Li et al. 10.1016/j.scitotenv.2024.174583
- Monitoring the Time-Lagged Response of Land Subsidence to Groundwater Fluctuations via InSAR and Distributed Fiber-Optic Strain Sensing Q. He et al. 10.3390/app15147991
- A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing M. Li et al. 10.3390/rs15225361
- State updating of root zone soil moisture estimates of an unsaturated zone metamodel for operational water resources management M. Pezij et al. 10.1016/j.hydroa.2019.100040
- Validation of SMAP L2 passive-only soil moisture products using upscaled in situ measurements collected in Twente, the Netherlands R. van der Velde et al. 10.5194/hess-25-473-2021
- Anatomy of the 2018 agricultural drought in the Netherlands using in situ soil moisture and satellite vegetation indices J. Buitink et al. 10.5194/hess-24-6021-2020
- A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry D. Blank et al. 10.5194/hess-27-2413-2023
- Remote sensing of root zone soil moisture: A review of methods and products A. Kasim et al. 10.1016/j.jhydrol.2025.133002
- Improved subsurface soil moisture prediction from surface soil moisture through the integration of the (de)coupling effect Z. Xu et al. 10.1016/j.jhydrol.2022.127634
- Twelve years of profile soil moisture and temperature measurements in Twente, the Netherlands R. van der Velde et al. 10.5194/essd-15-1889-2023
- Assessing the Potential of Combined SMAP and In-Situ Soil Moisture for Improving Streamflow Forecast S. Wakigari & R. Leconte 10.3390/hydrology10020031
- Multi-layer grid-scale soil moisture estimation using spatiotemporal deep learning methods with physical constraints T. Zhang et al. 10.1016/j.jhydrol.2025.133086
16 citations as recorded by crossref.
- Biophysical constraints on evapotranspiration partitioning for a conductance-based two source energy balance model J. Bu et al. 10.1016/j.jhydrol.2021.127179
- Towards interpreting machine learning models for predicting soil moisture droughts F. Huang et al. 10.1088/1748-9326/acdbe0
- Short-Term Hydrological Forecast Using Artificial Neural Network Models with Different Combinations and Spatial Representations of Hydrometeorological Inputs R. Jougla & R. Leconte 10.3390/w14040552
- Responsive characteristics of soil water regimes to rainfall events in a boreal larch forest in China: Dynamic processes and decoupling effects Z. Xu et al. 10.1016/j.geoderma.2023.116741
- Relationships among soil moisture at various depths under diverse climate, land cover and soil texture N. Li et al. 10.1016/j.scitotenv.2024.174583
- Monitoring the Time-Lagged Response of Land Subsidence to Groundwater Fluctuations via InSAR and Distributed Fiber-Optic Strain Sensing Q. He et al. 10.3390/app15147991
- A Review of Root Zone Soil Moisture Estimation Methods Based on Remote Sensing M. Li et al. 10.3390/rs15225361
- State updating of root zone soil moisture estimates of an unsaturated zone metamodel for operational water resources management M. Pezij et al. 10.1016/j.hydroa.2019.100040
- Validation of SMAP L2 passive-only soil moisture products using upscaled in situ measurements collected in Twente, the Netherlands R. van der Velde et al. 10.5194/hess-25-473-2021
- Anatomy of the 2018 agricultural drought in the Netherlands using in situ soil moisture and satellite vegetation indices J. Buitink et al. 10.5194/hess-24-6021-2020
- A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry D. Blank et al. 10.5194/hess-27-2413-2023
- Remote sensing of root zone soil moisture: A review of methods and products A. Kasim et al. 10.1016/j.jhydrol.2025.133002
- Improved subsurface soil moisture prediction from surface soil moisture through the integration of the (de)coupling effect Z. Xu et al. 10.1016/j.jhydrol.2022.127634
- Twelve years of profile soil moisture and temperature measurements in Twente, the Netherlands R. van der Velde et al. 10.5194/essd-15-1889-2023
- Assessing the Potential of Combined SMAP and In-Situ Soil Moisture for Improving Streamflow Forecast S. Wakigari & R. Leconte 10.3390/hydrology10020031
- Multi-layer grid-scale soil moisture estimation using spatiotemporal deep learning methods with physical constraints T. Zhang et al. 10.1016/j.jhydrol.2025.133086
Latest update: 08 Aug 2025
Short summary
Remote sensing has been popular for mapping surface soil moisture. However, estimating subsurface values using surface soil moisture remains a challenge, as decoupling can occur. Depth-integrated soil moisture values used in hydrological models are affected by vertical variability. Using statistical methods, we investigate vertical variability between the surface (5 cm) and subsurface (40 cm) to quantify decoupling. We also discuss potential controls for decoupling during wet and dry conditions.
Remote sensing has been popular for mapping surface soil moisture. However, estimating...
