Articles | Volume 22, issue 8
https://doi.org/10.5194/hess-22-4473-2018
© 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-22-4473-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
22 Aug 2018
Research article | Highlight paper |
| 22 Aug 2018
| 22 Aug 2018
Estimating time-dependent vegetation biases in the SMAP soil moisture product
Department of Geography, University of Guelph, Guelph, Ontario, Canada
Department of Environmental Engineering, ETH Zurich, Zurich, Switzerland
Andreas Colliander
NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
Michael H. Cosh
USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, Maryland, USA
José Martínez-Fernández
Instituto Hispano Luso de Investigaciones Agrarias, Universidad de Salamanca, Salamanca, Spain
Heather McNairn
Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada
Patrick J. Starks
USDA-ARS Grazinglands Research Laboratory, El Reno, Oklahoma, USA
Marc Thibeault
Comisión Nacional de Actividades Espaciales, Buenos Aires, Argentina
Aaron Berg
Department of Geography, University of Guelph, Guelph, Ontario, Canada
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33 citations as recorded by crossref.
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- Spatial-temporal variability pattern of multi-depth soil moisture jointly driven by climatic and human factors in China Y. Liu & Y. Yang 10.1016/j.jhydrol.2023.129313
- Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests J. Whitcomb et al. 10.1109/JSTARS.2020.3033591
- Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture E. Cho et al. 10.1016/j.rse.2020.111668
- Evaluating the utility of remotely sensed soil moisture for the characterization of runoff response over Canadian watersheds E. Wadsworth et al. 10.1080/07011784.2019.1691943
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- A Monte Carlo based adaptive Kalman filtering framework for soil moisture data assimilation A. Gruber et al. 10.1016/j.rse.2019.04.003
- Forecasting monthly soil moisture at broad spatial scales in sub-Saharan Africa using three time-series models: evidence from four decades of remotely sensed data S. Tesfamichael et al. 10.1080/22797254.2023.2246638
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- A global-scale intercomparison of Triple Collocation Analysis- and ground-based soil moisture time-variant errors derived from different rescaling techniques K. Wu et al. 10.1016/j.rse.2022.113387
- Comparison of high-resolution airborne soil moisture retrievals to SMAP soil moisture during the SMAP validation experiment 2016 (SMAPVEX16) A. Colliander et al. 10.1016/j.rse.2019.04.004
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- Investigating multiple causes of time-varying SMAP soil moisture biases based on core validation sites data X. Fan et al. 10.1016/j.jhydrol.2022.128151
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- Validation practices for satellite soil moisture retrievals: What are (the) errors? A. Gruber et al. 10.1016/j.rse.2020.111806
- Vegetation signal crosstalk present in official SMAP surface soil moisture retrievals W. Crow & A. Feldman 10.1016/j.rse.2024.114466
- Soil Salinity Dynamics Impairs Radiometer-Based Soil Moisture Retrieval Over Global Cropland X. Fan et al. 10.1109/TGRS.2022.3181586
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- Improving Permafrost Modeling by Assimilating Remotely Sensed Soil Moisture S. Zwieback et al. 10.1029/2018WR023247
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- Analyzing Effects of Crops on SMAP Satellite-Based Soil Moisture Using a Rainfall–Runoff Model in the U.S. Corn Belt N. Jadidoleslam et al. 10.1109/JSTARS.2021.3131133
- Comprehensive assessment of Fengyun-3 satellites derived soil moisture with in-situ measurements across the globe Y. Liu et al. 10.1016/j.jhydrol.2020.125949
- SMAP underestimates soil moisture in vegetation-disturbed areas primarily as a result of biased surface temperature data X. Fan et al. 10.1016/j.rse.2020.111914
- Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands C. Park et al. 10.3390/rs12182939
- L-band remote-sensing increases sampled levels of global soil moisture-air temperature coupling strength J. Dong & W. Crow 10.1016/j.rse.2018.10.024
- Validation of Soil Moisture Data Products From the NASA SMAP Mission A. Colliander et al. 10.1109/JSTARS.2021.3124743
- Impact of vegetation water content information on soil moisture retrievals in agricultural regions: An analysis based on the SMAPVEX16-MicroWEX dataset J. Judge et al. 10.1016/j.rse.2021.112623
- Assessment of five SMAP soil moisture products using ISMN ground-based measurements over varied environmental conditions C. Yi et al. 10.1016/j.jhydrol.2023.129325
33 citations as recorded by crossref.
- Error Propagation in Microwave Soil Moisture and Vegetation Optical Depth Retrievals A. Feldman et al. 10.1109/JSTARS.2021.3124857
- Spatial-temporal variability pattern of multi-depth soil moisture jointly driven by climatic and human factors in China Y. Liu & Y. Yang 10.1016/j.jhydrol.2023.129313
- Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests J. Whitcomb et al. 10.1109/JSTARS.2020.3033591
- Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture E. Cho et al. 10.1016/j.rse.2020.111668
- Evaluating the utility of remotely sensed soil moisture for the characterization of runoff response over Canadian watersheds E. Wadsworth et al. 10.1080/07011784.2019.1691943
- A new SMAP soil moisture and vegetation optical depth product (SMAP-IB): Algorithm, assessment and inter-comparison X. Li et al. 10.1016/j.rse.2022.112921
- Bias-Corrected RADARSAT-2 Soil Moisture Dynamics Reveal Discharge Hysteresis at An Agricultural Watershed J. Lee & K. Lindenschmidt 10.3390/rs15102677
- Validation of Remotely Sensed and Modeled Soil Moisture at Forested and Unforested NEON Sites E. Ayres et al. 10.1109/JSTARS.2024.3430928
- A Monte Carlo based adaptive Kalman filtering framework for soil moisture data assimilation A. Gruber et al. 10.1016/j.rse.2019.04.003
- Forecasting monthly soil moisture at broad spatial scales in sub-Saharan Africa using three time-series models: evidence from four decades of remotely sensed data S. Tesfamichael et al. 10.1080/22797254.2023.2246638
- Short-Term and Long-Term Surface Soil Moisture Memory Time Scales Are Spatially Anticorrelated at Global Scales K. McColl et al. 10.1175/JHM-D-18-0141.1
- A global-scale intercomparison of Triple Collocation Analysis- and ground-based soil moisture time-variant errors derived from different rescaling techniques K. Wu et al. 10.1016/j.rse.2022.113387
- Comparison of high-resolution airborne soil moisture retrievals to SMAP soil moisture during the SMAP validation experiment 2016 (SMAPVEX16) A. Colliander et al. 10.1016/j.rse.2019.04.004
- Satellite detection of varying seasonal water supply restrictions on grassland productivity in the Missouri basin, USA G. A et al. 10.1016/j.rse.2019.111623
- Time-variant error characterization of SMAP and ASCAT soil moisture using Triple Collocation Analysis K. Wu et al. 10.1016/j.rse.2021.112324
- Investigating multiple causes of time-varying SMAP soil moisture biases based on core validation sites data X. Fan et al. 10.1016/j.jhydrol.2022.128151
- Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters M. Maertens et al. 10.5194/hess-25-4099-2021
- Validation practices for satellite soil moisture retrievals: What are (the) errors? A. Gruber et al. 10.1016/j.rse.2020.111806
- Vegetation signal crosstalk present in official SMAP surface soil moisture retrievals W. Crow & A. Feldman 10.1016/j.rse.2024.114466
- Soil Salinity Dynamics Impairs Radiometer-Based Soil Moisture Retrieval Over Global Cropland X. Fan et al. 10.1109/TGRS.2022.3181586
- Validation of Four Satellite-Derived Soil Moisture Products Using Ground-Based In Situ Observations over Northern China W. Liu et al. 10.3390/rs14061419
- Clarifications on the equations and the sample number in triple collocation analysis using SST observations C. Tsamalis 10.1016/j.rse.2022.112936
- Improving Permafrost Modeling by Assimilating Remotely Sensed Soil Moisture S. Zwieback et al. 10.1029/2018WR023247
- Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology A. Gruber et al. 10.5194/essd-11-717-2019
- Vegetation–soil moisture coupling metrics from dual-polarization microwave radiometry using regularization S. Zwieback et al. 10.1016/j.rse.2019.111257
- Analyzing Effects of Crops on SMAP Satellite-Based Soil Moisture Using a Rainfall–Runoff Model in the U.S. Corn Belt N. Jadidoleslam et al. 10.1109/JSTARS.2021.3131133
- Comprehensive assessment of Fengyun-3 satellites derived soil moisture with in-situ measurements across the globe Y. Liu et al. 10.1016/j.jhydrol.2020.125949
- SMAP underestimates soil moisture in vegetation-disturbed areas primarily as a result of biased surface temperature data X. Fan et al. 10.1016/j.rse.2020.111914
- Parameterization of Vegetation Scattering Albedo in the Tau-Omega Model for Soil Moisture Retrieval on Croplands C. Park et al. 10.3390/rs12182939
- L-band remote-sensing increases sampled levels of global soil moisture-air temperature coupling strength J. Dong & W. Crow 10.1016/j.rse.2018.10.024
- Validation of Soil Moisture Data Products From the NASA SMAP Mission A. Colliander et al. 10.1109/JSTARS.2021.3124743
- Impact of vegetation water content information on soil moisture retrievals in agricultural regions: An analysis based on the SMAPVEX16-MicroWEX dataset J. Judge et al. 10.1016/j.rse.2021.112623
- Assessment of five SMAP soil moisture products using ISMN ground-based measurements over varied environmental conditions C. Yi et al. 10.1016/j.jhydrol.2023.129325
Latest update: 02 Apr 2025
Short summary
Satellite soil moisture products can provide critical information on incipient droughts and the interplay between vegetation and water availability. However, time-variant systematic errors in the soil moisture products may impede their usefulness. Using a novel statistical approach, we detect such errors (associated with changing vegetation) in the SMAP soil moisture product. The vegetation-associated biases impede drought detection and the quantification of vegetation–water interactions.
Satellite soil moisture products can provide critical information on incipient droughts and the...
