Articles | Volume 26, issue 13
https://doi.org/10.5194/hess-26-3337-2022
© Author(s) 2022. 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-26-3337-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Jul 2022
Research article |
| 04 Jul 2022
| 04 Jul 2022
Exploring the combined use of SMAP and Sentinel-1 data for downscaling soil moisture beyond the 1 km scale
Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
now at: Hydrogeology and Landscape Hydrology Group, Institute for
Biology and Environmental Sciences, Carl von Ossietzky University of Oldenburg, Ammerländer Heerstr. 114–118, 26129 Oldenburg, Germany
Wenmin Zhang
Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
Søren Julsgaard Kragh
Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
Mie Andreasen
Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
Karsten Høgh Jensen
Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
Rasmus Fensholt
Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
Simon Stisen
Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
Majken C. Looms
Department of Geosciences and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, 1350 Copenhagen, Denmark
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Cited
13 citations as recorded by crossref.
- Toward Large‐Scale Soil Moisture Monitoring Using Rail‐Based Cosmic Ray Neutron Sensing D. Altdorff et al. 10.1029/2022WR033514
- Estimation of flood inundation in river basins of Uttar Pradesh using Sentinel 1A-SAR data on Sentinel Application Platform (SNAP) P. Gautam et al. 10.1007/s12517-024-11910-x
- Within‐field soil moisture variability and time‐invariant spatial structures of agricultural fields in the US Midwest Y. Yang et al. 10.1002/vzj2.20337
- Sentinel-1 Backscatter and Interferometric Coherence for Soil Moisture Retrieval in Winter Wheat Fields Within a Semiarid South-Mediterranean Climate: Machine Learning Versus Semiempirical Models J. Ezzahar et al. 10.1109/JSTARS.2023.3339616
- Field Testing of Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field S. Becker et al. 10.3390/s24072223
- Historical Hazard Assessment of Climate and Land Use–Land Cover Effects on Soil Erosion Using Remote Sensing: Case Study of Oman S. Shojaeezadeh et al. 10.3390/rs16162976
- A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest Q. Yang et al. 10.1016/j.rse.2023.113880
- An inter-comparison of approaches and frameworks to quantify irrigation from satellite data S. Kragh et al. 10.5194/hess-28-441-2024
- Estimation of Sentinel-1 derived soil moisture using modified Dubois model P. Settu & M. Ramaiah 10.1007/s10668-024-05460-1
- PIML-SM: Physics-Informed Machine Learning to Estimate Surface Soil Moisture From Multisensor Satellite Images by Leveraging Swarm Intelligence A. Singh & K. Gaurav 10.1109/TGRS.2024.3502618
- Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data J. Wang et al. 10.3390/app142411875
- Assessing road construction effects on turbidity in adjacent water bodies using Sentinel-1 and Sentinel-2 M. Mooselu et al. 10.1016/j.scitotenv.2024.177554
- 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
12 citations as recorded by crossref.
- Toward Large‐Scale Soil Moisture Monitoring Using Rail‐Based Cosmic Ray Neutron Sensing D. Altdorff et al. 10.1029/2022WR033514
- Estimation of flood inundation in river basins of Uttar Pradesh using Sentinel 1A-SAR data on Sentinel Application Platform (SNAP) P. Gautam et al. 10.1007/s12517-024-11910-x
- Within‐field soil moisture variability and time‐invariant spatial structures of agricultural fields in the US Midwest Y. Yang et al. 10.1002/vzj2.20337
- Sentinel-1 Backscatter and Interferometric Coherence for Soil Moisture Retrieval in Winter Wheat Fields Within a Semiarid South-Mediterranean Climate: Machine Learning Versus Semiempirical Models J. Ezzahar et al. 10.1109/JSTARS.2023.3339616
- Field Testing of Gamma-Spectroscopy Method for Soil Water Content Estimation in an Agricultural Field S. Becker et al. 10.3390/s24072223
- Historical Hazard Assessment of Climate and Land Use–Land Cover Effects on Soil Erosion Using Remote Sensing: Case Study of Oman S. Shojaeezadeh et al. 10.3390/rs16162976
- A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest Q. Yang et al. 10.1016/j.rse.2023.113880
- An inter-comparison of approaches and frameworks to quantify irrigation from satellite data S. Kragh et al. 10.5194/hess-28-441-2024
- Estimation of Sentinel-1 derived soil moisture using modified Dubois model P. Settu & M. Ramaiah 10.1007/s10668-024-05460-1
- PIML-SM: Physics-Informed Machine Learning to Estimate Surface Soil Moisture From Multisensor Satellite Images by Leveraging Swarm Intelligence A. Singh & K. Gaurav 10.1109/TGRS.2024.3502618
- Downscaling of Remote Sensing Soil Moisture Products That Integrate Microwave and Optical Data J. Wang et al. 10.3390/app142411875
- Assessing road construction effects on turbidity in adjacent water bodies using Sentinel-1 and Sentinel-2 M. Mooselu et al. 10.1016/j.scitotenv.2024.177554
Latest update: 24 Dec 2024
Short summary
The amount and spatio-temporal distribution of soil moisture, the water in the upper soil, is of great relevance for agriculture and water management. Here, we investigate whether the established downscaling algorithm combining different satellite products to estimate medium-scale soil moisture is applicable to higher resolutions and whether results can be improved by accounting for land cover types. Original satellite data and downscaled soil moisture are compared with ground observations.
The amount and spatio-temporal distribution of soil moisture, the water in the upper soil, is of...
