Articles | Volume 16, issue 5
https://doi.org/10.5194/gmd-16-1553-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1553-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
17 Mar 2023
Model description paper |
| 17 Mar 2023
| 17 Mar 2023
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
Jiangtao Liu
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
David Hughes
Department of Entomology, The Pennsylvania State University,
University Park, PA, USA
Department of Biology, The Pennsylvania State University, University
Park, PA, USA
The Current and Emerging Threat to Crop Innovation Lab, The
Pennsylvania State University, University Park, PA, USA
Farshid Rahmani
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
Kathryn Lawson
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
Department of Civil and Environmental Engineering, The Pennsylvania
State University, University Park, PA, USA
Viewed
Total article views: 6,175 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Sep 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 5,209 | 850 | 116 | 6,175 | 282 | 144 | 172 |
- HTML: 5,209
- PDF: 850
- XML: 116
- Total: 6,175
- Supplement: 282
- BibTeX: 144
- EndNote: 172
Total article views: 4,779 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 17 Mar 2023)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 4,203 | 480 | 96 | 4,779 | 160 | 130 | 159 |
- HTML: 4,203
- PDF: 480
- XML: 96
- Total: 4,779
- Supplement: 160
- BibTeX: 130
- EndNote: 159
Total article views: 1,396 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 30 Sep 2022)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 1,006 | 370 | 20 | 1,396 | 122 | 14 | 13 |
- HTML: 1,006
- PDF: 370
- XML: 20
- Total: 1,396
- Supplement: 122
- BibTeX: 14
- EndNote: 13
Viewed (geographical distribution)
Total article views: 6,175 (including HTML, PDF, and XML)
Thereof 6,062 with geography defined
and 113 with unknown origin.
Total article views: 4,779 (including HTML, PDF, and XML)
Thereof 4,698 with geography defined
and 81 with unknown origin.
Total article views: 1,396 (including HTML, PDF, and XML)
Thereof 1,364 with geography defined
and 32 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
13 citations as recorded by crossref.
- Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification Q. Li et al. 10.1016/j.catena.2025.108743
- Comparative analysis of machine learning based dissolved oxygen predictions in the Yellow River Basin: The role of diverse environmental predictors L. Liu et al. 10.1016/j.jenvman.2025.127138
- A novel diversity-aware sampling method for global soil moisture prediction Q. Xiao et al. 10.1016/j.jhydrol.2025.133851
- Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations Y. Song et al. 10.1016/j.jhydrol.2024.131573
- Probing the limit of hydrologic predictability with the Transformer network J. Liu et al. 10.1016/j.jhydrol.2024.131389
- Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments K. Ma et al. 10.1016/j.jhydrol.2024.130841
- Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning H. Ji et al. 10.1038/s41467-025-64367-1
- Enhanced global soil moisture prediction through a sampling-weighted sensitive learning strategy applied to various LSTM-based models X. Li et al. 10.1016/j.cageo.2025.106068
- Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
- A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. 10.5194/bg-20-2671-2023
- Improving global soil moisture prediction through cluster-averaged sampling strategy Q. Li et al. 10.1016/j.geoderma.2024.116999
- Advances in remote sensing based soil moisture retrieval: applications, techniques, scales and challenges for combining machine learning and physical models A. Abbes et al. 10.1007/s10462-024-10734-1
- Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. 10.1029/2023WR035337
13 citations as recorded by crossref.
- Improving global soil moisture prediction based on Meta-Learning model leveraging Köppen-Geiger climate classification Q. Li et al. 10.1016/j.catena.2025.108743
- Comparative analysis of machine learning based dissolved oxygen predictions in the Yellow River Basin: The role of diverse environmental predictors L. Liu et al. 10.1016/j.jenvman.2025.127138
- A novel diversity-aware sampling method for global soil moisture prediction Q. Xiao et al. 10.1016/j.jhydrol.2025.133851
- Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations Y. Song et al. 10.1016/j.jhydrol.2024.131573
- Probing the limit of hydrologic predictability with the Transformer network J. Liu et al. 10.1016/j.jhydrol.2024.131389
- Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments K. Ma et al. 10.1016/j.jhydrol.2024.130841
- Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning H. Ji et al. 10.1038/s41467-025-64367-1
- Enhanced global soil moisture prediction through a sampling-weighted sensitive learning strategy applied to various LSTM-based models X. Li et al. 10.1016/j.cageo.2025.106068
- Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
- A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations D. Aboelyazeed et al. 10.5194/bg-20-2671-2023
- Improving global soil moisture prediction through cluster-averaged sampling strategy Q. Li et al. 10.1016/j.geoderma.2024.116999
- Advances in remote sensing based soil moisture retrieval: applications, techniques, scales and challenges for combining machine learning and physical models A. Abbes et al. 10.1007/s10462-024-10734-1
- Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T. Bindas et al. 10.1029/2023WR035337
Latest update: 28 Oct 2025
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Under-monitored regions like Africa need high-quality soil moisture predictions to help with...
