A unified ensemble soil moisture dataset across the continental United States
Abstract A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiot...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04657-x |
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| author | Lingcheng Li Xinming Lin Yilin Fang Z. Jason Hou L. Ruby Leung Yaoping Wang Jiafu Mao Yaping Xu Elias Massoud Mingjie Shi |
| author_facet | Lingcheng Li Xinming Lin Yilin Fang Z. Jason Hou L. Ruby Leung Yaoping Wang Jiafu Mao Yaping Xu Elias Massoud Mingjie Shi |
| author_sort | Lingcheng Li |
| collection | DOAJ |
| description | Abstract A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM’s spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies in temporal scales across datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land–atmosphere interactions and making recommendations for drought response planning. |
| format | Article |
| id | doaj-art-4685a9f417fe411cb0c4a0eb4172dbb2 |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-4685a9f417fe411cb0c4a0eb4172dbb22025-08-20T02:08:08ZengNature PortfolioScientific Data2052-44632025-04-0112111410.1038/s41597-025-04657-xA unified ensemble soil moisture dataset across the continental United StatesLingcheng Li0Xinming Lin1Yilin Fang2Z. Jason Hou3L. Ruby Leung4Yaoping Wang5Jiafu Mao6Yaping Xu7Elias Massoud8Mingjie Shi9Pacific Northwest National LaboratoryPacific Northwest National LaboratoryPacific Northwest National LaboratoryPacific Northwest National LaboratoryPacific Northwest National LaboratoryEnvironmental Sciences Division and Climate Change Science Institute, Oak Ridge National LaboratoryEnvironmental Sciences Division and Climate Change Science Institute, Oak Ridge National LaboratoryDepartment of Environmental and Geosciences, Sam Houston State UniversityIntegrated Computational Earth Sciences group, Oak Ridge National LaboratoryPacific Northwest National LaboratoryAbstract A unified ensemble soil moisture (SM) package has been developed over the Continental United States (CONUS). The data package includes 19 products from land surface models, remote sensing, reanalysis, and machine learning models. All datasets are unified to a 0.25-degree and monthly spatiotemporal resolution, providing a comprehensive view of surface SM dynamics. The statistical analysis of the datasets leverages the Koppen-Geiger Climate Classification to explore surface SM’s spatiotemporal variabilities. The extracted SM characteristics highlight distinct patterns, with the western CONUS showing larger coefficient of variation values and the eastern CONUS exhibiting higher SM values. Remote sensing datasets tend to be drier, while reanalysis products present wetter conditions. In-situ SM observations serve as the basis for wavelet power spectrum analyses to explain discrepancies in temporal scales across datasets facilitating daily SM records. This study provides a comprehensive soil moisture data package and an analysis framework that can be used for Earth system model evaluations and uncertainty quantification, quantifying drought impacts and land–atmosphere interactions and making recommendations for drought response planning.https://doi.org/10.1038/s41597-025-04657-x |
| spellingShingle | Lingcheng Li Xinming Lin Yilin Fang Z. Jason Hou L. Ruby Leung Yaoping Wang Jiafu Mao Yaping Xu Elias Massoud Mingjie Shi A unified ensemble soil moisture dataset across the continental United States Scientific Data |
| title | A unified ensemble soil moisture dataset across the continental United States |
| title_full | A unified ensemble soil moisture dataset across the continental United States |
| title_fullStr | A unified ensemble soil moisture dataset across the continental United States |
| title_full_unstemmed | A unified ensemble soil moisture dataset across the continental United States |
| title_short | A unified ensemble soil moisture dataset across the continental United States |
| title_sort | unified ensemble soil moisture dataset across the continental united states |
| url | https://doi.org/10.1038/s41597-025-04657-x |
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