SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data
Abstract Accurate and continuous monitoring of soil moisture (SM) is crucial for a wide range of applications in agriculture, hydrology, and climate modelling. In this study, we present a novel machine learning (ML) based framework for generating a continuously updated, multilayer global SM dataset:...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05511-w |
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| author | Yuhan Liu Yuanyuan Zha Gulin Ran Yonggen Zhang Liangsheng Shi |
| author_facet | Yuhan Liu Yuanyuan Zha Gulin Ran Yonggen Zhang Liangsheng Shi |
| author_sort | Yuhan Liu |
| collection | DOAJ |
| description | Abstract Accurate and continuous monitoring of soil moisture (SM) is crucial for a wide range of applications in agriculture, hydrology, and climate modelling. In this study, we present a novel machine learning (ML) based framework for generating a continuously updated, multilayer global SM dataset: SMRFR (Soil Moisture via Random Forest Regression). Leveraging publicly available reanalysis and remote sensing data, SMRFR provides daily SM estimates at five soil layers (0–5, 5–10, 10–30, 30–50 and 50–100 cm) with a spatial resolution of 9 km, covering the period from 2000 to 2023. Evaluation results demonstrate that SMRFR effectively captures both spatial and temporal SM variability. It also exhibits strong generalization capacity, successfully transferring knowledge across continents and accurately capturing transient and seasonal SM dynamics following rainfall events. SMRFR achieved an unbiased root mean square error of 0.0339 m3/m3 on the validation set. Our novel SM dataset offers a basis and valuable reference for agricultural, hydrological, and ecological research, enabling improved analysis and modelling of SM dynamics at regional to global scales. |
| format | Article |
| id | doaj-art-e38bcaf97a11473694292e61c27ea1c6 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-e38bcaf97a11473694292e61c27ea1c62025-08-20T03:45:45ZengNature PortfolioScientific Data2052-44632025-07-0112111610.1038/s41597-025-05511-wSMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source dataYuhan Liu0Yuanyuan Zha1Gulin Ran2Yonggen Zhang3Liangsheng Shi4State Key Laboratory of Water Resources Engineering and Management, Wuhan UniversityState Key Laboratory of Water Resources Engineering and Management, Wuhan UniversityState Key Laboratory of Water Resources Engineering and Management, Wuhan UniversityInstitute of Surface-Earth System Science, School of Earth System Science, Tianjin UniversityState Key Laboratory of Water Resources Engineering and Management, Wuhan UniversityAbstract Accurate and continuous monitoring of soil moisture (SM) is crucial for a wide range of applications in agriculture, hydrology, and climate modelling. In this study, we present a novel machine learning (ML) based framework for generating a continuously updated, multilayer global SM dataset: SMRFR (Soil Moisture via Random Forest Regression). Leveraging publicly available reanalysis and remote sensing data, SMRFR provides daily SM estimates at five soil layers (0–5, 5–10, 10–30, 30–50 and 50–100 cm) with a spatial resolution of 9 km, covering the period from 2000 to 2023. Evaluation results demonstrate that SMRFR effectively captures both spatial and temporal SM variability. It also exhibits strong generalization capacity, successfully transferring knowledge across continents and accurately capturing transient and seasonal SM dynamics following rainfall events. SMRFR achieved an unbiased root mean square error of 0.0339 m3/m3 on the validation set. Our novel SM dataset offers a basis and valuable reference for agricultural, hydrological, and ecological research, enabling improved analysis and modelling of SM dynamics at regional to global scales.https://doi.org/10.1038/s41597-025-05511-w |
| spellingShingle | Yuhan Liu Yuanyuan Zha Gulin Ran Yonggen Zhang Liangsheng Shi SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data Scientific Data |
| title | SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data |
| title_full | SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data |
| title_fullStr | SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data |
| title_full_unstemmed | SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data |
| title_short | SMRFR: A global multilayer soil moisture dataset generated using Random Forest from multi-source data |
| title_sort | smrfr a global multilayer soil moisture dataset generated using random forest from multi source data |
| url | https://doi.org/10.1038/s41597-025-05511-w |
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