Articles | Volume 15, issue 5
https://doi.org/10.5194/essd-15-2055-2023
© 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/essd-15-2055-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
23 May 2023
Data description paper |
| 23 May 2023
| 23 May 2023
Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning
Yufang Zhang
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Department of Geography, The University of Hong Kong, Hong Kong
999077, China
Department of Geography, The University of Hong Kong, Hong Kong
999077, China
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Qian Wang
State Key Laboratory of Remote Sensing Science, Beijing Normal
University, Beijing 100875, China
Bing Li
Key Research Institute of Yellow River Civilization and Sustainable
Development and Collaborative Innovation Center on Yellow River
Civilization of Henan Province, Henan University, Kaifeng 475001, China
Jianglei Xu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Guodong Zhang
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Xiaobang Liu
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
Changhao Xiong
School of Remote Sensing and Information Engineering, Wuhan
University, Wuhan 430079, China
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Cited
12 citations as recorded by crossref.
- An Integrated Learning Framework for Seamless High-Resolution Soil Moisture Estimation Y. Jing et al. 10.1109/TGRS.2024.3461717
- Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques M. Tahmouresi et al. 10.1038/s41598-024-77050-0
- Exploring the actual spatial resolution of 1 km satellite soil moisture products L. Brocca et al. 10.1016/j.scitotenv.2024.174087
- Spatial Machine Learning for Exploring the Variability in Low Height‐For‐Age From Socioeconomic, Agroecological, and Climate Features in the Northern Province of Rwanda G. Nduwayezu et al. 10.1029/2024GH001027
- Probabilistic Evaluation of Drought Propagation Using Satellite Data and Deep Learning Model: From Precipitation to Soil Moisture and Groundwater J. Seo & S. Lee 10.1109/JSTARS.2023.3290685
- Identification of the dominant factors and altitudinal variation in water use efficiency in the Qinling–Daba Mountains T. Zhao et al. 10.1016/j.ecolind.2024.111626
- Estimating Land Surface All-Wave Daily Net Radiation From VIIRS Top-of-Atmosphere Data X. Yin et al. 10.1109/LGRS.2024.3412731
- Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale Q. Han et al. 10.5194/gmd-16-5825-2023
- Improving SMAP soil moisture spatial resolution in different climatic conditions using remote sensing data F. Imanpour et al. 10.1007/s10661-023-12107-7
- How Big Data Can Help to Monitor the Environment and to Mitigate Risks due to Climate Change: A review J. Montillet et al. 10.1109/MGRS.2024.3379108
- Generation of global 1 km all-weather instantaneous and daily mean land surface temperatures from MODIS data B. Li et al. 10.5194/essd-16-3795-2024
- A Review on Soil Moisture Dynamics Monitoring in Semi-Arid Ecosystems: Methods, Techniques, and Tools Applied at Different Scales E. Duarte & A. Hernandez 10.3390/app14177677
12 citations as recorded by crossref.
- An Integrated Learning Framework for Seamless High-Resolution Soil Moisture Estimation Y. Jing et al. 10.1109/TGRS.2024.3461717
- Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques M. Tahmouresi et al. 10.1038/s41598-024-77050-0
- Exploring the actual spatial resolution of 1 km satellite soil moisture products L. Brocca et al. 10.1016/j.scitotenv.2024.174087
- Spatial Machine Learning for Exploring the Variability in Low Height‐For‐Age From Socioeconomic, Agroecological, and Climate Features in the Northern Province of Rwanda G. Nduwayezu et al. 10.1029/2024GH001027
- Probabilistic Evaluation of Drought Propagation Using Satellite Data and Deep Learning Model: From Precipitation to Soil Moisture and Groundwater J. Seo & S. Lee 10.1109/JSTARS.2023.3290685
- Identification of the dominant factors and altitudinal variation in water use efficiency in the Qinling–Daba Mountains T. Zhao et al. 10.1016/j.ecolind.2024.111626
- Estimating Land Surface All-Wave Daily Net Radiation From VIIRS Top-of-Atmosphere Data X. Yin et al. 10.1109/LGRS.2024.3412731
- Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale Q. Han et al. 10.5194/gmd-16-5825-2023
- Improving SMAP soil moisture spatial resolution in different climatic conditions using remote sensing data F. Imanpour et al. 10.1007/s10661-023-12107-7
- How Big Data Can Help to Monitor the Environment and to Mitigate Risks due to Climate Change: A review J. Montillet et al. 10.1109/MGRS.2024.3379108
- Generation of global 1 km all-weather instantaneous and daily mean land surface temperatures from MODIS data B. Li et al. 10.5194/essd-16-3795-2024
- A Review on Soil Moisture Dynamics Monitoring in Semi-Arid Ecosystems: Methods, Techniques, and Tools Applied at Different Scales E. Duarte & A. Hernandez 10.3390/app14177677
Latest update: 23 Nov 2024
Short summary
Soil moisture observations are important for a range of earth system applications. This study generated a long-term (2000–2020) global seamless soil moisture product with both high spatial and temporal resolutions (1 km, daily) using an XGBoost model and multisource datasets. Evaluation of this product against dense in situ soil moisture datasets and microwave soil moisture products showed that this product has reliable accuracy and more complete spatial coverage.
Soil moisture observations are important for a range of earth system applications. This study...
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