Articles | Volume 14, issue 6
https://doi.org/10.5194/essd-14-2613-2022
© 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/essd-14-2613-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
08 Jun 2022
Data description paper |
| 08 Jun 2022
| 08 Jun 2022
A 1 km daily surface soil moisture dataset of enhanced coverage under all-weather conditions over China in 2003–2019
Peilin Song
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, The Chinese
Academy of Sciences, Beijing 100101, China
State Key Laboratory of Remote Sensing Science, Aerospace
Information Research Institute, Chinese Academy of Sciences. Beijing 100101,
China
now at: School of Electronic Science and Engineering, Xi'an
Jiaotong University, Xi'an, 710049, China
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, The Chinese
Academy of Sciences, Beijing 100101, China
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Jiancheng Shi
National Space Science Center, Chinese Academy of Sciences, Beijing
100190, China
Tianjie Zhao
State Key Laboratory of Remote Sensing Science, Aerospace
Information Research Institute, Chinese Academy of Sciences. Beijing 100101,
China
Bing Tong
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
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Cited
17 citations as recorded by crossref.
- Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning Y. Zhang et al. 10.5194/essd-15-2055-2023
- Trend of Vegetation and Environmental Factors and Their Feedback in the Karst Regions of Southwestern China K. Huang et al. 10.3390/su142315941
- Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China H. Cui et al. 10.1109/JSTARS.2022.3216267
- High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau Z. Li et al. 10.1080/10095020.2024.2307931
- Estimation and Spatiotemporal Evolution Analysis of Actual Evapotranspiration in Turpan and Hami Cities Based on Multi-Source Data L. Wang et al. 10.3390/rs15102565
- 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
- Inconsistency and correction of manually observed ground surface temperatures over snow-covered regions B. Cao et al. 10.1016/j.agrformet.2023.109518
- Spatiotemporal assessment and scenario simulation of the risk potential of industrial sites at the regional scale Y. Jiang et al. 10.1016/j.scitotenv.2023.167537
- Disaggregation of remote sensing and model-based data for 1 km daily seamless soil moisture L. Zhu et al. 10.1016/j.jag.2023.103572
- Application of a two source energy balance model coupled with satellite based soil moisture and thermal infrared data L. Song et al. 10.1016/j.isprsjprs.2023.08.009
- Valuing ecological restoration benefits cannot fully support landscape sustainability: a case study in Inner Mongolia, China C. Wang et al. 10.1007/s10980-023-01697-9
- Exploring the dominant drivers affecting soil water content and vegetation growth by decoupling meteorological indicators X. Mao et al. 10.1016/j.jhydrol.2024.130722
- Estimation of quasi-full spatial coverage soil moisture with fine resolution in China from the combined use of ERA5-Land reanalysis and TRIMS land surface temperature product Y. Zhang et al. 10.1016/j.agwat.2022.107990
- Estimation of all-weather land surface temperature through correcting cloud-shadowing bias simulated by hourly cloud information P. Song et al. 10.1016/j.jag.2024.103703
- Multi-Source Soil Moisture Data Fusion Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation in the Western U.S. H. Chen et al. 10.3390/s23198019
- A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by Advanced Very High Resolution Radiometer observations from 1981 to 2021 J. Li et al. 10.5194/essd-15-2189-2023
- An improved process-based evapotranspiration/heat fluxes remote sensing algorithm based on the Bayesian and Sobol’ uncertainty analysis framework using eddy covariance observations of Tibetan grasslands J. Feng et al. 10.1016/j.jhydrol.2022.128384
17 citations as recorded by crossref.
- Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning Y. Zhang et al. 10.5194/essd-15-2055-2023
- Trend of Vegetation and Environmental Factors and Their Feedback in the Karst Regions of Southwestern China K. Huang et al. 10.3390/su142315941
- Investigating the Potential of Downscaling Approaches for SMAP Radiometer Soil Moisture in Agroforestry Areas, China H. Cui et al. 10.1109/JSTARS.2022.3216267
- High-spatial-resolution surface soil moisture retrieval using the Deep Forest model in the cloud environment over the Tibetan Plateau Z. Li et al. 10.1080/10095020.2024.2307931
- Estimation and Spatiotemporal Evolution Analysis of Actual Evapotranspiration in Turpan and Hami Cities Based on Multi-Source Data L. Wang et al. 10.3390/rs15102565
- 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
- Inconsistency and correction of manually observed ground surface temperatures over snow-covered regions B. Cao et al. 10.1016/j.agrformet.2023.109518
- Spatiotemporal assessment and scenario simulation of the risk potential of industrial sites at the regional scale Y. Jiang et al. 10.1016/j.scitotenv.2023.167537
- Disaggregation of remote sensing and model-based data for 1 km daily seamless soil moisture L. Zhu et al. 10.1016/j.jag.2023.103572
- Application of a two source energy balance model coupled with satellite based soil moisture and thermal infrared data L. Song et al. 10.1016/j.isprsjprs.2023.08.009
- Valuing ecological restoration benefits cannot fully support landscape sustainability: a case study in Inner Mongolia, China C. Wang et al. 10.1007/s10980-023-01697-9
- Exploring the dominant drivers affecting soil water content and vegetation growth by decoupling meteorological indicators X. Mao et al. 10.1016/j.jhydrol.2024.130722
- Estimation of quasi-full spatial coverage soil moisture with fine resolution in China from the combined use of ERA5-Land reanalysis and TRIMS land surface temperature product Y. Zhang et al. 10.1016/j.agwat.2022.107990
- Estimation of all-weather land surface temperature through correcting cloud-shadowing bias simulated by hourly cloud information P. Song et al. 10.1016/j.jag.2024.103703
- Multi-Source Soil Moisture Data Fusion Based on Spherical Cap Harmonic Analysis and Helmert Variance Component Estimation in the Western U.S. H. Chen et al. 10.3390/s23198019
- A global historical twice-daily (daytime and nighttime) land surface temperature dataset produced by Advanced Very High Resolution Radiometer observations from 1981 to 2021 J. Li et al. 10.5194/essd-15-2189-2023
- An improved process-based evapotranspiration/heat fluxes remote sensing algorithm based on the Bayesian and Sobol’ uncertainty analysis framework using eddy covariance observations of Tibetan grasslands J. Feng et al. 10.1016/j.jhydrol.2022.128384
Latest update: 26 Apr 2024
Short summary
Soil moisture information is crucial for understanding the earth surface, but currently available satellite-based soil moisture datasets are imperfect either in their spatiotemporal resolutions or in ensuring image completeness from cloudy weather. In this study, therefore, we developed one soil moisture data product over China that has tackled most of the above problems. This data product has the potential to promote the investigation of earth hydrology and be extended to the global scale.
Soil moisture information is crucial for understanding the earth surface, but currently...
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