Articles | Volume 13, issue 1
https://doi.org/10.5194/essd-13-1-2021
© Author(s) 2021. 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-13-1-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
05 Jan 2021
Data description paper |
| 05 Jan 2021
| 05 Jan 2021
An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018
Yongzhe Chen
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085,
China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049,
China
Xiaoming Feng
CORRESPONDING AUTHOR
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085,
China
Bojie Fu
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085,
China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049,
China
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Short summary
Soil moisture can greatly influence the ecosystem but is hard to monitor at the global scale. By calibrating and combining 11 different products derived from satellite observation, we developed a new global surface soil moisture dataset spanning from 2003 to 2018 with high accuracy. Using this new dataset, not only can the global long-term trends be derived, but also the seasonal variation and spatial distribution of surface soil moisture at different latitudes can be better studied.
Soil moisture can greatly influence the ecosystem but is hard to monitor at the global scale. By...
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