Articles | Volume 14, issue 10
https://doi.org/10.5194/essd-14-4473-2022
© 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-14-4473-2022
© 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
| 
06 Oct 2022
Data description paper |
| 06 Oct 2022
| 06 Oct 2022
SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022
Qiang Zhang
Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, China
School of Geodesy and Geomatics, Wuhan University, China
Taoyong Jin
CORRESPONDING AUTHOR
School of Geodesy and Geomatics, Wuhan University, China
Meiping Song
Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, China
Fujun Sun
CASIC Research Institute of Intelligent Decision Engineering, Beijing, China
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Short summary
Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0 enlarges the temporal scope from 2002 to 2022. By fusing auxiliary precipitation information with the long short-term memory convolutional neural network (LSTM-CNN) model, SGD-SM 2.0 can consider sudden extreme weather conditions for 1 d in global daily soil moisture products and is significant for full-coverage global daily hydrologic monitoring, rather than averaging monthly–quarterly–yearly results.
Compared to previous seamless global daily soil moisture (SGD-SM 1.0) products, SGD-SM 2.0...
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