Articles | Volume 14, issue 10
https://doi.org/10.5194/essd-14-4473-2022
https://doi.org/10.5194/essd-14-4473-2022
Data description paper
 | 
06 Oct 2022
Data description paper |  | 06 Oct 2022

SGD-SM 2.0: an improved seamless global daily soil moisture long-term dataset from 2002 to 2022

Qiang Zhang, Qiangqiang Yuan, Taoyong Jin, Meiping Song, and Fujun Sun

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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.
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