Articles | Volume 15, issue 5
https://doi.org/10.5194/essd-15-2055-2023
https://doi.org/10.5194/essd-15-2055-2023
Data description paper
 | 
23 May 2023
Data description paper |  | 23 May 2023

Generation of global 1 km daily soil moisture product from 2000 to 2020 using ensemble learning

Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Qian Wang, Bing Li, Jianglei Xu, Guodong Zhang, Xiaobang Liu, and Changhao Xiong

Viewed

Total article views: 3,398 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,619 727 52 3,398 41 54
  • HTML: 2,619
  • PDF: 727
  • XML: 52
  • Total: 3,398
  • BibTeX: 41
  • EndNote: 54
Views and downloads (calculated since 13 Jan 2023)
Cumulative views and downloads (calculated since 13 Jan 2023)

Viewed (geographical distribution)

Total article views: 3,398 (including HTML, PDF, and XML) Thereof 3,254 with geography defined and 144 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Apr 2024
Download
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.
Altmetrics
Final-revised paper
Preprint