Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 17, issue 10
Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5181-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-5181-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 10
Data description paper
 | 
07 Oct 2025
Data description paper |  | 07 Oct 2025

A seamless global daily 5 km soil moisture product from 1982 to 2021 using AVHRR satellite data and an attention-based deep learning model

Yufang Zhang, Shunlin Liang, Han Ma, Tao He, Feng Tian, Guodong Zhang, and Jianglei Xu

Viewed

Total article views: 1,966 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,678 243 45 1,966 51 46
  • HTML: 1,678
  • PDF: 243
  • XML: 45
  • Total: 1,966
  • BibTeX: 51
  • EndNote: 46
Views and downloads (calculated since 16 Jan 2025)
Cumulative views and downloads (calculated since 16 Jan 2025)

Viewed (geographical distribution)

Total article views: 1,966 (including HTML, PDF, and XML) Thereof 1,966 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Oct 2025
Download
Short summary
Soil moisture (SM) plays a vital role in climate, agriculture, and hydrology, yet reliable long-term, seamless global datasets remain scarce. To fill this gap, we developed a four-decade seamless global daily 5 km SM product using multi-source datasets and deep learning models. This product has long-term coverage, spatial and temporal integrity, and high accuracy, making it a valuable resource for applications like SM trend analysis, drought monitoring, and assessment of vegetation responses.
Read more
Share
Altmetrics
Final-revised paper
Preprint