SOIL
SOIL
SOIL
Articles | Volume 11, issue 1
Articles | Volume 11, issue 1
https://doi.org/10.5194/soil-11-287-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-11-287-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 1
Original research article
 | 
08 Apr 2025
Original research article |  | 08 Apr 2025

Mapping near-real-time soil moisture dynamics over Tasmania with transfer learning

Marliana Tri Widyastuti, José Padarian, Budiman Minasny, Mathew Webb, Muh Taufik, and Darren Kidd

Viewed

Total article views: 4,546 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,116 818 612 4,546 286 165 302
  • HTML: 3,116
  • PDF: 818
  • XML: 612
  • Total: 4,546
  • Supplement: 286
  • BibTeX: 165
  • EndNote: 302
Views and downloads (calculated since 06 Aug 2024)
Cumulative views and downloads (calculated since 06 Aug 2024)

Viewed (geographical distribution)

Total article views: 4,546 (including HTML, PDF, and XML) Thereof 4,489 with geography defined and 57 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 16 May 2026
Download
Short summary
This work aims to predict soil water content at a fine spatiotemporal resolution (80 m grids, daily) to support agricultural management in Tasmania. It proves that transfer learning can improve the accuracy of deep learning models to predict multilevel soil moisture. We address the challenge of mapping soil moisture at field-scale resolution and integrate the model into a near-real-time monitoring system. 
Read more
Share