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.
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.
the Creative Commons Attribution 4.0 License.
Original research article
| 
08 Apr 2025
Original research article |
| 08 Apr 2025
| 08 Apr 2025
Mapping near-real-time soil moisture dynamics over Tasmania with transfer learning
Marliana Tri Widyastuti
CORRESPONDING AUTHOR
School of Life and Environmental Sciences & Sydney Institute of Agriculture, The University of Sydney, Sydney, New South Wales, Australia
José Padarian
School of Life and Environmental Sciences & Sydney Institute of Agriculture, The University of Sydney, Sydney, New South Wales, Australia
Budiman Minasny
School of Life and Environmental Sciences & Sydney Institute of Agriculture, The University of Sydney, Sydney, New South Wales, Australia
Mathew Webb
Department of Natural Resources and Environment Tasmania, Prospect, Tasmania, Australia
Muh Taufik
Department of Geophysics and Meteorology, IPB University, Jalan Meranti Wing 19 Level 4 Darmaga Campus, Bogor, 16680, Indonesia
Darren Kidd
Department of Natural Resources and Environment Tasmania, Prospect, Tasmania, Australia
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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.
This work aims to predict soil water content at a fine spatiotemporal resolution (80 m grids,...
