Articles | Volume 11, issue 1
https://doi.org/10.5194/soil-11-287-2025
https://doi.org/10.5194/soil-11-287-2025
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

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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-333,https://doi.org/10.5194/essd-2024-333, 2024
Revised manuscript not accepted
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Cited articles

Aas, K., Jullum, M., and Løland, A.: Explaining individual predictions when features are dependent: More accurate approximations to Shapley values, Artif. Intell., 298, 103502, https://doi.org/10.1016/j.artint.2021.103502, 2021. 
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., and Devin, M.: TensorFlow: Large-scale machine learning on heterogeneous systems, arXiv [preprint], https://doi.org/10.48550/arXiv.1603.04467, 2015. 
Alemohammad, S. H., Kolassa, J., Prigent, C., Aires, F., and Gentine, P.: Global downscaling of remotely sensed soil moisture using neural networks, Hydrol. Earth Syst. Sci., 22, 5341–5356, https://doi.org/10.5194/hess-22-5341-2018, 2018. 
Behrens, T., Schmidt, K., MacMillan, R. A., and Viscarra Rossel, R. A.: Multi-scale digital soil mapping with deep learning, Sci. Rep.-UK, 8, 15244, https://doi.org/10.1038/s41598-018-33516-6, 2018. 
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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. 
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