Publication Details

Category Text Publication
Reference Category Journals
DOI 10.1002/vzj2.70011
Licence creative commons licence
Title (Primary) Machine-learning based spatiotemporal prediction of soil moisture in a grassland hillslope
Author Houben, T.; Ebeling, P.; Khurana, S.; Schmid, J.S.; Boog, J.
Source Titel Vadose Zone Journal
Year 2025
Department OESA; CHS; ENVINF; HDG; MET; AME
Volume 24
Issue 2
Page From e70011
Language englisch
Topic T5 Future Landscapes
Data and Software links https://doi.org/10.5281/zenodo.14871615
https://doi.org/10.5281/zenodo.14871758
Supplements https://acsess.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fvzj2.70011&file=vzj270011-sup-0001-SuppMat.docx
Abstract Soil moisture (SM) plays a significant role in the earth's water balance and in optimizing land management practices. However, SM at the field scale is difficult to map from available point measurements due to the inherent heterogeneity of soil and terrain properties and temporal dynamics of weather conditions. In this study, we explored the potential of four machine learning (ML) methods (random forest, gradient boosted regression trees, support vector regression, and neural networks) to predict SM in a grassland hillslope in space and time using auxiliary variables on soil and terrain properties and weather conditions. For training and testing the ML models, we used SM point measurements obtained by a sensor network. Performance metrics varied between the ML methods and the training-test data split (R2 = 0.48–0.69, root-mean-square error [RMSE] = 0.06–0.10). Random forests and gradient-boosted regression trees turned out to be promising and easy to parametrize as first choices to explore the potential of ML techniques. The day of the year emerged as an important feature to predict SM across models and can thus serve as a proxy for seasonal hydroclimatic variability. To enable the transfer of the application to other contexts or sites, we provide the modeling workflow as an open-source computational Python module.
Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=30501
Houben, T., Ebeling, P., Khurana, S., Schmid, J.S., Boog, J. (2025):
Machine-learning based spatiotemporal prediction of soil moisture in a grassland hillslope
Vadose Zone J. 24 (2), e70011 10.1002/vzj2.70011