Articles | Volume 28, issue 11
https://doi.org/10.5194/hess-28-2357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-2357-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Machine learning and global vegetation: random forests for downscaling and gap filling
Barry van Jaarsveld
CORRESPONDING AUTHOR
Department of Physical Geography, Utrecht University, Princetonlaan 8a, Utrecht, the Netherlands
Sandra M. Hauswirth
Department of Physical Geography, Utrecht University, Princetonlaan 8a, Utrecht, the Netherlands
Niko Wanders
Department of Physical Geography, Utrecht University, Princetonlaan 8a, Utrecht, the Netherlands
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- Spatiotemporal dynamics and drivers of water and carbon use efficiency in the Yellow River basin with harmonized MODIS and GLASS data S. Zhou et al. https://doi.org/10.1016/j.jhydrol.2026.135291
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- Extreme heat exposure and ventilation corridors in high-density historic districts: Evidence from three sites in Chongqing A. Wang et al. https://doi.org/10.1016/j.buildenv.2026.114403
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- Synergistic construction of an annual 2 m mangrove species dataset from 2016 to 2023 using structural features and hybrid stacked deep learning models—Beibu Gulf, Guangxi, China J. Sun et al. https://doi.org/10.1016/j.isprsjprs.2025.10.007
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- Multi-scale machine learning model for long-term blue and green water consumption scenarios for crop production Z. Li et al. https://doi.org/10.1016/j.jia.2026.04.015
Saved (final revised paper)
Latest update: 05 Jun 2026
Short summary
Drought often manifests itself in vegetation; however, obtaining high-resolution remote-sensing products that are spatially and temporally consistent is difficult. In this study, we show that machine learning (ML) can fill data gaps in existing products. We also demonstrate that ML can be used as a downscaling tool. By relying on ML for gap filling and downscaling, we can obtain a more holistic view of the impacts of drought on vegetation.
Drought often manifests itself in vegetation; however, obtaining high-resolution remote-sensing...