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Downscaling soil moisture to sub-km resolutions with simple machine learning ensembles

MPS-Authors
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Poehls,  Jeran
IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society;
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Silva,  Lazaro Alonso
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Koirala,  Sujan
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Carvalhais,  Nuno
Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Reichstein,  Markus       
Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

External Resource

https://doi.org/10.2139/ssrn.4743411
(Preprint)

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Fulltext (public)

BGC4509pr.pdf
(Preprint), 13MB

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Citation

Poehls, J., Silva, L. A., Koirala, S., Carvalhais, N., & Reichstein, M. (2024). Downscaling soil moisture to sub-km resolutions with simple machine learning ensembles. SSRN Conference Paper Series. doi:10.2139/ssrn.4743411.


Cite as: https://hdl.handle.net/21.11116/0000-000E-7FE3-4
Abstract
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