Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks

Vegetation state is largely driven by climate and the complexity of involved processes leads to non-linear interactions over multiple time-scales. Recently, the role of temporally lagged dependencies, so-called memory effects, has been emphasized and studied using data-driven methods, relying on a v...

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Detalles Bibliográficos
Autores principales: Kraft, Basil, Jung, Martin, Körner, Marco, Requena Mesa, Christian, Cortés, José, Reichstein, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Big Data
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931900/
https://www.ncbi.nlm.nih.gov/pubmed/33693354
http://dx.doi.org/10.3389/fdata.2019.00031

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7931900/
https://www.ncbi.nlm.nih.gov/pubmed/33693354
http://dx.doi.org/10.3389/fdata.2019.00031

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