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  Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs

Robin, C., Requena Mesa, C., Benson, V., Alonso, L., Poehls, J., Carvalhais, N., et al. (2022). Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs. arXiv. doi:10.48550/arXiv.2210.13648.

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 Creators:
Robin, Claire1, Author           
Requena Mesa, Christian1, 2, Author           
Benson, Vitus2, 3, Author           
Alonso, Lazaro1, Author           
Poehls, Jeran1, 2, Author           
Carvalhais, Nuno1, Author           
Reichstein, Markus3, Author           
Affiliations:
1Model-Data Integration, Dr. Nuno Carvalhais, Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1938310              
2IMPRS International Max Planck Research School for Global Biogeochemical Cycles, Max Planck Institute for Biogeochemistry, Max Planck Society, Hans-Knöll-Str. 10, 07745 Jena, DE, ou_1497757              
3Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1688139              

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 Dates: 2022-11-302022-11
 Publication Status: Published online
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 Rev. Type: No review
 Identifiers: Other: BGC4252
DOI: 10.48550/arXiv.2210.13648
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Project name : DeepCube
Grant ID : 101004188
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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