Exploring the Earth System
We contribute to the understanding of how living organisms – including humans - exchange fundamental resources like water, carbon, nitrogen and energy with their environment, and how this affects and responds to global climate and environmental change.
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Recent Publications
City life of mycorrhizal and wood-inhabiting macrofungi: importance of urban areas for maintaining fungal biodiversity
Purahong, W., Günther, A., Gminder, A., Tanunchai, B., Gossner, M. M., Buscot, F., Schulze, E. D. (in press) Landscape and Urban Planning.
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Forest canopy nitrogen uptake can supply entire foliar demand
Ferraretto, D., Nair, R., Shah, N. W., Reay, D., Mencuccini, M., Spencer, M., Heal, K. V. (in press) Functional Ecology. doi:10.1111/1365-2435.14005
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Introduction
Camps-Valls, G., Zhu, X. X., Tuia, D., Reichstein, M. (2021) In G. Camps-Valls, D. Tuia, X. X. Zhu, M. Reichstein (Eds.), Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences. doi:10.1002/9781119646181.ch1
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Generative adversarial networks in the Geosciences
Mateo-García, G., Laparra, V., Requena Mesa, C., Gómez-Chova, L. (2021) In G. Camps-Valls, D. Tuia, X. X. Zhu, M. Reichstein (Eds.), Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences. 24-36. doi:10.1002/9781119646181.ch3
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Spatio-temporal Autoencoders in Weather and Climate Research
Tibau, X.-A., Reimers, C., Requena Mesa, C., Runge, J. (2021) In G. Camps-Valls, D. Tuia, X. X. Zhu, M. Reichstein (Eds.), Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences. 186-203. doi:10.1002/9781119646181.ch13
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Emulating ecological memory with recurrent neural networks
Kraft, B., Besnard, S., Koirala, S. (2021) In G. Camps-Valls, D. Tuia, X. X. Zhu, M. Reichstein (Eds.), Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences. 269-281. doi:10.1002/9781119646181.ch18
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