An der Stelle eines ehemaligen Waldes ragen noch einige schlanke, abgestorbene Baumstämme aus dem Boden, der jetzt mit hohen Gräsern und nur noch wenigen Bäumen bewachsen ist.

Publikationen von Christian Requena Mesa

Zeitschriftenartikel (3)

1.
Zeitschriftenartikel
Son, R.; Stacke, T.; Gayler, V.; Nabel, J. E. M. S.; Schnur, R.; Silva, L. A.; Requena Mesa, C.; Winkler, A.; Hantson, S.; Zaehle, S. et al.; Weber, U.; Carvalhais, N.: Integration of a deep-learning-based fire model into a global land surface model. Journal of Advances in Modeling Earth Systems 16 (1), e2023MS003710 (2024)
2.
Zeitschriftenartikel
Bates, A. E.; Primack, R. B.; Biggar, B. S.; Bird, T. J.; Clinton, M. E.; Command, R. J.; Richards, C.; Shellard, M.; Geraldi, N. R.; Vergara, V. et al.; Acevedo-Charry, O.; Requena Mesa, C.; et, a.: Global COVID-19 lockdown highlights humans as both threats and custodians of the environment. Biological Conservation 263, 109175 (2021)
3.
Zeitschriftenartikel
Kraft, B.; Jung, M.; Körner, M.; Requena Mesa, C.; Cortés, J.; Reichstein, M.: Identifying dynamic memory effects on vegetation state using recurrent neural networks. Frontiers in Big Data 2, 31 (2019)

Buchkapitel (3)

4.
Buchkapitel
Mateo-García, G.; Laparra, V.; Requena Mesa, C.; Gómez-Chova, L.: Generative adversarial networks in the Geosciences. In: Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, S. 24 - 36 (Hg. Camps-Valls, G.; Tuia, D.; Zhu, X. X.; Reichstein, M.). John Wiley & Sons Ltd, Hoboken, New Jersey (2021)
5.
Buchkapitel
Tibau, X.-A.; Reimers, C.; Requena Mesa, C.; Runge, J.: Spatio-temporal Autoencoders in Weather and Climate Research. In: Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, S. 186 - 203 (Hg. Camps-Valls, G.; Tuia, D.; Zhu, X. X.; Reichstein, M.). John Wiley & Sons Ltd, Hoboken, New Jersey (2021)
6.
Buchkapitel
Requena-Mesa, C.; Reichstein, M.; Mahecha, M. D.; Kraft, B.; Denzler, J.: Predicting landscapes from environmental conditions using generative networks. In: Pattern Recognition, DAGM GCPR 2019, S. 203 - 217 (Hg. FInk, G. A.; Frintrop, S.; Jiang, X.). Springer, Cham (2019)

Konferenzbeitrag (2)

7.
Konferenzbeitrag
Requena Mesa, C.; Benson, V.; Reichstein, M.; Runge, J.; Denzler, J.: EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW, Nashville, TN, USA , 19. Juni 2021 - 25. Juni 2021. IEEE, Nashville, TN, USA (2021)
8.
Konferenzbeitrag
Requena-Mesa, C.; Reichstein, M.; Mahecha, M. D.; Kraft, B.; Denzler, J.: Predicting landscapes as seen from space from environmental conditions. In: 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), S. 1768 - 1771. (2018)

Preprint (2)

9.
Preprint
Benson, V.; Requena Mesa, C.; Robin, C.; Alonso, L.; Cortes, J.; Gao, Z.; Linscheid, N.; Weynants, M.; Reichstein, M.: Forecasting localized weather impacts on vegetation as seen from space with meteo-guided video prediction. arXiv (2023)
10.
Preprint
Robin, C.; Requena Mesa, C.; Benson, V.; Alonso, L.; Poehls, J.; Carvalhais, N.; Reichstein, M.: Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs. arXiv (2022)
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