Publications of Christian Requena Mesa

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)

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)

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)

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, pp. 24 - 36 (Eds. Camps-Valls, G.; Tuia, D.; Zhu, X. X.; Reichstein, M.). John Wiley & Sons Ltd, Hoboken, New Jersey (2021)

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, pp. 186 - 203 (Eds. Camps-Valls, G.; Tuia, D.; Zhu, X. X.; Reichstein, M.). John Wiley & Sons Ltd, Hoboken, New Jersey (2021)

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, pp. 203 - 217 (Eds. FInk, G. A.; Frintrop, S.; Jiang, X.). Springer, Cham (2019)

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 , June 19, 2021 - June 25, 2021. IEEE, Nashville, TN, USA (2021)

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), pp. 1768 - 1771. (2018)

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)

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)