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Reichstein, M.; Ahrens, B.; Kraft, B.; Camps-Valls, G.; Carvalhais, N.; Gans, F.; Gentine, P.; Winkler, A.: Combining system modeling and machine learning into hybrid ecosystem modeling. In: Knowledge-Guided Machine Learning, 9781003143376, pp. 327 - 352 (Eds. Kannan, R.; Kumar, V.). Chapman & Hall, London (2022)

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Poulter, B.; Bastos, A.; Canadell, J. G.; Ciais, P.; Huntzinger, D.; Houghton, R. A.; Kurz, W.; Petrescu, A. M. R.; Pongratz, J.; Sitch, S. et al.; Luyssaert, S.: Bottom-up approaches for estimating terrestrial GHG budgets: Bookkeeping, process-based modeling, and data-driven methods. In: Balancing greenhouse gas budgets:, pp. 59 - 85 (Ed. Poulter, B.). Elsevier, Amsterdam (2022)

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Kraft, B.; Besnard, S.; Koirala, S.: Emulating ecological memory with recurrent neural networks. In: Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, pp. 269 - 281 (Eds. Camps-Valls, G.; Tuia, D.; Zhu, X. X.; Reichstein, M.). John Wiley & Sons Ltd, Hoboken, New Jersey (2021)

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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)

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Reichstein, M.; Frank, D.; Sillmann, J.; Sippel, S.: Outlook: Challenges for societal resilience under climate extremes. In: Climate extremes and their implications for impact and risk assessment, pp. 341 - 353 (Eds. Sillmann, J.; Sippel, S.; Russo, S.). Elsevier, Amsterdam (2019)

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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)

Shadaydeh, M.; Denzler, J.; Garcia, Y. G.; Mahecha, M. D.: Time-frequency causal inference uncovers anomalous events in environmental systems. In: Pattern Recognition, DAGM GCPR 2019, pp. 499 - 512 (Eds. FInk, G. A.; Frintrop, S.; Jiang, X.). Springer, Cham (2019)

Trifunov, V. T.; Shadaydeh, M.; Runge, J.; Eyring, V.; Reichstein, M.; Denzler, J.: Nonlinear causal link estimation under hidden confounding with an application to time series anomaly detection. In: Pattern Recognition, DAGM GCPR 2019, pp. 261 - 273 (Eds. Fink, G. A.; Frintrop, S.; Jiang, X.). Springer, Cham (2019)

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