Stegehuis, A. I.; Teuling, A. J.; Ciais, P.; Vautard, R.; Jung, M.: Future European temperature change uncertainties reduced by using land heat flux observations. Geophysical Research Letters 40 (10), pp. 2242 - 2245 (2013)

Migliavacca, M.; Reichstein, M.; Richardson, A. D.; Colombo, R.; Sutton, M. A.; Lasslop, G.; Tomelleri, E.; Wohlfahrt, G.; Carvalhais, N.; Cescatti, A. et al.; Mahecha, M. D.; Montagnani, L.; Papale, D.; Zaehle, S.; Arain, A.; Arneth, A.; Black, T. A.; Carrara, A.; Dore, S.; Gianelle, D.; Helfter, C.; Hollinger, D.; Kutsch, W. L.; Lafleur, P. M.; Nouvellon, Y.; Rebmann, C.; Rocha, D.; Rodeghiero, M.; Roupsard, O.; Sebastià, M.-T.; Seufert, G.; Soussana, J.-F.; Molen, V. D.: Semiempirical modeling of abiotic and biotic factors controlling ecosystem respiration across eddy covariance sites. Global Change Biology 17 (1), pp. 390 - 409 (2011)

Reichstein, M.: Journal club: a biogeochemist looks at where all the emitted carbon dioxide is going. Nature 464, 7286 (2010)

Richardson, A. D.; Black, T. A.; Ciais, P.; Delbart, N.; Friedl, M. A.; Gobron, N.; Hollinger, D. Y.; Kutsch, W. L.; Longdoz, B.; Luyssaert, S. et al.; Migliavacca, M.; Montagnani, L.; Munger, J. W.; Moors, E.; Piao, S.; Rebmann, C.; Reichstein, M.; Saigusa, N.; Tomelleri, E.; Vargas, R.; Varlagin, A.: Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences 365 (1555), pp. 3227 - 3246 (2010)

Reichstein, M.; Ciais, P.; Beer, C.; Beier, C.; Ibrom, A.; Janssens, I.; Jung, M.; Misson, L.; Seneviratne, S.; Smith, P. et al.; Williams, C.; Wirth, C.: The role of climate variability and extremes for global terrestrial carbon dynamics: lessons learnt from multiple observations and experiments. IOP Conference Series: Earth and Environmental Science 6, 042006 (2009)

Reichstein, M.: Integration of FLUXNET and Earth observation data with biogeochemical modelling. iLEAPS Newsletter 3, pp. 32 - 34 (2006)

Camps-Valls, G.; Tuia, D.; Zhu, X. X.; Reichstein, M. (Eds.): Deep learning for the earth sciences: A comprehensive approach to remote sensing, climate science, and geosciences. John Wiley & Sons Ltd, Hoboken, New Jersey (2021), 405 pp.

Bastos, A.: Forest disturbances and carbon sinks: Chapter 13. In: European climate risk assessment: EEA Report, pp. 1 - 38 (2024)

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)

Jacob, D.; Birkmann, J.; Bollig, M.; Bonn, A.; Nöthlings, U.; Ott, K.; Quaas, M.; Reichstein, M.; Scholz, I.; Malburg-Graf, B. et al.; Sonntag, S.: Research priorities for sustainability science: DKN position paper. In: German Committee Future Earth, Hamburg, Germany. (2022)

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)

Camps-Valls, G.; Zhu, X. X.; Tuia, D.; Reichstein, M.: Introduction. In: Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences (Eds. Camps-Valls, G.; Tuia, D.; Zhu, X. X.; Reichstein, M.). John Wiley & Sons Ltd, Hoboken, New Jersey (2021)

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)

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)

Reichstein, M.; Camps-Valls, G.; Tuia, D.; Zhu, X. X.: Outlook. In: Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, pp. 328 - 330 (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)

Reimers, C.; Bodesheim, P.; Runge, J.; Denzler, J.: Conditional adversarial debiasing: Towards learning unbiased classifiers from biased data. In: Pattern Recognition. DAGM GCPR 2021. Lecture Notes in Computer Science, Vol. 13024, pp. 48 - 62. Springer International Publishing, Cham (2021)

Murray, V.; Abrahams, J.; Abdallah, C.; Ahmed, K.; Angeles, L.; Benouar, D.; Torres, B. A.; Choe, H. C.; Cox, S.; Douris, J. et al.; Fagan, L.; Paleo, F. U.; Han, Q.; Handmer, J.; Hodson, S.; Khim, W.; Mayner, L.; Moody, N.; Luiz Leal, M.; Osvaldo, N.; Michael, N. J.; Peduzzi, P.; Perwaiz, A.; Peters, K.; Radisch, J.; Reichstein, M.; Schneider, J.; Smith, A.; Souch, C.; Stevance, A.-S.; Triyanti, A.; Weir, M.; Wright, N.: Hazard Information Profiles: Supplement to UNDRR-ISC Hazard Definition & Classification Review. In: UNDRR-ISC Hazard Definition & Classification Review: Technical Report: Geneva, Switzerland, United Nations Office for Disaster Risk Reduction; Paris, France, International Science Council. UNDDR, Geneva (2020)

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)

Mahecha, M. D.; Guha-Sapir, D.; Smits, J.; Gans, F.; Kraemer, G.: Data challenges limit our global understanding of humanitarian disasters triggered by climate extremes. In: Climate Extremes and Their Implications for Impact and Risk Assessment, pp. 243 - 256 (Eds. Sillmann, J.; Sippel, S.; Russo, S.). Elsevier, Amsterdam (2019)