View from above looking straight down at the top of the large ATTO tower, which stands out as an orange square above the green tree tops far below. Diagonally, the guy wires run from the tower into the forest.

Publications of Christian Reimers

Journal Article (4)

1.
Journal Article
Benson, V.; Bastos, A.; Reimers, C.; Winkler, A.; Yang, F.; Reichstein, M.: Atmospheric transport modeling of CO2 with neural networks. Journal of Advances in Modeling Earth Systems 17 (2), e2024MS004655 (2025)
2.
Journal Article
Liu, G.; Migliavacca, M.; Reimers, C.; Kraft, B.; Reichstein, M.; Richardson, A. D.; Wingate, L.; Delpierre, N.; Yang, H.; Winkler, A.: DeepPhenoMem V1.0: Deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology. Geoscientific Model Development 17 (17), pp. 6683 - 6701 (2024)
3.
Journal Article
Winkler, A.; Myneni, R.; Reimers, C.; Reichstein, M.; Brovkin, V.: Carbon system state determines warming potential of emissions. PLOS ONE 19 (8), e0306128 (2024)
4.
Journal Article
Wutzler, T.; Reimers, C.; Ahrens, B.; Schrumpf, M.: Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)). Geoscientific Model Development 17 (7), pp. 2705 - 2725 (2024)

Book Chapter (1)

5.
Book Chapter
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)

Conference Paper (1)

6.
Conference Paper
Friede, D.; Reimers, C.; Stuckenschmidt, H.; Niepert, M.: Learning disentangled discrete representations. Machine learning and knowledge discovery in databases: Research track. ECML PKDD 2023. Lecture Notes in Computer Science 14172, pp. 593 - 609 (2023)

Preprint (1)

7.
Preprint
Reimers, C.; Hafezi Rachti, D.; Liu, G.; Winkler, A.: Comparing data-driven and mechanistic models for predicting phenology in deciduous broadleaf forests. arXiv (2024)
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