Publikationen von Basil Kraft

Upton, S.; Reichstein, M.; Gans, F.; Peters, W.; Kraft, B.; Bastos, A.: Constraining biospheric carbon dioxide fluxes by combined top-down and bottom-up approaches. Atmospheric Chemistry and Physics 24 (4), S. 2555 - 2582 (2024)

Li, W.; Reichstein, M.; O, S.; May, C.; Destouni, G.; Migliavacca, M.; Kraft, B.; Weber, U.; Orth, R.: Contrasting drought propagation into the terrestrial water cycle between dry and wet regions. Earth's Future 11 (7), e2022EF003441 (2023)

Lee, H. T.; Jung, M.; Carvalhais, N.; Trautmann, T.; Kraft, B.; Reichstein, M.; Forkel, M.; Koirala, S.: Diagnosing modeling errors in global terrestrial water storage interannual variability. Hydrology and Earth System Sciences 27 (7), S. 1531 - 1563 (2023)

ElGhawi, R.; Kraft, B.; Reimers, C.; Reichstein, M.; Körner, M.; Gentine, P.; Winkler, A. J.: Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning. Environmental Research 18, 034039 (2023)

Kraft, B.; Jung, M.; Körner, M.; Koirala, S.; Reichstein, M.: Towards hybrid modeling of the global hydrological cycle. Hydrology and Earth System Sciences 26 (6), S. 1579 - 1614 (2022)

Kraft, B.; Jung, M.; Körner, M.; Reichstein, M.: Hybrid modeling: Fusion of a deep approach and physics-based model for global hydrological modeling. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020, S. 1537 - 1544 (2020)

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)

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, S. 327 - 352 (Hg. Kannan, R.; Kumar, V.). Chapman & Hall, London (2022)

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, S. 269 - 281 (Hg. 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, S. 203 - 217 (Hg. FInk, G. A.; Frintrop, S.; Jiang, X.). Springer, Cham (2019)

Reichstein, M.; Besnard, S.; Carvalhais, N.; Gans, F.; Jung, M.; Kraft, B.; Mahecha, M. D.: Modelling landsurface time-series with recurrent neural nets. In: 2018 IEEE International geoscience and remote sensing symposium (IGARSS), S. 7640 - 7643. Valencia, 2018 (2018)

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)

Kraft, B.: Deep learning and hybrid modeling of global vegetation and hydrology. Dissertation, Technical University of Munich, München (2022)

Liu, G.; Migliavacca, M.; Reimers, C.; Kraft, B.; Reichstein, M.; Richardson, A.; 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. (2024)

Nelson, J. A.; Walther, S.; Gans, F.; Kraft, B.; Weber, U.; Novick, K.; Buchmann, N.; Migliavacca, M.; Wohlfahrt, G.; Šigut, L. et al.; Ibrom, A.; Papale, D.; Göckede, M.; Duveiller, G.; Knohl, A.; Hörtnagl, L.; Scott, R. L.; Zhang, W.; Hamdi, Z. M.; Reichstein, M.; Aranda-Barranco, S.; Ardö, J.; de Beeck, M. O.; Billdesbach, D.; Bowling, D.; Bracho, R.; Brümmer, C.; Camps-Valls, G.; Chen, S.; Cleverly, J. R.; Desai, A.; Dong, G.; El-Madany, T. S.; Euskirchen, E. S.; Feigenwinter, I.; Galvagno, M.; Gerosa, G.; Gielen, B.; Goded, I.; Goslee, S.; Gough, C. M.; Heinesch, B.; Ichii, K.; Jackowicz-Korczynski, M. A.; Klosterhalfen, A.; Knox, S.; Kobayashi, H.; Kohonen, K.-M.; Korkiakoski, M.; Mammarella, I.; Mana, G.; Marzuoli, R.; Matamala, R.; Metzger, S.; Montagnani, L.; Nicolini, G.; O'Halloran, T.; Ourcival, J.-M.; Peichl, M.; Pendall, E.; Reverter, B. R.; Roland, M.; Sabbatini, S.; Sachs, T.; Schmidt, M.; Schwalm, C. R.; Shekhar, A.; Silberstein, R.; Silveira, M. L.; Spano, D.; Tagesson, T.; Tramontana, G.; Trotta, C.; Turco, F.; Vesala, T.; Vincke, C.; Vitale, D.; Vivoni, E. R.; Wang, Y.; Woodgate, W.; Yepez, E. A.; Zhang, J.; Zona, D.; Jung, M.: X-BASE: the first terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X. (2024)