| Towards hybrid modeling of the global hydrological cycle B Kraft, M Jung, M Körner, S Koirala, M Reichstein Hydrology and Earth System Sciences 26 (6), 1579-1614, 2022 | 59 | 2022 |
| Hybrid modeling: Fusion of a deep learning approach and a physics-based model for global hydrological modeling B Kraft, M Jung, M Körner, M Reichstein The International Archives of Photogrammetry, Remote Sensing and Spatial …, 2020 | 32 | 2020 |
| Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks B Kraft, M Jung, M Körner, C Requena Mesa, J Cortés, M Reichstein Frontiers in Big Data 2, 31, 2019 | 31* | 2019 |
| Modelling landsurface time-series with recurrent neural nets M Reichstein, S Besnard, N Carvalhais, F Gans, M Jung, B Kraft, ... IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018 | 16 | 2018 |
| Predicting landscapes from environmental conditions using generative networks C Requena-Mesa, M Reichstein, M Mahecha, B Kraft, J Denzler German Conference on Pattern Recognition, 203-217, 2019 | 10 | 2019 |
| Predicting landscapes as seen from space from environmental conditions C Requena-Mesa, M Reichstein, M Mahecha, B Kraft, J Denzler IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018 | 10 | 2018 |
| Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning R ElGhawi, B Kraft, C Reimers, M Reichstein, M Körner, P Gentine, ... Environmental Research Letters 18 (3), 034039, 2023 | 9 | 2023 |
| Contrasting drought propagation into the terrestrial water cycle between dry and wet regions W Li, M Reichstein, S O, C May, G Destouni, M Migliavacca, B Kraft, ... Earth's Future 11 (7), e2022EF003441, 2023 | 7 | 2023 |
| Combining System Modeling and Machine Learning into Hybrid Ecosystem Modeling M Reichstein, B Ahrens, B Kraft, G Camps-Valls, N Carvalhais, F Gans, ... Knowledge-Guided Machine Learning, 327-352, 2022 | 4 | 2022 |
| Emulating ecological memory with recurrent neural networks B Kraft, S Besnard, S Koirala Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021 | 2 | 2021 |
| Hybrid Modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning R ElGhawi, B Kraft, C Reimers, M Reichstein, M Körner, P Gentine, ... Authorea Preprints, 2022 | 1 | 2022 |
| Diagnosing modeling errors of global terrestrial water storage interannual variability H Lee, M Jung, N Carvalhais, T Trautmann, B Kraft, M Reichstein, ... Hydrology and Earth System Sciences Discussions 2022, 1-44, 2022 | 1 | 2022 |
| Hybrid Modelling of Land-Atmosphere Fluxes: Estimating Evapotranspiration using Combined Physics-Based and Data-Driven Machine Learning R ElGhawi, AJ Winkler, B Kraft, C Reimers, M Körner, M Reichstein EGU General Assembly Conference Abstracts, EGU22-9890, 2022 | 1 | 2022 |
| Estimating global terrestrial water storage components by a physically constrained recurrent neural network B Kraft, M Jung, M Körner, S Koirala, M Reichstein EGU General Assembly Conference Abstracts, EGU22-6085, 2022 | 1 | 2022 |
| CH-RUN: A data-driven spatially contiguous runoff monitoring product for Switzerland B Kraft, M Schirmer, WH Aeberhard, M Zappa, SI Seneviratne, ... EGUsphere 2024, 1-30, 2024 | | 2024 |
| Terrestrial carbon and water flux products from an extended data-driven scaling framework, FLUXCOM-X JA Nelson, S Walther, B Kraft, F Gans, G Duveiller, U Weber, ZM Hamdi, ... EGU24, 2024 | | 2024 |
| A data-driven reconstruction of spatially contiguous daily small catchment runoff for flood and drought monitoring in Switzerland B Kraft, W Aeberhard, M Schirmer, SI Seneviratne, M Zappa, ... EGU24, 2024 | | 2024 |
| Deep learning based differentiable/hybrid modelling of the global hydrological cycle Z Baghirov, B Kraft, M Jung, M Körner, M Reichstein EGU24, 2024 | | 2024 |
| DeepPhenoMem V1. 0: Deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology G Liu, M Migliavacca, C Reimers, B Kraft, M Reichstein, A Richardson, ... | | 2024 |
| Constraining biospheric carbon dioxide fluxes by combined top-down and bottom-up approaches S Upton, M Reichstein, F Gans, W Peters, B Kraft, A Bastos Atmospheric Chemistry and Physics 24 (4), 2555-2582, 2024 | | 2024 |
Markus ReichsteinDepartment of Biogeochemical Integration, Max-Planck-Institute for BiogeochemistryVerified email at bgc-jena.mpg.de
