Publications

Shijie Jiang, Lily‐belle Sweet, Georgios Blougouras, Alexander Brenning, Wantong Li, Markus Reichstein, Joachim Denzler, Wei Shangguan, Guo Yu, Feini Huang, and Jakob Zscheischler. How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences. Earth’s Future, (12)7American Geophysical Union (AGU), July 2024. [PUMA: yaff] URL

Teja Kattenborn, Sebastian Wieneke, David Montero, Miguel D. Mahecha, Ronny Richter, Claudia Guimarães-Steinicke, Christian Wirth, Olga Ferlian, Hannes Feilhauer, Lena Sachsenmaier, Nico Eisenhauer, and Benjamin Dechant. Temporal dynamics in vertical leaf angles can confound vegetation indices widely used in Earth observations. Communications Earth & Environment, (5)1Springer Science and Business Media LLC, October 2024. [PUMA: zno] URL

Oscar J. Pellicer-Valero, Miguel-Ángel Fernández-Torres, Chaonan Ji, Miguel D. Mahecha, and Gustau Camps-Valls. Explainable Earth Surface Forecasting under Extreme Events. arXiv, 2024. [PUMA: zno] URL

Marco Hannemann, Almudena Garc\'ıa-Garc\'ıa, Rafael Poyatos, Miguel D Mahecha, and Jian Peng. Estimating transpiration globally by integrating the Priestley-Taylor model with neural networks. Environ. Res. Lett., (19)11:114089, IOP Publishing, November 2024. [PUMA: zno]

Jan Sodoge, Christian Kuhlicke, Miguel D. Mahecha, and Mariana Madruga de Brito. Text mining uncovers the unique dynamics of socio-economic impacts of the 2018–2022 multi-year drought in Germany. Natural Hazards and Earth System Sciences, (24)5:1757–1777, Copernicus GmbH, May 2024. [PUMA: Zno] URL

Maximilian Söchting, Miguel D. Mahecha, David Montero, and Gerik Scheuermann. Lexcube: Interactive Visualization of Large Earth System Data Cubes. IEEE Computer Graphics and Applications, (44)1:25–37, Institute of Electrical and Electronics Engineers (IEEE), January 2024. [PUMA: zno] URL

M. D. Mahecha, A. Bastos, F. J. Bohn, N. Eisenhauer, H. Feilhauer, T. Hickler, H. Kalesse‐Los, M. Migliavacca, F. E. L. Otto, J. Peng, S. Sippel, I. Tegen, A. Weigelt, M. Wendisch, C. Wirth, D. Al‐Halbouni, H. Deneke, D. Doktor, S. Dunker, G. Duveiller, A. Ehrlich, A. Foth, A. García‐García, C. A. Guerra, C. Guimarães‐Steinicke, H. Hartmann, S. Henning, H. Herrmann, P. Hu, C. Ji, T. Kattenborn, N. Kolleck, M. Kretschmer, I. Kühn, M. L. Luttkus, M. Maahn, M. Mönks, K. Mora, M. Pöhlker, M. Reichstein, N. Rüger, B. Sánchez‐Parra, M. Schäfer, F. Stratmann, M. Tesche, B. Wehner, S. Wieneke, A. J. Winkler, S. Wolf, S. Zaehle, J. Zscheischler, and J. Quaas. Biodiversity and Climate Extremes: Known Interactions and Research Gaps. Earth’s Future, (12)6American Geophysical Union (AGU), June 2024. [PUMA: zno] URL

Sophie Wolf, Miguel D. Mahecha, Francesco Maria Sabatini, Christian Wirth, Helge Bruelheide, Jens Kattge, Álvaro Moreno Martínez, Karin Mora, and Teja Kattenborn. Citizen science plant observations encode global trait patterns. Nature Ecology & Evolution, (6)12:1850–1859, Springer Science and Business Media LLC, October 2022. [PUMA: zno] URL

David Montero, César Aybar, Chaonan Ji, Guido Kraemer, Maximilian Söchting, Khalil Teber, and Miguel D. Mahecha. On-Demand Earth System Data Cubes. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 7529–7532, IEEE, July 2024. [PUMA: zno] URL

Salim Soltani, Olga Ferlian, Nico Eisenhauer, Hannes Feilhauer, and Teja Kattenborn. From simple labels to semantic image segmentation: leveraging citizen science plant photographs for tree species mapping in drone imagery. Biogeosciences, (21)11:2909–2935, Copernicus GmbH, June 2024. [PUMA: xack] URL

Karin Mora, Michael Rzanny, Jana Wäldchen, Hannes Feilhauer, Teja Kattenborn, Guido Kraemer, Patrick Mäder, Daria Svidzinska, Sophie Wolf, and Miguel D. Mahecha. Macrophenological dynamics from citizen science plant occurrence data. Methods in Ecology and Evolution, (15)8:1422–1437, Wiley, July 2024. [PUMA: Yaff] URL

Sina Mehrdad, Dörthe Handorf, Ines Höschel, Khalil Karami, Johannes Quaas, Sudhakar Dipu, and Christoph Jacobi. A Physics-informed Deep Learning Based Clustering to Investigate the Impact of Regional European Radiative Forcing on Arctic Climate and Upper Atmospheric Dynamics. EGU24-16391Copernicus Meetings, 2024. [PUMA: yaff]

Maria C Novitasari, Johannes Quaas, and Miguel Rodrigues. ALAS: Active Learning for Autoconversion Rates Prediction from Satellite Data. 3358-3366, PMLR, 2024. [PUMA: Yaff]

Julien Lenhardt, Johannes Quaas, Dino Sejdinovic, and Daniel Klocke. CloudViT: classifying cloud types in global satellite data and in kilometre-resolution simulations using vision transformers. EGUsphere, (2024):1-31, Copernicus Publications, 2024. [PUMA: yaff]

Julien Lenhardt, Johannes Quaas, Dino Sejdinovic, and Daniel Klocke. Leveraging surface observations and passive satellite retrievals of cloud properties: Applications to cloud type classification and cloud base height retrieval. EGU24-18214Copernicus Meetings, 2024. [PUMA: yaff]

Louise Cavalcante, David W Walker, Sarra Kchouk, Germano Ribeiro Neto, Taís Maria Nunes Carvalho, Mariana Madruga de Brito, Wieke Pot, Art Dewulf, and Pieter van Oel. From insufficient rainfall to livelihoods: understanding the cascade of drought impacts and policy implications. EGUsphere, (2024):1-20, Copernicus Publications, 2024. [PUMA: zno]

Francisco Assis Souza Filho, Ticiana Marinho Carvalho Studart, Joao Dehon Pontes Filho, Eduardo Sávio Passos Rodrigues Martins, Sergio Rodrigues Ayrimoraes, Carlos Alberto Perdigão Pessoa, Larissa Zaira Rafael Rolim, Luiz Martins Araujo Junior, Samiria Maria Oliveira Silva, Taís Maria Nunes Carvalho, and Sandra Helena Silva Aquino. Integrated proactive drought management in hydrosystems and cities: building a nine-step participatory planning methodology. Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, (115)3:2179-2204, Springer & International Society for the Prevention and Mitigation of Natural Hazards, 2023. [PUMA: zno]

Taís Maria Nunes Carvalho, and Francisco de Assis de Souza Filho. Variational Mode Decomposition Hybridized With Gradient Boost Regression for Seasonal Forecast of Residential Water Demand. Water Resources Management, (35)10:3431-3445, Springer Netherlands, 2021. [PUMA: zno]

Thaís Antero de Oliveira, Francisco de Assis de Souza Filho, Gabriela de Azevedo Reis, and Taís Maria Nunes Carvalho. Identification of correlation between residential water demand and average income using the pool regression model: Study case in Fortaleza-Brazil. Water Utility Journal, 2020. [PUMA: zno]

Sina Mehrdad, Dörthe Handorf, Ines Höschel, Khalil Karami, Johannes Quaas, Sudhakar Dipu, and Christoph Jacobi. Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach. EGUsphere, (2024):1-55, Copernicus Publications, 2024. [PUMA: Yaff topic_earthenvironment]