Forkel, M.; Carvalhais, N.; Verbesselt, J.; Mahecha, M. D.; Neigh, C. S.R.; Reichstein, M.: Trend change detection in NDVI time series: Effects of inter-annual variability and methodology. Remote Sensing 5 (5), pp. 2113 - 2144 (2013)
Benali, A.; Carvalho, A.; Nunes, J.; Carvalhais, N.; Santos, A.: Estimating air surface temperature in Portugal using MODIS LST data. Remote Sensing of Environment 124, pp. 108 - 121 (2012)
Wu, J.; Van Der Linden, L.; Lasslop, G.; Carvalhais, N.; Pilegaard, K.; Beier, C.; Ibrom, A.: Effects of climate variability and functional changes on the interannual variation of the carbon balance in a temperate deciduous forest. Biogeosciences 9 (2), p. 715 - 715 (2012)
Mahecha, M. D.; Reichstein, M.; Carvalhais, N.; Lasslop, G.; Lange, H.; Seneviratne, S. I.; Vargas, R.; Ammann, C.; Arain, M. A.; Cescatti, A.et al.; Janssens, I. A.; Migliavacca, M.; Montagnani, L.; Richardson, A. D.: Response to Comment on "Global Convergence in the Temperature Sensitivity of Respiration at Ecosystem Level". Science 331 (6022), p. 1265d (2011)
Carvalhais, N.; Reichstein, M.; Ciais, P.; Collatz, G. J.; Mahecha, M. D.; Montagnani, L.; Papale, D.; Rambal, S.; Seixas, J.: Identification of vegetation and soil carbon pools out of equilibrium in a process model via eddy covariance and biometric constraints. Global Change Biology 16 (10), pp. 2813 - 2829 (2010)
Carvalhais, N.; Reichstein, M.; Collatz, G. J.; Mahecha, M. D.; Migliavacca, M.; Neigh, C. S. R.; Tomelleri, E.; Benali, A. A.; Papale, D.; Seixas, J.: Deciphering the components of regional net ecosystem fluxes following a bottom-up approach for the Iberian Peninsula. Biogeosciences 7 (11), pp. 3707 - 3729 (2010)
Mahecha, M. D.; Reichstein, M.; Carvalhais, N.; Lasslop, G.; Lange, H.; Seneviratne, S. I.; Vargas, R.; Ammann, C.; Arain, M. A.; Cescatti, A.et al.; Janssens, I. A.; Migliavacca, M.; Montagnani, L.; Richardson, A. D.: Global Convergence in the Temperature Sensitivity of Respiration at Ecosystem Level. Science 329 (5993), pp. 838 - 840 (2010)
Williams, M.; Richardson, A. D.; Reichstein, M.; Stoy, P. C.; Peylin, P.; Verbeeck, H.; Carvalhais, N.; Jung, M.; Hollinger, D. Y.; Kattge, J.et al.; Leuning, R.; Luo, Y.; Tomelleri, E.; Trudinger, C. M.; Wang, Y. P.: Improving land surface models with FLUXNET data. Biogeosciences 6 (7), pp. 1341 - 1359 (2009)
Carvalhais, N.; Reichstein, M.; Seixas, J.; Collatz, G. J.; Pereira, J. S.; Berbigier, P.; Carrara, A.; Granier, A.; Montagnani, L.; Papale, D.et al.; Rambal, S.; Sanz, M. J.; Valentini, R.: Implications of the carbon cycle steady state assumption for biogeochemical modeling performance and inverse parameter retrieval. Global Biogeochemical Cycles 22 (2), p. Gb2007 (2008)
Mahecha, M. D.; Reichstein, M.; Lange, H.; Carvalhais, N.; Bernhofer, C.; Grunwald, T.; Papale, D.; Seufert, G.: Characterizing ecosystem-atmosphere interactions from short to interannual time scales. Biogeosciences 4 (5), pp. 743 - 758 (2007)
Nunes, J. P.; Vieira, G. N.; Seixas, J.; Gonçalves, P.; Carvalhais, N.: Evaluating the MEFIDIS model for runoff and soil erosion prediction during rainfall events. Catena 61 (2-3), pp. 210 - 228 (2005)
Reichstein, M.; Richardson, A. D.; Migliavacca, M.; Carvalhais, N.: Plant–environment interactions across multiple scales. In: Ecology and the Environment, pp. 1 - 27 (Ed. Monson, R. K.). Springer, New York (2014)
The BIOMASS satellite was successfully launched into orbit on 29 April 2025. The BIOMASS mission is designed to map and monitor global forests. It will map the structure of different forest types and provide data on above-ground biomass.
Thanks to FLUXCOM-X, the next generation of data driven, AI-based earth system models, scientists can now see the Earth’s metabolism at unprecedented detail – assessed everywhere on land and every hour of the day.
David Hafezi Rachti was awarded twice: for his EGU poster with this year’s “Outstanding Student and PhD candidate Presentation” (OSPP) and for his Bachelor thesis, he received the 1st prize of the “Young Climate Scientist Award 2024”.
A recent study by scientists from the Max Planck Institute for Biogeochemistry and the University of Leipzig suggests that increasing droughts in the tropics and changing carbon cycle responses due to climate change are not primarily responsible for the strong tropical response to rising temperatures. Instead, a few particularly strong El Niño events could be the cause.
EU funds the international research project AI4PEX to further improve Earth system models and thus scientific predictions of climate change. Participating scientists from 9 countries met at the end of May 2024 to launch the project at the MPI for Biogeochemistry in Jena, which is leading the project.
From the Greek philosopher Aristotle to Charles Darwin to the present day, scientists have dealt with this fundamental question of biology. Contrary to public perception, however, it is still largely unresolved. Scientists have now presented a new approach for the identification and delimitation of species using artificial intelligence (AI).
The 73rd Lindau Nobel Laureate Meeting was dedicated to physics and was held from June 30 to July 5, 2024. It brought together around 40 Nobel Laureates and 635 young scientists from more than 90 nations.
A research team led by the German Centre for Integrative Biodiversity Research (iDiv) and Leipzig University has developed an algorithm that analyses observational data from the Flora Incognita app. The novel can be used to derive ecological patterns that could provide valuable information about the effects of climate change on plants.
Tropical forests are continuously being fragmented and damaged by human influences. Using remote sensing data and cutting-edge data analysis methods, researchers can now show for the first time that the impact of this damage is greater than previously estimated.
The new research project "PollenNet" aims to use artificial intelligence to accurately predict the spread of pollen. In order to improve allergy prevention, experts are bringing together the latest interdisciplinary findings from a wide range of fields.
If rivers overflow their banks, the consequences can be devastating. Using methods of explainable machine learning, researchers at the Helmholtz Centre for Environmental Research (UFZ) have shown that floods are more extreme when several factors are involved in their development.