Articles | Volume 26, issue 6
https://doi.org/10.5194/nhess-26-2785-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-26-2785-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Jun 2026
Research article |
| 12 Jun 2026
| 12 Jun 2026
Hybrid forest disturbance classification using Sentinel-1 and inventory data: a case-study for Southeastern USA
Franziska Müller
CORRESPONDING AUTHOR
Institute for Earth System Science and Remote Sensing, University Leipzig, Talstr. 35, 04103 Leipzig, Germany
Max-Planck Institute of Biogeochemistry, Dept. of Biogeochemical Integration, Hans-Knöll-Straße 10, 07745 Jena, Germany
Laura Eifler
Institute for Earth System Science and Remote Sensing, University Leipzig, Talstr. 35, 04103 Leipzig, Germany
Max-Planck Institute of Biogeochemistry, Dept. of Biogeochemical Integration, Hans-Knöll-Straße 10, 07745 Jena, Germany
Felix Cremer
Max-Planck Institute of Biogeochemistry, Dept. of Biogeochemical Integration, Hans-Knöll-Straße 10, 07745 Jena, Germany
Pieter Beck
European Commission, Joint Research Centre, Ispra, Italy
Gustau Camps-Valls
Universitat de València, C/Cat. Agustín Escardino Benlloch, 9 46980 Paterna, València, Spain
Ana Bastos
Institute for Earth System Science and Remote Sensing, University Leipzig, Talstr. 35, 04103 Leipzig, Germany
Max-Planck Institute of Biogeochemistry, Dept. of Biogeochemical Integration, Hans-Knöll-Straße 10, 07745 Jena, Germany
Related authors
Laura Eifler, Franziska Müller, and Ana Bastos
Biogeosciences, 23, 1291–1325, https://doi.org/10.5194/bg-23-1291-2026, https://doi.org/10.5194/bg-23-1291-2026, 2026
Short summary
Short summary
Forests provide ecosystem services and biodiversity, but they are increasingly affected by disturbances. We evaluate five forest disturbance datasets across the Unites States to assess their consistency in space, timing, and disturbance agents. While datasets show good agreement in disturbance timing, spatial overlap and agent attribution differ substantially. This emphasizes the need for enhanced data quality assessment, integration, and accuracy to better understand forest disturbances.
Anna T. Schackow, Susan C. Steele-Dunne, David T. Milodowski, Jean-Marc Limousin, and Ana Bastos
Biogeosciences, 23, 3365–3386, https://doi.org/10.5194/bg-23-3365-2026, https://doi.org/10.5194/bg-23-3365-2026, 2026
Short summary
Short summary
Plants regulate how much water they lose and how much carbon they take in, but rising heat and dryness make this balance harder. We studied how water movement inside plant stems changes during the day and relates to dryness in the air and soil. By analyzing these daily patterns, we identified signals of stress that could be tracked not only with sensors in plants but also from satellites, offering new ways to monitor global vegetation health.
Guohua Liu, Philippe Ciais, Shengli Tao, Hui Yang, Chenwei Xiao, and Ana Bastos
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-246, https://doi.org/10.5194/essd-2026-246, 2026
Preprint under review for ESSD
Short summary
Short summary
We developed a long-term, moderate-resolution global dataset of forest above ground biomass, from satellite radar data and machine learning. The significance of this dataset lies in its ability to provide a continuous, moderate-resolution view of biomass carbon dynamics, offering a valuable resource for supporting studies of climate change, disturbance, and land use, and will help improve Earth System models and future carbon cycle.
Mara Y. McPartland, Tomas Lovato, Charles Koven, Jamie D. Wilson, Briony Turner, Colleen M. Petrik, José Licón-Saláiz, Fang Li, Fanny Lhardy, Jaclyn Clement Kinney, Michio Kawamiya, Birgit Hassler, Nathan P. Gillett, Cheikh Modou Noreyni Fall, Christopher Danek, Chris M. Brierley, Ana Bastos, and Oliver Andrews
Geosci. Model Dev., 19, 2849–2880, https://doi.org/10.5194/gmd-19-2849-2026, https://doi.org/10.5194/gmd-19-2849-2026, 2026
Short summary
Short summary
The Coupled Model Intercomparison Project (CMIP) is an international consortium of climate modeling groups that produce coordinated experiments in order to evaluate human influence on the climate and test knowledge of Earth systems. This paper describes the data requested for Earth systems research in CMIP7. We detail the request for model output of the carbon cycle, the flows of energy among the atmosphere, land and the oceans, and interactions between these and the global climate.
Samuel Upton, Markus Reichstein, Wouter Peters, Santiago Botía, Jacob A. Nelson, Sophia Walther, Martin Jung, Fabian Gans, László Haszpra, and Ana Bastos
Atmos. Chem. Phys., 26, 2561–2595, https://doi.org/10.5194/acp-26-2561-2026, https://doi.org/10.5194/acp-26-2561-2026, 2026
Short summary
Short summary
We create a hybrid ecosystem-level carbon flux model using both eddy-covariance observations and observations of the atmospheric mole fraction of CO2 at three tall-tower observatories. Our study uses an atmospheric transport model (STILT) to connect the atmospheric signal to the ecosystem-level model. We show that this inclusion of atmospheric information meaningfully improves the model's representation of the interannual variability of the global net flux of CO2.
Laura Eifler, Franziska Müller, and Ana Bastos
Biogeosciences, 23, 1291–1325, https://doi.org/10.5194/bg-23-1291-2026, https://doi.org/10.5194/bg-23-1291-2026, 2026
Short summary
Short summary
Forests provide ecosystem services and biodiversity, but they are increasingly affected by disturbances. We evaluate five forest disturbance datasets across the Unites States to assess their consistency in space, timing, and disturbance agents. While datasets show good agreement in disturbance timing, spatial overlap and agent attribution differ substantially. This emphasizes the need for enhanced data quality assessment, integration, and accuracy to better understand forest disturbances.
Na Li, Sebastian Sippel, Nora Linscheid, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
Biogeosciences, 23, 767–792, https://doi.org/10.5194/bg-23-767-2026, https://doi.org/10.5194/bg-23-767-2026, 2026
Short summary
Short summary
We study when anthropogenic signal becomes detectable in the global land carbon sink, which has risen since the 1950s due to CO₂ fertilization and mid- to high-latitude warming. The signal emerges earlier at the global than at regional scales. Future scenarios (2016–2100) take longer to detect than the historical period (1851–2014) because the signal is weaker relative to larger natural variability. Removing circulation-induced variability with dynamical adjustment shortens the detection time.
Peter Pfleiderer, Anna Merrifield, István Dunkl, Homer Durand, Enora Cariou, Julien Cattiaux, Gustau Camps-Valls, and Sebastian Sippel
Weather Clim. Dynam., 7, 89–108, https://doi.org/10.5194/wcd-7-89-2026, https://doi.org/10.5194/wcd-7-89-2026, 2026
Short summary
Short summary
Due to changes in atmospheric circulation some regions are warming quicker than others. Statistical methods are used to estimate how much of the local summer temperature changes are due to circulation changes. We evaluate these methods by comparing their estimates to special simulations representing only temperature changes related to circulation changes. By applying the methods to observations of 1979–2023 we find that half of the warming over parts of Europe is related to circulation changes.
Siyuan Wang, Hui Yang, Sujan Koirala, Maurizio Santoro, Ulrich Weber, Claire Robin, Felix Cremer, Matthias Forkel, Markus Reichstein, and Nuno Carvalhais
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-670, https://doi.org/10.5194/essd-2025-670, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
Forest disturbances are difficult to predict in models because they occur randomly. We discovered that the long-term rules of disturbance known as "regime" leave a unique footprint in a forest's spatial biomass patterns. We trained a model on millions of computer simulations to learn this link. By applying this model to detailed satellite biomass, we could read these patterns to infer the disturbance regime globally, helping make climate projections more accurate.
Friedrich J. Bohn, Giles B. Sioen, Ana Bastos, Yolandi Ernst, Marcin P. Jarzebski, Niak S. Koh, Romina Martin, Anja Rammig, Alex Godoy-Faúndez, Alexandros Gasparatos, Alvaro G. Gutiérrez, Amanda J. Aceituno, Andra-Ioana Horcea-Milcu, Andrea Marais-Potgieter, Ayyoob Sharifi, Caroline Howe, Cornelia B. Krug, Eduardo E. Acosta, Emmanuel F. Nzunda, Erik Andersson, Hans-Otto Pörtner, Helen Sooväli-Sepping, Ishihara Hiroe, Ivan Palmegiani, Kaera Coetzer, Kirsten Thonike, Krizler Tanalgo, Lisa Biber-Freudenberger, Nicholas O. Oguge, Mi S. Park, Milena Gross, Pablo De La Cruz, Paula R. Prist, Peng Bi, Rivera Diego, Roman Isaac, Rosemary McFarlane, Sinikka J. Paulus, Stefanie Burkhart, Sung-Ching Lee, Susanne Müller, Uchi D. Terhile, Wan-Yu Shih, William K. Smith, Viola Hakkarainen, Virginia Murray, Yuki Yoshida, Yohannes T. Damtew, and Zeenat Niazi
EGUsphere, https://doi.org/10.5194/egusphere-2025-3619, https://doi.org/10.5194/egusphere-2025-3619, 2025
Short summary
Short summary
The aim of this series is to provide decision-makers with valuable insights into the current state of biosphere research. Firstly, it is intended to ensure the flow of information between the comprehensive assessment reports of the IPCC and IPBES. On the other hand, it is intended to support economic and political decisions closely related to the biosphere with scientifically sound findings – including uncertainties – and comprehensive polysolutions, helping to solve the earth system polycrisis.
Zhixuan Guo, Wei Li, Philippe Ciais, Stephen Sitch, Guido R. van der Werf, Simon P. K. Bowring, Ana Bastos, Florent Mouillot, Jiaying He, Minxuan Sun, Lei Zhu, Xiaomeng Du, Nan Wang, and Xiaomeng Huang
Earth Syst. Sci. Data, 17, 3599–3618, https://doi.org/10.5194/essd-17-3599-2025, https://doi.org/10.5194/essd-17-3599-2025, 2025
Short summary
Short summary
To address the limitations of short time spans in satellite data and spatiotemporal discontinuity in site records, we reconstructed global monthly burned area maps at a 0.5° resolution for 1901–2020 using machine learning models. The global burned area is predicted at 3.46 × 106–4.58 × 106 km² per year, showing a decline from 1901 to 1978, an increase from 1978 to 2008 and a sharper decrease from 2008 to 2020. This dataset provides a benchmark for studies on fire ecology and the carbon cycle.
Guohua Liu, Philippe Ciais, Shengli Tao, Hui Yang, and Ana Bastos
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-330, https://doi.org/10.5194/essd-2025-330, 2025
Manuscript not accepted for further review
Short summary
Short summary
We have developed a long-term and high-resolution global map of above-ground biomass changes from 1993 to 2020 using radar data and machine learning approach. This dataset can help understand the effects of disturbances, land-use changes, and extreme events on global carbon cycle, and enhance the representation of these processes in Earth System Models.
Theertha Kariyathan, Ana Bastos, Markus Reichstein, Wouter Peters, and Julia Marshall
Atmos. Chem. Phys., 25, 7863–7878, https://doi.org/10.5194/acp-25-7863-2025, https://doi.org/10.5194/acp-25-7863-2025, 2025
Short summary
Short summary
The carbon uptake period (CUP) is the time period when land absorbs more CO2 than it emits. While atmospheric CO2 mole fraction measurements can be used to assess CUP changes, atmospheric transport and asynchronous timing across regions reduce the accuracy of the estimates. Forward model experiments show that only ~ 50 % of prescribed shifts in CUP timing applied to surface fluxes (ΔCUPNEE) are captured in simulated CO2 mole fraction data at monitoring sites like the Barrow Atmospheric Baseline Observatory.
Aytaç Paçal, Birgit Hassler, Katja Weigel, Miguel-Ángel Fernández-Torres, Gustau Camps-Valls, and Veronika Eyring
EGUsphere, https://doi.org/10.5194/egusphere-2025-2460, https://doi.org/10.5194/egusphere-2025-2460, 2025
Short summary
Short summary
Heatwaves are among the deadliest natural hazards, yet their causes and changes over time are not fully understood. We analyzed European heatwaves using a machine learning method that detects atmospheric patterns from these data. Our findings show that recent summer heatwaves differ from historical ones, indicating a shift in atmospheric dynamics consistent with climate change. This approach improves our understanding of the temporal evolution of heatwaves.
Friedrich J. Bohn, Ana Bastos, Romina Martin, Anja Rammig, Niak Sian Koh, Giles B. Sioen, Bram Buscher, Louise Carver, Fabrice DeClerck, Moritz Drupp, Robert Fletcher, Matthew Forrest, Alexandros Gasparatos, Alex Godoy-Faúndez, Gregor Hagedorn, Martin C. Hänsel, Jessica Hetzer, Thomas Hickler, Cornelia B. Krug, Stasja Koot, Xiuzhen Li, Amy Luers, Shelby Matevich, H. Damon Matthews, Ina C. Meier, Mirco Migliavacca, Awaz Mohamed, Sungmin O, David Obura, Ben Orlove, Rene Orth, Laura Pereira, Markus Reichstein, Lerato Thakholi, Peter H. Verburg, and Yuki Yoshida
Biogeosciences, 22, 2425–2460, https://doi.org/10.5194/bg-22-2425-2025, https://doi.org/10.5194/bg-22-2425-2025, 2025
Short summary
Short summary
An interdisciplinary collaboration of 36 international researchers from 35 institutions highlights recent findings in biosphere research. Within eight themes, they discuss issues arising from climate change and other anthropogenic stressors and highlight the co-benefits of nature-based solutions and ecosystem services. Based on an analysis of these eight topics, we have synthesized four overarching insights.
Zhu Deng, Philippe Ciais, Liting Hu, Adrien Martinez, Marielle Saunois, Rona L. Thompson, Kushal Tibrewal, Wouter Peters, Brendan Byrne, Giacomo Grassi, Paul I. Palmer, Ingrid T. Luijkx, Zhu Liu, Junjie Liu, Xuekun Fang, Tengjiao Wang, Hanqin Tian, Katsumasa Tanaka, Ana Bastos, Stephen Sitch, Benjamin Poulter, Clément Albergel, Aki Tsuruta, Shamil Maksyutov, Rajesh Janardanan, Yosuke Niwa, Bo Zheng, Joël Thanwerdas, Dmitry Belikov, Arjo Segers, and Frédéric Chevallier
Earth Syst. Sci. Data, 17, 1121–1152, https://doi.org/10.5194/essd-17-1121-2025, https://doi.org/10.5194/essd-17-1121-2025, 2025
Short summary
Short summary
This study reconciles national greenhouse gas (GHG) inventories with updated atmospheric inversion results to evaluate discrepancies for three principal GHG fluxes at the national level. Compared to our previous study, new satellite-based CO2 inversions were included and an updated mask of managed lands was used, improving agreement for Brazil and Canada. The proposed methodology can be regularly applied as a check to assess the gap between top-down inversions and bottom-up inventories.
István Dunkl, Ana Bastos, and Tatiana Ilyina
Earth Syst. Dynam., 16, 151–167, https://doi.org/10.5194/esd-16-151-2025, https://doi.org/10.5194/esd-16-151-2025, 2025
Short summary
Short summary
While the El Niño–Southern Oscillation, a climate mode, has a similar impact on CO2 growth rates across Earth system models, there is significant uncertainty in the processes behind this relationship. We found a compensatory effect that masks differences in the sensitivity of carbon fluxes to climate anomalies and observed that the carbon fluxes contributing to global CO2 anomalies originate from different regions and are caused by different drivers.
Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Jiří Dušek, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck, Dave Billesbach, David Bowling, Rosvel Bracho, Christian Brümmer, Gustau Camps-Valls, Shiping Chen, Jamie Rose Cleverly, Ankur Desai, Gang Dong, Tarek S. El-Madany, Eugenie Susanne Euskirchen, Iris Feigenwinter, Marta Galvagno, Giacomo A. Gerosa, Bert Gielen, Ignacio Goded, Sarah Goslee, Christopher Michael Gough, Bernard Heinesch, Kazuhito Ichii, Marcin Antoni Jackowicz-Korczynski, Anne Klosterhalfen, Sara Knox, Hideki Kobayashi, Kukka-Maaria Kohonen, Mika Korkiakoski, Ivan Mammarella, Mana Gharun, Riccardo Marzuoli, Roser Matamala, Stefan Metzger, Leonardo Montagnani, Giacomo Nicolini, Thomas O'Halloran, Jean-Marc Ourcival, Matthias Peichl, Elise Pendall, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Marius Schmidt, Christopher R. Schwalm, Ankit Shekhar, Richard Silberstein, Maria Lucia Silveira, Donatella Spano, Torbern Tagesson, Gianluca Tramontana, Carlo Trotta, Fabio Turco, Timo Vesala, Caroline Vincke, Domenico Vitale, Enrique R. Vivoni, Yi Wang, William Woodgate, Enrico A. Yepez, Junhui Zhang, Donatella Zona, and Martin Jung
Biogeosciences, 21, 5079–5115, https://doi.org/10.5194/bg-21-5079-2024, https://doi.org/10.5194/bg-21-5079-2024, 2024
Short summary
Short summary
The movement of water, carbon, and energy from the Earth's surface to the atmosphere, or flux, is an important process to understand because it impacts our lives. Here, we outline a method called FLUXCOM-X to estimate global water and CO2 fluxes based on direct measurements from sites around the world. We go on to demonstrate how these new estimates of net CO2 uptake/loss, gross CO2 uptake, total water evaporation, and transpiration from plants compare to previous and independent estimates.
Francesco Martinuzzi, Miguel D. Mahecha, Gustau Camps-Valls, David Montero, Tristan Williams, and Karin Mora
Nonlin. Processes Geophys., 31, 535–557, https://doi.org/10.5194/npg-31-535-2024, https://doi.org/10.5194/npg-31-535-2024, 2024
Short summary
Short summary
We investigated how machine learning can forecast extreme vegetation responses to weather. Examining four models, no single one stood out as the best, though "echo state networks" showed minor advantages. Our results indicate that while these tools are able to generally model vegetation states, they face challenges under extreme conditions. This underlines the potential of artificial intelligence in ecosystem modeling, also pinpointing areas that need further research.
Anne F. Van Loon, Sarra Kchouk, Alessia Matanó, Faranak Tootoonchi, Camila Alvarez-Garreton, Khalid E. A. Hassaballah, Minchao Wu, Marthe L. K. Wens, Anastasiya Shyrokaya, Elena Ridolfi, Riccardo Biella, Viorica Nagavciuc, Marlies H. Barendrecht, Ana Bastos, Louise Cavalcante, Franciska T. de Vries, Margaret Garcia, Johanna Mård, Ileen N. Streefkerk, Claudia Teutschbein, Roshanak Tootoonchi, Ruben Weesie, Valentin Aich, Juan P. Boisier, Giuliano Di Baldassarre, Yiheng Du, Mauricio Galleguillos, René Garreaud, Monica Ionita, Sina Khatami, Johanna K. L. Koehler, Charles H. Luce, Shreedhar Maskey, Heidi D. Mendoza, Moses N. Mwangi, Ilias G. Pechlivanidis, Germano G. Ribeiro Neto, Tirthankar Roy, Robert Stefanski, Patricia Trambauer, Elizabeth A. Koebele, Giulia Vico, and Micha Werner
Nat. Hazards Earth Syst. Sci., 24, 3173–3205, https://doi.org/10.5194/nhess-24-3173-2024, https://doi.org/10.5194/nhess-24-3173-2024, 2024
Short summary
Short summary
Drought is a creeping phenomenon but is often still analysed and managed like an isolated event, without taking into account what happened before and after. Here, we review the literature and analyse five cases to discuss how droughts and their impacts develop over time. We find that the responses of hydrological, ecological, and social systems can be classified into four types and that the systems interact. We provide suggestions for further research and monitoring, modelling, and management.
Samuel Upton, Markus Reichstein, Fabian Gans, Wouter Peters, Basil Kraft, and Ana Bastos
Atmos. Chem. Phys., 24, 2555–2582, https://doi.org/10.5194/acp-24-2555-2024, https://doi.org/10.5194/acp-24-2555-2024, 2024
Short summary
Short summary
Data-driven eddy-covariance upscaled estimates of the global land–atmosphere net CO2 exchange (NEE) show important mismatches with regional and global estimates based on atmospheric information. To address this, we create a model with a dual constraint based on bottom-up eddy-covariance data and top-down atmospheric inversion data. Our model overcomes shortcomings of each approach, producing improved NEE estimates from local to global scale, helping to reduce uncertainty in the carbon budget.
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
Short summary
Short summary
We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Jan De Pue, Sebastian Wieneke, Ana Bastos, José Miguel Barrios, Liyang Liu, Philippe Ciais, Alirio Arboleda, Rafiq Hamdi, Maral Maleki, Fabienne Maignan, Françoise Gellens-Meulenberghs, Ivan Janssens, and Manuela Balzarolo
Biogeosciences, 20, 4795–4818, https://doi.org/10.5194/bg-20-4795-2023, https://doi.org/10.5194/bg-20-4795-2023, 2023
Short summary
Short summary
The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. To estimate this flux, models can rely on remote sensing data (RS-driven), meteorological data (meteo-driven) or a combination of both (hybrid). An intercomparison of 11 models demonstrated that RS-driven models lack the sensitivity to short-term anomalies. Conversely, the simulation of soil moisture dynamics and stress response remains a challenge in meteo-driven models.
Chenwei Xiao, Sönke Zaehle, Hui Yang, Jean-Pierre Wigneron, Christiane Schmullius, and Ana Bastos
Earth Syst. Dynam., 14, 1211–1237, https://doi.org/10.5194/esd-14-1211-2023, https://doi.org/10.5194/esd-14-1211-2023, 2023
Short summary
Short summary
Ecosystem resistance reflects their susceptibility during adverse conditions and can be changed by land management. We estimate ecosystem resistance to drought and temperature globally. We find a higher resistance to drought in forests compared to croplands and an evident loss of resistance to drought when primary forests are converted to secondary forests or they are harvested. Old-growth trees tend to be more resistant in some forests and crops benefit from irrigation during drought periods.
Theertha Kariyathan, Ana Bastos, Julia Marshall, Wouter Peters, Pieter Tans, and Markus Reichstein
Atmos. Meas. Tech., 16, 3299–3312, https://doi.org/10.5194/amt-16-3299-2023, https://doi.org/10.5194/amt-16-3299-2023, 2023
Short summary
Short summary
The timing and duration of the carbon uptake period (CUP) are sensitive to the occurrence of major phenological events, which are influenced by recent climate change. This study presents an ensemble-based approach for quantifying the timing and duration of the CUP and their uncertainty when derived from atmospheric CO2 measurements with noise and gaps. The CUP metrics derived with the approach are more robust and have less uncertainty than when estimated with the conventional methods.
Na Li, Sebastian Sippel, Alexander J. Winkler, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
Earth Syst. Dynam., 13, 1505–1533, https://doi.org/10.5194/esd-13-1505-2022, https://doi.org/10.5194/esd-13-1505-2022, 2022
Short summary
Short summary
Quantifying the imprint of large-scale atmospheric circulation dynamics and associated carbon cycle responses is key to improving our understanding of carbon cycle dynamics. Using a statistical model that relies on spatiotemporal sea level pressure as a proxy for large-scale atmospheric circulation, we quantify the fraction of interannual variability in atmospheric CO2 growth rate and the land CO2 sink that are driven by atmospheric circulation variability.
Melissa Ruiz-Vásquez, Sungmin O, Alexander Brenning, Randal D. Koster, Gianpaolo Balsamo, Ulrich Weber, Gabriele Arduini, Ana Bastos, Markus Reichstein, and René Orth
Earth Syst. Dynam., 13, 1451–1471, https://doi.org/10.5194/esd-13-1451-2022, https://doi.org/10.5194/esd-13-1451-2022, 2022
Short summary
Short summary
Subseasonal forecasts facilitate early warning of extreme events; however their predictability sources are not fully explored. We find that global temperature forecast errors in many regions are related to climate variables such as solar radiation and precipitation, as well as land surface variables such as soil moisture and evaporative fraction. A better representation of these variables in the forecasting and data assimilation systems can support the accuracy of temperature forecasts.
Xin Yu, René Orth, Markus Reichstein, Michael Bahn, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Mirco Migliavacca, Martina Mund, Jacob A. Nelson, Benjamin D. Stocker, Sophia Walther, and Ana Bastos
Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, https://doi.org/10.5194/bg-19-4315-2022, 2022
Short summary
Short summary
Identifying drought legacy effects is challenging because they are superimposed on variability driven by climate conditions in the recovery period. We develop a residual-based approach to quantify legacies on gross primary productivity (GPP) from eddy covariance data. The GPP reduction due to legacy effects is comparable to the concurrent effects at two sites in Germany, which reveals the importance of legacy effects. Our novel methodology can be used to quantify drought legacies elsewhere.
Zhu Deng, Philippe Ciais, Zitely A. Tzompa-Sosa, Marielle Saunois, Chunjing Qiu, Chang Tan, Taochun Sun, Piyu Ke, Yanan Cui, Katsumasa Tanaka, Xin Lin, Rona L. Thompson, Hanqin Tian, Yuanzhi Yao, Yuanyuan Huang, Ronny Lauerwald, Atul K. Jain, Xiaoming Xu, Ana Bastos, Stephen Sitch, Paul I. Palmer, Thomas Lauvaux, Alexandre d'Aspremont, Clément Giron, Antoine Benoit, Benjamin Poulter, Jinfeng Chang, Ana Maria Roxana Petrescu, Steven J. Davis, Zhu Liu, Giacomo Grassi, Clément Albergel, Francesco N. Tubiello, Lucia Perugini, Wouter Peters, and Frédéric Chevallier
Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, https://doi.org/10.5194/essd-14-1639-2022, 2022
Short summary
Short summary
In support of the global stocktake of the Paris Agreement on climate change, we proposed a method for reconciling the results of global atmospheric inversions with data from UNFCCC national greenhouse gas inventories (NGHGIs). Here, based on a new global harmonized database that we compiled from the UNFCCC NGHGIs and a comprehensive framework presented in this study to process the results of inversions, we compared their results of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).
Philippe Ciais, Ana Bastos, Frédéric Chevallier, Ronny Lauerwald, Ben Poulter, Josep G. Canadell, Gustaf Hugelius, Robert B. Jackson, Atul Jain, Matthew Jones, Masayuki Kondo, Ingrid T. Luijkx, Prabir K. Patra, Wouter Peters, Julia Pongratz, Ana Maria Roxana Petrescu, Shilong Piao, Chunjing Qiu, Celso Von Randow, Pierre Regnier, Marielle Saunois, Robert Scholes, Anatoly Shvidenko, Hanqin Tian, Hui Yang, Xuhui Wang, and Bo Zheng
Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, https://doi.org/10.5194/gmd-15-1289-2022, 2022
Short summary
Short summary
The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO2, CH4, and N2O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
Ana Bastos, René Orth, Markus Reichstein, Philippe Ciais, Nicolas Viovy, Sönke Zaehle, Peter Anthoni, Almut Arneth, Pierre Gentine, Emilie Joetzjer, Sebastian Lienert, Tammas Loughran, Patrick C. McGuire, Sungmin O, Julia Pongratz, and Stephen Sitch
Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, https://doi.org/10.5194/esd-12-1015-2021, 2021
Short summary
Short summary
Temperate biomes in Europe are not prone to recurrent dry and hot conditions in summer. However, these conditions may become more frequent in the coming decades. Because stress conditions can leave legacies for many years, this may result in reduced ecosystem resilience under recurrent stress. We assess vegetation vulnerability to the hot and dry summers in 2018 and 2019 in Europe and find the important role of inter-annual legacy effects from 2018 in modulating the impacts of the 2019 event.
Kerstin Hartung, Ana Bastos, Louise Chini, Raphael Ganzenmüller, Felix Havermann, George C. Hurtt, Tammas Loughran, Julia E. M. S. Nabel, Tobias Nützel, Wolfgang A. Obermeier, and Julia Pongratz
Earth Syst. Dynam., 12, 763–782, https://doi.org/10.5194/esd-12-763-2021, https://doi.org/10.5194/esd-12-763-2021, 2021
Short summary
Short summary
In this study, we model the relative importance of several contributors to the land-use and land-cover change (LULCC) flux based on a LULCC dataset including uncertainty estimates. The uncertainty of LULCC is as relevant as applying wood harvest and gross transitions for the cumulative LULCC flux over the industrial period. However, LULCC uncertainty matters less than the other two factors for the LULCC flux in 2014; historical LULCC uncertainty is negligible for estimates of future scenarios.
Ana Bastos, Kerstin Hartung, Tobias B. Nützel, Julia E. M. S. Nabel, Richard A. Houghton, and Julia Pongratz
Earth Syst. Dynam., 12, 745–762, https://doi.org/10.5194/esd-12-745-2021, https://doi.org/10.5194/esd-12-745-2021, 2021
Short summary
Short summary
Fluxes from land-use change and management (FLUC) are a large source of uncertainty in global and regional carbon budgets. Here, we evaluate the impact of different model parameterisations on FLUC. We show that carbon stock densities and allocation of carbon following transitions contribute more to uncertainty in FLUC than response-curve time constants. Uncertainty in FLUC could thus, in principle, be reduced by available Earth-observation data on carbon densities at a global scale.
Cited articles
Aloisi, A.: Commoditized Workers. Case Study Research on Labour Law Issues Arising from a Set of “On-Demand/Gig Economy” Platforms, https://doi.org/10.2139/ssrn.2637485, 2016. a
Altman, J., Fibich, P., Trotsiuk, V., and Altmanova, N.: Global pattern of forest disturbances and its shift under climate change, Sci. Total Environ., 915, 170117, https://doi.org/10.1016/j.scitotenv.2024.170117, 2024. a
Anderegg, W. R. L., Hicke, J. A., Fisher, R. A., Allen, C. D., Aukema, J., Bentz, B., Hood, S., Lichstein, J. W., Macalady, A. K., Mcdowell, N., Pan, Y., Raffa, K., Sala, A., Shaw, J. D., Stephenson, N. L., Tague, C., and Zeppel, M.: Research review Tree mortality from drought, insects, and their interactions in a changing climate, New Phytol., 208, 674–683, https://doi.org/10.1111/nph.13477, 2015. a
Andresini, G., Appice, A., and Malerba, D.: A Deep Semantic Segmentation Approach to Map Forest Tree Dieback in Sentinel-2 Data, IEEE J. Sel. Top. Appl., 17, 17075–17086, https://doi.org/10.1109/JSTARS.2024.3460981, 2024. a
Andrus, R. A., Hicke, J. A., and Meddens, A. J. H.: Spatiotemporal characteristics of tree mortality from bark beetle outbreaks vary within and among bark beetle-host tree associations in the western United States, Forest Ecol. Manag., 576, 122382, https://doi.org/10.1016/j.foreco.2024.122382, 2025. a
Backsen, J. C. and Howell, B.: Comparing Aerial Detection and Photo Interpretation for Conducting Forest Health Surveys, West. J. Appl. For., 28, 3–8, https://doi.org/10.5849/wjaf.12-010, 2013. a, b
Bae, S., Müller, J., Förster, B., Hilmers, T., Hochrein, S., Jacobs, M., Leroy, B. M. L., Pretzsch, H., Weisser, W. W., and Mitesser, O.: Tracking the temporal dynamics of insect defoliation by high-resolution radar satellite data, Methods Ecol. Evol., 13, 121–132, https://doi.org/10.1111/2041-210X.13726, 2022. a
Bauer-Marschallinger, B., Sabel, D., and Wagner, W.: Optimisation of global grids for high-resolution remote sensing data, Comput. Geosci., 72, 84–93, https://doi.org/10.1016/j.cageo.2014.07.005, 2014. a
Bauman, D., Fortunel, C., Delhaye, G., Malhi, Y., Cernusak, L. A., Bentley, L. P., Rifai, S. W., Aguirre-Gutiérrez, J., Menor, I. O., Phillips, O. L., McNellis, B. E., Bradford, M., Laurance, S. G. W., Hutchinson, M. F., Dempsey, R., Santos-Andrade, P. E., Ninantay-Rivera, H. R., Chambi Paucar, J. R., and McMahon, S. M.: Tropical tree mortality has increased with rising atmospheric water stress, Nature, 608, 528–533, https://doi.org/10.1038/s41586-022-04737-7, 2022. a
Baumann, M., Ozdogan, M., Wolter, P. T., Krylov, A., Vladimirova, N., and Radeloff, V. C.: Landsat remote sensing of forest windfall disturbance, Remote Sens. Environ., 143, 171–179, https://doi.org/10.1016/j.rse.2013.12.020, 2014. a
Borlaf-Mena, I., Santoro, M., Villard, L., Badea, O., and Tanase, M.: Investigating the Impact of Digital Elevation Models on Sentinel-1 Backscatter and Coherence Observations, Remote Sens., 12, 3016, https://doi.org/10.3390/rs12183016, 2020. a
Bruggisser, M., Dorigo, W., Dostálová, A., Hollaus, M., Navacchi, C., Schlaffer, S., and Pfeifer, N.: Potential of Sentinel-1 C-Band Time Series to Derive Structural Parameters of Temperate Deciduous Forests, Remote Sens., 13, 798, https://doi.org/10.3390/rs13040798, 2021. a
Byrne, B., Liu, J., Bowman, K. W., Pascolini-Campbell, M., Chatterjee, A., Pandey, S., Miyazaki, K., van der Werf, G. R., Wunch, D., Wennberg, P. O., Roehl, C. M., and Sinha, S.: Carbon emissions from the 2023 Canadian wildfires, Nature, 633, 835–839, https://doi.org/10.1038/s41586-024-07878-z, 2024. a
Bárta, V., Lukeš, P., and Homolová, L.: Early detection of bark beetle infestation in Norway spruce forests of Central Europe using Sentinel-2, Int. J. Appl. Earth Obs., 100, 102335, https://doi.org/10.1016/j.jag.2021.102335, 2021. a
Canadell, J. G., Meyer, C. P. M., Cook, G. D., Dowdy, A., Briggs, P. R., Knauer, J., Pepler, A., and Haverd, V.: Multi-decadal increase of forest burned area in Australia is linked to climate change, Nat. Commun., 12, 6921, https://doi.org/10.1038/s41467-021-27225-4, 2021a. a
Canadell, J. G., Monteiro, P. M., Costa, M. H., Cotrim da Cunha, L., Cox, P. M., Eliseev, A. V., Hensen, S., Ishii, M., Jaccard, S., Koven, C., Lohila, A., Patra, P. K., Piao, S., Rogelj, J., Syampungani, S., Zaehle, S., and Zickfeld, K.: Global carbon and other biogeochemical cycles and feedbacks, IPCC, https://doi.org/10.1017/9781009157896.007, 2021b. a
Candotti, A., De Giglio, M., Dubbini, M., and Tomelleri, E.: A Sentinel-2 Based Multi-Temporal Monitoring Framework for Wind and Bark Beetle Detection and Damage Mapping, Remote Sens., 14, 6105, https://doi.org/10.3390/rs14236105, 2022. a, b
Chen, X., Zhao, W., Chen, J., Qu, Y., Wu, D., and Chen, X.: Mapping Large-Scale Forest Disturbance Types with Multi-Temporal CNN Framework, Remote Sens., 13, https://doi.org/10.3390/rs13245177, 2021. a
Chuvieco, E., Lizundia-Loiola, J., Pettinari, M. L., Ramo, R., Padilla, M., Tansey, K., Mouillot, F., Laurent, P., Storm, T., Heil, A., and Plummer, S.: Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies, Earth Syst. Sci. Data, 10, 2015–2031, https://doi.org/10.5194/essd-10-2015-2018, 2018. a
Cohen, W. B., Yang, Z., and Kennedy, R. E.: Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. Time series analysis, Remote Sens. Environ., 114, 2897–2910, https://doi.org/10.1016/j.rse.2010.07.010, 2010. a
Coleman, T. W., Graves, A. D., Heath, Z., Flowers, R. W., Hanavan, R. P., Cluck, D. R., and Ryerson, D.: Accuracy of aerial detection surveys for mapping insect and disease disturbances in the United States, Forest Ecol. Manag., 430, 321–336, https://doi.org/10.1016/j.foreco.2018.08.020, 2018. a, b, c, d, e, f, g, h
Cremer, F. and Gans, F.: Satellite-Based Forest Disturbance Dataset from Sentinel-1 SAR (2016–2021) (1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.16903909, 2025. a
Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A., and Hansen, M. C.: Classifying drivers of global forest loss, Science, 361, 1108–1111, https://doi.org/10.1126/science.aau3445, 2018. a
Dalponte, M., Sassi, R., Gianelle, D., Bruzzone, L., and Marinelli, D.: Exploring the Detection of Bark Beetle Attacks in Norway Spruce Forests in Sentinel-1 Image Time Series, IEEE, https://doi.org/10.1109/IGARSS53475.2024.10641531, 2024. a
Eklundh, L., Johansson, T., and Solberg, S.: Mapping insect defoliation in Scots pine with MODIS time-series data, Remote Sens. Environ., 113, 1566–1573, https://doi.org/10.1016/j.rse.2009.03.008, 2009. a, b
ESA: ESA's Living Planet Programme: Scientific Achievements and Future Challenges – Scientific Context of the Earth Observation Science Strategy for ESA, ESA SP-1329/2, European Space Agency (ESA), Noordwijk, the Netherlands, ISBN 978-92-9221-427-2, 2015. a
European Commission: Proposal for a Regulation of the European Parliament and of the Council on a Monitoring Framework for Resilient European Forests, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52023PC0728, (last access: 22 November 2023), 2023. a
European Environment Agency (EU body or agency): European climate risk assessment: executive summary, Publications Office of the European Union, ISBN 978-92-9480-627-7, https://doi.org/10.2800/204249, 2024. a
Forzieri, G., Pecchi, M., Girardello, M., Mauri, A., Klaus, M., Nikolov, C., Rüetschi, M., Gardiner, B., Tomaštík, J., Small, D., Nistor, C., Jonikavicius, D., Spinoni, J., Feyen, L., Giannetti, F., Comino, R., Wolynski, A., Pirotti, F., Maistrelli, F., Savulescu, I., Wurpillot-Lucas, S., Karlsson, S., Zieba-Kulawik, K., Strejczek-Jazwinska, P., Mokroš, M., Franz, S., Krejci, L., Haidu, I., Nilsson, M., Wezyk, P., Catani, F., Chen, Y.-Y., Luyssaert, S., Chirici, G., Cescatti, A., and Beck, P. S. A.: A spatially explicit database of wind disturbances in European forests over the period 2000–2018, Earth Syst. Sci. Data, 12, 257–276, https://doi.org/10.5194/essd-12-257-2020, 2020. a, b, c, d
Forzieri, G., Girardello, M., Ceccherini, G., Spinoni, J., Feyen, L., Hartmann, H., Beck, P. S. A., Camps-Valls, G., Chirici, G., Mauri, A., and Cescatti, A.: Emergent vulnerability to climate-driven disturbances in European forests, Nat. Commun., 12, 1081, https://doi.org/10.1038/s41467-021-21399-7, 2021. a
Forzieri, G., Dutrieux, L. P., Elia, A., Eckhardt, B., Caudullo, G., Álvarez Taboada, F., Andriolo, A., Bălăcenoiu, F., Bastos, A., Buzatu, A., Castedo Dorado, F., Dobrovolný, L., Duduman, M.-L., Fernandez-Carrillo, A., Cescatti, A., A Beck, P. S., Giovanni Forzieri, C., and Commission, E.: The Database of European Forest Insect and Disease Disturbances: DEFID2, Glob. Change Biol., https://doi.org/10.1111/gcb.16912, 2023. a, b, c, d, e
Gibson, R., Danaher, T., Hehir, W., and Collins, L.: A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest, Remote Sens. Environ., 240, 111702, https://doi.org/10.1016/j.rse.2020.111702, 2020. a
Graziosi, I., Tembo, M., Kuate, J., and Muchugi, A.: Pests and diseases of trees in Africa: A growing continental emergency, Plants, People, Planet, 2, 14–28, https://doi.org/10.1002/ppp3.31, 2020. a
Hall, R., Castilla, G., White, J., Cooke, B., and Skakun, R.: Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective, Can. Entomol., 148, S296–S356, https://doi.org/10.4039/tce.2016.11, 2016. a, b, c, d
Hammond, W. M., H., Williams, A. P., Abatzoglou, J. T., Adams, H. D., Klein, T., López Rodríguez, R., Sáenz-Romero, C., Hartmann, H., Breshears, D. D., and Allen, C. D.: Global field observations of tree die-off reveal hotter-drought fingerprint for Earth's forests, Nat. Commun., 13, 1761, https://doi.org/10.1038/s41467-022-29289-2, 2022. a
Hansen, M., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S., Tyukavina, A., Thau, D., Stehman, S., Goetz, S., Loveland, T., Kommareddy, A., Egorov, A., Chini, L., Justice, C., and Townshed, J.: Global Forest Change 2000–2024 Data Download, https://storage.googleapis.com/earthenginepartners-hansen/GFC-2024-v1.12/download.html (last access: 22 July 2024), 2024. a
Hansen, M. C. and Loveland, T. R.: A review of large area monitoring of land cover change using Landsat data, Remote Sens. Environ., 122, 66–74, https://doi.org/10.1016/j.rse.2011.08.024, 2012. a
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R., Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J. R.: High-resolution global maps of 21st-century forest cover change, Science, 342, 850–853, https://doi.org/10.1126/SCIENCE.1244693, 2013. a
Harris, N. L., Brown, S., Hagen, S. C., Saatchi, S. S., Petrova, S., Salas, W., Hansen, M. C., Potapov, P. V., and Lotsch, A.: Baseline Map of Carbon Emissions from Deforestation in Tropical Regions, Science, 336, 1573–1576, https://doi.org/10.1126/science.1217962, 2012. a
Harris, N. L., Hagen, S. C., Saatchi, S. S., Pearson, T. R. H., Woodall, C. W., Domke, G. M., Braswell, B. H., Walters, B. F., Brown, S., Salas, W., Fore, A., and Yu, Y.: Attribution of net carbon change by disturbance type across forest lands of the conterminous United States, Carbon Balance and Management, 11, 24, https://doi.org/10.1186/s13021-016-0066-5, 2016. a, b, c, d, e
Hartmann, H., Bastos, A., Das, A. J., Esquivel-Muelbert, A., Hammond, W. M., Martínez-Vilalta, J., McDowell, N. G., Powers, J. S., Pugh, T. A., and Ruthrof, K. X.: Climate change risks to global forest health: emergence of unexpected events of elevated tree mortality worldwide, Annu. Rev. Plant Biolo., 73, 673–702, 2022. a
Hawkins, W. and Mittelstadt, B.: The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices, in: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT '23, pp. 261–270, Association for Computing Machinery, New York, NY, USA, ISBN 979-8-4007-0192-4, https://doi.org/10.1145/3593013.3593995, 2023. a
Hawryło, P., Bednarz, B., Wężyk, P., and Szostak, M.: Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2, Eur. J. Remote Sens., 51, 194–204, https://doi.org/10.1080/22797254.2017.1417745, 2018. a
Heaton, R., Song, B., Williams, T., Conner, W., Baucom, Z., and Williams, B.: Twenty-Seven Year Response of South Carolina Coastal Plain Forests Affected by Hurricane Hugo, Plants, 12, 691, https://doi.org/10.3390/plants12040691, 2023. a, b
Heinrich, V. H. A., Dalagnol, R., Cassol, H. L. G., Rosan, T. M., de Almeida, C. T., Silva Junior, C. H. L., Campanharo, W. A., House, J. I., Sitch, S., Hales, T. C., Adami, M., Anderson, L. O., and Aragão, L. E. O. C.: Large carbon sink potential of secondary forests in the Brazilian Amazon to mitigate climate change, Nat. Commun., 12, 1785, https://doi.org/10.1038/s41467-021-22050-1, 2021. a
Helbig, C. E., Müller, M. G., and Landgraf, D.: Effects of Leaf Loss by Artificial Defoliation on the Growth of Different Poplar and Willow Varieties, Forests, 12, https://doi.org/10.3390/f12091224, 2021. a
Helmer, E. H., Ruzycki, T. S., Wunderle Jr., J. M., Vogesser, S., Ruefenacht, B., Kwit, C., Brandeis, T. J., and Ewert, D. N.: Mapping tropical dry forest height, foliage height profiles and disturbance type and age with a time series of cloud-cleared Landsat and ALI image mosaics to characterize avian habitat, Remote Sens. Environ., 114, 2457–2473, https://doi.org/10.1016/j.rse.2010.05.021, 2010. a
Hicke, J. A., Allen, C. D., Desai, A. R., Dietze, M. C., Hall, R. J., Kashian, D. M., Moore, D., Raffa, K. F., Sturrock, R. N., Vogelmann, J., and others: Effects of biotic disturbances on forest carbon cycling in the United States and Canada, Glob. Change Biol., 18, 7–34, 2012. a
Hicke, J. A., Meddens, A. J., and Kolden, C. A.: Recent Tree Mortality in the Western United States from Bark Beetles and Forest Fires, Forest Sci., 62, 141–153, https://doi.org/10.5849/forsci.15-086, 2016. a, b
Hicke, J. A., Xu, B., Meddens, A. J. H., and Egan, J. M.: Characterizing recent bark beetle-caused tree mortality in the western United States from aerial surveys, Forest Ecol. Manag., 475, 118402, https://doi.org/10.1016/j.foreco.2020.118402, 2020. a, b, c
Hirschmugl, M., Gallaun, H., Dees, M., Datta, P., Deutscher, J., Koutsias, N., and Schardt, M.: Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review, Current Forestry Reports, 3, 32–45, https://doi.org/10.1007/s40725-017-0047-2, 2017. a
Housman, I. W., Heyer, J. P., Ruefenacht, B., Schleeweis, K., Megown, K., Bogle, S., Reischmann, J., and Ryerson, D.: National Land Cover Database Tree Canopy Cover Methods v2023.5, Technical Report GO-10268-RPT2, U.S. Department of Agriculture, Forest Service, Field Services and Innovation Center–Geospatial Office, Salt Lake City, UT, https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/docs/TCC_v2023-5_Methods.pdf (last access: 1 June 2026), 2025. a
Huang, X., Ziniti, B., Torbick, N., and Ducey, M. J.: Assessment of Forest above Ground Biomass Estimation Using Multi-Temporal C-band Sentinel-1 and Polarimetric L-band PALSAR-2 Data, Remote Sens., 10, https://doi.org/10.3390/rs10091424, 2018. a
Imperatore, P.: SAR Imaging Distortions Induced by Topography: A Compact Analytical Formulation for Radiometric Calibration, Remote Sens., 13, 3318, https://doi.org/10.3390/rs13163318, 2021. a
Jaccard, P.: Lois de distribution florale dans la zone alpine, Bulletin de la Société Vaudoise des Sciences Naturelles, 38, https://doi.org/10.5169/SEALS-266762, 1902. a
Jones, M. W., Abatzoglou, J. T., Veraverbeke, S., Andela, N., Lasslop, G., Forkel, M., Smith, A. J. P., Burton, C., Betts, R. A., van der Werf, G. R., Sitch, S., Canadell, J. G., Santín, C., Kolden, C., Doerr, S. H., and Le Quéré, C.: Global and Regional Trends and Drivers of Fire Under Climate Change, Rev. Geophys., 60, e2020RG000726, https://doi.org/10.1029/2020RG000726, 2022. a
Justice, C., Vermote, E., Townshend, J., Defries, R., Roy, D., Hall, D., Salomonson, V., Privette, J., Riggs, G., Strahler, A., Lucht, W., Myneni, R., Knyazikhin, Y., Running, S., Nemani, R., Wan, Z., Huete, A., van Leeuwen, W., Wolfe, R., Giglio, L., Muller, J., Lewis, P., and Barnsley, M.: The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research, IEEE T. Geosci. Remote, 36, 1228–1249, https://doi.org/10.1109/36.701075, 1998. a
Karel, T. H. and Man, G.: Major forest insect and disease conditions in the United States: 2015, 45 pp., U.S. Department of Agriculture, Forest Service, https://www.fs.usda.gov/foresthealth/publications/ConditionsReport_2015.pdf (last access: 10 June 2026), 2017. a
Kautz, M., Meddens, A. J. H., Hall, R. J., and Arneth, A.: Biotic disturbances in Northern Hemisphere forests-a synthesis of recent data, uncertainties and implications for forest monitoring and modelling, Global Ecol. Biogeogr., 26, 533–552, https://doi.org/10.1111/geb.12558, 2017. a, b, c, d, e, f, g, h
Kautz, M., Anthoni, P., Meddens, A. J. H., Pugh, T. A. M., and Arneth, A.: Simulating the recent impacts of multiple biotic disturbances on forest carbon cycling across the United States, Glob. Change Biol., 24, 2079–2092, https://doi.org/10.1111/gcb.13974, 2018. a
Kautz, M., Feurer, J., and Adler, P.: Early detection of bark beetle (Ips typographus) infestations by remote sensing – A critical review of recent research, Forest Ecol. Manag., 556, 121595, https://doi.org/10.1016/j.foreco.2023.121595, 2024. a, b
Kellndorfer, J.: Using SAR data for mapping deforestation and forest degradation, in: Synthetic Aperture Radar (SAR) Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation, edited by: Flores-Anderson, A. I., Herndon, K. E., Thapa, R. B., and Cherrington, E., chap. 3, SERVIR Global Science Coordination Office, National Space Science and Technology Center, Huntsville, AL, 47–66, https://doi.org/10.25966/nr2c-s697, 2019. a
Kennedy, R. E., Yang, Z., and Cohen, W. B.: Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr – Temporal segmentation algorithms, Remote Sens. Environ., 114, 2897–2910, https://doi.org/10.1016/j.rse.2010.07.008, 2010. a
Kislov, D. E., Korznikov, K. A., Altman, J., Vozmishcheva, A. S., and Krestov, P. V.: Extending deep learning approaches for forest disturbance segmentation on very high-resolution satellite images, Remote Sensing in Ecology and Conservation, 7, 355–368, https://doi.org/10.1002/rse2.194, 2021. a
Konings, A. G., Saatchi, S. S., Frankenberg, C., Keller, M., Leshyk, V., Anderegg, W. R. L., Humphrey, V., Matheny, A. M., Trugman, A., Sack, L., Agee, E., Barnes, M. L., Binks, O., Cawse-Nicholson, K., Christoffersen, B. O., Entekhabi, D., Gentine, P., Holtzman, N. M., Katul, G. G., Liu, Y., Longo, M., Martinez-Vilalta, J., McDowell, N., Meir, P., Mencuccini, M., Mrad, A., Novick, K. A., Oliveira, R. S., Siqueira, P., Steele-Dunne, S. C., Thompson, D. R., Wang, Y., Wehr, R., Wood, J. D., Xu, X., and Zuidema, P. A.: Detecting forest response to droughts with global observations of vegetation water content, Glob. Change Biol., 27, 6005–6024, https://doi.org/10.1111/gcb.15872, 2021. a
Korosuo, A., Pilli, R., Abad Viñas, R., Blujdea, V. N. B., Colditz, R. R., Fiorese, G., Rossi, S., Vizzarri, M., and Grassi, G.: The role of forests in the EU climate policy: are we on the right track?, Carbon Balance and Management, 18, 15, https://doi.org/10.1186/s13021-023-00234-0, 2023. a
Kurz, W. A., Dymond, C. C., Stinson, G., Rampley, G. J., Neilson, E. T., Carroll, A. L., Ebata, T., and Safranyik, L.: Mountain pine beetle and forest carbon feedback to climate change, Nature, 452, 987–990, https://doi.org/10.1038/nature06777, 2008. a
Markham, B. L. and Helder, D. L.: Forty-year calibrated record of earth-reflected radiance from Landsat: A review, Remote Sens. Environ., 122, 30–40, https://doi.org/10.1016/j.rse.2011.06.026, 2012. a
Marwan, N., Romano, M. C., Thiel, M., and Kurths, J.: Recurrence Plots for the Analysis of Complex Systems, Phys. Rep., 438, 237–329, https://doi.org/10.1016/j.physrep.2006.11.001, 2007. a
McConnell, T. J.: A Guide to Conducting Aerial Sketchmapping Surveys, U.S. Department of Agriculture, Forest Service, Forest Health Technology Enterprise Team, U.S. Department of Agriculture internal, https://www.fs.usda.gov/foresthealth/technology/pdfs/Sketchmapping.pdf (last access: 10 June 2026), 2000. a, b, c, d
McDowell, N. G., Coops, N. C., Beck, P. S., Chambers, J. Q., Gangodagamage, C., Hicke, J. A., Huang, C.-y., Kennedy, R., Krofcheck, D. J., Litvak, M., Meddens, A. J., Muss, J., Negrón-Juarez, R., Peng, C., Schwantes, A. M., Swenson, J. J., Vernon, L. J., Williams, A. P., Xu, C., Zhao, M., Running, S. W., and Allen, C. D.: Global satellite monitoring of climate-induced vegetation disturbances, Trends Plant Sci., 20, 114–123, 2015. a, b, c, d, e, f, g
McDowell, N. G., Allen, C. D., Anderson-Teixeira, K., Aukema, B. H., Bond-Lamberty, B., Chini, L., Clark, J. S., Dietze, M., Grossiord, C., Hanbury-Brown, A., Hurtt, G. C., Jackson, R. B., Johnson, D. J., Kueppers, L., Lichstein, J. W., Ogle, K., Poulter, B., Pugh, T. A. M., Seidl, R., Turner, M. G., Uriarte, M., Walker, A. P., and Xu, C.: Pervasive shifts in forest dynamics in a changing world, Science, 368, eaaz9463, https://doi.org/10.1126/science.aaz9463, 2020. a, b
Migliavacca, M., Grassi, G., Bastos, A., Ceccherini, G., Ciais, P., Janssens-Maenhout, G., Lugato, E., Mahecha, M., Novick, K., Peñuelas, J., Pilli, R., Reichstein, M., Avitabile, V., Beck, P. S. A., Barredo, J. I., Forzieri, G., Herold, M., Korosuo, A., Mansuy, N., Mubareka, S., Orth, R., Rougieux, P., and Cescatti, A.: Securing the forest carbon sink for the European Union’s climate ambition: Scientific action to anticipate the evolution of the forest carbon sink, Nature, 643, 1203–1213, https://doi.org/10.1038/s41586-025-08967-3, 2025. a
Mitchell, S. J.: Wind as a natural disturbance agent in forests: a synthesis, Forestry, 86, 147–157, https://doi.org/10.1093/forestry/cps058, 2013. a
Müller, F., Eifler, L., Cremer, F., Beck, P. S. A., Camps-Valls, G., and Bastos, A.: Sentinel-1 Disturbance Map. In Natural Hazards and Earth System Sciences (NHESS) (1.0.0), European Geosciences Union General Assembly 2024 (EGU 2024), Vienna, Zenodo [data set], https://doi.org/10.5281/zenodo.15672487, 2026. a
Nabuurs, G.-J., Lindner, M., Verkerk, H., Gunia, K., Deda, P., Michalak, R., and Grassi, G.: First signs of carbon sink saturation in European forest biomass, Nat. Clim. Change, 3, https://doi.org/10.1038/nclimate1853, 2013. a
Negrón-Juárez, R. I., Holm, J. A., Marra, D. M., Rifai, S. W., Riley, W. J., Chambers, J. Q., Koven, C. D., Knox, R. G., McGroddy, M. E., Vittorio, A. V. D., Urquiza-Muñoz, J., Tello-Espinoza, R., Muñoz, W. A., Ribeiro, G. H. P. M., and Higuchi, N.: Vulnerability of Amazon forests to storm-driven tree mortality, Environ. Res. Lett., 13, 054021, https://doi.org/10.1088/1748-9326/aabe9f, 2018. a, b
Oeser, J., Pflugmacher, D., Senf, C., Heurich, M., and Hostert, P.: Using Intra-Annual Landsat Time Series for Attributing Forest Disturbance Agents in Central Europe, Forests, 8, 251, https://doi.org/10.3390/f8070251, 2017. a
Pan, Y., Birdsey, R. A., Phillips, O. L., Houghton, R. A., Fang, J., Kauppi, P. E., Keith, H., Kurz, W. A., Ito, A., Lewis, S. L., Nabuurs, G.-J., Shvidenko, A., Hashimoto, S., Lerink, B., Schepaschenko, D., Castanho, A., and Murdiyarso, D.: The enduring world forest carbon sink, Nature, 631, 563–569, https://doi.org/10.1038/s41586-024-07602-x, 2024. a
Popp, T., Hegglin, M. I., Hollmann, R., Ardhuin, F., Bartsch, A., Bastos, A., Bennett, V., Boutin, J., Brockmann, C., Buchwitz, M., Chuvieco, E., Ciais, P., Dorigo, W., Ghent, D., Jones, R., Lavergne, T., Merchant, C. J., Meyssignac, B., Paul, F., Quegan, S., Sathyendranath, S., Scanlon, T., Schröder, M., Simis, S. G. H., and Willén, U.: Consistency of Satellite Climate Data Records for Earth System Monitoring, B. Am. Meteor. Soc., 101, E1948–E1971, https://doi.org/10.1175/BAMS-D-19-0127.1, 2020. a
Richter, R., Ballasus, H., Engelmann, R. A., Zielhofer, C., Sanaei, A., and Wirth, C.: Tree species matter for forest microclimate regulation during the drought year 2018: disentangling environmental drivers and biotic drivers, Sci. Re., 12, 17559, https://doi.org/10.1038/s41598-022-22582-6, 2022. a
Rodríguez Paulino, E., Schlerf, M., Röder, A., Stoffels, J., and Udelhoven, T.: Forest disturbance characterization in the era of earth observation big data: A mapping review, Int. J. Appl. Earth Obs., 128, 103755, https://doi.org/10.1016/j.jag.2024.103755, 2024. a, b
Rüetschi, M., Small, D., and Waser, L. T.: Rapid Detection of Windthrows Using Sentinel-1 C-Band SAR Data, Remote Sens., 11, 115, https://doi.org/10.3390/rs11020115, 2019. a
Schleeweis, K. G., Moisen, G. G., Schroeder, T. A., Toney, C., Freeman, E. A., Goward, S. N., Huang, C., and Dungan, J. L.: US National Maps Attributing Forest Change: 1986–2010, Forests, 11, 653, https://doi.org/10.3390/f11060653, 2020. a, b, c
Schuldt, B., Buras, A., Arend, M., Vitasse, Y., Beierkuhnlein, C., Damm, A., Gharun, M., Grams, T. E. E., Hauck, M., Hajek, P., Hartmann, H., Hiltbrunner, E., Hoch, G., Holloway-Phillips, M., Körner, C., Larysch, E., Lübbe, T., Nelson, D. B., Rammig, A., Rigling, A., Rose, L., Ruehr, N. K., Schumann, K., Weiser, F., Werner, C., Wohlgemuth, T., Zang, C. S., and Kahmen, A.: A first assessment of the impact of the extreme 2018 summer drought on Central European forests, Basic Appl. Ecol., 45, 86–103, https://doi.org/10.1016/j.baae.2020.04.003, 2020. a
Seidl, R. and Turner, M. G.: Post-disturbance reorganization of forest ecosystems in a changing world, P. Natl. Acad. Sci. USA, 119, e2202190119, https://doi.org/10.1073/pnas.2202190119, 2022. a
Seidl, R., Thom, D., Kautz, M., Martin-Benito, D., Peltoniemi, M., Vacchiano, G., Wild, J., Ascoli, D., Petr, M., Honkaniemi, J., Lexer, M. J., Trotsiuk, V., Mairota, P., Svoboda, M., Fabrika, M., Nagel, T. A., and Reyer, C. P.: Forest disturbances under climate change, Nat. Clim. Change, 7, 395–402, https://doi.org/10.1038/NCLIMATE3303, 2017. a, b
Senf, C. and Seidl, R.: Natural disturbances are spatially diverse but temporally synchronized across temperate forest landscapes in Europe, Glob. Change Biol., 24, 1201–1211, https://doi.org/10.1111/gcb.13897, 2018. a, b, c, d
Senf, C. and Seidl, R.: Persistent impacts of the 2018 drought on forest disturbance regimes in Europe, Biogeosciences, 18, 5223–5230, https://doi.org/10.5194/bg-18-5223-2021, 2021a. a
Senf, C. and Seidl, R.: Storm and fire disturbances in Europe: Distribution and trends, Glob. Change Biol., 27, 3605–3619, https://doi.org/10.1111/gcb.15679, 2021b. a, b
Senf, C., Pflugmacher, D., Wulder, M. A., and Hostert, P.: Characterizing spectral-temporal patterns of defoliator and bark beetle disturbances using Landsat time series, Remote Sens. Environ., 170, 166–177, https://doi.org/10.1016/j.rse.2015.09.019, 2015. a, b, c, d
Senf, C., Müller, J., and Seidl, R.: Post-disturbance recovery of forest cover and tree height differ with management in Central Europe, Landscape Ecol., 34, 2837–2850, https://doi.org/10.1007/s10980-019-00921-9, 2019. a
Senf, C., Buras, A., Zang, C. S., Rammig, A., and Seidl, R.: Excess forest mortality is consistently linked to drought across Europe, Nat. Commun., 11, 6200, https://doi.org/10.1038/s41467-020-19924-1, 2020. a
Shi, C., Zuo, X., Zhang, J., Zhu, D., Li, Y., and Bu, J.: Accuracy Assessment of Geometric-Distortion Identification Methods for Sentinel-1 Synthetic Aperture Radar Imagery in Highland Mountainous Regions, Sensors, 24, 2834, https://doi.org/10.3390/s24092834, 2024. a
Small, D.: Flattening Gamma: Radiometric Terrain Correction for SAR Imagery, IEEE T. Geosci. Remote, 49, 3081–3093, https://doi.org/10.1109/TGRS.2011.2120616, 2011. a
Stahl, A. T., Andrus, R., Hicke, J. A., Hudak, A. T., Bright, B. C., and Meddens, A. J. H.: Automated attribution of forest disturbance types from remote sensing data: A synthesis, Remote Sens. Environ., 285, 113416, https://doi.org/10.1016/j.rse.2022.113416, 2023. a
Tanase, M. A., Villard, L., Pitar, D., Apostol, B., Petrila, M., Chivulescu, S., Leca, S., Borlaf-Mena, I., Pascu, I.-S., Dobre, A.-C., Pitar, D., Guiman, G., Lorent, A., Anghelus, C., Ciceu, A., Nedea, G., Stanculeanu, R., Popescu, F., Aponte, C., and Badea, O.: Synthetic aperture radar sensitivity to forest changes: A simulations-based study for the Romanian forests, Sci. Total Environ., 689, 1104–1114, 2019. a
Thom, D. and Seidl, R.: Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests, Biol. Rev., 91, 760–781, https://doi.org/10.1111/brv.12193, 2016. a
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E., Potin, P., Rommen, B., Floury, N., Brown, M., Navas Traver, I., Deghaye, P., Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L'Abbate, M., Croci, R., Pietropaolo, A., Huchler, M., and Rostan, F.: GMES Sentinel-1 mission, Remote Sens. Environ., 120, 9–24, https://doi.org/10.1016/j.rse.2011.05.028, 2012. a, b
Trumbore, S., Brando, P., and Hartmann, H.: Forest health and global change, Science, 349, 814–818, https://doi.org/10.1126/science.aac6759, 2015. a
Urquiza-Muñoz, J. D., Trumbore, S., Negrón-Juárez, R. I., Feng, Y., Brenning, A., Vasquez-Parana, C. M., and Marra, D. M.: Increased Occurrence of Large-Scale Windthrows Across the Amazon Basin, AGU Advances, 5, e2023AV001030, https://doi.org/10.1029/2023AV001030, 2024. a
US Forest Service: US Forest Service – FIA Forest Types of the Southeastern United States – Data Basin, https://databasin.org/datasets/e874a247b7e44693ad0792fb1a252aba/ (last access: 30 January 2025), 2013. a
USDA Forest Service: Aerial Survey Geographic Information System Handbook: Sketchmaps to Digital Geographic Information, USDA Forest Service, Forest Health Monitoring Program, State and Private Forestry, Forest Health Protection, aerial Survey GIS Handbook, https://www.fs.usda.gov/foresthealth/technology/pdfs/GISHandbook_body_apndxA-C.pdf (last access: 10 June 2026), 2005. a, b
USDA Forest Service: Forest Health Detection Survey GIS Handbook and Data Conformity Standards, USDA Forest Service, forest Health Monitoring Program, https://www.fs.usda.gov/foresthealth/technology/docs/DMSM_Tutorial/story_content/external_files/GIS-Handbook-for-Forest-Health-Detection-Survey.pdf (last access: 10 June 2026), 2022. a
van der Woude, S., Reiche, J., Balling, J., Nabuurs, G.-J., Sterck, F., Welsink, A.-J., Slagter, B., and Herold, M.: European forest disturbance alerting using Sentinel-1, Remote Sens. Environ., 337, 115325, https://doi.org/10.1016/j.rse.2026.115325, 2026. a
Vanguri, R., Laneve, G., and Hościło, A.: Mapping forest tree species and its biodiversity using EnMAP hyperspectral data along with Sentinel-2 temporal data: An approach of tree species classification and diversity indices, Ecol. Indic., 167, 112671, https://doi.org/10.1016/j.ecolind.2024.112671, 2024. a
Verbesselt, J., Hyndman, R. J., Newnham, G., and Culvenor, D.: Detecting trend and seasonal changes in satellite image time series, Remote Sens. Environ., 114, 106–115, https://doi.org/10.1016/j.rse.2009.08.014, 2010. a
Viana-Soto, A. and Senf, C.: The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive, Earth Syst. Sci. Data, 17, 2373–2404, https://doi.org/10.5194/essd-17-2373-2025, 2025. a, b
Vicente-Serrano, S. M., Gouveia, C., Camarero, J. J., Beguería, S., Trigo, R., López-Moreno, J. I., Azorín-Molina, C., Pasho, E., Lorenzo-Lacruz, J., Revuelto, J., Morán-Tejeda, E., and Sanchez-Lorenzo, A.: Response of vegetation to drought time-scales across global land biomes, P. Natl. Acad. Sci. USA, 110, 52–57, https://doi.org/10.1073/pnas.1207068110, 2013. a
Wagner, W., Bauer-Marschallinger, B., Navacchi, C., Reuß, F., Cao, S., Reimer, C., Schramm, M., and Briese, C.: A Sentinel-1 Backscatter Datacube for Global Land Monitoring Applications, Remote Sens., 13, 4622, https://doi.org/10.3390/rs13224622, 2021. a
Winkler, K., Yang, H., Ganzenmüller, R., Fuchs, R., Ceccherini, G., Duveiller, G., Grassi, G., Pongratz, J., Bastos, A., Shvidenko, A., Araza, A., Herold, M., Wigneron, J.-P., and Ciais, P.: Changes in land use and management led to a decline in Eastern Europe's terrestrial carbon sink, Commun. Earth Environ., 4, 1–14, https://doi.org/10.1038/s43247-023-00893-4, 2023. a
Woodman, S. G., Khoury, S., Fournier, R. E., Emilson, E. J. S., Gunn, J. M., Rusak, J. A., and Tanentzap, A. J.: Forest defoliator outbreaks alter nutrient cycling in northern waters, Nat. Commun., 12, 6355, https://doi.org/10.1038/s41467-021-26666-1, 2021. a
Woudenberg, S. W., Conkling, B. L., O'Connell, B. M., LaPoint, E. B., Turner, J. A., and Waddell, K. L.: The Forest Inventory and Analysis Database: Database description and users manual version 4.0 for Phase 2, U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, https://doi.org/10.2737/rmrs-gtr-245, 2010. a, b, c
Wu, Y., Chen, Y., Tian, C., Yun, T., and Li, M.: Estimation of Subtropical Forest Aboveground Biomass Using Active and Passive Sentinel Data with Canopy Height, Remote Sens., 17, https://doi.org/10.3390/rs17142509, 2025. a
Zhu, Z. and Woodcock, C. E.: Continuous change detection and classification of land cover using all available Landsat data, Remote Sens. Environ., 144, 152–171, https://doi.org/10.1016/j.rse.2014.01.011, 2014. a
Short summary
Forest health is increasingly threatened, but disturbances like wind damage and insect outbreaks are hard to track. Our Sentinel-1 Disturbance Mapping (S1DM) approach combines satellite radar with survey data, improving detection for wind and bark beetle impacts and often spotting them earlier. Defoliators remain difficult to capture, but this method strengthens monitoring and supports better forest management.
Forest health is increasingly threatened, but disturbances like wind damage and insect outbreaks...
Altmetrics
Final-revised paper
Preprint