Articles | Volume 13, issue 8
https://doi.org/10.5194/essd-13-3927-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-3927-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
11 Aug 2021
Data description paper |
| 11 Aug 2021
| 11 Aug 2021
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations
Gamma Remote Sensing, 3073 Gümligen, Switzerland
Oliver Cartus
Gamma Remote Sensing, 3073 Gümligen, Switzerland
Nuno Carvalhais
Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
Danaë M. A. Rozendaal
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Plant Production Systems Group, Wageningen University and Research,
P.O. Box 430, 6700 AK Wageningen, the Netherlands
Centre for Crop Systems Analysis, Wageningen University and Research, P.O. Box 430, 6700 AK Wageningen, the Netherlands
Valerio Avitabile
Joint Research Centre, European Commission, Ispra, Italy
Arnan Araza
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Sytze de Bruin
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Martin Herold
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Shaun Quegan
National Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, UK
Pedro Rodríguez-Veiga
Centre for Landscape and Climate Research, School of Geography,
Geology and the Environment, University of Leicester, LE1 7RH, UK
National Centre for Earth Observation (NCEO), Leicester, LE1 7RH,
UK
Heiko Balzter
Centre for Landscape and Climate Research, School of Geography,
Geology and the Environment, University of Leicester, LE1 7RH, UK
National Centre for Earth Observation (NCEO), Leicester, LE1 7RH,
UK
João Carreiras
National Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, UK
Dmitry Schepaschenko
International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, Austria
Center of Forest Ecology and Productivity, Russian Academy of Sciences, Profsoyuznaya 84/32/14, 117997 Moscow, Russia
Institute of Ecology and Geography, Siberian Federal University, 79 Svobodny Prospect,
660041 Krasnoyarsk, Russia
Mikhail Korets
Laboratory of Ecophysiology of Permafrost Systems, V.N. Sukachev
Institute of Forest of the Siberian Branch of the Russian Academy of Sciences –
separated department of the KSC SB RAS, 660036 Krasnoyarsk, Russia
Masanobu Shimada
Tokyo Denki University, School of Science and Engineering, Division of Architectural, Civil and Environmental Engineering, Ishizaka, Hatoyama, Hiki, Saitama, 350-0394, Japan
Takuya Itoh
Remote Sensing Technology Center of Japan, Tokyu Reit Toranomon Bldg, 3f, 3-17-1 Toranomon, Minato-Ku, Tokyo, 105-0001, Japan
Álvaro Moreno Martínez
Image Processing Laboratory (IPL), Universitat de València,
València, Spain
Numerical Terradynamic Simulation Group (NTSG), University of
Montana, Missoula, MT, USA
Jura Cavlovic
Department of Forest Inventory and Management, Faculty of Forestry and Wood Technology, University of Zagreb, Svetosimunska cesta 23,
10000 Zagreb, Croatia
Roberto Cazzolla Gatti
Biological Institute, Tomsk State University, 634050 Tomsk, Russia
Polyanna da Conceição Bispo
Centre for Landscape and Climate Research, School of Geography,
Geology and the Environment, University of Leicester, LE1 7RH, UK
Department of
Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, M13 9PL Manchester, UK
Nasheta Dewnath
Guyana Forestry Commission, 1 Water Street, Kingston, Georgetown,
Guyana
Nicolas Labrière
Laboratoire Évolution et Diversité Biologique, UMR 5174
(CNRS/IRD/UPS), 31062 Toulouse CEDEX 9, France
Jingjing Liang
Department of Forestry and Natural Resources, Purdue University, 715 W State St, West Lafayette, IN 47907, USA
Jeremy Lindsell
A Rocha International, Cambridge, UK
The RSPB Centre for Conservation Science, Bedfordshire, UK
Edward T. A. Mitchard
School of GeoSciences, University of Edinburgh, Crew Building, The
King's Buildings, Edinburgh, EH9 3FF, UK
Alexandra Morel
Department of Geography and Environmental Sciences, University of
Dundee, Dundee, UK
Ana Maria Pacheco Pascagaza
Centre for Landscape and Climate Research, School of Geography,
Geology and the Environment, University of Leicester, LE1 7RH, UK
Department of
Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, M13 9PL Manchester, UK
Casey M. Ryan
School of GeoSciences, University of Edinburgh, Crew Building, The
King's Buildings, Edinburgh, EH9 3FF, UK
Ferry Slik
Faculty of Science, University Brunei Darussalam, Jln Tungku Link,
Gadong, BE1410, Brunei Darussalam
amma Remote Sensing, 3073 Gümligen, Switzerland
Gaia Vaglio Laurin
Department for Innovation in Biological, Agro-Food and Forest Systems
(DIBAF), University of Tuscia, 01100 Viterbo, Italy
Hans Verbeeck
CAVElab – Computational and Applied Vegetation Ecology, Department
of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium
Arief Wijaya
Department of
Research, Data and Innovation, World Resources Institute Indonesia (WRI Indonesia), Wisma PMI, 3rd Floor, Jl. Wijaya I/63,
Kebayoran Baru, South Jakarta, Indonesia
Simon Willcock
School of Natural Sciences, Bangor University, Bangor, Gwynedd, UK
Related authors
Simon Besnard, Alba Viana-Soto, Henrik Hartmann, Marco Patacca, Viola H. A. Heinrich, Katja Kowalski, Maurizio Santoro, Wanda De Keersmaecker, Ruben Van De Kerchove, Martin Herold, and Cornelius Senf
EGUsphere, https://doi.org/10.5194/egusphere-2025-6288, https://doi.org/10.5194/egusphere-2025-6288, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Europe’s forests store vast amounts of carbon, but climate-driven disturbances are becoming more frequent. By combining satellite records with information on forest age and structure, we show that recent disturbances increasingly affect the oldest and most carbon-rich forests, particularly spruce forests in Central Europe. This emerging pattern puts long-accumulated carbon at risk and may reduce the long-term climate benefits provided by Europe’s forests.
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
Preprint 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.
Tazio Strozzi, Erik Schytt Mannerfelt, Oliver Cartus, Maurizio Santoro, Thomas Schellenberger, and Andreas Kääb
EGUsphere, https://doi.org/10.5194/egusphere-2025-5011, https://doi.org/10.5194/egusphere-2025-5011, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
By analysing 30 years of satellite SAR data, we have found that the number of glacier surges over Svalbard has tripled since 2015. We show that this increase is unlikely to be explained solely by improvements in data quality or by random fluctuations in surge frequency, suggesting that this trend is caused by an external forcing mechanism. Given our incomplete understanding of surge initiation, the cause of the observed threefold increase remains however uncertain.
Simon Besnard, Sujan Koirala, Maurizio Santoro, Ulrich Weber, Jacob Nelson, Jonas Gütter, Bruno Herault, Justin Kassi, Anny N'Guessan, Christopher Neigh, Benjamin Poulter, Tao Zhang, and Nuno Carvalhais
Earth Syst. Sci. Data, 13, 4881–4896, https://doi.org/10.5194/essd-13-4881-2021, https://doi.org/10.5194/essd-13-4881-2021, 2021
Short summary
Short summary
Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. Yet, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. In this paper, we introduced a new global distribution of forest age inferred from forest inventory, remote sensing and climate data in support of a better understanding of the global dynamics in the forest water and carbon cycles.
Yuanyuan Huang, Phillipe Ciais, Maurizio Santoro, David Makowski, Jerome Chave, Dmitry Schepaschenko, Rose Z. Abramoff, Daniel S. Goll, Hui Yang, Ye Chen, Wei Wei, and Shilong Piao
Earth Syst. Sci. Data, 13, 4263–4274, https://doi.org/10.5194/essd-13-4263-2021, https://doi.org/10.5194/essd-13-4263-2021, 2021
Short summary
Short summary
Roots play a key role in our Earth system. Here we combine 10 307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (~ 1 km) root biomass dataset. In total, 142 ± 25 (95 % CI) Pg of live dry-matter biomass is stored belowground, representing a global average root : shoot biomass ratio of 0.25 ± 0.10.
Simon Besnard, Alba Viana-Soto, Henrik Hartmann, Marco Patacca, Viola H. A. Heinrich, Katja Kowalski, Maurizio Santoro, Wanda De Keersmaecker, Ruben Van De Kerchove, Martin Herold, and Cornelius Senf
EGUsphere, https://doi.org/10.5194/egusphere-2025-6288, https://doi.org/10.5194/egusphere-2025-6288, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Europe’s forests store vast amounts of carbon, but climate-driven disturbances are becoming more frequent. By combining satellite records with information on forest age and structure, we show that recent disturbances increasingly affect the oldest and most carbon-rich forests, particularly spruce forests in Central Europe. This emerging pattern puts long-accumulated carbon at risk and may reduce the long-term climate benefits provided by Europe’s forests.
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
Preprint 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.
Myroslava Lesiv, Steffen Fritz, Martina Dürauer, Ivelina Georgieva, Marcel Buchhorn, Luc Bertels, Nandika Tsendbazar, Ruben Van De Kerchove, Daniele Zanaga, Dmitry Schepaschenko, Linda See, Martin Herold, Bruno Smets, Michael Cherlet, Andreas Brink, and Ian McCallum
Earth Syst. Sci. Data, 17, 6149–6155, https://doi.org/10.5194/essd-17-6149-2025, https://doi.org/10.5194/essd-17-6149-2025, 2025
Short summary
Short summary
This paper presents a unique global reference data set for land cover mapping at a 10 m resolution, aligned with Sentinel-2 imagery for the year 2015. It contains more than 16.5 million data records at a 10 m resolution (or 165 K data records at 100 m) and information on 12 different land cover classes. The data set was collected by a group of experts through visual interpretation of very high resolution imagery, along with other sources of information provided in the Geo-Wiki platform.
Tazio Strozzi, Erik Schytt Mannerfelt, Oliver Cartus, Maurizio Santoro, Thomas Schellenberger, and Andreas Kääb
EGUsphere, https://doi.org/10.5194/egusphere-2025-5011, https://doi.org/10.5194/egusphere-2025-5011, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
By analysing 30 years of satellite SAR data, we have found that the number of glacier surges over Svalbard has tripled since 2015. We show that this increase is unlikely to be explained solely by improvements in data quality or by random fluctuations in surge frequency, suggesting that this trend is caused by an external forcing mechanism. Given our incomplete understanding of surge initiation, the cause of the observed threefold increase remains however uncertain.
Inês Vieira, Félicien Meunier, Maria Carolina Duran Rojas, Stephen Sitch, Flossie Brown, Giacomo Gerosa, Silvano Fares, Pascal Boeckx, Marijn Bauters, and Hans Verbeeck
Biogeosciences, 22, 6205–6223, https://doi.org/10.5194/bg-22-6205-2025, https://doi.org/10.5194/bg-22-6205-2025, 2025
Short summary
Short summary
We used a computer model to study how ozone pollution reduces plant growth in six European forests, from Finland to Italy. Combining field data and simulations, we found that ozone can lower carbon uptake by up to 6 % each year, especially in Mediterranean areas. Our study shows that local climate and forest type influence ozone damage and highlights the need to include ozone effects in forest and climate models.
Wim Verbruggen, David Wårlind, Stéphanie Horion, Félicien Meunier, Hans Verbeeck, Aleksander Wieckowski, Torbern Tagesson, and Guy Schurgers
Geosci. Model Dev., 18, 6623–6645, https://doi.org/10.5194/gmd-18-6623-2025, https://doi.org/10.5194/gmd-18-6623-2025, 2025
Short summary
Short summary
We improved the representation of soil water movement in a state-of-the-art dynamic vegetation model. This is important for dry ecosystems, as they are often driven by changes in soil water availability. We showed that this update resulted in a better match with observations and that the updated model is more sensitive to soil texture. The new model can also simulate a groundwater table. This updated model can help us to better understand the future of dry ecosystems under climate change.
Cecilia Chavana-Bryant, Phil Wilkes, Wanxin Yang, Andrew Burt, Peter Vines, Amy C. Bennett, Georgia C. Pickavance, Declan L. M. Cooper, Simon L. Lewis, Oliver L. Phillips, Benjamin Brede, Alvaro Lau, Martin Herold, Iain McNicol, Edward T. A. Mitchard, David A. Coomes, Toby Jackson, Loic Makaga, Heddy O. Milamizokou Napo, Alfred Ngomanda, Stephan Ntie, Vincent Medjibe, Pacome Dimbonda, Luna Soenens, Virginie Daelemans, Laetitia Proux, Reuben Nilus, Nicolas Labriere, Kathryn Jeffery, David F. R. P. Burslem, Daniel Clewley, David Moffat, Lan Qie, Harm Bartholomeus, Vincent Gregoire, Nicolas Barbier, Geraldine Derroire, Katharine Abernethy, Klaus Scipal, and Mat Disney
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-67, https://doi.org/10.5194/essd-2025-67, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
The ForestScan project provides a comprehensive set of datasets of tropical forest 3D structural measurements using terrestrial, unpiloted aerial vehicle and aerial laser scanning, plus tree census data. Collected at three sites in French Guiana, Gabon, and Malaysia, these datasets are crucial for calibrating and validating earth observation-derived forest biomass estimates, therefore, expanding and enhancing their use, and aiding global conservation efforts.
Derrick Muheki, Bas Vercruysse, Krishna Kumar Thirukokaranam Chandrasekar, Christophe Verbruggen, Julie M. Birkholz, Koen Hufkens, Hans Verbeeck, Pascal Boeckx, Seppe Lampe, Ed Hawkins, Peter Thorne, Dominique Kankonde Ntumba, Olivier Kapalay Moulasa, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2024-3779, https://doi.org/10.5194/egusphere-2024-3779, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Archives worldwide host vast records of observed weather data crucial for understanding climate variability. However, most of these records are still in paper form, limiting their use. To address this, we developed MeteoSaver, an open-source tool, to transcribe these records to machine-readable format. Applied to ten handwritten temperature sheets, it achieved a median accuracy of 74%. This tool offers a promising solution to preserve records from archives and unlock historical weather insights.
Min Feng, Joseph O. Sexton, Panshi Wang, Paul M. Montesano, Leonardo Calle, Nuno Carvalhais, Benjamin Poulter, Matthew J. Macander, Michael A. Wulder, Margaret Wooten, William Wagner, Akiko Elders, Saurabh Channan, and Christopher S. R. Neigh
EGUsphere, https://doi.org/10.5194/egusphere-2025-2268, https://doi.org/10.5194/egusphere-2025-2268, 2025
Short summary
Short summary
The boreal forest, warming fastest among forested biomes, shows a northward shift in tree cover. Using the longest, highest-resolution satellite maps, we found an 0.844 million km² increase in tree cover and a 0.45° northward shift from 1985–2020, especially in northern latitudes. Stable disturbance rates suggest climate-driven growth. Young forests' biomass may help reduce global CO2, despite uncertainties in carbon balance, disturbance, and respiration.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Mathew Williams, David T. Milodowski, T. Luke Smallman, Kyle G. Dexter, Gabi C. Hegerl, Iain M. McNicol, Michael O'Sullivan, Carla M. Roesch, Casey M. Ryan, Stephen Sitch, and Aude Valade
Biogeosciences, 22, 1597–1614, https://doi.org/10.5194/bg-22-1597-2025, https://doi.org/10.5194/bg-22-1597-2025, 2025
Short summary
Short summary
Southern African woodlands are important in both regional and global carbon cycles. A new carbon analysis created by combining satellite data with ecosystem modelling shows that the region has a neutral C balance overall but with important spatial variations. Patterns of biomass and C balance across the region are the outcome of climate controls on production and vegetation–fire interactions, which determine the mortality of vegetation and spatial variations in vegetation function.
Flossie Brown, Gerd Folberth, Stephen Sitch, Paulo Artaxo, Marijn Bauters, Pascal Boeckx, Alexander W. Cheesman, Matteo Detto, Ninong Komala, Luciana Rizzo, Nestor Rojas, Ines dos Santos Vieira, Steven Turnock, Hans Verbeeck, and Alfonso Zambrano
Atmos. Chem. Phys., 24, 12537–12555, https://doi.org/10.5194/acp-24-12537-2024, https://doi.org/10.5194/acp-24-12537-2024, 2024
Short summary
Short summary
Ozone is a pollutant that is detrimental to human and plant health. Ozone monitoring sites in the tropics are limited, so models are often used to understand ozone exposure. We use measurements from the tropics to evaluate ozone from the UK Earth system model, UKESM1. UKESM1 is able to capture the pattern of ozone in the tropics, except in southeast Asia, although it systematically overestimates it at all sites. This work highlights that UKESM1 can capture seasonal and hourly variability.
Tao Chen, Félicien Meunier, Marc Peaucelle, Guoping Tang, Ye Yuan, and Hans Verbeeck
Biogeosciences, 21, 2253–2272, https://doi.org/10.5194/bg-21-2253-2024, https://doi.org/10.5194/bg-21-2253-2024, 2024
Short summary
Short summary
Chinese subtropical forest ecosystems are an extremely important component of global forest ecosystems and hence crucial for the global carbon cycle and regional climate change. However, there is still great uncertainty in the relationship between subtropical forest carbon sequestration and its drivers. We provide first quantitative estimates of the individual and interactive effects of different drivers on the gross primary productivity changes of various subtropical forest types in China.
J. J. Bilolo, J. V. Dida, and A. Araza
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W8-2023, 53–60, https://doi.org/10.5194/isprs-archives-XLVIII-4-W8-2023-53-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W8-2023-53-2024, 2024
Hui Yang, Krzysztof Stereńczak, Zbigniew Karaszewski, and Nuno Carvalhais
EGUsphere, https://doi.org/10.5194/egusphere-2023-2691, https://doi.org/10.5194/egusphere-2023-2691, 2023
Preprint archived
Short summary
Short summary
Wood density is crucial for ecological and carbon stock assessment, yet its labor-intensive analysis limits studies across species and spaces. Our study, based on 48,000 samples from Central Europe, reveals that, even without species information, 91% of inter-tree variations can be predicted by vegetation indexes, topography, and soil texture. Importantly, we highlight neglected intra-tree variation, showing substantial variations vertically along the height and radially from the center to bark.
Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala
Hydrol. Earth Syst. Sci., 27, 1531–1563, https://doi.org/10.5194/hess-27-1531-2023, https://doi.org/10.5194/hess-27-1531-2023, 2023
Short summary
Short summary
We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
Joseph Okello, Marijn Bauters, Hans Verbeeck, Samuel Bodé, John Kasenene, Astrid Françoys, Till Engelhardt, Klaus Butterbach-Bahl, Ralf Kiese, and Pascal Boeckx
Biogeosciences, 20, 719–735, https://doi.org/10.5194/bg-20-719-2023, https://doi.org/10.5194/bg-20-719-2023, 2023
Short summary
Short summary
The increase in global and regional temperatures has the potential to drive accelerated soil organic carbon losses in tropical forests. We simulated climate warming by translocating intact soil cores from higher to lower elevations. The results revealed increasing temperature sensitivity and decreasing losses of soil organic carbon with increasing elevation. Our results suggest that climate warming may trigger enhanced losses of soil organic carbon from tropical montane forests.
Artem G. Lim, Ivan V. Krickov, Sergey N. Vorobyev, Mikhail A. Korets, Sergey Kopysov, Liudmila S. Shirokova, Jan Karlsson, and Oleg S. Pokrovsky
Biogeosciences, 19, 5859–5877, https://doi.org/10.5194/bg-19-5859-2022, https://doi.org/10.5194/bg-19-5859-2022, 2022
Short summary
Short summary
In order to quantify C transport and emission and main environmental factors controlling the C cycle in Siberian rivers, we investigated the largest tributary of the Ob River, the Ket River basin, by measuring spatial and seasonal variations in carbon CO2 and CH4 concentrations and emissions together with hydrochemical analyses. The obtained results are useful for large-scale modeling of C emission and export fluxes from permafrost-free boreal rivers of an underrepresented region of the world.
Félicien Meunier, Wim Verbruggen, Hans Verbeeck, and Marc Peaucelle
Geosci. Model Dev., 15, 7573–7591, https://doi.org/10.5194/gmd-15-7573-2022, https://doi.org/10.5194/gmd-15-7573-2022, 2022
Short summary
Short summary
Drought stress occurs in plants when water supply (i.e. root water uptake) is lower than the water demand (i.e. atmospheric demand). It is strongly related to soil properties and expected to increase in intensity and frequency in the tropics due to climate change. In this study, we show that contrary to the expectations, state-of-the-art terrestrial biosphere models are mostly insensitive to soil texture and hence probably inadequate to reproduce in silico the plant water status in drying soils.
Flossie Brown, Gerd A. Folberth, Stephen Sitch, Susanne Bauer, Marijn Bauters, Pascal Boeckx, Alexander W. Cheesman, Makoto Deushi, Inês Dos Santos Vieira, Corinne Galy-Lacaux, James Haywood, James Keeble, Lina M. Mercado, Fiona M. O'Connor, Naga Oshima, Kostas Tsigaridis, and Hans Verbeeck
Atmos. Chem. Phys., 22, 12331–12352, https://doi.org/10.5194/acp-22-12331-2022, https://doi.org/10.5194/acp-22-12331-2022, 2022
Short summary
Short summary
Surface ozone can decrease plant productivity and impair human health. In this study, we evaluate the change in surface ozone due to climate change over South America and Africa using Earth system models. We find that if the climate were to change according to the worst-case scenario used here, models predict that forested areas in biomass burning locations and urban populations will be at increasing risk of ozone exposure, but other areas will experience a climate benefit.
Selena Georgiou, Edward T. A. Mitchard, Bart Crezee, Paul I. Palmer, Greta C. Dargie, Sofie Sjögersten, Corneille E. N. Ewango, Ovide B. Emba, Joseph T. Kanyama, Pierre Bola, Jean-Bosco N. Ndjango, Nicholas T. Girkin, Yannick E. Bocko, Suspense A. Ifo, and Simon L. Lewis
EGUsphere, https://doi.org/10.5194/egusphere-2022-580, https://doi.org/10.5194/egusphere-2022-580, 2022
Preprint archived
Short summary
Short summary
Two major vegetation types, hardwood trees and palms, overlay the Central Congo Basin peatland complex, each dominant in different locations. We investigated the influence of terrain and climatological variables on their distribution, using a regression model, and found elevation and seasonal rainfall and temperature contribute significantly. There are indications of an optimal range of net water input for palm swamp to dominate, above and below which hardwood swamp dominates.
Félicien Meunier, Sruthi M. Krishna Moorthy, Marc Peaucelle, Kim Calders, Louise Terryn, Wim Verbruggen, Chang Liu, Ninni Saarinen, Niall Origo, Joanne Nightingale, Mathias Disney, Yadvinder Malhi, and Hans Verbeeck
Geosci. Model Dev., 15, 4783–4803, https://doi.org/10.5194/gmd-15-4783-2022, https://doi.org/10.5194/gmd-15-4783-2022, 2022
Short summary
Short summary
We integrated state-of-the-art observations of the structure of the vegetation in a temperate forest to constrain a vegetation model that aims to reproduce such an ecosystem in silico. We showed that the use of this information helps to constrain the model structure, its critical parameters, as well as its initial state. This research confirms the critical importance of the representation of the vegetation structure in vegetation models and proposes a method to overcome this challenge.
Sophia Walther, Simon Besnard, Jacob Allen Nelson, Tarek Sebastian El-Madany, Mirco Migliavacca, Ulrich Weber, Nuno Carvalhais, Sofia Lorena Ermida, Christian Brümmer, Frederik Schrader, Anatoly Stanislavovich Prokushkin, Alexey Vasilevich Panov, and Martin Jung
Biogeosciences, 19, 2805–2840, https://doi.org/10.5194/bg-19-2805-2022, https://doi.org/10.5194/bg-19-2805-2022, 2022
Short summary
Short summary
Satellite observations help interpret station measurements of local carbon, water, and energy exchange between the land surface and the atmosphere and are indispensable for simulations of the same in land surface models and their evaluation. We propose generalisable and efficient approaches to systematically ensure high quality and to estimate values in data gaps. We apply them to satellite data of surface reflectance and temperature with different resolutions at the stations.
Tina Trautmann, Sujan Koirala, Nuno Carvalhais, Andreas Güntner, and Martin Jung
Hydrol. Earth Syst. Sci., 26, 1089–1109, https://doi.org/10.5194/hess-26-1089-2022, https://doi.org/10.5194/hess-26-1089-2022, 2022
Short summary
Short summary
We assess the effect of how vegetation is defined in a global hydrological model on the composition of total water storage (TWS). We compare two experiments, one with globally uniform and one with vegetation parameters that vary in space and time. While both experiments are constrained against observational data, we found a drastic change in the partitioning of TWS, highlighting the important role of the interaction between groundwater–soil moisture–vegetation in understanding TWS variations.
J. Pacheco-Labrador, U. Weber, X. Ma, M. D. Mahecha, N. Carvalhais, C. Wirth, A. Huth, F. J. Bohn, G. Kraemer, U. Heiden, FunDivEUROPE members, and M. Migliavacca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1-W1-2021, 49–55, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-49-2022, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-49-2022, 2022
Simon Besnard, Sujan Koirala, Maurizio Santoro, Ulrich Weber, Jacob Nelson, Jonas Gütter, Bruno Herault, Justin Kassi, Anny N'Guessan, Christopher Neigh, Benjamin Poulter, Tao Zhang, and Nuno Carvalhais
Earth Syst. Sci. Data, 13, 4881–4896, https://doi.org/10.5194/essd-13-4881-2021, https://doi.org/10.5194/essd-13-4881-2021, 2021
Short summary
Short summary
Forest age can determine the capacity of a forest to uptake carbon from the atmosphere. Yet, a lack of global diagnostics that reflect the forest stage and associated disturbance regimes hampers the quantification of age-related differences in forest carbon dynamics. In this paper, we introduced a new global distribution of forest age inferred from forest inventory, remote sensing and climate data in support of a better understanding of the global dynamics in the forest water and carbon cycles.
Sergey N. Vorobyev, Jan Karlsson, Yuri Y. Kolesnichenko, Mikhail A. Korets, and Oleg S. Pokrovsky
Biogeosciences, 18, 4919–4936, https://doi.org/10.5194/bg-18-4919-2021, https://doi.org/10.5194/bg-18-4919-2021, 2021
Short summary
Short summary
In order to quantify riverine carbon (C) exchange with the atmosphere in permafrost regions, we report a first assessment of CO2 and CH4 concentration and fluxes of the largest permafrost-affected river, the Lena River, during the peak of spring flow. The results allowed identification of environmental factors controlling GHG concentrations and emission in the Lena River watershed; this new knowledge can be used for foreseeing future changes in C balance in permafrost-affected Arctic rivers.
Yuanyuan Huang, Phillipe Ciais, Maurizio Santoro, David Makowski, Jerome Chave, Dmitry Schepaschenko, Rose Z. Abramoff, Daniel S. Goll, Hui Yang, Ye Chen, Wei Wei, and Shilong Piao
Earth Syst. Sci. Data, 13, 4263–4274, https://doi.org/10.5194/essd-13-4263-2021, https://doi.org/10.5194/essd-13-4263-2021, 2021
Short summary
Short summary
Roots play a key role in our Earth system. Here we combine 10 307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (~ 1 km) root biomass dataset. In total, 142 ± 25 (95 % CI) Pg of live dry-matter biomass is stored belowground, representing a global average root : shoot biomass ratio of 0.25 ± 0.10.
Paula Alejandra Lamprea Pineda, Marijn Bauters, Hans Verbeeck, Selene Baez, Matti Barthel, Samuel Bodé, and Pascal Boeckx
Biogeosciences, 18, 413–421, https://doi.org/10.5194/bg-18-413-2021, https://doi.org/10.5194/bg-18-413-2021, 2021
Short summary
Short summary
Tropical forest soils are an important source and sink of greenhouse gases (GHGs) with tropical montane forests having been poorly studied. In this pilot study, we explored soil fluxes of CO2, CH4, and N2O in an Ecuadorian neotropical montane forest, where a net consumption of N2O at higher altitudes was observed. Our results highlight the importance of short-term variations in N2O and provide arguments and insights for future, more detailed studies on GHG fluxes from montane forest soils.
Wim Verbruggen, Guy Schurgers, Stéphanie Horion, Jonas Ardö, Paulo N. Bernardino, Bernard Cappelaere, Jérôme Demarty, Rasmus Fensholt, Laurent Kergoat, Thomas Sibret, Torbern Tagesson, and Hans Verbeeck
Biogeosciences, 18, 77–93, https://doi.org/10.5194/bg-18-77-2021, https://doi.org/10.5194/bg-18-77-2021, 2021
Short summary
Short summary
A large part of Earth's land surface is covered by dryland ecosystems, which are subject to climate extremes that are projected to increase under future climate scenarios. By using a mathematical vegetation model, we studied the impact of single years of extreme rainfall on the vegetation in the Sahel. We found a contrasting response of grasses and trees to these extremes, strongly dependent on the way precipitation is spread over the rainy season, as well as a long-term impact on CO2 uptake.
Cited articles
Avitabile, V. and Camia, A.: An assessment of forest biomass maps in Europe
using harmonized national statistics and inventory plots, Forest Ecol. Manag.,
409, 489–498, https://doi.org/10.1016/j.foreco.2017.11.047, 2018.
Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J.,
Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y.,
Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A., and Willcock, S.: An integrated pan-tropical
biomass map using multiple reference datasets, Glob. Change Biol., 22,
1406–1420, https://doi.org/10.1111/gcb.13139, 2016.
Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M.,
Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A.,
Samanta, S., and Houghton, R. A.: Estimated carbon dioxide emissions from
tropical deforestation improved by carbon-density maps, Nat. Clim. Change,
2, 182–185, https://doi.org/10.1038/nclimate1354, 2012.
Baccini, A., Walker, W., Carvalho, L., Farina, M., Sulla-Menashe, D., and
Houghton, R. A.: Tropical forests are a net carbon source based on
aboveground measurements of gain and loss, Science, 358, 230–234,
https://doi.org/10.1126/science.aam5962, 2017.
Bar-On, Y. M., Phillips, R., and Milo, R.: The biomass distribution on
Earth, P. Natl. Acad. Sci. USA, 115, 6506–6511,
https://doi.org/10.1073/pnas.1711842115, 2018.
Bloom, A. A., Exbrayat, J.-F., van der Velde, I. R., Feng, L., and Williams, M.: The decadal state of the terrestrial carbon cycle: Global retrievals of
terrestrial carbon allocation, pools, and residence times, P. Natl. Acad. Sci. USA, 113, 1285–1290, https://doi.org/10.1073/pnas.1515160113, 2016.
Bouvet, A., Mermoz, S., Le Toan, T., Villard, L., Mathieu, R., Naidoo, L.,
and Asner, G. P.: An above-ground biomass map of African savannahs and
woodlands at 25 m resolution derived from ALOS PALSAR, Remote Sens.
Environ., 206, 156–173, https://doi.org/10.1016/j.rse.2017.12.030, 2018.
Brown, S.: Estimating biomass and biomass change of tropical forests: A
primer, Food and Agriculture Organization, Rome, Italy, 55 pp., 1987.
Cartus, O. and Santoro, M.: Exploring combinations of multi-temporal and
multi-frequency radar backscatter observations to estimate above-ground
biomass of tropical forest, Remote Sens. Environ., 232, 111313,
https://doi.org/10.1016/j.rse.2019.111313, 2019.
Cartus, O., Kellndorfer, J., Rombach, M., and Walker, W.: Mapping canopy
height and growing stock volume using airborne LiDAR, ALOS PALSAR and
Landsat ETM+, Remote Sens.-Basel, 4, 3320–3345,
https://doi.org/10.3390/rs4113320, 2012a.
Cartus, O., Santoro, M., and Kellndorfer, J.: Mapping forest aboveground
biomass in the Northeastern United States with ALOS PALSAR dual-polarization
L-band, Remote Sens. Environ., 124, 466–478,
https://doi.org/10.1016/j.rse.2012.05.029, 2012b.
Carvalhais, N., Forkel, M., Khomik, M., Bellarby, J., Jung, M., Migliavacca, M., Mu, M., Saatchi, S., Santoro, M., Thurner, M., Weber, U.,
Ahrens, B., Beer, C., Cescatti, A., Randerson, J. T., and Reichstein, M.:
Global covariation of carbon turnover times with climate in terrestrial
ecosystems, Nature, 514, 213–217, https://doi.org/10.1038/nature13731,
2014.
Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., and Zanne, A. E.: Towards a worldwide wood economics spectrum, Ecol. Lett., 12,
351–366, https://doi.org/10.1111/j.1461-0248.2009.01285.x, 2009.
Ciais, P., Dolman, A. J., Bombelli, A., Duren, R., Peregon, A., Rayner, P. J., Miller, C., Gobron, N., Kinderman, G., Marland, G., Gruber, N.,
Chevallier, F., Andres, R. J., Balsamo, G., Bopp, L., Bréon, F.-M.,
Broquet, G., Dargaville, R., Battin, T. J., Borges, A., Bovensmann, H.,
Buchwitz, M., Butler, J., Canadell, J. G., Cook, R. B., DeFries, R.,
Engelen, R., Gurney, K. R., Heinze, C., Heimann, M., Held, A., Henry, M.,
Law, B., Luyssaert, S., Miller, J., Moriyama, T., Moulin, C., Myneni, R. B.,
Nussli, C., Obersteiner, M., Ojima, D., Pan, Y., Paris, J.-D., Piao, S. L.,
Poulter, B., Plummer, S., Quegan, S., Raymond, P., Reichstein, M., Rivier, L., Sabine, C., Schimel, D., Tarasova, O., Valentini, R., Wang, R., van der
Werf, G., Wickland, D., Williams, M., and Zehner, C.: Current systematic
carbon-cycle observations and the need for implementing a policy-relevant
carbon observing system, Biogeosciences, 11, 3547–3602,
https://doi.org/10.5194/bg-11-3547-2014, 2014.
Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S.,
Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., Armston, J., Tang, H.,
Duncanson, L., Hancock, S., Jantz, P., Marselis, S., Patterson, P., Qi, W.,
and Silva, C.: The Global Ecosystem Dynamics Investigation: High-resolution
laser ranging of the Earth's forests and topography, Sci. Remote Sens., 1,
100002, https://doi.org/10.1016/j.srs.2020.100002, 2020.
Erb, K.-H., Kastner, T., Plutzar, C., Bais, A. L. S., Carvalhais, N.,
Fetzel, T., Gingrich, S., Haberl, H., Lauk, C., Niedertscheider, M.,
Pongratz, J., Thurner, M., and Luyssaert, S.: Unexpectedly large impact of
forest management and grazing on global vegetation biomass, Nature, 553,
73–76, https://doi.org/10.1038/nature25138, 2018.
Exbrayat, J.-F., Bloom, A. A., Carvalhais, N., Fischer, R., Huth, A.,
MacBean, N., and Williams, M.: Understanding the Land Carbon Cycle with
Space Data: Current Status and Prospects, Surv. Geophys., 40, 735–755,
https://doi.org/10.1007/s10712-019-09506-2, 2019.
FAO: Global Forest Resources Assessment 2010, Rome, 2010.
Gibbs, H. K., Brown, S., Niles, J. O., and Foley, J. A.: Monitoring and
estimating tropical forest carbon stocks: making REDD a reality, Environ.
Res. Lett., 2, 045023, https://doi.org/10.1088/1748-9326/2/4/045023, 2007.
Gillis, M. D., Omule, A. Y., and Brierley, T.: Monitoring Canada's forests:
The National Forest Inventory, Forest Chron., 81, 214–221,
https://doi.org/10.5558/tfc81214-2, 2005.
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. G.: High-resolution global maps of 21-st century forest cover change,
Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013.
Herold, M., Carter, S., Avitabile, V., Espejo, A. B., Jonckheere, I., Lucas, R., McRoberts, R. E., Næsset, E., Nightingale, J., Petersen, R., Reiche, J., Romijn, E., Rosenqvist, A., Rozendaal, D. M. A., Seifert, F. M., Sanz, M. J., and De Sy, V.: The Role and Need for Space-Based Forest
Biomass-Related Measurements in Environmental Management and Policy, Surv.
Geophys., 40, 757–778, https://doi.org/10.1007/s10712-019-09510-6, 2019.
Hofton, M. A., Minster, J. B., and Blair, J. B.: Decomposition of laser
altimeter waveforms, IEEE T. Geosci. Remote, 38, 1989–1996,
https://doi.org/10.1109/36.851780, 2000.
Houghton, R. A.: Aboveground forest biomass and the global carbon balance,
Glob. Change Biol., 11, 945–958,
https://doi.org/10.1111/j.1365-2486.2005.00955.x, 2005.
Houghton, R. A., Hall, F., and Goetz, S. J.: Importance of forest biomass in
the global carbon cycle, J. Geophys. Res., 114, G00E03,
https://doi.org/10.1029/2009JG000935, 2009.
Hu, T., Su, Y., Xue, B., Liu, J., Zhao, X., Fang, J., and Guo, Q.: Mapping
global forest aboveground biomass with spaceborne LiDAR, optical imagery,
and forest inventory data, Remote Sens.-Basel, 8, 565,
https://doi.org/10.3390/rs8070565, 2016.
IPCC: 2006 IPCC guidelines for national greenhouse gas inventories, Vol. 4: Agriculture, Forestry and Other Land Use, Institute for Global Environmental Strategies (IGES), Hayama, Japan on behalf of the IPCC, 2006.
Jenkins, J. C., Chojnacky, D. C., Heath, L. S., and Birdsey, R. A.:
National-scale biomass estimators for United States tree species, For. Sci.,
49, 12–35, 2003.
Kindermann, G. E., McCallum, I., Fritz, S., and Obersteiner, M.: A global
forest growing stock, biomass and carbon map based on FAO statistics, Silva
Fenn., 42, 387–396, https://doi.org/10.14214/sf.244, 2008.
Kurvonen, L., Pulliainen, J., and Hallikainen, M.: Retrieval of biomass in
boreal forests from multitemporal ERS-1 and JERS-1 SAR images, IEEE T. Geosci. Remote, 37, 198–205, https://doi.org/10.1109/36.739154, 1999.
Li, W., Ciais, P., Peng, S., Yue, C., Wang, Y., Thurner, M., Saatchi, S. S.,
Arneth, A., Avitabile, V., Carvalhais, N., Harper, A. B., Kato, E., Koven, C., Liu, Y. Y., Nabel, J. E. M. S., Pan, Y., Pongratz, J., Poulter, B.,
Pugh, T. A. M., Santoro, M., Sitch, S., Stocker, B. D., Viovy, N.,
Wiltshire, A., Yousefpour, R., and Zaehle, S.: Land-use and land-cover
change carbon emissions between 1901 and 2012 constrained by biomass
observations, Biogeosciences, 14, 5053–5067,
https://doi.org/10.5194/bg-14-5053-2017, 2017.
Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M., Canadell, J. G.,
McCabe, M. F., Evans, J. P., and Wang, G.: Recent reversal in loss of global
terrestrial biomass, Nat. Clim. Change, 5, 470–474,
https://doi.org/10.1038/nclimate2581, 2015.
Los, S. O., Rosette, J. A. B., Kljun, N., North, P. R. J., Chasmer, L.,
Suárez, J. C., Hopkinson, C., Hill, R. A., Van Gorsel, E., Mahoney, C.,
and Berni, J. A. J.: Vegetation height and cover fraction between
60∘ S and 60∘ N from ICESat GLAS data, Geosci. Model
Dev., 5, 413–432, https://doi.org/10.5194/gmd-5-413-2012, 2012.
Lu, D., Chen, Q., Wang, G., Liu, L., Li, G., and Moran, E.: A survey of
remote sensing-based aboveground biomass estimation methods in forest
ecosystems, Int. J. Digit. Earth, 9, 63–105,
https://doi.org/10.1080/17538947.2014.990526, 2016.
Lucas, R. M., Mitchell, A. L., and Armston, J.: Measurement of Forest
Above-Ground Biomass Using Active and Passive Remote Sensing at Large
(Subnational to Global) Scales, Curr. Forestry Rep., 1, 162–177,
https://doi.org/10.1007/s40725-015-0021-9, 2015.
Curr Forestry Rep
Mermoz, S., Bouvet, A., Toan, T. L., and Herold, M.: Impacts of the forest
definitions adopted by African countries on carbon conservation, Environ.
Res. Lett., 13, 104014, https://doi.org/10.1088/1748-9326/aae3b1, 2018.
Mitchard, E. T. A., Saatchi, S. S., Baccini, A., Asner, G. P., Goetz, S. J.,
Harris, N. L., and Brown, S.: Uncertainty in the spatial distribution of
tropical forest biomass: A comparison of pan-tropical maps, Carbon Balance
Manage., 8, 1, https://doi.org/10.1186/1750-0680-8-10, 2013.
Næsset, E., McRoberts, R. E., Pekkarinen, A., Saatchi, S., Santoro, M.,
Trier, Ø. D., Zahabu, E., and Gobakken, T.: Use of local and global maps
of forest canopy height and aboveground biomass to enhance local estimates
of biomass in miombo woodlands in Tanzania, Int. J. Appl. Earth Obs., 89, 102109, https://doi.org/10.1016/j.jag.2020.102109, 2020.
Ometto, J. P., Aguiar, A. P., Assis, T., Soler, L., Valle, P., Tejada, G.,
Lapola, D. M., and Meir, P.: Amazon forest biomass density maps: tackling
the uncertainty in carbon emission estimates, Climatic Change, 124, 545–560,
https://doi.org/10.1007/s10584-014-1058-7, 2014.
Pan, Y., Birdsey, R. A., Fang, J., Houghton, R., Kauppi, P. E., Kurz, W. A.,
Phillips, O. L., Shvidenko, A., Lewis, S. L., Canadell, J. G., Ciais, P.,
Jackson, R. B., Pacala, S. W., McGuire, A. D., Piao, S., Rautiainen, A.,
Sitch, S., and Hayes, D.: A large and persistent carbon sink in the world's
forests, Science, 333, 988–993, https://doi.org/10.1126/science.1201609,
2011.
Pulliainen, J. T., Heiska, K., Hyyppä, J., and Hallikainen, M. T.:
Backscattering properties of boreal forests at the C- and X bands, IEEE T. Geosci. Remote, 32, 1041–1050,
https://doi.org/10.1109/36.312892, 1994.
DUE GlobBiomass – Algorithm Theoretical Basis Document:
http://globbiomass.org/products/global-mapping/, last access: 9 August 2021.
Quegan, S., Le Toan, T., Chave, J., Dall, J., Exbrayat, J.-F., Minh, D. H. T., Lomas, M., D'Alessandro, M. M., Paillou, P., Papathanassiou, K., Rocca, F., Saatchi, S., Scipal, K., Shugart, H., Smallman, T. L., Soja, M. J.,
Tebaldini, S., Ulander, L., Villard, L., and Williams, M.: The European
Space Agency BIOMASS mission: Measuring forest above-ground biomass from
space, Remote Sens. Environ., 227, 44–60,
https://doi.org/10.1016/j.rse.2019.03.032, 2019.
Reichstein, M. and Carvalhais, N.: Aspects of Forest Biomass in the Earth
System: Its Role and Major Unknowns, Surv. Geophys., 40, 693–707,
https://doi.org/10.1007/s10712-019-09551-x, 2019.
Rodríguez-Veiga, P., Wheeler, J., Louis, V., Tansey, K., and Balzter, H.: Quantifying forest biomass carbon stocks from space, Curr. Forestry Rep., 3,
1–18, https://doi.org/10.1007/s40725-017-0052-5, 2017.
Rodríguez-Veiga, P., Quegan, S., Carreiras, J., Persson, H. J.,
Fransson, J. E. S., Hoscilo, A., Ziółkowski, D., Stereńczak, K.,
Lohberger, S., Stängel, M., Berninger, A., Siegert, F., Avitabile, V.,
Herold, M., Mermoz, S., Bouvet, A., Le Toan, T., Carvalhais, N., Santoro, M., Cartus, O., Rauste, Y., Mathieu, R., Asner, G. P., Thiel, C., Pathe, C.,
Schmullius, C., Seifert, F. M., Tansey, K., and Balzter, H.: Forest biomass
retrieval approaches from Earth Observation in different biomes, Int. J.
Appl. Earth Obs., 77, 53–68,
https://doi.org/10.1016/j.jag.2018.12.008, 2019.
Romijn, E., Lantican, C. B., Herold, M., Lindquist, E., Ochieng, R., Wijaya, A., Murdiyarso, D., and Verchot, L.: Assessing change in national forest
monitoring capacities of 99 tropical countries, Forest Ecol. Manag., 352,
109–123, https://doi.org/10.1016/j.foreco.2015.06.003, 2015.
Rosenqvist, A., Shimada, M., Ito, N., and Watanabe, M.: ALOS PALSAR: A
pathfinder mission for global-scale monitoring of the environment, IEEE T. Geosci. Remote, 45, 3307–3316,
https://doi.org/10.1109/TGRS.2007.901027, 2007.
Saatchi, S., Marlier, M., Chazdon, R. L., Clark, D. B., and Russell, A. E.:
Impact of spatial variability of tropical forest structure on radar
estimation of aboveground biomass, Remote Sens. Environ., 115, 2836–2849,
https://doi.org/10.1016/j.rse.2010.07.015, 2011a.
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A.,
Salas, W., Zutta, B. R., Buermann, W., Lewis, S. L., Hagen, S., Petrova, S.,
White, L., Silman, M., and Morel, A.: Benchmark map of forest carbon stocks
in tropical regions across three continents, P. Natl. Acad. Sci. USA, 108,
9899–9904, https://doi.org/10.1073/pnas.1019576108, 2011b.
Santoro, M.: GlobBiomass – global datasets of forest biomass, https://doi.org/10.1594/PANGAEA.894711, 2018.
Santoro, M. and Cartus, O.: Research pathways of forest above-ground biomass
estimation based on SAR backscatter and interferometric SAR observations, Remote Sens.-Basel, 10, 608, https://doi.org/10.3390/rs10040608, 2018.
Santoro, M., Askne, J., Smith, G., and Fransson, J. E. S.: Stem volume
retrieval in boreal forests from ERS-1/2 interferometry, Remote Sens.
Environ., 81, 19–35, https://doi.org/10.1016/S0034-4257(01)00329-7, 2002.
Santoro, M., Beer, C., Cartus, O., Schmullius, C., Shvidenko, A., McCallum, I., Wegmüller, U., and Wiesmann, A.: Retrieval of growing stock volume
in boreal forest using hyper-temporal series of Envisat ASAR ScanSAR
backscatter measurements, Remote Sens. Environ., 115, 490–507,
https://doi.org/10.1016/j.rse.2010.09.018, 2011.
Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J. E. S., Hall, R. J., Pathe, C., Schepaschenko, D., Schmullius, C., Shvidenko, A., Thurner, M., and Wegmüller, U.: Forest growing stock volume of the Northern Hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR
data, Remote Sens. Environ., 168, 316–334,
https://doi.org/10.1016/j.rse.2015.07.005, 2015a.
Santoro, M., Wegmüller, U., Lamarche, C., Bontemps, S., Defourny, P.,
and Arino, O.: Strengths and weaknesses of multi-year Envisat ASAR
backscatter measurements to map permanent open water bodies at global scale,
Remote Sens. Environ., 171, 185–201,
https://doi.org/10.1016/j.rse.2015.10.031, 2015b.
Schepaschenko, D., Kraxner, F., See, L., Fuss, S., McCallum, I., Fritz, S.,
Perger, C., Shvidenko, A., Kindermann, G., Frank, S., Tum, M., Schmid, E.,
Balkovič, J., and Günther, K.: Global Biomass Information: From Data
Generation to Application, in: Handbook of Clean Energy Systems, edited by:
Yan, J., John Wiley and Sons, Ltd, Chichester, UK, 1–23,
https://doi.org/10.1002/9781118991978.hces173, 2015.
Schepaschenko, D., Moltchanova, E., Fedorov, S., Karminov, V., Ontikov, P.,
Santoro, M., See, L., Kositsyn, V., Shvidenko, A., Romanovskaya, A.,
Korotkov, V., Lesiv, M., Bartalev, S., Fritz, S., Shchepashchenko, M., and
Kraxner, F.: Russian forest sequesters substantially more carbon than
previously reported, Sci. Rep.-UK, 11:12825, 7,
https://doi.org/10.1038/s41598-021-92152-9, 2021.
Schimel, D., Pavlick, R., Fisher, J. B., Asner, G. P., Saatchi, S.,
Townsend, P., Miller, C., Frankenberg, C., Hibbard, K., and Cox, P.:
Observing terrestrial ecosystems and the carbon cycle from space, Glob.
Change Biol., 21, 1762–1776, https://doi.org/10.1111/gcb.12822, 2015.
Shimada, M.: Ortho-rectification and slope correction of SAR data using DEM
and its accuracy evaluation, IEEE J. Sel. Top. Appl., 3, 657–671, https://doi.org/10.1109/JSTARS.2010.2072984, 2010.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy
height globally with spaceborne LiDAR, J. Geophys. Res., 116, G04021, https://doi.org/10.1029/2011JG001708, 2011.
Soto-Navarro, C., Ravilious, C., Arnell, A., de Lamo, X., Harfoot, M., Hill, S. L. L., Wearn, O. R., Santoro, M., Bouvet, A., Mermoz, S., Le Toan, T.,
Xia, J., Liu, S., Yuan, W., Spawn, S. A., Gibbs, H. K., Ferrier, S.,
Harwood, T., Alkemade, R., Schipper, A. M., Schmidt-Traub, G., Strassburg, B., Miles, L., Burgess, N. D., and Kapos, V.: Mapping co-benefits for carbon
storage and biodiversity to inform conservation policy and action, Philos.
T. R. Soc. B, 375, 20190128,
https://doi.org/10.1098/rstb.2019.0128, 2020.
Spawn, S. A., Sullivan, C. C., Lark, T. J., and Gibbs, H. K.: Harmonized
global maps of above and belowground biomass carbon density in the year
2010, Sci. Data, 7, 112, https://doi.org/10.1038/s41597-020-0444-4, 2020.
Stolbovoi, V. and McCallum, I.: Land Resources of Russia, International
Institute for Applied Systems Analysis and Russian Academy of Science,
Laxenburg, Austria, 2002.
Thum, T., MacBean, N., Peylin, P., Bacour, C., Santaren, D., Longdoz, B.,
Loustau, D., and Ciais, P.: The potential benefit of using forest biomass
data in addition to carbon and water flux measurements to constrain
ecosystem model parameters: Case studies at two temperate forest sites,
Agr. Forest Meteorol., 234–235, 48–65,
https://doi.org/10.1016/j.agrformet.2016.12.004, 2017.
Thurner, M., Beer, C., Santoro, M., Carvalhais, N., Wutzler, T.,
Schepaschenko, D., Shvidenko, A., Kompter, E., Ahrens, B., Levick, S. R.,
and Schmullius, C.: Carbon stock and density of northern boreal and
temperate forests, Global Ecol. Biogeogr., 23, 297–310,
https://doi.org/10.1111/geb.12125, 2014.
Thurner, M., Beer, C., Carvalhais, N., Forkel, M., Santoro, M., Tum, M., and
Schmullius, C.: Large-scale variation in boreal and temperate forest carbon
turnover rate related to climate, Geophys. Res. Lett., 43, 4576–4585,
https://doi.org/10.1002/2016GL068794, 2016.
Tomppo, E., Gschwantner, T., Lawrence, M., and McRoberts, R. E.: National
Forest Inventories – Pathways for common reporting, Springer Science and
Business Media, 612 pp., 2010.
Short summary
Forests play a crucial role in Earth’s carbon cycle. To understand the carbon cycle better, we generated a global dataset of forest above-ground biomass, i.e. carbon stocks, from satellite data of 2010. This dataset provides a comprehensive and detailed portrait of the distribution of carbon in forests, although for dense forests in the tropics values are somewhat underestimated. This dataset will have a considerable impact on climate, carbon, and socio-economic modelling schemes.
Forests play a crucial role in Earth’s carbon cycle. To understand the carbon cycle better, we...
Altmetrics
Final-revised paper
Preprint