Articles | Volume 17, issue 3
https://doi.org/10.5194/essd-17-1101-2025
© Author(s) 2025. 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-17-1101-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Aboveground biomass dataset from SMOS L-band vegetation optical depth and reference maps
Simon Boitard
CORRESPONDING AUTHOR
Centre d'Etudes Spatiales de la Biosphère, Univ. Toulouse, CNES/IRD/CNRS/INRAe, CESBIO, Toulouse, France
Arnaud Mialon
CORRESPONDING AUTHOR
Centre d'Etudes Spatiales de la Biosphère, Univ. Toulouse, CNES/IRD/CNRS/INRAe, CESBIO, Toulouse, France
Stéphane Mermoz
GlobEO (Global Earth Observation), Toulouse, France
Nemesio J. Rodríguez-Fernández
Centre d'Etudes Spatiales de la Biosphère, Univ. Toulouse, CNES/IRD/CNRS/INRAe, CESBIO, Toulouse, France
Philippe Richaume
Centre d'Etudes Spatiales de la Biosphère, Univ. Toulouse, CNES/IRD/CNRS/INRAe, CESBIO, Toulouse, France
Julio César Salazar-Neira
Centre d'Etudes Spatiales de la Biosphère, Univ. Toulouse, CNES/IRD/CNRS/INRAe, CESBIO, Toulouse, France
Stéphane Tarot
IFREMER, BP 70, 29280 Plouzané, France
Yann H. Kerr
Centre d'Etudes Spatiales de la Biosphère, Univ. Toulouse, CNES/IRD/CNRS/INRAe, CESBIO, Toulouse, France
Related authors
No articles found.
Kimmo Rautiainen, Manu Holmberg, Juval Cohen, Arnaud Mialon, Mike Schwank, Juha Lemmetyinen, Antonio de la Fuente, and Yann Kerr
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-68, https://doi.org/10.5194/essd-2025-68, 2025
Revised manuscript accepted for ESSD
Short summary
Short summary
The SMOS Soil Freeze Thaw State product uses satellite data to monitor seasonal soil freezing and thawing globally, with a focus on high latitude regions. This is important for understanding greenhouse gas emissions, as frozen soil is associated with methane release. The product provides accurate data on key events such as the first day of soil freezing in autumn, helping scientists to study climate change, ecosystem dynamics and its impact on our planet.
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.
Juliette Ortet, Arnaud Mialon, Alain Royer, Mike Schwank, Manu Holmberg, Kimmo Rautiainen, Simone Bircher-Adrot, Andreas Colliander, Yann Kerr, and Alexandre Roy
EGUsphere, https://doi.org/10.5194/egusphere-2024-3963, https://doi.org/10.5194/egusphere-2024-3963, 2025
Short summary
Short summary
We propose a new method to determine the ground surface temperature under the snowpack in the Arctic area from satellite observations. The obtained ground temperatures time series were evaluated over 21 reference sites in Northern Alaska and compared with ground temperatures obtained with global models. The method is excessively promising for monitoring ground temperature below the snowpack and studying the spatiotemporal variability thanks to 15 years of observations over the whole Arctic area.
Marta Bottani, Laurent Ferro-Famil, Juan Doblas, Stéphane Mermoz, Alexandre Bouvet, and Thierry Koleck
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 43–49, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-43-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-43-2024, 2024
Juan Doblas, Mariane Souza Reis, Stéphane Mermoz, Claudio Aparecido Almeida, Thierry Koleck, Cassiano Gustavo Messias, Luciana Soler, Alexandre Bouvet, and Sidnei J. S. Sant’Anna
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 127–133, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-127-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-127-2024, 2024
Thuy Le Toan, Ludovic Villard, Dinh Ho Tong Minh, Juan Doblas, Stephane Mermoz, Laurent Ferro-Famil, Thierry Koleck, Alexandre Bouvet, Milena Planells, and Laurent Polidori
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 287–293, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-287-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-287-2024, 2024
Remi Madelon, Nemesio J. Rodríguez-Fernández, Hassan Bazzi, Nicolas Baghdadi, Clement Albergel, Wouter Dorigo, and Mehrez Zribi
Hydrol. Earth Syst. Sci., 27, 1221–1242, https://doi.org/10.5194/hess-27-1221-2023, https://doi.org/10.5194/hess-27-1221-2023, 2023
Short summary
Short summary
We present an approach to estimate soil moisture (SM) at 1 km resolution using Sentinel-1 and Sentinel-3 satellites. The estimates were compared to other high-resolution (HR) datasets over Europe, northern Africa, Australia, and North America, showing good agreement. However, the discrepancies between the different HR datasets and their lower performances compared with in situ measurements and coarse-resolution datasets show the remaining challenges for large-scale HR SM mapping.
Emma Bousquet, Arnaud Mialon, Nemesio Rodriguez-Fernandez, Stéphane Mermoz, and Yann Kerr
Biogeosciences, 19, 3317–3336, https://doi.org/10.5194/bg-19-3317-2022, https://doi.org/10.5194/bg-19-3317-2022, 2022
Short summary
Short summary
Pre- and post-fire values of four climate variables and four vegetation variables were analysed at the global scale, in order to observe (i) the general fire likelihood factors and (ii) the vegetation recovery trends over various biomes. The main result of this study is that L-band vegetation optical depth (L-VOD) is the most impacted vegetation variable and takes the longest to recover over dense forests. L-VOD could then be useful for post-fire vegetation recovery studies.
Guillaume Marie, B. Sebastiaan Luyssaert, Cecile Dardel, Thuy Le Toan, Alexandre Bouvet, Stéphane Mermoz, Ludovic Villard, Vladislav Bastrikov, and Philippe Peylin
Geosci. Model Dev., 15, 2599–2617, https://doi.org/10.5194/gmd-15-2599-2022, https://doi.org/10.5194/gmd-15-2599-2022, 2022
Short summary
Short summary
Most Earth system models make use of vegetation maps to initialize a simulation at global scale. Satellite-based biomass map estimates for Africa were used to estimate cover fractions for the 15 land cover classes. This study successfully demonstrates that satellite-based biomass maps can be used to better constrain vegetation maps. Applying this approach at the global scale would increase confidence in assessments of present-day biomass stocks.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Joaquín Muñoz-Sabater, Emanuel Dutra, Anna Agustí-Panareda, Clément Albergel, Gabriele Arduini, Gianpaolo Balsamo, Souhail Boussetta, Margarita Choulga, Shaun Harrigan, Hans Hersbach, Brecht Martens, Diego G. Miralles, María Piles, Nemesio J. Rodríguez-Fernández, Ervin Zsoter, Carlo Buontempo, and Jean-Noël Thépaut
Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, https://doi.org/10.5194/essd-13-4349-2021, 2021
Short summary
Short summary
The creation of ERA5-Land responds to a growing number of applications requiring global land datasets at a resolution higher than traditionally reached. ERA5-Land provides operational, global, and hourly key variables of the water and energy cycles over land surfaces, at 9 km resolution, from 1981 until the present. This work provides evidence of an overall improvement of the water cycle compared to previous reanalyses, whereas the energy cycle variables perform as well as those of ERA5.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
Short summary
Short summary
The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Clément Albergel, Yongjun Zheng, Bertrand Bonan, Emanuel Dutra, Nemesio Rodríguez-Fernández, Simon Munier, Clara Draper, Patricia de Rosnay, Joaquin Muñoz-Sabater, Gianpaolo Balsamo, David Fairbairn, Catherine Meurey, and Jean-Christophe Calvet
Hydrol. Earth Syst. Sci., 24, 4291–4316, https://doi.org/10.5194/hess-24-4291-2020, https://doi.org/10.5194/hess-24-4291-2020, 2020
Short summary
Short summary
LDAS-Monde is a global offline land data assimilation system (LDAS) that jointly assimilates satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the ISBA (Interaction between Soil Biosphere and Atmosphere) land surface model (LSM). This study demonstrates that LDAS-Monde is able to detect, monitor and forecast the impact of extreme weather on land surface states.
Cited articles
Al Bitar, A., Mialon, A., Kerr, Y. H., Cabot, F., Richaume, P., Jacquette, E., Quesney, A., Mahmoodi, A., Tarot, S., Parrens, M., Al-Yaari, A., Pellarin, T., Rodriguez-Fernandez, N., and Wigneron, J.-P.: The global SMOS Level 3 daily soil moisture and brightness temperature maps, Earth Syst. Sci. Data, 9, 293–315, https://doi.org/10.5194/essd-9-293-2017, 2017. a, b, c
Avitabile, V., Herold, M., Heuvelink, G., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., and Berry, N. J.: An integrated pan-tropical biomass map using multiple reference datasets, Global Change Biol., 22, 1406–1420, https://doi.org/10.1111/gcb.13139, 2016. a, b, c, d, e, f, g, h, i
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, 2012. a, b
Baret, F. and Guyot, G.: Potentials and limits of vegetation indices for LAI and APAR assessment, Remote Sens. Environ., 35, 161–173, https://doi.org/10.1016/0034-4257(91)90009-U, 1991. a
Boitard, S., Mialon, A., Rodriguez-Fernandez, N., Richaume, P., Salazar Neira, J. C., and Kerr, Y. H.: Technical Note: AGB and TH estimation from SMOS LVOD, Tech. rep., Centre d'Etudes Spatiales de la Biosphère, Université de Toulouse, CNES/CNRS/IRD/UPS, https://data.catds.fr/cecsm/Land_products/L4_Above_Ground_Biomass/documentation/NT_AGB_maps_from_VOD.pdf (last access: 8 November 2024), 2023. a
Boitard, S., Mialon, A., Mermoz, S., Rodriguez-Fernandez, N., Richaume, P., Salazar Neira, J. C., Tarot, S., and Kerr, Y. H.: Above ground biomass dataset from SMOS L band vegetation optical depth and reference maps, Sextant [data set], https://doi.org/10.12770/95f76ff0-5d89-430d-80db-95fbdd77f543, 2024 (data available at: https://data.catds.fr/cecsm/Land_products/L4_Above_Ground_Biomass/, last access: 11 March 2025). a, b, c
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.: EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets, ISPRS Int. J. Geo-Inf., 1, 32–45, https://doi.org/10.3390/ijgi1010032, 2012. a
Brodzik, M. J., Billingsley, B., Haran, T., Raup, B., and Savoie, M. H.: Correction: Brodzik, M. J., et al. EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets. ISPRS International J. of Geo-Information 2012, 1, 32 45, ISPRS Int. J. Geo-Inf., 3, 1154–1156, https://doi.org/10.3390/ijgi3031154, 2014. a
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. a
Chaparro, D., Duveiller, G., Piles, M., Cescatti, A., Vall-Llossera, M., Camps, A., and Entekhabi, D.: Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices, Remote Sens. Environ., 232, 111303, https://doi.org/10.1016/j.rse.2019.111303, 2019. a
Chaubell, J., Yueh, S., Dunbar, R. S., Colliander, A., Entekhabi, D., Chan, S. K., Chen, F., Xu, X., Bindlish, R., O'Neill, P., Asanuma, J., Berg, A. A., Bosch, D. D., Caldwell, T., Cosh, M. H., Collins, C. H., Jensen, K. H., Martínez-Fernández, J., Seyfried, M., Starks, P. J., Su, Z., Thibeault, M., and Walker, J. P.: Regularized dual-channel algorithm for the retrieval of soil moisture and vegetation optical depth from SMAP measurements, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 15, 102–114, 2021. a
Clark, D. A.: Sources or sinks? The responses of tropical forests to current and future climate and atmospheric composition, Philos. T. Roy. Soc. Lond. B, 359, 477–491, https://doi.org/10.1098/rstb.2003.1426, 2004. a
Djomo, A. N., Knohl, A., and Gravenhorst, G.: Estimations of total ecosystem carbon pools distribution and carbon biomass current annual increment of a moist tropical forest, Forest Ecol. Manage., 261, 1448–1459, https://doi.org/10.1016/j.foreco.2011.01.031, 2011. a
Dou, Y., Tian, F., Wigneron, J.-P., Tagesson, T., Du, J., Brandt, M., Liu, Y., Zou, L., Kimball, J. S., and Fensholt, R.: Reliability of using vegetation optical depth for estimating decadal and interannual carbon dynamics, Remote Sens. Environ., 285, 113390, https://doi.org/10.1016/j.rse.2022.113390, 2023. a
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T., Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J., Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman, S. W., Tsang, L., and Van Zyl, J.: The Soil Moisture Active Passive (SMAP) Mission, Proc. IEEE, 98, 704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010. a
ESA: SMOS L2 SM V700, Version 700, ESA [data set], https://doi.org/10.57780/SM1-857c3d7, 2021. a
Fan, L., Wigneron, J.-P., Ciais, P., Chave, J., Brandt, M., Fensholt, R., Saatchi, S. S., Bastos, A., Al-Yaari, A. ad Hufkens, K., Qin, Y., Xiao, X., Chen, C., Myneni, R. B., Fernandez-Moran, R., Mialon, A., Rodriguez-Fernandez, N., Kerr, Y., Tian, F., and Peñuelas, J.: Satellite-observed pantropical carbon dynamics, Nat. Plants, 5, 944–951, 2019. a
Fernandez-Moran, R., Al-Yaari, A., Mialon, A., Mahmoodi, A., Al Bitar, A., De Lannoy, G., Rodriguez-Fernandez, N., Lopez-Baeza, E., Kerr, Y., and Wigneron, J.-P.: SMOS-IC: An alternative SMOS soil moisture and vegetation optical depth product, Remote Sens., 9, 457, https://doi.org/10.3390/rs9050457, 2017. a, b
Frappart, F., Wigneron, J.-P., Li, X., Liu, X., Al-Yaari, A., Fan, L., Wang, M., Moisy, C., Le Masson, E., Aoulad Lafkih, Z., Vallé, C., Ygorra, B., and Baghdadi, N.: Global monitoring of the vegetation dynamics from the Vegetation Optical Depth (VOD): A review, Remote Sens., 12, 2915, https://doi.org/10.3390/rs12182915, 2020. a
Grace, J.: Understanding and managing the global carbon cycle, J. Ecol., 92, 189–202, https://doi.org/10.1111/j.0022-0477.2004.00874.x, 2004. a
Grant, J., Wigneron, J.-P., Drusch, M., Williams, M., Law, B., Novello, N., and Kerr, Y.: Investigating temporal variations in vegetation water content derived from SMOS optical depth, in: 2012 IEEE International Geoscience and Remote Sensing Symposium, 22–27 July 2012, Munich, Germany, 3331–3334, https://doi.org/10.1109/IGARSS.2012.6350590, 2012. a
Hese, S., Lucht, W., Schmullius, C., Barnsley, M., Dubayah, R., Knorr, D., Neumann, K., Riedel, T., and Schröter, K.: Global biomass mapping for an improved understanding of the CO2 balance – the Earth observation mission Carbon-3D, Remote Sens. Environ., 94, 94–104, https://doi.org/10.1016/j.rse.2004.09.006, 2005. a
Houghton, R.: Aboveground forest biomass and the global carbon balance, Global Change Biol., 11, 945–958, https://doi.org/10.1111/j.1365-2486.2005.00955.x, 2005. a
Houghton, R., Hall, F., and Goetz, S. J.: Importance of biomass in the global carbon cycle, J. Geophys. Res.-Biogeo., 114, G00E03, https://doi.org/10.1029/2009JG000935, 2009. a
Jackson, T. and Schmugge, T.: Vegetation effects on the microwave emission of soils, Remote Sens. Environ., 36, 203–212, https://doi.org/10.1016/0034-4257(91)90057-D, 1991. a
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M.-J., Font, J., Reul, N., Gruhier, C., Juglea, S. E., Drinkwater, M. R., Hahne, A., Martín-Neira, M., and Mecklenburg, S.: The SMOS mission: New tool for monitoring key elements of the global water cycle, Proc. IEEE, 98, 666–687, https://doi.org/10.1109/JPROC.2010.2043032, 2010. a, b
Kerr, Y. H., Waldteufel, P., Richaume, P., Wigneron, J. P., Ferrazzoli, P., Mahmoodi, A., Al Bitar, A., Cabot, F., Gruhier, C., Juglea, S. E., Leroux, D., Mialon, A., and Delwart, S.: The SMOS Soil Moisture Retrieval Algorithm, IEEE T. Geosci. Remote, 50, 1384–1403, https://doi.org/10.1109/TGRS.2012.2184548, 2012. a, b, c, d
Konings, A. G., Piles, M., Das, N., and Entekhabi, D.: L-band vegetation optical depth and effective scattering albedo estimation from SMAP, Remote Sens. Environ., 198, 460–470, 2017. a
Le Toan, T., Quegan, S., Davidson, M., Balzter, H., Paillou, P., Papathanassiou, K., Plummer, S., Rocca, F., Saatchi, S., Shugart, H., and Ulander, L.: The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle, Remote Sens. Environ., 115, 2850–2860, 2011. a
Liu, Y. Y., Van Dijk, A. I., De Jeu, R. A., 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. a, b, c
Losi, C. J., Siccama, T. G., Condit, R., and Morales, J. E.: Analysis of alternative methods for estimating carbon stock in young tropical plantations, Forest Ecol. Manage. 184, 355–368, https://doi.org/10.1016/S0378-1127(03)00160-9, 2003. a
Lu, D.: The potential and challenge of remote sensing‐based biomass estimation, Int. J. Remote Sens., 27, 1297–1328, https://doi.org/10.1080/01431160500486732, 2006. a
Lu, Y., Coops, N. C., and Hermosilla, T.: Estimating urban vegetation fraction across 25 cities in pan-Pacific using Landsat time series data, ISPRS J. Photogram. Remote Sens., 126, 11–23, https://doi.org/10.1016/j.isprsjprs.2016.12.014, 2017. a
Luyssaert, S., Inglima, I., Jung, M., Richardson, A. D., Reichstein, M., Papale, D., Piao, S. L., Schulze, E. D., Wingate, L., Matteucci, G., Aragao, L., Aubinet, M., Beer, C., Bernhofer, C., Black, K. G., Bonal, D., Bonnefond, J. M., Chambers, J., Ciais, P., Cook, B., Davis, K. J., Dolman, A. J., Gielen, B., Goulden, M., Grace, J., Granier, A., Grelle, A., Griffis, T., Grünwald, T., Guidolotti, G., Hanson, P. J., Harding, R., Hollinger, D. Y., Hutyra, L. R., Kolari, P., Kruijt, B., Kutsch, W., Lagergren, F., Laurila, T., Law, B. E., Maire, G. L., Lindroth, A., Loustau, D., Malhi, Y., Mateus, J., Migliavacca, M., Misson, L., Montagnani, L., Moncrieff, J., Moors, E., Munger, J. W., Nikinmaa, E., Ollinger, S. V., Pita, G., Rebmann, C., Roupsard, O., Saigusa, N., Sanz, M. J., Seufert, G., Sierra, C., Smith, M. L., Tang, J., Valentini, R., Vesala, T., and Janssens, I. A.: CO2 Balance Of Boreal, Temperate, and Tropical Forests Derived From A Global Database, Global Change Biol., 13, 2509–2537, https://doi.org/10.1111/j.1365-2486.2007.01439.x, 2007. a
Mermoz, S., Réjou-Méchain, M., Villard, L., Le Toan, T., Rossi, V., and Gourlet-Fleury, S.: Decrease of L-band SAR backscatter with biomass of dense forests, Remote Sens. Environ., 159, 307–317, 2015. a
Mialon, A., Rodríguez-Fernández, N. J., Santoro, M., Saatchi, S., Mermoz, S., Bousquet, E., and Kerr, Y. H.: Evaluation of the sensitivity of SMOS L-VOD to forest above-ground biomass at global scale, Remote Sens., 12, 1450, https://doi.org/10.3390/rs12091450, 2020. a, b, c, d
Mitchard, E. T., Saatchi, S. S., Gerard, F., Lewis, S. L., and Meir, P.: Measuring woody encroachment along a forest–savanna boundary in Central Africa, Earth Interact., 13, 1–29, 2009. a
Mitchard, E. T., 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, Carb. Bal. Manage., 8, 1–13, https://doi.org/10.1186/1750-0680-8-10, 2013. a
Mo, T., Choudhury, B., Schmugge, T., Wang, J. R., and Jackson, T.: A model for microwave emission from vegetation-covered fields, J. Geophys. Res.-Oceans, 87, 11229–11237, https://doi.org/10.1029/JC087iC13p11229, 1982. a
Oliva, R., Daganzo, E., Richaume, P., Kerr, Y., Cabot, F., Soldo, Y., Anterrieu, E., Reul, N., Gutierrez, A., Barbosa, J., and Lopes, G.: Status of Radio Frequency Interference (RFI) in the 1400–1427 MHz passive band based on six years of SMOS mission, Remote Sens. Environ., 180, 64–75, https://doi.org/10.1016/j.rse.2016.01.013, 2016. a
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. a
Pan, Y., Birdsey, R. A., Phillips, O. L., and Jackson, R. B.: The Structure, Distribution, and Biomass of the World's Forests, Annu. Rev. Ecol. Evol. System., 44, 593–622, https://doi.org/10.1146/annurev-ecolsys-110512-135914, 2013. a
Prigent, C. and Jimenez, C.: An evaluation of the synergy of satellite passive microwave observations between 1.4 and 36 GHz, for vegetation characterization over the Tropics, Remote Sens. Environ., 257, 112346, https://doi.org/10.1016/j.rse.2021.112346, 2021. a
Purevdorj, T., Tateishi, R., Ishiyama, T., and Honda, Y.: Relationships between percent vegetation cover and vegetation indices, Int. J. Remote Sens., 19, 3519–3535, https://doi.org/10.1080/014311698213795, 1998. a
Rodríguez-Fernández, N. J., Mialon, A., Mermoz, S., Bouvet, A., Richaume, P., Al Bitar, A., Al-Yaari, A., Brandt, M., Kaminski, T., Le Toan, T., Kerr, Y. H., and Wigneron, J.-P.: An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa, Biogeosciences, 15, 4627–4645, https://doi.org/10.5194/bg-15-4627-2018, 2018. a, b, c, d, e, f
Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T., 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, 2011. a, b
Sahr, K., White, D., and Kimerling, A. J.: Geodesic discrete global grid systems, Cartogr. Geogr. Inf. Sci., 30, 121–134, https://doi.org/10.1559/152304003100011090, 2003. a
Salazar-Neira, J. C., Mialon, A., Richaume, P., Mermoz, S., Kerr, Y., Bouvet, A., Le Toan, T., Boitard, S., and Rodríguez-Fernández, N. J.: Above-Ground Biomass estimation based on multi-angular L-Band passive microwaves brightness temperatures, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 16, 5813–5827, https://doi.org/10.1109/JSTARS.2023.3285288, 2023. a
Santoro, M. and Cartus, O.: ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5.01, NERC EDS Centre for Environmental Data Analysis, https://doi.org/10.5285/bf535053562141c6bb7ad831f5998d77, 2024. a, b, c, d, e, f, g
Santoro, M., Cartus, O., Lucas, R., Kay, H., and Quegan, S.: CCI Biomass Algorithm Theoretical Basis Document v4, Tech. rep., European Space Agency [data set], https://climate.esa.int/media/documents/D2_2_Algorithm_Theoretical_Basis_Document_ATBD_V4.0_20230317.pdf (last access: 16 October 2024), 2023. a
Schwank, M., Zhou, Y., Mialon, A., Richaume, P., Kerr, Y., and Mätzler, C.: Temperature dependence of L-band vegetation optical depth over the boreal forest from 2011 to 2022, Remote Sens. Environ., 315, 114470, https://doi.org/10.1016/j.rse.2024.114470, 2024. a
Vittucci, C., Laurin, G. V., Tramontana, G., Ferrazzoli, P., Guerriero, L., and Papale, D.: Vegetation optical depth at L-band and above ground biomass in the tropical range: Evaluating their relationships at continental and regional scales, Int. J. Appl.Earth Obs. Geoinf., 77, 151–161, https://doi.org/10.1016/j.jag.2019.01.006, 2019. a
Wang, M., Fan, L., Frappart, F., Ciais, P., Sun, R., Liu, Y., Li, X., Liu, X., Moisy, C., and Wigneron, J.-P.: An alternative AMSR2 vegetation optical depth for monitoring vegetation at large scales, Remote Sens. Environ., 263, 112556, https://doi.org/10.1016/j.rse.2021.112556, 2021. a
Wear, D. N. and Coulston, J. W.: From sink to source: Regional variation in US forest carbon futures, Sci. Rep., 5, 1–11, https://doi.org/10.1038/srep16518, 2015. a
Wigneron, J., Fan, L., Ciais, P., Bastos, A., Brandt, M., Chave, J., Saatchi, S., Baccini, A., and Fensholt, R.: Tropical forests did not recover from the strong 2015–2016 El Niño event, Sci. Adv., 6, eaay4603, https://doi.org/10.1126/sciadv.aay4603, 2020. a, b
Wigneron, J.-P., Kerr, Y., Waldteufel, P., Saleh, K., Escorihuela, M.-J., Richaume, P., Ferrazzoli, P., de Rosnay, P., Gurney, R., Calvet, J.-C., Grant, J., Guglielmetti, M., Hornbuckle, B., Mätzler, C., Pellarin, T., and Schwank, M.: L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields, Remote Sens. Environ., 107, 639–655, https://doi.org/10.1016/j.rse.2006.10.014, 2007. a, b
Xu, L., Saatchi, S. S., Yang, Y., Yu, Y., Pongratz, J., Bloom, A. A., Bowman, K., Worden, J., Liu, J., Yin, Y., Domke, G., McRoberts, R. E., Woodall, C., Nabuurs, G.-J., de Miguel, S., Keller, M., Harris, N., Maxwell, S., and Schimel, D.: Dataset for “Changes in Global Terrestrial Live Biomass over the 21st Century”, Zenodo [data set], https://doi.org/10.5281/zenodo.4161694, 2021a. a, b, c
Xu, L., Saatchi, S. S., Yang, Y., Yu, Y., Pongratz, J., Bloom, A. A., Bowman, K., Worden, J., Liu, J., Yin, Y., Domke, G., McRoberts, R. E., Woodall, C., Nabuurs, G.-J., de Miguel, S., Keller, M., Harris, N., Maxwell, S., and Schimel, D.: Changes in global terrestrial live biomass over the 21st century, Sci. Adv., 7, eabe9829, https://doi.org/10.1126/sciadv.abe9829, 2021b. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s
Yu, Y. and Saatchi, S.: Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests, Remote Sens., 8, 522, https://doi.org/10.3390/rs8060522, 2016. a
Zeng, Y., Hao, D., Huete, A., Dechant, B., Berry, J., Chen, J. M., Joiner, J., Frankenberg, C., Bond-Lamberty, B., Ryu, Y., Xiao, J., Asrar, G. R., and Chen, M.: Optical vegetation indices for monitoring terrestrial ecosystems globally, Nat. Rev. Earth Environ., 3, 477–493, https://doi.org/10.1038/s43017-022-00298-5, 2022. a
Short summary
Aboveground biomass (AGB) is a critical component of the Earth's carbon cycle. The presented dataset aims to help monitor this essential climate variable with AGB time series from 2011 onward, derived with a carefully calibrated spatial relationship between the measurements of the Soil Moisture and Ocean Salinity (SMOS) mission and pre-existing AGB maps. The produced dataset has been extensively compared with other available AGB time series and can be used in AGB studies.
Aboveground biomass (AGB) is a critical component of the Earth's carbon cycle. The presented...
Altmetrics
Final-revised paper
Preprint