Articles | Volume 27, issue 1
https://doi.org/10.5194/hess-27-39-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-39-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jan 2023
Research article |
| 02 Jan 2023
| 02 Jan 2023
Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth
Matthias Forkel
CORRESPONDING AUTHOR
Faculty of Environmental Sciences, Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, 01062 Dresden, Germany
Luisa Schmidt
Faculty of Environmental Sciences, Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, 01062 Dresden, Germany
Ruxandra-Maria Zotta
Department of Geodesy and Geoinformation, Technische Universität Wien, 1040 Vienna, Austria
Wouter Dorigo
Department of Geodesy and Geoinformation, Technische Universität Wien, 1040 Vienna, Austria
Marta Yebra
Fenner School of Environment and Society, Australian National
University, ACT 2601 Canberra, Australia
School of Engineering, Australian National University, ACT 2601 Canberra, Australia
Related authors
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.
Evripidis Avouris, Christopher Marrs, Kristina Beetz, Lucie Kudláčková, Markéta Poděbradská, Miroslav Trnka, and Matthias Forkel
EGUsphere, https://doi.org/10.5194/egusphere-2025-4859, https://doi.org/10.5194/egusphere-2025-4859, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Wildfires are increasing in Central Europe. We studied how they could threaten settlements in the Saxon–Czech border region. Using satellite information, local data, and computer simulations, we mapped where fires are most likely and how intense they could be. We tested the model against a destructive fire that occurred in 2022. The results are shared in an interactive web map with the aim of helping residents and agencies improve preparedness and coordinate cross-border disaster response.
Adrianus de Laat, Vincent Huijnen, Niels Andela, and Matthias Forkel
EGUsphere, https://doi.org/10.5194/egusphere-2024-732, https://doi.org/10.5194/egusphere-2024-732, 2024
Preprint archived
Short summary
Short summary
This study assesses state-of-the art and more advanced and innovative satellite-observation-based (bottom-up) wildfire emission estimates. They are evaluated by comparison with satellite observation of single fire emission plumes. Results indicate that more advanced fire emission estimates – more information – are more realistic but that especially for a limited number of very large fires certain differences remain – for unknown reasons.
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.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
Short summary
Short summary
Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
Short summary
Short summary
Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
Short summary
Short summary
In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
Short summary
Short summary
Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Irene E. Teubner, Matthias Forkel, Benjamin Wild, Leander Mösinger, and Wouter Dorigo
Biogeosciences, 18, 3285–3308, https://doi.org/10.5194/bg-18-3285-2021, https://doi.org/10.5194/bg-18-3285-2021, 2021
Short summary
Short summary
Vegetation optical depth (VOD), which contains information on vegetation water content and biomass, has been previously shown to be related to gross primary production (GPP). In this study, we analyzed the impact of adding temperature as model input and investigated if this can reduce the previously observed overestimation of VOD-derived GPP. In addition, we could show that the relationship between VOD and GPP largely holds true along a gradient of dry or wet conditions.
Pierre Laluet, Chiara Corbari, Oscar Baez-Villanueva, Sophia Walther, Yongqiang Zhang, Joaquín Muñoz-Sabater, Gabriel B. Senay, Clément Albergel, and Wouter Dorigo
EGUsphere, https://doi.org/10.5194/egusphere-2025-5716, https://doi.org/10.5194/egusphere-2025-5716, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
We examined how well global datasets estimate evapotranspiration, the water moving from land to the air, in irrigated areas. We compared six widely used products with irrigation maps, satellite estimates, and field measurements across different climate regions. Some datasets better capture irrigation effects, especially those using temperature and vegetation satellite data. This work helps guide dataset selection and supports better inclusion of irrigation in hydrological and climate models.
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.
Evripidis Avouris, Christopher Marrs, Kristina Beetz, Lucie Kudláčková, Markéta Poděbradská, Miroslav Trnka, and Matthias Forkel
EGUsphere, https://doi.org/10.5194/egusphere-2025-4859, https://doi.org/10.5194/egusphere-2025-4859, 2025
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Wildfires are increasing in Central Europe. We studied how they could threaten settlements in the Saxon–Czech border region. Using satellite information, local data, and computer simulations, we mapped where fires are most likely and how intense they could be. We tested the model against a destructive fire that occurred in 2022. The results are shared in an interactive web map with the aim of helping residents and agencies improve preparedness and coordinate cross-border disaster response.
Wolfgang Preimesberger, Pietro Stradiotti, and Wouter Dorigo
Earth Syst. Sci. Data, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, https://doi.org/10.5194/essd-17-4305-2025, 2025
Short summary
Short summary
We introduce the official ESA CCI Soil Moisture GAPFILLED climate data record. A univariate interpolation algorithm is applied to predict missing data points without relying on ancillary variables. The dataset includes gap-free uncertainty estimates for all predictions and was validated with independent in situ reference measurements. Our data record is recommended for applications which require global long-term gap-free satellite soil moisture data.
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.
Bethan L. Harris, Christopher M. Taylor, Wouter Dorigo, Ruxandra-Maria Zotta, Darren Ghent, and Iván Noguera
EGUsphere, https://doi.org/10.5194/egusphere-2025-1489, https://doi.org/10.5194/egusphere-2025-1489, 2025
Short summary
Short summary
An improved understanding of land-atmosphere coupling processes during flash (rapid-onset) droughts is needed to aid the development of forecasts for these events. We use satellite observations to investigate the surface energy budget during flash droughts globally. The most intense events show a perturbed surface energy budget months before onset. In some regions, vegetation observations 1–2 months before onset provide information on the likelihood of heat extremes during an event.
Martin Hirschi, Pietro Stradiotti, Bas Crezee, Wouter Dorigo, and Sonia I. Seneviratne
Hydrol. Earth Syst. Sci., 29, 397–425, https://doi.org/10.5194/hess-29-397-2025, https://doi.org/10.5194/hess-29-397-2025, 2025
Short summary
Short summary
We investigate the potential of long-term satellite and reanalysis products for characterising soil drying by analysing their 2000–2022 soil moisture trends and their representation of agroecological drought events of this period. Soil moisture trends are globally diverse and partly contradictory between products. This also affects the products' drought-detection capacity. Based on the best-estimate products, consistent soil drying is observed over more than 40 % of the land area covered.
Ruxandra-Maria Zotta, Leander Moesinger, Robin van der Schalie, Mariette Vreugdenhil, Wolfgang Preimesberger, Thomas Frederikse, Richard de Jeu, and Wouter Dorigo
Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, https://doi.org/10.5194/essd-16-4573-2024, 2024
Short summary
Short summary
VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA v2 comprises two products: VODCA CXKu, spanning 34 years of observations (1987–2021), suitable for monitoring upper canopy dynamics, and VODCA L (2010–2021), for above-ground biomass monitoring. VODCA v2 has lower noise levels than the previous product version and provides valuable insights into plant water dynamics and biomass changes, even in areas where optical data are limited.
Adrianus de Laat, Vincent Huijnen, Niels Andela, and Matthias Forkel
EGUsphere, https://doi.org/10.5194/egusphere-2024-732, https://doi.org/10.5194/egusphere-2024-732, 2024
Preprint archived
Short summary
Short summary
This study assesses state-of-the art and more advanced and innovative satellite-observation-based (bottom-up) wildfire emission estimates. They are evaluated by comparison with satellite observation of single fire emission plumes. Results indicate that more advanced fire emission estimates – more information – are more realistic but that especially for a limited number of very large fires certain differences remain – for unknown reasons.
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023, https://doi.org/10.5194/hess-27-4087-2023, 2023
Short summary
Short summary
We explored different options for data assimilation (DA) of the remotely sensed leaf area index (LAI). We found strong biases between LAI predicted by Noah-MP and observations. LAI DA that does not take these biases into account can induce unphysical patterns in the resulting LAI and flux estimates and leads to large changes in the climatology of root zone soil moisture. We tested two bias-correction approaches and explored alternative solutions to treating bias in LAI DA.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
Short summary
Short summary
We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
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.
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.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
Short summary
Short summary
Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
Short summary
Short summary
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Leander Moesinger, Ruxandra-Maria Zotta, Robin van der Schalie, Tracy Scanlon, Richard de Jeu, and Wouter Dorigo
Biogeosciences, 19, 5107–5123, https://doi.org/10.5194/bg-19-5107-2022, https://doi.org/10.5194/bg-19-5107-2022, 2022
Short summary
Short summary
The standardized vegetation optical depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage, spans the past 3 decades and is derived from multiple spaceborne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data even if the data are not gap-free.
Robin van der Schalie, Mendy van der Vliet, Clément Albergel, Wouter Dorigo, Piotr Wolski, and Richard de Jeu
Hydrol. Earth Syst. Sci., 26, 3611–3627, https://doi.org/10.5194/hess-26-3611-2022, https://doi.org/10.5194/hess-26-3611-2022, 2022
Short summary
Short summary
Climate data records of surface soil moisture, vegetation optical depth, and land surface temperature can be derived from passive microwave observations. The ability of these datasets to properly detect anomalies and extremes is very valuable in climate research and can especially help to improve our insight in complex regions where the current climate reanalysis datasets reach their limitations. Here, we present a case study over the Okavango Delta, where we focus on inter-annual variability.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
Short summary
Short summary
Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
Stefan Schlaffer, Marco Chini, Wouter Dorigo, and Simon Plank
Hydrol. Earth Syst. Sci., 26, 841–860, https://doi.org/10.5194/hess-26-841-2022, https://doi.org/10.5194/hess-26-841-2022, 2022
Short summary
Short summary
Prairie wetlands are important for biodiversity and water availability. Knowledge about their variability and spatial distribution is of great use in conservation and water resources management. In this study, we propose a novel approach for the classification of small water bodies from satellite radar images and apply it to our study area over 6 years. The retrieved dynamics show the different responses of small and large wetlands to dry and wet periods.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
Short summary
Short summary
The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
Short summary
Short summary
In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
Short summary
Short summary
Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Irene E. Teubner, Matthias Forkel, Benjamin Wild, Leander Mösinger, and Wouter Dorigo
Biogeosciences, 18, 3285–3308, https://doi.org/10.5194/bg-18-3285-2021, https://doi.org/10.5194/bg-18-3285-2021, 2021
Short summary
Short summary
Vegetation optical depth (VOD), which contains information on vegetation water content and biomass, has been previously shown to be related to gross primary production (GPP). In this study, we analyzed the impact of adding temperature as model input and investigated if this can reduce the previously observed overestimation of VOD-derived GPP. In addition, we could show that the relationship between VOD and GPP largely holds true along a gradient of dry or wet conditions.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
Short summary
Short summary
We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Cited articles
Abbott, K. N., Leblon, B., Staples, G. C., Maclean, D. A., and Alexander, M.
E.: Fire danger monitoring using RADARSAT-1 over northern boreal forests,
Int. J. Remote Sens., 28, 1317–1338, https://doi.org/10.1080/01431160600904956, 2007.
Bonan, G.: Ecological Climatology: Concepts and Applications, 3rd Edn.,
Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9781107339200, 2015.
Bowyer, P. and Danson, F. M.: Sensitivity of spectral reflectance to variation in live fuel moisture content at leaf and canopy level, Remote
Sens. Environ., 92, 297–308, https://doi.org/10.1016/j.rse.2004.05.020, 2004.
Caccamo, G., Chisholm, L. A., Bradstock, R. A., Puotinen, M. L., Pippen, B.
G., Caccamo, G., Chisholm, L. A., Bradstock, R. A., Puotinen, M. L., and Pippen, B. G.: Monitoring live fuel moisture content of heathland, shrubland
and sclerophyll forest in south-eastern Australia using MODIS data, Int. J.
Wildland Fire, 21, 257–269, https://doi.org/10.1071/WF11024, 2011.
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.
Chuvieco, E., Riaño, D., Aguado, I., and Cocero, D.: Estimation of fuel
moisture content from multitemporal analysis of Landsat Thematic Mapper
reflectance data: Applications in fire danger assessment, Int. J. Remote
Sens., 23, 2145–2162, https://doi.org/10.1080/01431160110069818, 2002.
Chuvieco, E., Aguado, I., Yebra, M., Nieto, H., Salas, J., Martín, M. P., Vilar, L., Martínez, J., Martín, S., Ibarra, P., de la Riva, J., Baeza, J., Rodríguez, F., Molina, J. R., Herrera, M. A., and Zamora, R.: Development of a framework for fire risk assessment using remote sensing and geographic information system technologies, Ecol. Model., 221, 46–58, https://doi.org/10.1016/j.ecolmodel.2008.11.017, 2010.
Cleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I.: STL: A
Seasonal-Trend Decomposition Procedure Based on Loess, J. Off. Stat., 6, 3–73, 1990.
Crocetti, L., Forkel, M., Fischer, M., Jurečka, F., Grlj, A., Salentinig, A., Trnka, M., Anderson, M., Ng, W.-T., Kokalj, Ž., Bucur, A., and Dorigo, W.: Earth Observation for agricultural drought monitoring in the Pannonian Basin (southeastern Europe): current state and future directions, Reg. Environ. Change, 20, 123.1–123.17, https://doi.org/10.1007/s10113-020-01710-w, 2020.
Danson, F. M. and Bowyer, P.: Estimating live fuel moisture content from
remotely sensed reflectance, Remote Sens. Environ., 92, 309–321,
https://doi.org/10.1016/j.rse.2004.03.017, 2004.
DeSoto, L., Cailleret, M., Sterck, F., Jansen, S., Kramer, K., Robert, E. M.
R., Aakala, T., Amoroso, M. M., Bigler, C., Camarero, J. J., Čufar, K.,
Gea-Izquierdo, G., Gillner, S., Haavik, L. J., Hereş, A.-M., Kane, J.
M., Kharuk, V. I., Kitzberger, T., Klein, T., Levanič, T., Linares, J.
C., Mäkinen, H., Oberhuber, W., Papadopoulos, A., Rohner, B., Sangüesa-Barreda, G., Stojanovic, D. B., Suárez, M. L., Villalba,
R., and Martínez-Vilalta, J.: Low growth resilience to drought is related to future mortality risk in trees, Nat. Commun., 11, 1–9,
https://doi.org/10.1038/s41467-020-14300-5, 2020.
de Nijs, A. H. A., Parinussa, R. M., de Jeu, R. A. M., Schellekens, J., and
Holmes, T. R. H.: A Methodology to Determine Radio-Frequency Interference in
AMSR2 Observations, IEEE T. Geosci. Remote, 53, 5148–5159,
https://doi.org/10.1109/TGRS.2015.2417653, 2015.
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L.,
Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, P. D., Hirschi, M., Ikonen, J., de Jeu, R., Kidd, R., Lahoz, W., Liu, Y. Y., Miralles, D., Mistelbauer, T., Nicolai-Shaw, N., Parinussa, R., Pratola, C., Reimer, C., van der Schalie, R., Seneviratne, S. I., Smolander, T., and Lecomte, P.: ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions, Remote Sens. Environ., 203, 185–215, https://doi.org/10.1016/j.rse.2017.07.001, 2017.
Fan, L., Wigneron, J.-P., Xiao, Q., Al-Yaari, A., Wen, J., Martin-StPaul, N., Dupuy, J.-L., Pimont, F., Al Bitar, A., Fernandez-Moran, R., and Kerr, Y. H.: Evaluation of microwave remote sensing for monitoring live fuel moisture content in the Mediterranean region, Remote Sens. Environ., 205, 210–223, https://doi.org/10.1016/j.rse.2017.11.020, 2018.
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas, Int. J. Climatol., 37, 4302–4315,
https://doi.org/10.1002/joc.5086, 2017.
Forkel, M., Dorigo, W., Lasslop, G., Teubner, I., Chuvieco, E., and Thonicke, K.: A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1), Geosci. Model Dev., 10, 4443–4476, https://doi.org/10.5194/gmd-10-4443-2017, 2017.
Forkel, M., Dorigo, W. A., Lasslop, G., Chuvieco, E., Hantson, S., Heil, A.,
Teubner, I., Thonicke, K., and Harrison, S. P.: Recent global and regional
trends in burned area and their compensating environmental controls, Environ. Res. Commun., 1, 051005, https://doi.org/10.1088/2515-7620/ab25d2, 2019.
Forkel, M., Schmidt, L., Zotta, R.-M., Dorigo, W., and Yebra, M.: Leaf moisture content (live-fuel moisture content) at global scale from passive microwave satellite observations of vegetation optical depth (VOD2LFMC), Zenodo [data set], https://doi.org/10.5281/zenodo.6545571, 2022.
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.
García, M., Chuvieco, E., Nieto, H., and Aguado, I.: Combining AVHRR and meteorological data for estimating live fuel moisture content, Remote Sens. Environ., 112, 3618–3627, https://doi.org/10.1016/j.rse.2008.05.002, 2008.
Global Drought Observatory – JRC European Commission: EDO and GDO Data Download, https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2112, last access: 18 July 2022.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition
of the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Hantson, S., Kelley, D. I., Arneth, A., Harrison, S. P., Archibald, S., Bachelet, D., Forrest, M., Hickler, T., Lasslop, G., Li, F., Mangeon, S.,
Melton, J. R., Nieradzik, L., Rabin, S. S., Prentice, I. C., Sheehan, T.,
Sitch, S., Teckentrup, L., Voulgarakis, A., and Yue, C.: Quantitative
assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project, Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, 2020.
Holtzman, N. M., Anderegg, L. D. L., Kraatz, S., Mavrovic, A., Sonnentag, O., Pappas, C., Cosh, M. H., Langlois, A., Lakhankar, T., Tesser, D., Steiner, N., Colliander, A., Roy, A., and Konings, A. G.: L-band vegetation optical depth as an indicator of plant water potential in a temperate deciduous forest stand, Biogeosciences, 18, 739–753, https://doi.org/10.5194/bg-18-739-2021, 2021.
Hovmöller, E.: The Trough-and-Ridge diagram, Tellus, 1, 62–66,
https://doi.org/10.3402/tellusa.v1i2.8498, 1949.
Jackson, T. J. and Schmugge, T. J.: 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.
Jackson, T. J., Schmugge, T. J., and Wang, J. R.: Passive microwave sensing
of soil moisture under vegetation canopies, Water Resour. Res., 18, 1137–1142, https://doi.org/10.1029/WR018i004p01137, 1982.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled seamless
SRTM data V4, CIAT – International Centre for Tropical Agriculture, CGIAR [data set], https://cgiarcsi.community/data/srtm-90m-digital-elevation-database-v4-1/
(last access: 22 December 2022), 2008.
Jarvis, P. G.: The interpretation of the variations in leaf water potential
and stomatal conductance found in canopies in the field, Philos. T. Roy. Soc. Lond. B, 273, 593–610, https://doi.org/10.1098/rstb.1976.0035, 1976.
Jia, S., Kim, S. H., Nghiem, S. V., and Kafatos, M.: Estimating Live Fuel
Moisture Using SMAP L-Band Radiometer Soil Moisture for Southern California,
USA, Remote Sens., 11, 1575, https://doi.org/10.3390/rs11131575, 2019.
Jiao, W., Wang, L., and McCabe, M. F.: Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for
the future, Remote Sens. Environ., 256, 112313, https://doi.org/10.1016/j.rse.2021.112313, 2021.
Jolly, W. M., Cochrane, M. A., Freeborn, P. H., Holden, Z. A., Brown, T. J.,
Williamson, G. J., and Bowman, D. M. J. S.: Climate-induced variations in
global wildfire danger from 1979 to 2013, Nat. Commun., 6, 7537,
https://doi.org/10.1038/ncomms8537, 2015.
Konings, A. G., Rao, K., and Steele-Dunne, S. C.: Macro to micro: microwave
remote sensing of plant water content for physiology and ecology, New Phytol., 223, 1166–1172, https://doi.org/10.1111/nph.15808, 2019.
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, Global Change Biol., 27, 6005–6024, https://doi.org/10.1111/gcb.15872, 2021a.
Konings, A. G., Holtzman, N. M., Rao, K., Xu, L., and Saatchi, S. S.: Interannual Variations of Vegetation Optical Depth are Due to Both Water
Stress and Biomass Changes, Geophys. Res. Lett., 48, e2021GL095267,
https://doi.org/10.1029/2021GL095267, 2021b.
Kuhn-Régnier, A., Voulgarakis, A., Nowack, P., Forkel, M., Prentice, I.
C., and Harrison, S. P.: The importance of antecedent vegetation and drought
conditions as global drivers of burnt area, Biogeosciences, 18, 3861–3879,
https://doi.org/10.5194/bg-18-3861-2021, 2021.
Leblon, B., Kasischke, E., Alexander, M., Doyle, M., and Abbott, M.: Fire
Danger Monitoring Using ERS-1 SAR Images in the Case of Northern Boreal
Forests, Nat. Hazards, 27, 231–255, https://doi.org/10.1023/A:1020375721520, 2002.
Li, F., Val Martin, M., Andreae, M. O., Arneth, A., Hantson, S., Kaiser, J.
W., Lasslop, G., Yue, C., Bachelet, D., Forrest, M., Kluzek, E., Liu, X.,
Mangeon, S., Melton, J. R., Ward, D. S., Darmenov, A., Hickler, T., Ichoku,
C., Magi, B. I., Sitch, S., van der Werf, G. R., Wiedinmyer, C., and Rabin,
S. S.: Historical (1700–2012) global multi-model estimates of the fire
emissions from the Fire Modeling Intercomparison Project (FireMIP), Atmos. Chem. Phys., 19, 12545–12567, https://doi.org/10.5194/acp-19-12545-2019, 2019.
Li, W., Ciais, P., MacBean, N., Peng, S., Defourny, P., and Bontemps, S.:
Major forest changes and land cover transitions based on plant functional
types derived from the ESA CCI Land Cover product, Int. J. Appl. Earth Obs.
Geoinf., 47, 30–39, https://doi.org/10.1016/j.jag.2015.12.006, 2016.
Li, W., Migliavacca, M., Forkel, M., Walther, S., Reichstein, M., and Orth,
R.: Revisiting Global Vegetation Controls Using Multi-Layer Soil Moisture,
Geophys. Res. Lett., 48, e2021GL092856, https://doi.org/10.1029/2021GL092856, 2021.
Li, X., Wigneron, J.-P., Frappart, F., Fan, L., Ciais, P., Fensholt, R.,
Entekhabi, D., Brandt, M., Konings, A. G., Liu, X., Wang, M., Al-Yaari, A.,
and Moisy, C.: Global-scale assessment and inter-comparison of recently
developed/reprocessed microwave satellite vegetation optical depth products,
Remote Sens. Environ., 253, 112208, https://doi.org/10.1016/j.rse.2020.112208, 2021.
Liaw, A. and Wiener, M.: Classification and Regression by randomForest, R News, 2, 18–22, 2002.
Lu, Y. and Wei, C.: Evaluation of microwave soil moisture data for monitoring live fuel moisture content (LFMC) over the coterminous United States, Sci. Total Environ., 771, 145410, https://doi.org/10.1016/j.scitotenv.2021.145410, 2021.
Matthews, S.: Dead fuel moisture research: 1991–2012, Int. J. Wildland Fire, 23, 78–92, 2014.
McDowell, N. G.: Mechanisms Linking Drought, Hydraulics, Carbon Metabolism, and Vegetation Mortality, Plant Physiol., 155, 1051–1059, https://doi.org/10.1104/pp.110.170704, 2011.
Mebane, W. R. and Sekhon, J. S.: Genetic Optimization Using Derivatives: The
rgenoud Package for R, J. Stat. Softw., 42, 1–26, https://doi.org/10.18637/jss.v042.i11, 2011.
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.
Mo, T., Choudhury, B. J., Schmugge, T. J., Wang, J. R., and Jackson, T. J.: A model for microwave emission from vegetation-covered fields, J. Geophys.
Res.-Oceans, 87, 11229–11237, https://doi.org/10.1029/JC087iC13p11229, 1982.
Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T.,
Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177–196,
https://doi.org/10.5194/essd-12-177-2020, 2020.
Momen, M., Wood, J. D., Novick, K. A., Pangle, R., Pockman, W. T., McDowell,
N. G., and Konings, A. G.: Interacting Effects of Leaf Water Potential and
Biomass on Vegetation Optical Depth, J. Geophys. Res.-Biogeo., 122, 3031–3046, https://doi.org/10.1002/2017JG004145, 2017.
Myneni, R. B., Knyazikhin, Y., and Park, T.: MOD15A2 MODIS/Terra Leaf Area
Index/FPAR 8-Day L4 Global 1 km SIN Grid, Boston University and MODAPS SIPS,
NASA [data set], https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD15A2/
(last acess: 22 December 2022), 2015.
Njoku, E., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S. V.: Soil
moisture retrieval from AMSR-E, IEEE T. Geosci. Remote, 41, 215–229, 2003.
Njoku, E. G. and Entekhabi, D.: Passive microwave remote sensing of soil
moisture, J. Hydrol., 184, 101–129, https://doi.org/10.1016/0022-1694(95)02970-2, 1996.
Nolan, R. H., Boer, M. M., Resco de Dios, V., Caccamo, G., and Bradstock, R.
A.: Large-scale, dynamic transformations in fuel moisture drive wildfire
activity across southeastern Australia, Geophys. Res. Lett., 43, 2016GL068614, https://doi.org/10.1002/2016GL068614, 2016.
Owe, M., de Jeu, R., and Walker, J.: A methodology for surface soil moisture
and vegetation optical depth retrieval using the microwave polarization
difference index, IEEE T. Geosci. Remote, 39, 1643–1654, https://doi.org/10.1109/36.942542, 2001.
Paloscia, S. and Pampaloni, P.: Microwave polarization index for monitoring
vegetation growth, IEEE T. Geosci. Remote, 26, 617–621, https://doi.org/10.1109/36.7687, 1988.
Poulter, B., MacBean, N., Hartley, A., Khlystova, I., Arino, O., Betts, R.,
Bontemps, S., Boettcher, M., Brockmann, C., Defourny, P., Hagemann, S., Herold, M., Kirches, G., Lamarche, C., Lederer, D., Ottlé, C., Peters,
M., and Peylin, P.: Plant functional type classification for earth system
models: results from the European Space Agency's Land Cover Climate Change
Initiative, Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, 2015.
Quan, X., Yebra, M., Riaño, D., He, B., Lai, G., and Liu, X.: Global
fuel moisture content mapping from MODIS, Int. J. Appl. Earth Obs. Geoinf., 101, 102354, https://doi.org/10.1016/j.jag.2021.102354, 2021.
Rabin, S. S., Melton, J. R., Lasslop, G., Bachelet, D., Forrest, M., Hantson, S., Kaplan, J. O., Li, F., Mangeon, S., Ward, D. S., Yue, C., Arora, V. K., Hickler, T., Kloster, S., Knorr, W., Nieradzik, L., Spessa, A., Folberth, G. A., Sheehan, T., Voulgarakis, A., Kelley, D. I., Prentice, I. C., Sitch, S., Harrison, S., and Arneth, A.: The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions, Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, 2017.
Rao, K., Williams, A. P., Flefil, J. F., and Konings, A. G.: SAR-enhanced
mapping of live fuel moisture content, Remote Sens. Environ., 245, 111797,
https://doi.org/10.1016/j.rse.2020.111797, 2020.
Riano, D., Vaughan, P., Chuvieco, E., Zarco-Tejada, P. J., and Ustin, S. L.:
Estimation of fuel moisture content by inversion of radiative transfer models to simulate equivalent water thickness and dry matter content: analysis at leaf and canopy level, IEEE T. Geosci. Remote, 43, 819–826, https://doi.org/10.1109/TGRS.2005.843316, 2005.
Rodríguez-Fernández, N. J., Mialon, A., Mermoz, S., Bouvet, A.,
Richaume, P., Bitar, A. A., Al-Yaari, A., Brandt, M., Kaminski, T., Toan, T.
L., 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.
Saatchi, S. S. and Moghaddam, M.: Estimation of crown and stem water content
and biomass of boreal forest using polarimetric SAR imagery, IEEE T. Geosci. Remote, 38, 697–709, https://doi.org/10.1109/36.841999, 2000.
Sawada, Y., Tsutsui, H., Koike, T., Rasmy, M., Seto, R., and Fujii, H.: A
Field Verification of an Algorithm for Retrieving Vegetation Water Content
From Passive Microwave Observations, IEEE T. Geosci. Remote, 54, 2082–2095, https://doi.org/10.1109/TGRS.2015.2495365, 2016.
Sawada, Y., Koike, T., Aida, K., Toride, K., and Walker, J. P.: Fusing Microwave and Optical Satellite Observations to Simultaneously Retrieve Surface Soil Moisture, Vegetation Water Content, and Surface Soil Roughness,
IEEE T. Geosci. Remote, 55, 6195–6206, https://doi.org/10.1109/TGRS.2017.2722468, 2017.
Scholze, M., Buchwitz, M., Dorigo, W., Guanter, L., and Quegan, S.: Reviews
and syntheses: Systematic Earth observations for use in terrestrial carbon
cycle data assimilation systems, Biogeosciences, 14, 3401–3429,
https://doi.org/10.5194/bg-14-3401-2017, 2017.
Sippel, S., Reichstein, M., Ma, X., Mahecha, M. D., Lange, H., Flach, M.,
and Frank, D.: Drought, Heat, and the Carbon Cycle: a Review, Curr. Clim.
Change Rep., 4, 266–286, https://doi.org/10.1007/s40641-018-0103-4, 2018.
Song, X.-P., Hansen, M. C., Stehman, S. V., Potapov, P. V., Tyukavina, A.,
Vermote, E. F., and Townshend, J. R.: Global land change from 1982 to 2016,
Nature, 560, 639–643, https://doi.org/10.1038/s41586-018-0411-9, 2018.
Stocks, B. J., Fosberg, M. A., Lynham, T. J., Mearns, L., Wotton, M., Yang,
Q., Jin, J. Z., Lawrence, K., Hartley, G. R., Mason, J. A., and McKenney, D.
W.: Climate Change and Forest Fire Potential in Russian and Canadian Boreal
Forests, Climatic Change, 38, 1–13, https://doi.org/10.1023/a:1005306001055, 1998.
Thonicke, K., Spessa, A., Prentice, I. C., Harrison, S. P., Dong, L., and Carmona-Moreno, C.: The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model, Biogeosciences, 7, 1991–2011, https://doi.org/10.5194/bg-7-1991-2010, 2010.
Tian, F., Brandt, M., Liu, Y. Y., Verger, A., Tagesson, T., Diouf, A. A.,
Rasmussen, K., Mbow, C., Wang, Y., and Fensholt, R.: Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD)
using AVHRR NDVI and in situ green biomass data over West African Sahel,
Remote Sens. Environ., 177, 265–276, https://doi.org/10.1016/j.rse.2016.02.056, 2016.
Ulaby, F., Bradley, G. A., and Dobson, M.: Microwave Backscatter Dependence
on Surface Roughness, Soil Moisture, and Soil Texture: Par II – Vegetation-Covered Soil, IEEE T. Geosci. Electron., 17, 33–40, https://doi.org/10.1109/TGE.1979.294626, 1979.
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave remote sensing: Active and Passive. Volume 1 – Microwave remote sensing fundamentals and radiometry, Artech House Publishers, Norwood, MA, USA, ISBN 10:0890061904,
ISBN 13:978-0890061909, 1981.
US Drought Monitor: Drought Severity and Coverage Index, US Drought Monitor [data set], https://droughtmonitor.unl.edu/DmData/DataDownload/DSCI.aspx, last access: 18 July 2022.
van der Schalie, R., de Jeu, R. A. M., Kerr, Y. H., Wigneron, J. P.,
Rodríguez-Fernández, N. J., Al-Yaari, A., Parinussa, R. M., Mecklenburg, S., and Drusch, M.: The merging of radiative transfer based
surface soil moisture data from SMOS and AMSR-E, Remote Sens. Environ., 189,
180–193, https://doi.org/10.1016/j.rse.2016.11.026, 2017.
Viney, N.: A Review of Fine Fuel Moisture Modelling, Int. J. Wildland Fire, 1, 215–234, https://doi.org/10.1071/WF9910215, 1991.
Wang, L., Quan, X., He, B., Yebra, M., Xing, M., and Liu, X.: Assessment of
the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content
Estimation, Remote Sens., 11, 1568, https://doi.org/10.3390/rs11131568, 2019.
Wang, M., Wigneron, J.-P., Sun, R., Fan, L., Frappart, F., Tao, S., Chai, L., Li, X., Liu, X., Ma, H., Moisy, C., and Ciais, P.: A consistent record of vegetation optical depth retrieved from the AMSR-E and AMSR2 X-band observations, Int. J. Appl. Earth Obs. Geoinf., 105, 102609,
https://doi.org/10.1016/j.jag.2021.102609, 2021.
Wigneron, J.-P., Schmugge, T., Chanzy, A., Calvet, J.-C., and Kerr, Y.: Use
of passive microwave remote sensing to monitor soil moisture, Agronomie, 18,
27–43, https://doi.org/10.1051/agro:19980102, 1998.
Wigneron, J.-P., Li, X., Frappart, F., Fan, L., Al-Yaari, A., De Lannoy, G.,
Liu, X., Wang, M., Le Masson, E., and Moisy, C.: SMOS-IC data record of soil
moisture and L-VOD: Historical development, applications and perspectives,
Remote Sens. Environ., 254, 112238, https://doi.org/10.1016/j.rse.2020.112238, 2021.
Wild, B., Teubner, I., Moesinger, L., Zotta, R.-M., Forkel, M., van der
Schalie, R., Sitch, S., and Dorigo, W.: VODCA2GPP – a new, global, long-term (1988–2020) gross primary production dataset from microwave remote sensing, Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, 2022.
Yebra, M., Chuvieco, E., and Riaño, D.: Estimation of live fuel moisture
content from MODIS images for fire risk assessment, Agr. Forest Meteorol.,
148, 523–536, https://doi.org/10.1016/j.agrformet.2007.12.005, 2008.
Yebra, M., Dennison, P. E., Chuvieco, E., Riaño, D., Zylstra, P., Hunt
Jr., E. R., Danson, F. M., Qi, Y., and Jurdao, S.: A global review of remote
sensing of live fuel moisture content for fire danger assessment: Moving
towards operational products, Remote Sens. Environ., 136, 455–468,
https://doi.org/10.1016/j.rse.2013.05.029, 2013.
Yebra, M., Quan, X., Riaño, D., Rozas Larraondo, P., van Dijk, A. I. J. M., and Cary, G. J.: A fuel moisture content and flammability monitoring
methodology for continental Australia based on optical remote sensing, Remote Sens. Environ., 212, 260–272, https://doi.org/10.1016/j.rse.2018.04.053, 2018.
Yebra, M., Scortechini, G., Badi, A., Beget, M. E., Boer, M. M., Bradstock,
R., Chuvieco, E., Danson, F. M., Dennison, P., de Dios, V. R., Bella, C. M.
D., Forsyth, G., Frost, P., Garcia, M., Hamdi, A., He, B., Jolly, M.,
Kraaij, T., Martín, M. P., Mouillot, F., Newnham, G., Nolan, R. H., Pellizzaro, G., Qi, Y., Quan, X., Riaño, D., Roberts, D., Sow, M., and
Ustin, S.: Globe-LFMC, a global plant water status database for vegetation
ecophysiology and wildfire applications, Sci. Data, 6, 1–8,
https://doi.org/10.1038/s41597-019-0164-9, 2019.
Zhang, Y., Zhou, S., Gentine, P., and Xiao, X.: Can vegetation optical depth
reflect changes in leaf water potential during soil moisture dry-down events?, Remote Sens. Environ., 234, 111451, https://doi.org/10.1016/j.rse.2019.111451, 2019.
Zhu, L., Webb, G. I., Yebra, M., Scortechini, G., Miller, L., and Petitjean,
F.: Live fuel moisture content estimation from MODIS: A deep learning
approach, ISPRS J. Photogram. Remote Sens., 179, 81–91,
https://doi.org/10.1016/j.isprsjprs.2021.07.010, 2021.
Short summary
The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.
The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We...
