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
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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
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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
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Leander Moesinger, Ruxandra-Maria Zotta, Robin van der Schalie, Tracy Scanlon, Richard de Jeu, and Wouter Dorigo
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Robin van der Schalie, Mendy van der Vliet, Clément Albergel, Wouter Dorigo, Piotr Wolski, and Richard de Jeu
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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
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Stefan Schlaffer, Marco Chini, Wouter Dorigo, and Simon Plank
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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
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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
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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.
Kurt C. Solander, Brent D. Newman, Alessandro Carioca de Araujo, Holly R. Barnard, Z. Carter Berry, Damien Bonal, Mario Bretfeld, Benoit Burban, Luiz Antonio Candido, Rolando Célleri, Jeffery Q. Chambers, Bradley O. Christoffersen, Matteo Detto, Wouter A. Dorigo, Brent E. Ewers, Savio José Filgueiras Ferreira, Alexander Knohl, L. Ruby Leung, Nate G. McDowell, Gretchen R. Miller, Maria Terezinha Ferreira Monteiro, Georgianne W. Moore, Robinson Negron-Juarez, Scott R. Saleska, Christian Stiegler, Javier Tomasella, and Chonggang Xu
Hydrol. Earth Syst. Sci., 24, 2303–2322, https://doi.org/10.5194/hess-24-2303-2020, https://doi.org/10.5194/hess-24-2303-2020, 2020
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We evaluate the soil moisture response in the humid tropics to El Niño during the three most recent super El Niño events. Our estimates are compared to in situ soil moisture estimates that span five continents. We find the strongest and most consistent soil moisture decreases in the Amazon and maritime southeastern Asia, while the most consistent increases occur over eastern Africa. Our results can be used to improve estimates of soil moisture in tropical ecohydrology models at multiple scales.
Angelika Xaver, Luca Zappa, Gerhard Rab, Isabella Pfeil, Mariette Vreugdenhil, Drew Hemment, and Wouter Arnoud Dorigo
Geosci. Instrum. Method. Data Syst., 9, 117–139, https://doi.org/10.5194/gi-9-117-2020, https://doi.org/10.5194/gi-9-117-2020, 2020
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Miguel D. Mahecha, Fabian Gans, Gunnar Brandt, Rune Christiansen, Sarah E. Cornell, Normann Fomferra, Guido Kraemer, Jonas Peters, Paul Bodesheim, Gustau Camps-Valls, Jonathan F. Donges, Wouter Dorigo, Lina M. Estupinan-Suarez, Victor H. Gutierrez-Velez, Martin Gutwin, Martin Jung, Maria C. Londoño, Diego G. Miralles, Phillip Papastefanou, and Markus Reichstein
Earth Syst. Dynam., 11, 201–234, https://doi.org/10.5194/esd-11-201-2020, https://doi.org/10.5194/esd-11-201-2020, 2020
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The ever-growing availability of data streams on different subsystems of the Earth brings unprecedented scientific opportunities. However, researching a data-rich world brings novel challenges. We present the concept of
Earth system data cubesto study the complex dynamics of multiple climate and ecosystem variables across space and time. Using a series of example studies, we highlight the potential of effectively considering the full multivariate nature of processes in the Earth system.
Leander Moesinger, Wouter Dorigo, Richard de Jeu, Robin van der Schalie, Tracy Scanlon, Irene Teubner, and Matthias Forkel
Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, https://doi.org/10.5194/essd-12-177-2020, 2020
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Vegetation optical depth (VOD) is measured by satellites and is related to the density of vegetation and its water content. VOD has a wide range of uses, including drought, wildfire danger, biomass, and carbon stock monitoring. For the past 30 years there have been various VOD data sets derived from space-borne microwave sensors, but biases between them prohibit a combined use. We removed these biases and merged the data to create the global long-term VOD Climate Archive (VODCA).
Markus Drüke, Matthias Forkel, Werner von Bloh, Boris Sakschewski, Manoel Cardoso, Mercedes Bustamante, Jürgen Kurths, and Kirsten Thonicke
Geosci. Model Dev., 12, 5029–5054, https://doi.org/10.5194/gmd-12-5029-2019, https://doi.org/10.5194/gmd-12-5029-2019, 2019
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This work shows the successful application of a systematic model–data integration setup, as well as the implementation of a new fire danger formulation, in order to optimize a process-based fire-enabled dynamic global vegetation model. We have demonstrated a major improvement in the fire representation within LPJmL4-SPITFIRE in terms of the spatial pattern and the interannual variability of burned area in South America as well as in the modelling of biomass and the distribution of plant types.
Alexander Gruber, Tracy Scanlon, Robin van der Schalie, Wolfgang Wagner, and Wouter Dorigo
Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019, https://doi.org/10.5194/essd-11-717-2019, 2019
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Soil moisture is a key variable in our Earth system. Knowledge of soil moisture and its dynamics across scales is vital for many applications such as the prediction of agricultural yields or irrigation demands, flood and drought monitoring, weather forecasting and climate modelling. To date, the ESA CCI SM products are the only consistent long-term multi-satellite soil moisture data sets available. This paper reviews the evolution of these products and their underlying merging methodology.
Felix Zaussinger, Wouter Dorigo, Alexander Gruber, Angelica Tarpanelli, Paolo Filippucci, and Luca Brocca
Hydrol. Earth Syst. Sci., 23, 897–923, https://doi.org/10.5194/hess-23-897-2019, https://doi.org/10.5194/hess-23-897-2019, 2019
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About 70 % of global freshwater is consumed by irrigation. Yet, policy-relevant estimates of irrigation water use (IWU) are virtually lacking at regional to global scales. To bridge this gap, we develop a method for quantifying IWU from a combination of state-of-the-art remotely sensed and modeled soil moisture products and apply it over the United States for the period 2013–2016. Overall, our estimates agree well with reference data on irrigated area and irrigation water withdrawals.
Victor Pellet, Filipe Aires, Simon Munier, Diego Fernández Prieto, Gabriel Jordá, Wouter Arnoud Dorigo, Jan Polcher, and Luca Brocca
Hydrol. Earth Syst. Sci., 23, 465–491, https://doi.org/10.5194/hess-23-465-2019, https://doi.org/10.5194/hess-23-465-2019, 2019
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This study is an effort for a better understanding and quantification of the water cycle and related processes in the Mediterranean region, by dealing with satellite products and their uncertainties. The aims of the paper are 3-fold: (1) developing methods with hydrological constraints to integrate all the datasets, (2) giving the full picture of the Mediterranean WC, and (3) building a model-independent database that can evaluate the numerous regional climate models (RCMs) for this region.
Matthias Forkel, Niels Andela, Sandy P. Harrison, Gitta Lasslop, Margreet van Marle, Emilio Chuvieco, Wouter Dorigo, Matthew Forrest, Stijn Hantson, Angelika Heil, Fang Li, Joe Melton, Stephen Sitch, Chao Yue, and Almut Arneth
Biogeosciences, 16, 57–76, https://doi.org/10.5194/bg-16-57-2019, https://doi.org/10.5194/bg-16-57-2019, 2019
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Weather, humans, and vegetation control the occurrence of fires. In this study we find that global fire–vegetation models underestimate the strong increase of burned area with higher previous-season plant productivity in comparison to satellite-derived relationships.
Albert I. J. M. van Dijk, Jaap Schellekens, Marta Yebra, Hylke E. Beck, Luigi J. Renzullo, Albrecht Weerts, and Gennadii Donchyts
Hydrol. Earth Syst. Sci., 22, 4959–4980, https://doi.org/10.5194/hess-22-4959-2018, https://doi.org/10.5194/hess-22-4959-2018, 2018
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Evaporation from wetlands, lakes and irrigation areas needs to be measured to understand water scarcity. So far, this has only been possible for small regions. Here, we develop a solution that can be applied at a very high resolution globally by making use of satellite observations. Our results show that 16% of global water resources evaporate before reaching the ocean, mostly from surface water. Irrigation water use is less than 1% globally but is a very large water user in several dry basins.
Sibyll Schaphoff, Werner von Bloh, Anja Rammig, Kirsten Thonicke, Hester Biemans, Matthias Forkel, Dieter Gerten, Jens Heinke, Jonas Jägermeyr, Jürgen Knauer, Fanny Langerwisch, Wolfgang Lucht, Christoph Müller, Susanne Rolinski, and Katharina Waha
Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, https://doi.org/10.5194/gmd-11-1343-2018, 2018
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Here we provide a comprehensive model description of a global terrestrial biosphere model, named LPJmL4, incorporating the carbon and water cycle and the quantification of agricultural production. The model allows for the consistent and joint quantification of climate and land use change impacts on the biosphere. The model represents the key ecosystem functions, but also the influence of humans on the biosphere. It comes with an evaluation paper to demonstrate the credibility of LPJmL4.
Sibyll Schaphoff, Matthias Forkel, Christoph Müller, Jürgen Knauer, Werner von Bloh, Dieter Gerten, Jonas Jägermeyr, Wolfgang Lucht, Anja Rammig, Kirsten Thonicke, and Katharina Waha
Geosci. Model Dev., 11, 1377–1403, https://doi.org/10.5194/gmd-11-1377-2018, https://doi.org/10.5194/gmd-11-1377-2018, 2018
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Here we provide a comprehensive evaluation of the now launched version 4.0 of the LPJmL biosphere, water, and agricultural model. The article is the second part to a comprehensive description of the LPJmL4 model. We have evaluated the model against various datasets of satellite observations, agricultural statistics, and in situ measurements by applying a range of metrics. We are able to show that the LPJmL4 model simulates many parameters and relations reasonably.
Luca Ciabatta, Christian Massari, Luca Brocca, Alexander Gruber, Christoph Reimer, Sebastian Hahn, Christoph Paulik, Wouter Dorigo, Richard Kidd, and Wolfgang Wagner
Earth Syst. Sci. Data, 10, 267–280, https://doi.org/10.5194/essd-10-267-2018, https://doi.org/10.5194/essd-10-267-2018, 2018
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In this study, rainfall is estimated starting from satellite soil moisture observation on a global scale, using the ESA CCI soil moisture datasets. The new obtained rainfall product has proven to correctly identify rainfall events, showing performance sometimes higher than those obtained by using classical rainfall estimation approaches.
Matthias Forkel, Wouter Dorigo, Gitta Lasslop, Irene Teubner, Emilio Chuvieco, and Kirsten Thonicke
Geosci. Model Dev., 10, 4443–4476, https://doi.org/10.5194/gmd-10-4443-2017, https://doi.org/10.5194/gmd-10-4443-2017, 2017
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Wildfires affect infrastructures, vegetation, and the atmosphere. However, it is unclear how fires should be accurately represented in global vegetation models. We introduce here a new flexible data-driven fire modelling approach that allows us to explore sensitivities of burned areas to satellite and climate datasets. Our results suggest combining observations with data-driven and process-oriented fire models to better understand the role of fires in the Earth system.
Clément Albergel, Simon Munier, Delphine Jennifer Leroux, Hélène Dewaele, David Fairbairn, Alina Lavinia Barbu, Emiliano Gelati, Wouter Dorigo, Stéphanie Faroux, Catherine Meurey, Patrick Le Moigne, Bertrand Decharme, Jean-Francois Mahfouf, and Jean-Christophe Calvet
Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, https://doi.org/10.5194/gmd-10-3889-2017, 2017
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LDAS-Monde, a global land data assimilation system, is applied over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables. It is able to ingest information from satellite-derived surface soil moisture (SSM) and leaf area index (LAI) observations to constrain the ISBA land surface model coupled with the CTRIP continental hydrological system. Assimilation of SSM and LAI leads to a better representation of evapotranspiration and gross primary production.
Marko Scholze, Michael Buchwitz, Wouter Dorigo, Luis Guanter, and Shaun Quegan
Biogeosciences, 14, 3401–3429, https://doi.org/10.5194/bg-14-3401-2017, https://doi.org/10.5194/bg-14-3401-2017, 2017
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This paper briefly reviews data assimilation techniques in carbon cycle data assimilation and the requirements of data assimilation systems on observations. We provide a non-exhaustive overview of current observations and their uncertainties for use in terrestrial carbon cycle data assimilation, focussing on relevant space-based observations.
Jaap Schellekens, Emanuel Dutra, Alberto Martínez-de la Torre, Gianpaolo Balsamo, Albert van Dijk, Frederiek Sperna Weiland, Marie Minvielle, Jean-Christophe Calvet, Bertrand Decharme, Stephanie Eisner, Gabriel Fink, Martina Flörke, Stefanie Peßenteiner, Rens van Beek, Jan Polcher, Hylke Beck, René Orth, Ben Calton, Sophia Burke, Wouter Dorigo, and Graham P. Weedon
Earth Syst. Sci. Data, 9, 389–413, https://doi.org/10.5194/essd-9-389-2017, https://doi.org/10.5194/essd-9-389-2017, 2017
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The dataset combines the results of 10 global models that describe the global continental water cycle. The data can be used as input for water resources studies, flood frequency studies etc. at different scales from continental to medium-scale catchments. We compared the results with earth observation data and conclude that most uncertainties are found in snow-dominated regions and tropical rainforest and monsoon regions.
Brecht Martens, Diego G. Miralles, Hans Lievens, Robin van der Schalie, Richard A. M. de Jeu, Diego Fernández-Prieto, Hylke E. Beck, Wouter A. Dorigo, and Niko E. C. Verhoest
Geosci. Model Dev., 10, 1903–1925, https://doi.org/10.5194/gmd-10-1903-2017, https://doi.org/10.5194/gmd-10-1903-2017, 2017
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Terrestrial evaporation is a key component of the hydrological cycle and reliable data sets of this variable are of major importance. The Global Land Evaporation Amsterdam Model (GLEAM, www.GLEAM.eu) is a set of algorithms which estimates evaporation based on satellite observations. The third version of GLEAM, presented in this study, includes an improved parameterization of different model components. As a result, the accuracy of the GLEAM data sets has been improved upon previous versions.
Christina Papagiannopoulou, Diego G. Miralles, Stijn Decubber, Matthias Demuzere, Niko E. C. Verhoest, Wouter A. Dorigo, and Willem Waegeman
Geosci. Model Dev., 10, 1945–1960, https://doi.org/10.5194/gmd-10-1945-2017, https://doi.org/10.5194/gmd-10-1945-2017, 2017
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Global satellite observations provide a means to unravel the influence of climate on vegetation. Common statistical methods used to study the relationships between climate and vegetation are often too simplistic to capture the complexity of these relationships. Here, we present a novel causality framework that includes data fusion from various databases, time series decomposition, and machine learning techniques. Results highlight the highly non-linear nature of climate–vegetation interactions.
Markus Enenkel, Christoph Reimer, Wouter Dorigo, Wolfgang Wagner, Isabella Pfeil, Robert Parinussa, and Richard De Jeu
Hydrol. Earth Syst. Sci., 20, 4191–4208, https://doi.org/10.5194/hess-20-4191-2016, https://doi.org/10.5194/hess-20-4191-2016, 2016
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Soil moisture is a crucial variable for a variety of applications, ranging from weather forecasting and agricultural production to the monitoring of floods and droughts. Satellite observations are particularly important in regions where no in situ measurements are available. Our study presents a method to integrate global near-real-time satellite observations from different sensors into one harmonized, daily data set. A first validation shows good results on a global scale.
S. Sippel, F. E. L. Otto, M. Forkel, M. R. Allen, B. P. Guillod, M. Heimann, M. Reichstein, S. I. Seneviratne, K. Thonicke, and M. D. Mahecha
Earth Syst. Dynam., 7, 71–88, https://doi.org/10.5194/esd-7-71-2016, https://doi.org/10.5194/esd-7-71-2016, 2016
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We introduce a novel technique to bias correct climate model output for impact simulations that preserves its physical consistency and multivariate structure. The methodology considerably improves the representation of extremes in climatic variables relative to conventional bias correction strategies. Illustrative simulations of biosphere–atmosphere carbon and water fluxes with a biosphere model (LPJmL) show that the novel technique can be usefully applied to drive climate impact models.
M. Forkel, N. Carvalhais, S. Schaphoff, W. v. Bloh, M. Migliavacca, M. Thurner, and K. Thonicke
Biogeosciences, 11, 7025–7050, https://doi.org/10.5194/bg-11-7025-2014, https://doi.org/10.5194/bg-11-7025-2014, 2014
C. Szczypta, J.-C. Calvet, F. Maignan, W. Dorigo, F. Baret, and P. Ciais
Geosci. Model Dev., 7, 931–946, https://doi.org/10.5194/gmd-7-931-2014, https://doi.org/10.5194/gmd-7-931-2014, 2014
A. Loew, T. Stacke, W. Dorigo, R. de Jeu, and S. Hagemann
Hydrol. Earth Syst. Sci., 17, 3523–3542, https://doi.org/10.5194/hess-17-3523-2013, https://doi.org/10.5194/hess-17-3523-2013, 2013
Related subject area
Subject: Ecohydrology | Techniques and Approaches: Remote Sensing and GIS
Circumarctic landcover diversity considering wetness gradients
Multi-decadal Floodplain Classification and Trend Analysis in the Upper Columbia River Valley, British Columbia
Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture
Untangling irrigation effects on maize water and heat stress alleviation using satellite data
Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture
Assessing the large-scale plant–water relations in the humid, subtropical Pearl River basin of China
Technical note: Accounting for snow in the estimation of root zone water storage capacity from precipitation and evapotranspiration fluxes
Long-term water stress and drought assessment of Mediterranean oak savanna vegetation using thermal remote sensing
Temporal interpolation of land surface fluxes derived from remote sensing – results with an unmanned aerial system
Pattern and structure of microtopography implies autogenic origins in forested wetlands
The influence of water table depth on evapotranspiration in the Amazon arc of deforestation
Does the Normalized Difference Vegetation Index explain spatial and temporal variability in sap velocity in temperate forest ecosystems?
Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales
Evolution of the vegetation system in the Heihe River basin in the last 2000 years
Laser vision: lidar as a transformative tool to advance critical zone science
Attribution of satellite-observed vegetation trends in a hyper-arid region of the Heihe River basin, Western China
Evapotranspiration and water yield over China's landmass from 2000 to 2010
Satellite-based analysis of recent trends in the ecohydrology of a semi-arid region
Soil moisture controls on patterns of grass green-up in Inner Mongolia: an index based approach
Groundwater surface water interactions and the role of phreatophytes in identifying recharge zones
Quantifying the performance of automated GIS-based geomorphological approaches for riparian zone delineation using digital elevation models
Climate change, growing season water deficit and vegetation activity along the north–south transect of eastern China from 1982 through 2006
Hydrological differentiation and spatial distribution of high altitude wetlands in a semi-arid Andean region derived from satellite data
The impact of in-canopy wind profile formulations on heat flux estimation in an open orchard using the remote sensing-based two-source model
The use of remote sensing to quantify wetland loss in the Choke Mountain range, Upper Blue Nile basin, Ethiopia
Annett Bartsch, Aleksandra Efimova, Barbara Widhalm, Xaver Muri, Clemens von Baeckmann, Helena Bergstedt, Ksenia Ermokhina, Gustaf Hugelius, Birgit Heim, and Marina Leibmann
EGUsphere, https://doi.org/10.5194/egusphere-2023-2295, https://doi.org/10.5194/egusphere-2023-2295, 2023
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Wetness gradients and landcover diversity for the entire Arctic tundra have been assessed using a novel satellite data based map. Patterns of lakes, wetlands, general soil moisture conditions and vegetation physiognomy are represented at 10 m. About 40 % of the area north of the treeline falls into three units of dry types with limited shrub growth. Wetter regions have higher landcover diversity than drier regions.
Italo Sampaio Rodrigues, Christopher Hopkinson, Laura Chasmer, Ryan MacDonald, Suzanne Bayley, and Brian Brisco
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-211, https://doi.org/10.5194/hess-2023-211, 2023
Revised manuscript accepted for HESS
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The research evaluated the trends and changes in land cover and river discharge in the Upper Columbia River Wetlands using remote sensing and hydroclimatic data. The river discharge increased during the peak flow season, resulting in a positive trend in the open water extent in the same period, while on an annual basis open water area is declining. Furthermore, since 2003 the peak flow has occurred eleven days earlier than 1903–1928, which has led to larger discharges in a shorter time.
Sinan Li, Li Zhang, Jingfeng Xiao, Rui Ma, Xiangjun Tian, and Min Yan
Hydrol. Earth Syst. Sci., 26, 6311–6337, https://doi.org/10.5194/hess-26-6311-2022, https://doi.org/10.5194/hess-26-6311-2022, 2022
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Accurate estimation for global GPP and ET is important in climate change studies. In this study, the GLASS LAI, SMOS, and SMAP datasets were assimilated jointly and separately in a coupled model. The results show that the performance of joint assimilation for GPP and ET is better than that of separate assimilation. The joint assimilation in water-limited regions performed better than in humid regions, and the global assimilation results had higher accuracy than other products.
Peng Zhu and Jennifer Burney
Hydrol. Earth Syst. Sci., 26, 827–840, https://doi.org/10.5194/hess-26-827-2022, https://doi.org/10.5194/hess-26-827-2022, 2022
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Satellite data were used to disentangle water and heat stress alleviation due to irrigation. Our findings are as follows. (1) Irrigation-induced cooling was captured by satellite LST but air temperature failed. (2) Irrigation extended maize growing season duration, especially during grain filling. (3) Water and heat stress alleviation constitutes 65 % and 35 % of the irrigation benefit. (4) The crop model simulating canopy temperature better captures the irrigation benefit.
Bonan Li and Stephen P. Good
Hydrol. Earth Syst. Sci., 25, 5029–5045, https://doi.org/10.5194/hess-25-5029-2021, https://doi.org/10.5194/hess-25-5029-2021, 2021
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We found that satellite retrieved soil moisture has large uncertainty, with uncertainty caused by the algorithm being closely related to the satellite soil moisture quality. The information provided by the two main inputs is mainly redundant. Such redundant components and synergy components provided by two main inputs to the satellite soil moisture are related to how the satellite algorithm performs. The satellite remote sensing algorithms may be improved by performing such analysis.
Hailong Wang, Kai Duan, Bingjun Liu, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 4741–4758, https://doi.org/10.5194/hess-25-4741-2021, https://doi.org/10.5194/hess-25-4741-2021, 2021
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Using remote sensing and reanalysis data, we examined the relationships between vegetation development and water resource availability in a humid subtropical basin. We found overall increases in total water storage and surface greenness and vegetation production, and the changes were particularly profound in cropland-dominated regions. Correlation analysis implies water availability leads the variations in greenness and production, and irrigation may improve production during dry periods.
David N. Dralle, W. Jesse Hahm, K. Dana Chadwick, Erica McCormick, and Daniella M. Rempe
Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, https://doi.org/10.5194/hess-25-2861-2021, 2021
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Root zone water storage capacity determines how much water can be stored belowground to support plants during periods without precipitation. Here, we develop a satellite remote sensing method to estimate this key variable at large scales that matter for management. Importantly, our method builds on previous approaches by accounting for snowpack, which may bias estimates from existing approaches. Ultimately, our method will improve large-scale understanding of plant access to subsurface water.
María P. González-Dugo, Xuelong Chen, Ana Andreu, Elisabet Carpintero, Pedro J. Gómez-Giraldez, Arnaud Carrara, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 755–768, https://doi.org/10.5194/hess-25-755-2021, https://doi.org/10.5194/hess-25-755-2021, 2021
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Drought is a devastating natural hazard and difficult to define, detect and quantify. Global meteorological data and remote-sensing products present new opportunities to characterize drought in an objective way. In this paper, we applied the surface energy balance model SEBS to estimate monthly evapotranspiration (ET) from 2001 to 2018 over the dehesa area of the Iberian Peninsula. ET anomalies were used to identify the main drought events and analyze their impacts on dehesa vegetation.
Sheng Wang, Monica Garcia, Andreas Ibrom, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 24, 3643–3661, https://doi.org/10.5194/hess-24-3643-2020, https://doi.org/10.5194/hess-24-3643-2020, 2020
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Remote sensing only provides snapshots of rapidly changing land surface variables; this limits its application for water resources and ecosystem management. To obtain continuous estimates of surface temperature, soil moisture, evapotranspiration, and ecosystem productivity, a simple and operational modelling scheme is presented. We demonstrate it with temporally sparse optical and thermal remote sensing data from an unmanned aerial system at a Danish bioenergy plantation eddy covariance site.
Jacob S. Diamond, Daniel L. McLaughlin, Robert A. Slesak, and Atticus Stovall
Hydrol. Earth Syst. Sci., 23, 5069–5088, https://doi.org/10.5194/hess-23-5069-2019, https://doi.org/10.5194/hess-23-5069-2019, 2019
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We found evidence for spatial patterning of soil elevation in forested wetlands that was well explained by hydrology. The patterns that we found were strongest at wetter sites, and were weakest at drier sites. When a site was wet, soil elevations typically only belonged to two groups: tall "hummocks" and low "hollows. The tall, hummock groups were spaced equally apart from each other and were a similar size. We believe this is evidence for a biota–hydrology feedback that creates hummocks.
John O'Connor, Maria J. Santos, Karin T. Rebel, and Stefan C. Dekker
Hydrol. Earth Syst. Sci., 23, 3917–3931, https://doi.org/10.5194/hess-23-3917-2019, https://doi.org/10.5194/hess-23-3917-2019, 2019
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The Amazon rainforest has undergone extensive land use change, which greatly reduces the rate of evapotranspiration. Forest with deep roots is replaced by agriculture with shallow roots. The difference in rooting depth can greatly reduce access to water, especially during the dry season. However, large areas of the Amazon have a sufficiently shallow water table that may provide access for agriculture. We used remote sensing observations to compare the impact of deep and shallow water tables.
Anne J. Hoek van Dijke, Kaniska Mallick, Adriaan J. Teuling, Martin Schlerf, Miriam Machwitz, Sibylle K. Hassler, Theresa Blume, and Martin Herold
Hydrol. Earth Syst. Sci., 23, 2077–2091, https://doi.org/10.5194/hess-23-2077-2019, https://doi.org/10.5194/hess-23-2077-2019, 2019
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Satellite images are often used to estimate land water fluxes over a larger area. In this study, we investigate the link between a well-known vegetation index derived from satellite data and sap velocity, in a temperate forest in Luxembourg. We show that the link between the vegetation index and transpiration is not constant. Therefore we suggest that the use of vegetation indices to predict transpiration should be limited to ecosystems and scales where the link has been confirmed.
Olanrewaju O. Abiodun, Huade Guan, Vincent E. A. Post, and Okke Batelaan
Hydrol. Earth Syst. Sci., 22, 2775–2794, https://doi.org/10.5194/hess-22-2775-2018, https://doi.org/10.5194/hess-22-2775-2018, 2018
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In recent decades, evapotranspiration estimation has been improved by remote sensing methods as well as by hydrological models. However, comparing these methods shows differences of up to 31 % at a spatial resolution of 1 km2. Land cover differences and catchment averaged climate data in the hydrological model were identified as the principal causes of the differences in results. The implication is that water management will have to deal with large uncertainty in estimated water balances.
Shoubo Li, Yan Zhao, Yongping Wei, and Hang Zheng
Hydrol. Earth Syst. Sci., 21, 4233–4244, https://doi.org/10.5194/hess-21-4233-2017, https://doi.org/10.5194/hess-21-4233-2017, 2017
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This study aims to investigate the evolution of natural and crop vegetation systems over the past 2000 years accommodated with the changes in water regimes at the basin scale. It is based on remote-sensing data and previous historical research. The methods developed and the findings obtained from this study could assist in understanding how current ecosystem problems were created in the past and what their implications for future river basin management are.
A. A. Harpold, J. A. Marshall, S. W. Lyon, T. B. Barnhart, B. A. Fisher, M. Donovan, K. M. Brubaker, C. J. Crosby, N. F. Glenn, C. L. Glennie, P. B. Kirchner, N. Lam, K. D. Mankoff, J. L. McCreight, N. P. Molotch, K. N. Musselman, J. Pelletier, T. Russo, H. Sangireddy, Y. Sjöberg, T. Swetnam, and N. West
Hydrol. Earth Syst. Sci., 19, 2881–2897, https://doi.org/10.5194/hess-19-2881-2015, https://doi.org/10.5194/hess-19-2881-2015, 2015
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This review's objective is to demonstrate the transformative potential of lidar by critically assessing both challenges and opportunities for transdisciplinary lidar applications in geomorphology, hydrology, and ecology. We find that using lidar to its full potential will require numerous advances, including more powerful open-source processing tools, new lidar acquisition technologies, and improved integration with physically based models and complementary observations.
Y. Wang, M. L. Roderick, Y. Shen, and F. Sun
Hydrol. Earth Syst. Sci., 18, 3499–3509, https://doi.org/10.5194/hess-18-3499-2014, https://doi.org/10.5194/hess-18-3499-2014, 2014
Y. Liu, Y. Zhou, W. Ju, J. Chen, S. Wang, H. He, H. Wang, D. Guan, F. Zhao, Y. Li, and Y. Hao
Hydrol. Earth Syst. Sci., 17, 4957–4980, https://doi.org/10.5194/hess-17-4957-2013, https://doi.org/10.5194/hess-17-4957-2013, 2013
M. Gokmen, Z. Vekerdy, W. Verhoef, and O. Batelaan
Hydrol. Earth Syst. Sci., 17, 3779–3794, https://doi.org/10.5194/hess-17-3779-2013, https://doi.org/10.5194/hess-17-3779-2013, 2013
H. Liu, F. Tian, H. C. Hu, H. P. Hu, and M. Sivapalan
Hydrol. Earth Syst. Sci., 17, 805–815, https://doi.org/10.5194/hess-17-805-2013, https://doi.org/10.5194/hess-17-805-2013, 2013
T. S. Ahring and D. R. Steward
Hydrol. Earth Syst. Sci., 16, 4133–4142, https://doi.org/10.5194/hess-16-4133-2012, https://doi.org/10.5194/hess-16-4133-2012, 2012
D. Fernández, J. Barquín, M. Álvarez-Cabria, and F. J. Peñas
Hydrol. Earth Syst. Sci., 16, 3851–3862, https://doi.org/10.5194/hess-16-3851-2012, https://doi.org/10.5194/hess-16-3851-2012, 2012
P. Sun, Z. Yu, S. Liu, X. Wei, J. Wang, N. Zegre, and N. Liu
Hydrol. Earth Syst. Sci., 16, 3835–3850, https://doi.org/10.5194/hess-16-3835-2012, https://doi.org/10.5194/hess-16-3835-2012, 2012
M. Otto, D. Scherer, and J. Richters
Hydrol. Earth Syst. Sci., 15, 1713–1727, https://doi.org/10.5194/hess-15-1713-2011, https://doi.org/10.5194/hess-15-1713-2011, 2011
C. Cammalleri, M. C. Anderson, G. Ciraolo, G. D'Urso, W. P. Kustas, G. La Loggia, and M. Minacapilli
Hydrol. Earth Syst. Sci., 14, 2643–2659, https://doi.org/10.5194/hess-14-2643-2010, https://doi.org/10.5194/hess-14-2643-2010, 2010
E. Teferi, S. Uhlenbrook, W. Bewket, J. Wenninger, and B. Simane
Hydrol. Earth Syst. Sci., 14, 2415–2428, https://doi.org/10.5194/hess-14-2415-2010, https://doi.org/10.5194/hess-14-2415-2010, 2010
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...
