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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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
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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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Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
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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
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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.
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...
