Articles | Volume 18, issue 11
https://doi.org/10.5194/bg-18-3285-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-3285-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of temperature and water availability on microwave-derived gross primary production
Irene E. Teubner
CORRESPONDING AUTHOR
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Zentralanstalt für Meteorologie und Geodynamik (ZAMG), Hohe Warte 38, 1190 Vienna, Austria
Matthias Forkel
Environmental Remote Sensing Group, Institute of Photogrammetry and Remote Sensing, Technische Universität Dresden, Helmholtzstraße 10, 01069 Dresden, Germany
Benjamin Wild
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Leander Mösinger
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
Wouter Dorigo
Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstraße 8, 1040 Vienna, Austria
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Hydrol. Earth Syst. Sci., 29, 397–425, https://doi.org/10.5194/hess-29-397-2025, https://doi.org/10.5194/hess-29-397-2025, 2025
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-W8-2024, 463–470, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-463-2024, https://doi.org/10.5194/isprs-archives-XLVIII-2-W8-2024-463-2024, 2024
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Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023, https://doi.org/10.5194/hess-27-4087-2023, 2023
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Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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Hydrol. Earth Syst. Sci., 27, 1221–1242, https://doi.org/10.5194/hess-27-1221-2023, https://doi.org/10.5194/hess-27-1221-2023, 2023
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Matthias Forkel, Luisa Schmidt, Ruxandra-Maria Zotta, Wouter Dorigo, and Marta Yebra
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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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Hydrol. Earth Syst. Sci., 26, 3611–3627, https://doi.org/10.5194/hess-26-3611-2022, https://doi.org/10.5194/hess-26-3611-2022, 2022
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Climate data records of surface soil moisture, vegetation optical depth, and land surface temperature can be derived from passive microwave observations. The ability of these datasets to properly detect anomalies and extremes is very valuable in climate research and can especially help to improve our insight in complex regions where the current climate reanalysis datasets reach their limitations. Here, we present a case study over the Okavango Delta, where we focus on inter-annual variability.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
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G. Verhoeven, B. Wild, J. Schlegel, M. Wieser, N. Pfeifer, S. Wogrin, L. Eysn, M. Carloni, B. Koschiček-Krombholz, A. Molada-Tebar, J. Otepka-Schremmer, C. Ressl, M. Trognitz, and A. Watzinger
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Stefan Schlaffer, Marco Chini, Wouter Dorigo, and Simon Plank
Hydrol. Earth Syst. Sci., 26, 841–860, https://doi.org/10.5194/hess-26-841-2022, https://doi.org/10.5194/hess-26-841-2022, 2022
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Prairie wetlands are important for biodiversity and water availability. Knowledge about their variability and spatial distribution is of great use in conservation and water resources management. In this study, we propose a novel approach for the classification of small water bodies from satellite radar images and apply it to our study area over 6 years. The retrieved dynamics show the different responses of small and large wetlands to dry and wet periods.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
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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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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
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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.
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.
Cited articles
Anav, A., Friedlingstein, P., Beer, C., Ciais, P., Harper, A., Jones, C., Murray-Tortarolo, G., Papale, D., Parazoo, N. C., Peylin, P., Piao, S., Sitch, S., Viovy, N., Wiltshire, A., and Zhao, M.:
Spatiotemporal patterns of terrestrial gross primary production: A review,
Rev. Geophys., 53, 785–818, 2015. a
Atkin, O. K., Atkinson, L. J., Fisher, R. A., Campbell, C. D.,
ZARAGOZA-CASTELLS, J., Pitchford, J. W., Woodward, F. I., and Hurry, V.:
Using temperature-dependent changes in leaf scaling relationships to
quantitatively account for thermal acclimation of respiration in a coupled
global climate–vegetation model, Glob. Change Biol., 14, 2709–2726,
2008. a
Beguería, S., Latorre, B., Reig, F., and Vicente-Serrano, S.:
sbegueria/SPEIbase: Version 2.5. 1, Glob. SPEI Database, available at: https://digital.csic.es/handle/10261/153475 (last access: 15 November 2019), 2017. a
Brandt, M., Wigneron, J.-P., Chave, J., Tagesson, T., Penuelas, J., Ciais, P., Rasmussen, K., Tian, F., Mbow, C., Al-Yaari, A., Rodriguez-Fernandez, N., Schurgers, G., Zhang, W., Chang, J., Kerr, Y., Verger, A., Tucker, C., Mialon, A., Rasmussen, L. V., Fan, L., and Fensholt, R.: Satellite passive
microwaves reveal recent climate-induced carbon losses in African drylands,
Nature Ecology & Evolution, 2, 827–835, 2018. a
Burri, S., Sturm, P., Prechsl, U. E., Knohl, A., and Buchmann, N.: The impact of extreme summer drought on the short-term carbon coupling of photosynthesis to soil CO2 efflux in a temperate grassland, Biogeosciences, 11, 961–975, https://doi.org/10.5194/bg-11-961-2014, 2014. a
C3S: C3S ERA5-Land reanalysis. Copernicus Climate Change Service,
available at: https://cds.climate.copernicus.eu/cdsapp#!/home, last access: 15 November 2019. a
Chambers, J. Q., Tribuzy, E. S., Toledo, L. C., Crispim, B. F., Higuchi, N.,
Santos, J. d., Araújo, A. C., Kruijt, B., Nobre, A. D., and Trumbore,
S. E.: Respiration from a tropical forest ecosystem: partitioning of sources
and low carbon use efficiency, Ecol. Appl., 14, 72–88, 2004. a
Chaparro, D., Piles, M., Vall-Llossera, M., Camps, A., Konings, A. G., and
Entekhabi, D.: L-band vegetation optical depth seasonal metrics for crop
yield assessment, Remote Sens. Environ., 212, 249–259, 2018. a
Chaparro, D., Duveiller, G., Piles, M., Cescatti, A., Vall-Llossera, M., Camps,
A., and Entekhabi, D.: Sensitivity of L-band vegetation optical depth to
carbon stocks in tropical forests: a comparison to higher frequencies and
optical indices, Remote Sens. Environ., 232, 111303, https://doi.org/10.1016/j.rse.2019.111303, 2019. a, b, c, d
Crocetti, L., Forkel, M., Fischer, M., Jurecka, 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, 1–17, 2020. a
Doughty, C. E., Metcalfe, D., Girardin, C. A., Amezquita, F. F., Durand, L., Huasco, W. H., Silva-Espejo, J. E., Araujo-Murakami, A., Da Costa, M., da Costa, A. C. L., Rocha, W., Meir, P., Galbraith, D., and Malhi, Y.: Source and sink carbon dynamics and carbon
allocation in the Amazon basin, Global Biogeochem. Cy., 29, 645–655,
2015. a, b
Drake, J. E., Tjoelker, M. G., Aspinwall, M. J., Reich, P. B., Barton, C. V.,
Medlyn, B. E., and Duursma, R. A.: Does physiological acclimation to climate
warming stabilize the ratio of canopy respiration to photosynthesis?, New
Phytol., 211, 850–863, 2016. a
El Hajj, M., Baghdadi, N., Wigneron, J.-P., Zribi, M., Albergel, C., Calvet,
J.-C., and Fayad, I.: First Vegetation Optical Depth Mapping from Sentinel-1
C-band SAR Data over Crop Fields, Remote Sensing, 11, 2769, https://doi.org/10.3390/rs11232769, 2019. a
Fan, L., Wigneron, J.-P., Ciais, P., Chave, J., Brandt, M., Fensholt, R., Saatchi, S. S., Bastos, A., Al-Yaari, A., Hufkens, K., Qin, Y., Xiao, X., Chen, C., Myneni, R. B., Fernandez-Moran, R., Mialon, A., Rodriguez-Fernandez, N., Kerr, Y., Tian, F., and Peñuelas, J.:
Satellite-observed pantropical carbon dynamics, Nat. Plants, 5, 944–951,
2019. a
Feldman, A. F., Gianotti, D. J. S., Konings, A. G., McColl, K. A., Akbar, R.,
Salvucci, G. D., and Entekhabi, D.: Moisture pulse-reserve in the soil-plant
continuum observed across biomes, Nat. Plants, 4, 1026–1033, 2018. a
FluxCom:
http://www.fluxcom.org, last access: 24 March 2017. a
Forkel, M., Dorigo, W., 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. a
Frappart, F., Wigneron, J.-P., Li, X., Liu, X., Al-Yaari, A., Fan, L., Wang, M., Moisy, C., Le Masson, E., Lafkih, Z. A., Vallé, C., Ygorra, B., and Baghdadi, N.: Global monitoring of the
vegetation dynamics from the Vegetation Optical Depth (VOD): A review, Remote
Sensing, 12, 2915, https://doi.org/10.3390/rs12182915, 2020. a, b, c
Giardina, F., Konings, A. G., Kennedy, D., Alemohammad, S. H., Oliveira, R. S.,
Uriarte, M., and Gentine, P.: Tall Amazonian forests are less sensitive to
precipitation variability, Nat. Geosci., 11, 405–409, 2018. a
Pastorello, G., Trotta, C., Canfora, E., et al.: The
FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance
data, Scientific Data, 7, 225, https://doi.org/10.1038/s41597-020-0534-3, 2020 (data available at: https://fluxnet.org/data/fluxnet2015-dataset/, last access: 9 June 2016). a, b, c
Goodrich, J., Campbell, D., Clearwater, M., Rutledge, S., and Schipper, L.:
High vapor pressure deficit constrains GPP and the light response of NEE at a
Southern Hemisphere bog, Agr. Forest Meteorol., 203, 54–63,
2015. a
Hastie, T. and Tibshirani, R.: Generalized additive models: some applications,
J. Am. Stat. Assoc., 82, 371–386, 1987. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, 2020. a
Jung, M., Schwalm, C., Migliavacca, M., Walther, S., Camps-Valls, G., Koirala, S., Anthoni, P., Besnard, S., Bodesheim, P., Carvalhais, N., Chevallier, F., Gans, F., Goll, D. S., Haverd, V., Köhler, P., Ichii, K., Jain, A. K., Liu, J., Lombardozzi, D., Nabel, J. E. M. S., Nelson, J. A., O'Sullivan, M., Pallandt, M., Papale, D., Peters, W., Pongratz, J., Rödenbeck, C., Sitch, S., Tramontana, G., Walker, A., Weber, U., and Reichstein, M.: Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach, Biogeosciences, 17, 1343–1365, https://doi.org/10.5194/bg-17-1343-2020, 2020. a, b, c, d
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, 2019. a
Kumar, S. V., Holmes, T. R., Bindlish, R., de Jeu, R., and Peters-Lidard, C.: Assimilation of vegetation optical depth retrievals from passive microwave radiometry, Hydrol. Earth Syst. Sci., 24, 3431–3450, https://doi.org/10.5194/hess-24-3431-2020, 2020. a, b
Li, L., Njoku, E. G., Im, E., Chang, P. S., and Germain, K. S.: A preliminary
survey of radio-frequency interference over the US in Aqua AMSR-E data, IEEE
T. Geosci. Remote, 42, 380–390, 2004. a
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. a, b, c
Liu, Y. Y., Van Dijk, A. I., De Jeu, R. A., Canadell, J. G., McCabe, M. F.,
Evans, J. P., and Wang, G.: Recent reversal in loss of global terrestrial
biomass, Nat. Clim. Change, 5, 470–474, 2015. a
MacBean, N., Maignan, F., Bacour, C., Lewis, P., Peylin, P., Guanter, L.,
Köhler, P., Gómez-Dans, J., and Disney, M.: Strong constraint on
modelled global carbon uptake using solar-induced chlorophyll fluorescence
data, Scientific Rep.-UK, 8, 1973, https://doi.org/10.1038/s41598-018-20024-w, 2018. a, b
Martínez-Vilalta, J., Sala, A., Asensio, D., Galiano, L., Hoch, G.,
Palacio, S., Piper, F. I., and Lloret, F.: Dynamics of non-structural
carbohydrates in terrestrial plants: a global synthesis, Ecol.
Monogr., 86, 495–516, 2016. a
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 (Version 1.0) [data set], Zenodo, https://doi.org/10.5281/zenodo.2575599, 2019. a
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. a, b, c, d
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, 2017. a
Monteith, J.: Solar radiation and productivity in tropical ecosystems, J. Appl. Ecol., 9, 747–766, 1972. a
Muñoz-Sabater, J.: First ERA5-Land dataset to be released this spring,
ECMWF, Reding, UK, 159, 2019. a
Njoku, E. G., Ashcroft, P., Chan, T. K., and Li, L.: Global survey and
statistics of radio-frequency interference in AMSR-E land observations, IEEE
T. Geosci. Remote, 43, 938–947, 2005. a
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, 2001. a
Piao, S., Luyssaert, S., Ciais, P., Janssens, I. A., Chen, A., Cao, C., Fang,
J., Friedlingstein, P., Luo, Y., and Wang, S.: Forest annual carbon cost: A
global-scale analysis of autotrophic respiration, Ecology, 91, 652–661,
2010. a
Rao, K., Anderegg, W. R., Sala, A., Martínez-Vilalta, J., and Konings,
A. G.: Satellite-based vegetation optical depth as an indicator of
drought-driven tree mortality, Remote Sens. Environ., 227, 125–136,
2019. a
Ribaut, J.-M., Betran, J., Monneveux, P., and Setter, T.: Drought tolerance in
maize, in: Handbook of maize: its biology, Springer, New York, NY, 311–344, 2009. a
Rodríguez-Fernández, N. J., Mialon, A., Mermoz, S., Bouvet, A., Richaume, P., Al Bitar, A., Al-Yaari, A., Brandt, M., Kaminski, T., Le Toan, T., Kerr, Y. H., and Wigneron, J.-P.: An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa, Biogeosciences, 15, 4627–4645, https://doi.org/10.5194/bg-15-4627-2018, 2018. a
Running, S., Mu, Q., and Zhao, M.: MOD17A2H MODIS/terra gross primary
productivity 8-day L4 global 500 m SIN grid V006, NASA EOSDIS Land Processes
DAAC, https://doi.org/10.5067/MODIS/MOD17A2H.006, 2018. a, b
Running, S. W., Nemani, R., Glassy, J. M., and Thornton, P. E.: MODIS daily
photosynthesis (PSN) and annual net primary production (NPP) product (MOD17)
Algorithm Theoretical Basis Document, University of Montana, SCF At-Launch
Algorithm ATBD Documents, available at: http://www.ntsg.umt.edu/files/modis/ATBD_MOD17_v21.pdf (last access: 1 October 2020), 1999. a, b
Running, S. W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., and
Hashimoto, H.: A continuous satellite-derived measure of global terrestrial
primary production, Bioscience, 54, 547–560, 2004. a
Sapes, G., Roskilly, B., Dobrowski, S., Maneta, M., Anderegg, W. R.,
Martinez-Vilalta, J., and Sala, A.: Plant water content integrates hydraulics
and carbon depletion to predict drought-induced seedling mortality, Tree
Physiol., 39, 1300–1312, 2019. a
Servén, D. and Brummitt, C.: pyGAM: generalized additive models in python,
Zenodo, https://doi.org/10.5281/zenodo.1208723, 2018. a, b
Song, L., Li, Y., Ren, Y., Wu, X., Guo, B., Tang, X., Shi, W., Ma, M., Han, X.,
and Zhao, L.: Divergent vegetation responses to extreme spring and summer
droughts in Southwestern China, Agr. Forest Meteorol., 279,
107703, https://doi.org/10.1016/j.agrformet.2019.107703, 2019. a
Sun, Y., Frankenberg, C., Jung, M., Joiner, J., Guanter, L., Köhler, P.,
and Magney, T.: Overview of Solar-Induced chlorophyll Fluorescence (SIF) from
the Orbiting Carbon Observatory-2: Retrieval, cross-mission comparison, and
global monitoring for GPP, Remote Sens. Environ., 209, 808–823,
2018. a
Teubner, I. E., Forkel, M., Jung, M., Liu, Y. Y., Miralles, D. G., Parinussa, R., van der Schalie, R., Vreugdenhil, M., Schwalm, C. R., Tramontana, G., Camps-Valls, G., and Dorigo, W. A.: Assessing the relationship between microwave vegetation optical depth
and gross primary production, Int. J. Applied Earth
Obs., 65, 79–91, 2018. a, b, c, d, e
Teubner, I. E., Forkel, M., Camps-Valls, G., Jung, M., Miralles, D. G.,
Tramontana, G., van der Schalie, R., Vreugdenhil, M., Mösinger, L., and
Dorigo, W. A.: A carbon sink-driven approach to estimate gross primary
production from microwave satellite observations, Remote Sens.
Environ., 229, 100–113, 2019. a, b, c, d, e, f
Tian, F., Wigneron, J., Ciais, P., Chave, J., Ogée, J., Peñuelas, J., Ræbild, A., Domec, J., Tong, X., Brandt, M., Mialon, A., Rodriguez-Fernandez, N., Tagesson, T., Al-Yaari, A., Kerr, Y., Chen, C., Myneni, R. B., Zhang, W., Ardö, J., and Fensholt, R.: Coupling of
ecosystem-scale plant water storage and leaf phenology observed by satellite,
Nature Ecology & Evolution, 2, 1428–1435, 2018. a
Tjoelker, M. G., Oleksyn, J., and Reich, P. B.: Modelling respiration of
vegetation: evidence for a general temperature-dependent Q10, Glob. Change
Biol., 7, 223–230, 2001. a
Tjoelker, M. G., Oleksyn, J., Reich, P. B., and Żytkowiak, R.: Coupling of
respiration, nitrogen, and sugars underlies convergent temperature
acclimation in Pinus banksiana across wide-ranging sites and populations,
Glob. Change Biol., 14, 782–797, 2008. a
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016. a
Turner, D. P., Ritts, W. D., Cohen, W. B., Maeirsperger, T. K., Gower, S. T., Kirschbaum, A. A., Running, S. W., Zhao, M., Wofsy, S. C., Dunn, A. L., Law, B. E., Campbell, J. L., Oechel, W. C., Kwon, H. J., Meyers, T. P., Small, E. E., Kurc, S. A., and Gamon, J. A.: Site-level evaluation of satellite-based global terrestrial gross
primary production and net primary production monitoring, Glob. Change
Biol., 11, 666–684, 2005. a
Turner, D. P., Ritts, W. D., Cohen, W. B., Gower, S. T., Running, S. W., Zhao, M., Costa, M. H., Kirschbaum, A. A., Ham, J. M., Saleska, S. R., and Ahl, D. E.:
Evaluation of MODIS NPP and GPP products across multiple biomes, Remote
Sens. Environ., 102, 282–292, 2006. a
van der Schalie, R., de Jeu, R. A., Kerr, Y., 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, 2017. a
Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., Angulo, M.,
and El Kenawy, A.: A new global 0.5 gridded dataset (1901–2006) of a
multiscalar drought index: comparison with current drought index datasets
based on the Palmer Drought Severity Index, J. Hydrometeorol., 11,
1033–1043, 2010. a, b
Vreugdenhil, M., Wagner, W., Bauer-Marschallinger, B., Pfeil, I., Teubner, I.,
Rüdiger, C., and Strauss, P.: Sensitivity of Sentinel-1 backscatter to
vegetation dynamics: An Austrian case study, Remote Sensing, 10, 1396, https://doi.org/10.3390/rs10091396, 2018.
a
Wigneron, J.-P., Fan, L., Ciais, P., Bastos, A., Brandt, M., Chave, J.,
Saatchi, S., Baccini, A., and Fensholt, R.: Tropical forests did not recover
from the strong 2015–2016 El Niño event, Science Advances, 6, eaay4603, https://doi.org/10.1126/sciadv.aay4603,
2020. a
Woodhouse, I. H.: Introduction to microwave remote sensing, CRC Press, Boca Raton, FL, 400 pp., 2017. a
Wu, M., Scholze, M., Kaminski, T., Voßbeck, M., and Tagesson, T.: Using
SMOS soil moisture data combining CO2 flask samples to constrain carbon
fluxes during 2010–2015 within a Carbon Cycle Data Assimilation System
(CCDAS), Remote Sens. Environ., 240, 111719, https://doi.org/10.1016/j.rse.2020.111719, 2020. a
Wythers, K. R., Reich, P. B., and Bradford, J. B.: Incorporating
temperature-sensitive Q10 and foliar respiration acclimation algorithms
modifies modeled ecosystem responses to global change, J. Geophys.
Res.-Biogeo., 118, 77–90, 2013. a
Zhang, Y., Xiao, X., Zhou, S., Ciais, P., McCarthy, H., and Luo, Y.: Canopy and
physiological controls of GPP during drought and heat wave, Geophys.
Res. Lett., 43, 3325–3333, 2016. a
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., and Dong, J.: A
global moderate resolution dataset of gross primary production of vegetation
for 2000–2016, Scientific Data, 4, 170165, https://doi.org/10.1038/sdata.2017.165, 2017. a
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. a
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.
Vegetation optical depth (VOD), which contains information on vegetation water content and...
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