Articles | Volume 26, issue 11
https://doi.org/10.5194/hess-26-2997-2022
© Author(s) 2022. 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-26-2997-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The influence of vegetation water dynamics on the ASCAT backscatter–incidence angle relationship in the Amazon
Ashwini Petchiappan
Department of Water Management, Delft University of Technology, Stevinweg 1, 2600 GA Delft, the Netherlands
Susan C. Steele-Dunne
CORRESPONDING AUTHOR
Department of Geoscience and Remote Sensing, Delft University of Technology, Stevinweg 1, 2600 GA Delft, the Netherlands
Mariette Vreugdenhil
Department of Geodesy and Geo-Information, TU Wien, 1040 Vienna, Austria
Sebastian Hahn
Department of Geodesy and Geo-Information, TU Wien, 1040 Vienna, Austria
Wolfgang Wagner
Department of Geodesy and Geo-Information, TU Wien, 1040 Vienna, Austria
Rafael Oliveira
Department of Plant Biology, Institute of Biology, P.O. Box 6109, University of Campinas – UNICAMP 13083-970, Campinas, SP, Brazil
Related authors
No articles found.
Jaime Gaona, Davide Bavera, Guido Fioravanti, Sebastian Hahn, Pietro Stradiotti, Paolo Filippucci, Stefania Camici, Luca Ciabatta, Hamidreza Mosaffa, Silvia Puca, Nicoletta Roberto, and Luca Brocca
Hydrol. Earth Syst. Sci., 29, 3865–3888, https://doi.org/10.5194/hess-29-3865-2025, https://doi.org/10.5194/hess-29-3865-2025, 2025
Short summary
Short summary
Soil moisture is crucial for the water cycle since it is at the front line of drought. Satellite, model and in situ data help identify soil moisture stress but are challenged by data uncertainties. This study evaluates trends and data coherence of common active/passive microwave sensors and model-based soil moisture data against in situ stations across Europe from 2007 to 2022. Data reliability is increasing, but combining data types improves soil moisture monitoring capabilities.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Ruxandra-Maria Zotta, Leander Moesinger, Robin van der Schalie, Mariette Vreugdenhil, Wolfgang Preimesberger, Thomas Frederikse, Richard de Jeu, and Wouter Dorigo
Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, https://doi.org/10.5194/essd-16-4573-2024, 2024
Short summary
Short summary
VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA v2 comprises two products: VODCA CXKu, spanning 34 years of observations (1987–2021), suitable for monitoring upper canopy dynamics, and VODCA L (2010–2021), for above-ground biomass monitoring. VODCA v2 has lower noise levels than the previous product version and provides valuable insights into plant water dynamics and biomass changes, even in areas where optical data are limited.
J. Zhao, F. Roth, B. Bauer-Marschallinger, W. Wagner, M. Chini, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 911–918, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023, 2023
Florian Roth, Bernhard Bauer-Marschallinger, Mark Edwin Tupas, Christoph Reimer, Peter Salamon, and Wolfgang Wagner
Nat. Hazards Earth Syst. Sci., 23, 3305–3317, https://doi.org/10.5194/nhess-23-3305-2023, https://doi.org/10.5194/nhess-23-3305-2023, 2023
Short summary
Short summary
In August and September 2022, millions of people were impacted by a severe flood event in Pakistan. Since many roads and other infrastructure were destroyed, satellite data were the only way of providing large-scale information on the flood's impact. Based on the flood mapping algorithm developed at Technische Universität Wien (TU Wien), we mapped an area of 30 492 km2 that was flooded at least once during the study's time period. This affected area matches about the total area of Belgium.
Jacopo Dari, Luca Brocca, Sara Modanesi, Christian Massari, Angelica Tarpanelli, Silvia Barbetta, Raphael Quast, Mariette Vreugdenhil, Vahid Freeman, Anaïs Barella-Ortiz, Pere Quintana-Seguí, David Bretreger, and Espen Volden
Earth Syst. Sci. Data, 15, 1555–1575, https://doi.org/10.5194/essd-15-1555-2023, https://doi.org/10.5194/essd-15-1555-2023, 2023
Short summary
Short summary
Irrigation is the main source of global freshwater consumption. Despite this, a detailed knowledge of irrigation dynamics (i.e., timing, extent of irrigated areas, and amounts of water used) are generally lacking worldwide. Satellites represent a useful tool to fill this knowledge gap and monitor irrigation water from space. In this study, three regional-scale and high-resolution (1 and 6 km) products of irrigation amounts estimated by inverting the satellite soil moisture signals are presented.
M. Tupas, C. Navacchi, F. Roth, B. Bauer-Marschallinger, F. Reuß, and W. Wagner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W1-2022, 495–502, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-495-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-495-2022, 2022
Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
Short summary
Short summary
This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
Paolo Filippucci, Luca Brocca, Raphael Quast, Luca Ciabatta, Carla Saltalippi, Wolfgang Wagner, and Angelica Tarpanelli
Hydrol. Earth Syst. Sci., 26, 2481–2497, https://doi.org/10.5194/hess-26-2481-2022, https://doi.org/10.5194/hess-26-2481-2022, 2022
Short summary
Short summary
A high-resolution (1 km) rainfall product with 10–30 d temporal resolution was obtained starting from SM data from Sentinel-1. Good performances are achieved using observed data (gauge and radar) over the Po River Valley, Italy, as a benchmark. The comparison with a product characterized by lower spatial resolution (25 km) highlights areas where the high spatial resolution of Sentinel-1 has great benefits. Possible applications include water management, agriculture and index-based insurances.
Rui Tong, Juraj Parajka, Borbála Széles, Isabella Greimeister-Pfeil, Mariette Vreugdenhil, Jürgen Komma, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 26, 1779–1799, https://doi.org/10.5194/hess-26-1779-2022, https://doi.org/10.5194/hess-26-1779-2022, 2022
Short summary
Short summary
The role and impact of using additional data (other than runoff) for the prediction of daily hydrographs in ungauged basins are not well understood. In this study, we assessed the model performance in terms of runoff, soil moisture, and snow cover predictions with the existing regionalization approaches. Results show that the best transfer methods are the similarity and the kriging approaches. The performance of the transfer methods differs between lowland and alpine catchments.
Paul C. Vermunt, Susan C. Steele-Dunne, Saeed Khabbazan, Jasmeet Judge, and Nick C. van de Giesen
Hydrol. Earth Syst. Sci., 26, 1223–1241, https://doi.org/10.5194/hess-26-1223-2022, https://doi.org/10.5194/hess-26-1223-2022, 2022
Short summary
Short summary
This study investigates the use of hydrometeorological sensors to reconstruct variations in internal vegetation water content of corn and relates these variations to the sub-daily behaviour of polarimetric L-band backscatter. The results show significant sensitivity of backscatter to the daily cycles of vegetation water content and dew, particularly on dry days and for vertical and cross-polarizations, which demonstrates the potential for using radar for studies on vegetation water dynamics.
Wouter Dorigo, Irene Himmelbauer, Daniel Aberer, Lukas Schremmer, Ivana Petrakovic, Luca Zappa, Wolfgang Preimesberger, Angelika Xaver, Frank Annor, Jonas Ardö, Dennis Baldocchi, Marco Bitelli, Günter Blöschl, Heye Bogena, Luca Brocca, Jean-Christophe Calvet, J. Julio Camarero, Giorgio Capello, Minha Choi, Michael C. Cosh, Nick van de Giesen, Istvan Hajdu, Jaakko Ikonen, Karsten H. Jensen, Kasturi Devi Kanniah, Ileen de Kat, Gottfried Kirchengast, Pankaj Kumar Rai, Jenni Kyrouac, Kristine Larson, Suxia Liu, Alexander Loew, Mahta Moghaddam, José Martínez Fernández, Cristian Mattar Bader, Renato Morbidelli, Jan P. Musial, Elise Osenga, Michael A. Palecki, Thierry Pellarin, George P. Petropoulos, Isabella Pfeil, Jarrett Powers, Alan Robock, Christoph Rüdiger, Udo Rummel, Michael Strobel, Zhongbo Su, Ryan Sullivan, Torbern Tagesson, Andrej Varlagin, Mariette Vreugdenhil, Jeffrey Walker, Jun Wen, Fred Wenger, Jean Pierre Wigneron, Mel Woods, Kun Yang, Yijian Zeng, Xiang Zhang, Marek Zreda, Stephan Dietrich, Alexander Gruber, Peter van Oevelen, Wolfgang Wagner, Klaus Scipal, Matthias Drusch, and Roberto Sabia
Hydrol. Earth Syst. Sci., 25, 5749–5804, https://doi.org/10.5194/hess-25-5749-2021, https://doi.org/10.5194/hess-25-5749-2021, 2021
Short summary
Short summary
The International Soil Moisture Network (ISMN) is a community-based open-access data portal for soil water measurements taken at the ground and is accessible at https://ismn.earth. Over 1000 scientific publications and thousands of users have made use of the ISMN. The scope of this paper is to inform readers about the data and functionality of the ISMN and to provide a review of the scientific progress facilitated through the ISMN with the scope to shape future research and operations.
A. Iglseder, M. Bruggisser, A. Dostálová, N. Pfeifer, S. Schlaffer, W. Wagner, and M. Hollaus
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 567–574, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-567-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-567-2021, 2021
Rui Tong, Juraj Parajka, Andreas Salentinig, Isabella Pfeil, Jürgen Komma, Borbála Széles, Martin Kubáň, Peter Valent, Mariette Vreugdenhil, Wolfgang Wagner, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 1389–1410, https://doi.org/10.5194/hess-25-1389-2021, https://doi.org/10.5194/hess-25-1389-2021, 2021
Short summary
Short summary
We used a new and experimental version of the Advanced Scatterometer (ASCAT) soil water index data set and Moderate Resolution Imaging Spectroradiometer (MODIS) C6 snow cover products for multiple objective calibrations of the TUWmodel in 213 catchments of Austria. Combined calibration to runoff, satellite soil moisture, and snow cover improves runoff (40 % catchments), soil moisture (80 % catchments), and snow (~ 100 % catchments) simulation compared to traditional calibration to runoff only.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
Short summary
Short summary
We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Cited articles
Andela, N., Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M., and McVicar, T. R.: Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data, Biogeosciences, 10, 6657–6676, https://doi.org/10.5194/bg-10-6657-2013, 2013. a
Anderson, C., Figa, J., Bonekamp, H., Wilson, J. J. W., Verspeek, J., Stoffelen, A., and Portabella, M.: Validation of Backscatter Measurements
from the Advanced Scatterometer on MetOp-A, J. Atmos. Ocean. Tech., 29, 77–88, https://doi.org/10.1175/JTECH-D-11-00020.1, 2011. a
Attema, E. P.: The active microwave instrument on-board the ERS-1 satellite,
Proc. IEEE, 79, 791–799, 1991. a
Buchhorn, M., Smets, B., Bertels, L., Roo, B. D., Lesiv, M., Tsendbazar, N.-E., Herold, M., and Fritz, S.: Copernicus Global Land Service: Land
Cover 100 m: collection 3: epoch 2015: Globe, Zenodo [data set],
https://doi.org/10.5281/zenodo.3939038, 2020. a, b
Camarão, A. P., Lourenço Júnior, J. D. B., and Dutra, S.: Flooded pasture production for grazing buffalo in the brazilian Amazon region, in: Embrapa Amazônia Oriental-Artigo em anais de congresso (ALICE), Belém, 68–82, https://www.alice.cnptia.embrapa.br/alice/handle/doc/403437 (last access: 14 June 2022), 2002. 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
Chave, J., Navarrete, D., Almeida, S., Álvarez, E., Aragão, L. E. O. C., Bonal, D., Châtelet, P., Silva-Espejo, J. E., Goret, J.-Y., von Hildebrand, P., Jiménez, E., Patiño, S., Peñuela, M. C., Phillips, O. L., Stevenson, P., and Malhi, Y.: Regional and seasonal patterns of litterfall in tropical South America, Biogeosciences, 7, 43–55, https://doi.org/10.5194/bg-7-43-2010, 2010. a, b
De Jeu, R. A.: Retrieval of land surface parameters using passive microwave
remote sensing, PhD thesis, Vrije Universiteit, Amsterdam, ISBN 9090164308, 2003. a
Eiten, G.: The cerrado vegetation of Brazil, Bot. Rev., 38, 201–341, 1972. a
Fernandez-Moran, R., Al-Yaari, A., Mialon, A., Mahmoodi, A., Al Bitar, A.,
De Lannoy, G., Rodriguez-Fernandez, N., Lopez-Baeza, E., Kerr, Y., and Wigneron, J.-P.: SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product, Remote Sens., 9, 457, https://doi.org/10.3390/rs9050457, 2017. a
Ferrazzoli, P., Paloscia, S., Pampaloni, P., Schiavon, G., Solimini, D., and
Coppo, P.: Sensitivity of microwave measurements to vegetation biomass and
soil moisture content: a case study, IEEE Transactions on Geoscience and
Remote Sens., 30, 750–756, 1992. a
Figa-Saldaña, J., Wilson, J. J., Attema, E., Gelsthorpe, R., Drinkwater,
M. R., and Stoffelen, A.: The advanced scatterometer (ASCAT) on the
meteorological operational (MetOp) platform: A follow on for European wind scatterometers, Can. J. Remote Sens., 28, 404–412, 2002. a
Forkel, M., Andela, N., Harrison, S. P., Lasslop, G., van Marle, M., Chuvieco, E., Dorigo, W., Forrest, M., Hantson, S., Heil, A., Li, F., Melton, J., Sitch, S., Yue, C., and Arneth, A.: Emergent relationships with respect to burned area in global satellite observations and fire-enabled vegetation
models, Biogeosciences, 16, 57–76, https://doi.org/10.5194/bg-16-57-2019, 2019. a
Friesen, J., Steele-Dunne, S. C., and van de Giesen, N.: Diurnal differences
in global ERS scatterometer backscatter observations of the land surface,
IEEE T. Geosci. Remote, 50, 2595–2602, 2012. a
Frison, P.-L. and Mougin, E.: Use of ERS-1 wind scatterometer data over land
surfaces, IEEE T. Geosci. Remote, 34, 550–560, 1996. a
Frison, P. L., Mougin, E., and Hiernaux, P.: Observations and interpretation of seasonal ERS-1 wind scatterometer data over northern Sahel (Mali), Remote Sens. Environ., 63, 233–242, 1998. a
Frolking, S., Hagen, S., Braswell, B., Milliman, T., Herrick, C., Peterson, S., Roberts, D., Keller, M., and Palace, M.: Evaluating multiple causes of
persistent low microwave backscatter from Amazon forests after the 2005 drought, PloS One, 12, e0183308, https://doi.org/10.1371/journal.pone.0183308, 2017. a
Greimeister-Pfeil, I., Wagner, W., Quast, R., Hahn, S., Steele-Dunne, S., and
Vreugdenhil, M.: Analysis of short-term soil moisture effects on the ASCAT
backscatter-incidence angle dependence, Sci. Remote Sens., 5, 100053, https://doi.org/10.1016/j.srs.2022.100053, 2022. a
Hamilton, S. K., Sippel, S. J., and Melack, J. M.: Seasonal inundation patterns in two large savanna floodplains of South America: the Llanos de
Moxos (Bolivia) and the Llanos del Orinoco (Venezuela and Colombia), Hydrol. Process., 18, 2103–2116, 2004. a
Hashimoto, H., Wang, W., Dungan, J. L., Li, S., Michaelis, A. R., Takenaka, H., Higuchi, A., Myneni, R. B., and Nemani, R. R.: New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests, Nat. Commun., 12, 684, https://doi.org/10.1038/s41467-021-20994-y, 2021. a
Hawkins, R., Attema, E., Crapolicchio, R., Lecomte, P., Closa, J., Meadows, P., and Srivastava, S.: Stability of Amazon Backscatter at C-Band: Spaceborne
Results from ERS- and RADARSAT-1, in: SAR workshop: CEOS Committee on
Earth Observation Satellites, vol. 450, p. 99, https://earth.esa.int/eogateway/documents/20142/37627/p103.pdf (last access: 14 June 2022), 2000. a
Hordijk, I., Meijer, F., Nissen, E., Boorsma, T., and Poorter, L.: Cattle
affect regeneration of the palm species Attalea princeps in a Bolivian
forest–savanna mosaic, Biotropica, 51, 28–38, 2019. a
Huffman, G. J., Adler, R. F., Bolvin, D. T., and Gu, G.: Improving the global
precipitation record: GPCP Version 2.1, Geophys. Res. Lett., 36, L17808, https://doi.org/10.1029/2009GL040000, 2009. a
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. a
Jarlan, L., Mougin, E., Frison, P. L., Mazzega, P., and Hiernaux, P.: Analysis of ERS wind scatterometer time series over Sahel (Mali), Remote Sens. Environ., 81, 404–415, https://doi.org/10.1016/S0034-4257(02)00015-9, 2002. a
Jiménez-Muñoz, J. C., Mattar, C., Barichivich, J., Santamaría-Artigas, A., Takahashi, K., Malhi, Y., Sobrino, J. A., and
Van Der Schrier, G.: Record-breaking warming and extreme drought in the
Amazon rainforest during the course of El Niño 2015–2016, Sci. Rep.,
6, 33130, https://doi.org/10.1038/srep33130, 2016. a, b
Kennett, R. G. and Li, F. K.: Seasat over-land scatterometer data. II. Selection of extended area and land-target sites for the calibration of
spaceborne scatterometers, IEEE T. Geosci. Remote, 27, 779–788, 1989. a
Khabbazan, S., Steele-Dunne, S. C., Vermunt, P., Judge, J., Vreugdenhil, M.,
and Gao, G.: The influence of surface canopy water on the relationship between L-band backscatter and biophysical variables in agricultural monitoring, Remote Sens. Environ., 268, 112789, https://doi.org/10.1016/j.rse.2021.112789, 2022. a
Konings, A. G., Piles, M., Rötzer, K., McColl, K. A., Chan, S. K., and
Entekhabi, D.: Vegetation optical depth and scattering albedo retrieval using
time series of dual-polarized L-band radiometer observations, Remote Sens. Environ., 172, 178–189, https://doi.org/10.1016/j.rse.2015.11.009, 2016. a
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. a, b
Konings, A. G., Saatchi, S. S., Frankenberg, C., Keller, M., Leshyk, V.,
Anderegg, W. R., 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, 2021. a
Landerer, F. W. and Swenson, S.: Accuracy of scaled GRACE terrestrial water
storage estimates, Water Resour. Res., 48, W04531, https://doi.org/10.1029/2011WR011453, 2012. a
Liu, Y. Y., de Jeu, R. A., McCabe, M. F., Evans, J. P., and van Dijk, A. I.:
Global long-term passive microwave satellite-based retrievals of vegetation
optical depth, Geophys. Res. Let., 38, L18402, https://doi.org/10.1029/2011GL048684, 2011. a
Liu, Y. Y., Dijk, A. I., McCabe, M. F., Evans, J. P., and Jeu, R. A.: Global
vegetation biomass change (1988–2008) and attribution to environmental and
human drivers, Global Ecol. Biogeogr., 22, 692–705, https://doi.org/10.1111/geb.12024, 2013. a
Liu, Y. Y., Van Dijk, A. I., De Jeu, R. A., Canadell, J. G., McCabe, M. F.,
Evans, J. P., and Wang, G.: Recent reversal in loss of global terrestrial
biomass, Nat. Clim. Change, 5, 470–474, 2015. a
Liu, Y. Y., van Dijk, A. I., Miralles, D. G., McCabe, M. F., Evans, J. P.,
de Jeu, R. A., Gentine, P., Huete, A., Parinussa, R. M., Wang, L., Guan, K., Berry, J., and Restrepo-Coupe, N.: Enhanced canopy growth precedes senescence in 2005 and 2010 Amazonian droughts, Remote Sens. Environ., 211, 26–37, https://doi.org/10.1016/j.rse.2018.03.035, 2018. a, b, c, d
Marengo, J. A., Tomasella, J., Alves, L. M., Soares, W. R., and Rodriguez, D. A.: The drought of 2010 in the context of historical droughts in the Amazon region, Geophys. Res. Lett., 38, L12703, https://doi.org/10.1029/2011GL047436, 2011. a
McNairn, H., Van der Sanden, J. J., Brown, R. J., and Ellis, J.: The potential of RADARSAT-2 for crop mapping and assessing crop condition, in: Second International Conference on Geospatial Information in Agriculture and Forestry, Lake Buena Vista, 10–12 January 2000, Florida, ftp://ftp.geogratis.gc.ca/pub/nrcan_rncan/publications/STPublications_PublicationsST/219/219589/4716.pdf (last access: 14 June 2022), 2000. 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
Naeimi, V., Scipal, K., Bartalis, Z., Hasenauer, S., and Wagner, W.: An
Improved Soil Moisture Retrieval Algorithm for ERS and METOP Scatterometer Observations, IEEE T. Geosci. Remote, 47, 1999–2013, https://doi.org/10.1109/TGRS.2008.2011617, 2009. a, b
Oliveira, R., Bezerra, L., Davidson, E., Pinto, F., Klink, C., Nepstad, D., and Moreira, A.: Deep root function in soil water dynamics in cerrado savannas of central Brazil, Funct. Ecol., 19, 574–581, 2005. a
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell,
G. V., Underwood, E. C., D'amico, J. A., Itoua, I., Strand, H. E., Morrison,
J. C., Loucks, C. J.. Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., and Kassem, K. R: Terrestrial Ecoregions of the World: A New Map of Life on Earth A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity, BioScience, 51, 933–938, https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2, 2001. a, b
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen–Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007. a
Pfeil, I., Wagner, W., Forkel, M., Dorigo, W., and Vreugdenhil, M.: Does ASCAT observe the spring reactivation in temperate deciduous broadleaf forests?, Remote Sens. Environ., 250, 112042, https://doi.org/10.1016/j.rse.2020.112042, 2020. a
Rao, K., Anderegg, W. R. L., 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,
https://doi.org/10.1016/j.rse.2019.03.026, 2019. a, b
Schroeder, R., McDonald, K. C., Azarderakhsh, M., and Zimmermann, R.: ASCAT
MetOp-A diurnal backscatter observations of recent vegetation drought patterns over the contiguous US: An assessment of spatial extent and
relationship with precipitation and crop yield, Remote Sents. Environ., 177, 153–159, 2016. a
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-year
high-resolution global dataset of meteorological forcings for land surface
modeling, J. Climate, 19, 3088–3111, 2006. a
Soares, B. S., Nepstad, D. C., Curran, L. M., Cerqueira, G. C., Garcia, R. A., Ramos, C. A., Voll, E., McDonald, A., Lefebvre, P., and Schlesinger, P.:
Modelling conservation in the Amazon basin, Nature, 440, 520–523, 2006. a
Steele-Dunne, S. C., Friesen, J., and van de Giesen, N.: Using diurnal
variation in backscatter to detect vegetation water stress, IEEE T. Geosci. Remote, 50, 2618–2629, 2012. a
Steele-Dunne, S. C., McNairn, H., Monsivais-Huertero, A., Judge, J., Liu,
P.-W., and Papathanassiou, K.: Radar remote sensing of agricultural canopies:
A review, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 10, 2249–2273, 2017. a
Stoffelen, A., Aaboe, S., Calvet, J.-C., Cotton, J., De Chiara, G., Saldana,
J. F., Mouche, A. A., Portabella, M., Scipal, K., and Wagner, W.: Scientific
developments and the EPS-SG scatterometer, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 10, 2086–2097, 2017. a
Swenson, S. and Wahr, J.: Post-processing removal of correlated errors in
GRACE data, Geophys. Res. Lett., 33, L08402, https://doi.org/10.1029/2005GL025285, 2006. a
Templ, B., Koch, E., Bolmgren, K., Ungersböck, M., Paul, A., Scheifinger,
H., Busto, M., Chmielewski, F.-M., Hájková, L., Hodzić, S., Kaspar, F., Pietragalla, B., Romero-Fresneda, R., Tolvanen, A., Vučetič, V., Zimmermann, K., and Zust, A.: Pan European Phenological database (PEP725): a single point of access for European data, Int. J. Climatol., 62, 1109–1113, 2018. a
Teubner, I., Forkel, M., Jung, M., Liu, Y., Miralles, D., Parinussa, R.,
van der Schalie, R., Vreugdenhil, M., Schwalm, C., Tramontana, G., Camps-Valls, G., and Dorigo, W.: Assessing the relationship between microwave
vegetation optical depth and gross primary production, Int. J. Appl. Earth Obs. Geoinform., 65, 79–91, https://doi.org/10.1016/j.jag.2017.10.006, 2018. a
Teubner, I., Forkel, M., Camps-Valls, G., Jung, M., Miralles, D., Tramontana,
G., van der Schalie, R., Vreugdenhil, M., Mösinger, L., and Dorigo, W.: A
carbon sink-driven approach to estimate gross primary production from
microwave satellite observations, Remote Sens. Environ., 229, 100–113, https://doi.org/10.1016/j.rse.2019.04.022, 2019. a
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. a
Townsend, P.: Relationships between forest structure and the detection of flood inundation in forested wetlands using C-band SAR, Int. J. Remote Sens., 23, 443–460, 2002. a
TU Wien, Department of Geodesy and Geoinformation: Soil WAter Retrieval Package (WARP): v5.10.0, 2022. a
Ulaby, F.: Radar response to vegetation, IEEE T. Antenn. Propagat., 23, 36–45, https://doi.org/10.1109/TAP.1975.1140999, 1975. a
Vermunt, P. C., Khabbazan, S., Steele-Dunne, S. C., Judge, J.,
Monsivais-Huertero, A., Guerriero, L., and Liu, P.-W.: Response of Subdaily
L-Band Backscatter to Internal and Surface Canopy Water Dynamics, IEEE T. Geosci. Remote, 59, 7322–7337, https://doi.org/10.1109/TGRS.2020.3035881, 2020. a
Wagner, F. H., Hérault, B., Bonal, D., Stahl, C., Anderson, L. O., Baker, T. R., Becker, G. S., Beeckman, H., Boanerges Souza, D., Botosso, P. C., Bowman, D. M. J. S., Bräuning, A., Brede, B., Brown, F. I., Camarero, J. J., Camargo, P. B., Cardoso, F. C. G., Carvalho, F. A., Castro, W., Chagas, R. K., Chave, J., Chidumayo, E. N., Clark, D. A., Costa, F. R. C., Couralet, C., da Silva Mauricio, P. H., Dalitz, H., de Castro, V. R., de Freitas Milani, J. E., de Oliveira, E. C., de Souza Arruda, L., Devineau, J.-L., Drew, D. M., Dünisch, O., Durigan, G., Elifuraha, E., Fedele, M., Ferreira Fedele, L., Figueiredo Filho, A., Finger, C. A. G., Franco, A. C., Freitas Júnior, J. L., Galvão, F., Gebrekirstos, A., Gliniars, R., Graça, P. M. L. D. A., Griffiths, A. D., Grogan, J., Guan, K., Homeier, J., Kanieski, M. R., Kho, L. K., Koenig, J., Kohler, S. V., Krepkowski, J., Lemos-Filho, J. P., Lieberman, D., Lieberman, M. E., Lisi, C. S., Longhi Santos, T., López Ayala, J. L., Maeda, E. E., Malhi, Y., Maria, V. R. B., Marques, M. C. M., Marques, R., Maza Chamba, H., Mbwambo, L., Melgaço, K. L. L., Mendivelso, H. A., Murphy, B. P., O'Brien, J. J., Oberbauer, S. F., Okada, N., Pélissier, R., Prior, L. D., Roig, F. A., Ross, M., Rossatto, D. R., Rossi, V., Rowland, L., Rutishauser, E., Santana, H., Schulze, M., Selhorst, D., Silva, W. R., Silveira, M., Spannl, S., Swaine, M. D., Toledo, J. J., Toledo, M. M., Toledo, M., Toma, T., Tomazello Filho, M., Valdez Hernández, J. I., Verbesselt, J., Vieira, S. A., Vincent, G., Volkmer de Castilho, C., Volland, F., Worbes, M., Zanon, M. L. B., and Aragão, L. E. O. C.: Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests, Biogeosciences, 13, 2537–2562, https://doi.org/10.5194/bg-13-2537-2016, 2016. a, b
Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S.,
Figa-Saldaña, J., de Rosnay, P., Jann, A., Schneider, S., Komma, J., Kubu, G., Brugger, K., Aubrecht, C., Züger, J., Gangkofner, U., Kienberger, S., Brocca, L., Wang, Y., Blöschl, G., Eitzinger, J., Steinnocher, K., Zeil, P., and Rubel, F.: The ASCAT Soil Moisture Product: A Review of Its Specifications, Validation Results, and Emerging Applications,
Meteorol. Z., 22, 5–33, https://doi.org/10.1127/0941-2948/2013/0399, 2013. a, b
Wahr, J., Molenaar, M., and Bryan, F.: Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACE, J. Geophys. Res.-Solid, 103, 30205–30229, 1998. a
Wismann, V. R., Boehnke, K., and Schmullius, C.: Monitoring ecological dynamics in Africa with the ERS-1 scatterometer, in: IEEE 1995 International Geoscience and Remote Sensing Symposium, IGARSS'95, Quantitative Remote Sensing for Science and Applications, vol. 2, 10–14 July 1995, 1523–1525, https://doi.org/10.1109/IGARSS.1995.521798, 1995. a
Woodhouse, I. ., van der Sanden, J. J., and Hoekman, D. H.: Scatterometer
observations of seasonal backscatter variation over tropical rain forest,
IEEE T. Geosci. Remote, 37, 859–861, 1999. a
Wright, S. J. and Van Schaik, C. P.: Light and the phenology of tropical trees, Am. Nat., 143, 192–199, 1994. a
WWF: Terrestrial Ecoregions|Biome Categories|WWF,
https://files.worldwildlife.org/wwfcmsprod/files/Publication/file/6kcchn7e3u_official_teow.zip?_ga=2.266153039.1704052247.1654773215-2069422009.1654773214 (last access: 6 June 2022), 2019. a
Executive editor
How vegetation responds to changing climate will have large implications for our global carbon and water cycles. This study shows the potential of using C-band scatterometer data to investigate vegetation status-- with over a 30 year record. Many applications related to biosphere-atmosphere interactions could potentially develop from here and improve weather and climate predictions, and model evaluation in terms of their representation of vegetation dynamics.
How vegetation responds to changing climate will have large implications for our global carbon...
Short summary
This study investigates spatial and temporal patterns in the incidence angle dependence of backscatter from the ASCAT C-band scatterometer and relates those to precipitation, humidity, and radiation data and GRACE equivalent water thickness in ecoregions in the Amazon. The results show that the ASCAT data record offers a unique perspective on vegetation water dynamics exhibiting sensitivity to moisture availability and demand and phenological change at interannual, seasonal, and diurnal scales.
This study investigates spatial and temporal patterns in the incidence angle dependence of...