Articles | Volume 26, issue 3
https://doi.org/10.5194/hess-26-841-2022
© 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-26-841-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Feb 2022
Research article |
| 15 Feb 2022
| 15 Feb 2022
Monitoring surface water dynamics in the Prairie Pothole Region of North Dakota using dual-polarised Sentinel-1 synthetic aperture radar (SAR) time series
Stefan Schlaffer
CORRESPONDING AUTHOR
Department of Geodesy and Geoinformation, Technische Universität Wien, Wiedner Hauptstr. 8–10, 1040 Vienna, Austria
Earth Observation Center, German Aerospace Center, Münchener Str. 20, 82234 Wessling, Germany
Marco Chini
Environmental Sensing and Modelling Unit, Luxembourg Institute of Science and Technology, 41 rue du Brill, 4422 Belvaux, Luxembourg
Wouter Dorigo
Department of Geodesy and Geoinformation, Technische Universität Wien, Wiedner Hauptstr. 8–10, 1040 Vienna, Austria
Simon Plank
Earth Observation Center, German Aerospace Center, Münchener Str. 20, 82234 Wessling, Germany
Related authors
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
Wolfgang Preimesberger, Pietro Stradiotti, and Wouter Dorigo
Earth Syst. Sci. Data, 17, 4305–4329, https://doi.org/10.5194/essd-17-4305-2025, https://doi.org/10.5194/essd-17-4305-2025, 2025
Short summary
Short summary
We introduce the official ESA CCI Soil Moisture GAPFILLED climate data record. A univariate interpolation algorithm is applied to predict missing data points without relying on ancillary variables. The dataset includes gap-free uncertainty estimates for all predictions and was validated with independent in situ reference measurements. Our data record is recommended for applications which require global long-term gap-free satellite soil moisture data.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Bethan L. Harris, Christopher M. Taylor, Wouter Dorigo, Ruxandra-Maria Zotta, Darren Ghent, and Iván Noguera
EGUsphere, https://doi.org/10.5194/egusphere-2025-1489, https://doi.org/10.5194/egusphere-2025-1489, 2025
Short summary
Short summary
An improved understanding of land-atmosphere coupling processes during flash (rapid-onset) droughts is needed to aid the development of forecasts for these events. We use satellite observations to investigate the surface energy budget during flash droughts globally. The most intense events show a perturbed surface energy budget months before onset. In some regions, vegetation observations 1–2 months before onset provide information on the likelihood of heat extremes during an event.
Martin Hirschi, Pietro Stradiotti, Bas Crezee, Wouter Dorigo, and Sonia I. Seneviratne
Hydrol. Earth Syst. Sci., 29, 397–425, https://doi.org/10.5194/hess-29-397-2025, https://doi.org/10.5194/hess-29-397-2025, 2025
Short summary
Short summary
We investigate the potential of long-term satellite and reanalysis products for characterising soil drying by analysing their 2000–2022 soil moisture trends and their representation of agroecological drought events of this period. Soil moisture trends are globally diverse and partly contradictory between products. This also affects the products' drought-detection capacity. Based on the best-estimate products, consistent soil drying is observed over more than 40 % of the land area covered.
Ruxandra-Maria Zotta, Leander Moesinger, Robin van der Schalie, Mariette Vreugdenhil, Wolfgang Preimesberger, Thomas Frederikse, Richard de Jeu, and Wouter Dorigo
Earth Syst. Sci. Data, 16, 4573–4617, https://doi.org/10.5194/essd-16-4573-2024, https://doi.org/10.5194/essd-16-4573-2024, 2024
Short summary
Short summary
VODCA v2 is a dataset providing vegetation indicators for long-term ecosystem monitoring. VODCA v2 comprises two products: VODCA CXKu, spanning 34 years of observations (1987–2021), suitable for monitoring upper canopy dynamics, and VODCA L (2010–2021), for above-ground biomass monitoring. VODCA v2 has lower noise levels than the previous product version and provides valuable insights into plant water dynamics and biomass changes, even in areas where optical data are limited.
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
Samuel Scherrer, Gabriëlle De Lannoy, Zdenko Heyvaert, Michel Bechtold, Clement Albergel, Tarek S. El-Madany, and Wouter Dorigo
Hydrol. Earth Syst. Sci., 27, 4087–4114, https://doi.org/10.5194/hess-27-4087-2023, https://doi.org/10.5194/hess-27-4087-2023, 2023
Short summary
Short summary
We explored different options for data assimilation (DA) of the remotely sensed leaf area index (LAI). We found strong biases between LAI predicted by Noah-MP and observations. LAI DA that does not take these biases into account can induce unphysical patterns in the resulting LAI and flux estimates and leads to large changes in the climatology of root zone soil moisture. We tested two bias-correction approaches and explored alternative solutions to treating bias in LAI DA.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
Short summary
Short summary
We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Remi Madelon, Nemesio J. Rodríguez-Fernández, Hassan Bazzi, Nicolas Baghdadi, Clement Albergel, Wouter Dorigo, and Mehrez Zribi
Hydrol. Earth Syst. Sci., 27, 1221–1242, https://doi.org/10.5194/hess-27-1221-2023, https://doi.org/10.5194/hess-27-1221-2023, 2023
Short summary
Short summary
We present an approach to estimate soil moisture (SM) at 1 km resolution using Sentinel-1 and Sentinel-3 satellites. The estimates were compared to other high-resolution (HR) datasets over Europe, northern Africa, Australia, and North America, showing good agreement. However, the discrepancies between the different HR datasets and their lower performances compared with in situ measurements and coarse-resolution datasets show the remaining challenges for large-scale HR SM mapping.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
Short summary
Short summary
Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
Short summary
Short summary
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Matthias Forkel, Luisa Schmidt, Ruxandra-Maria Zotta, Wouter Dorigo, and Marta Yebra
Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, https://doi.org/10.5194/hess-27-39-2023, 2023
Short summary
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.
Leander Moesinger, Ruxandra-Maria Zotta, Robin van der Schalie, Tracy Scanlon, Richard de Jeu, and Wouter Dorigo
Biogeosciences, 19, 5107–5123, https://doi.org/10.5194/bg-19-5107-2022, https://doi.org/10.5194/bg-19-5107-2022, 2022
Short summary
Short summary
The standardized vegetation optical depth index (SVODI) can be used to monitor the vegetation condition, such as whether the vegetation is unusually dry or wet. SVODI has global coverage, spans the past 3 decades and is derived from multiple spaceborne passive microwave sensors of that period. SVODI is based on a new probabilistic merging method that allows the merging of normally distributed data even if the data are not gap-free.
Edgar U. Zorn, Aiym Orynbaikyzy, Simon Plank, Andrey Babeyko, Herlan Darmawan, Ismail Fata Robbany, and Thomas R. Walter
Nat. Hazards Earth Syst. Sci., 22, 3083–3104, https://doi.org/10.5194/nhess-22-3083-2022, https://doi.org/10.5194/nhess-22-3083-2022, 2022
Short summary
Short summary
Tsunamis caused by volcanoes are a challenge for warning systems as they are difficult to predict and detect. In Southeast Asia there are many active volcanoes close to the coast, so it is important to identify the most likely volcanoes to cause tsunamis in the future. For this purpose, we developed a point-based score system, allowing us to rank volcanoes by the hazard they pose. The results may be used to improve local monitoring and preparedness in the affected areas.
Robin van der Schalie, Mendy van der Vliet, Clément Albergel, Wouter Dorigo, Piotr Wolski, and Richard de Jeu
Hydrol. Earth Syst. Sci., 26, 3611–3627, https://doi.org/10.5194/hess-26-3611-2022, https://doi.org/10.5194/hess-26-3611-2022, 2022
Short summary
Short summary
Climate data records of surface soil moisture, vegetation optical depth, and land surface temperature can be derived from passive microwave observations. The ability of these datasets to properly detect anomalies and extremes is very valuable in climate research and can especially help to improve our insight in complex regions where the current climate reanalysis datasets reach their limitations. Here, we present a case study over the Okavango Delta, where we focus on inter-annual variability.
W. Wang, M. Motagh, S. Plank, A. Orynbaikyzy, and S. Roessner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1181–1187, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1181-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1181-2022, 2022
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
Short summary
Short summary
Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
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.
Concetta Di Mauro, Renaud Hostache, Patrick Matgen, Ramona Pelich, Marco Chini, Peter Jan van Leeuwen, Nancy K. Nichols, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 4081–4097, https://doi.org/10.5194/hess-25-4081-2021, https://doi.org/10.5194/hess-25-4081-2021, 2021
Short summary
Short summary
This study evaluates how the sequential assimilation of flood extent derived from synthetic aperture radar data can help improve flood forecasting. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. Our empirical results demonstrate the efficiency of the proposed data assimilation framework, as forecasting errors are substantially reduced as a result of the assimilation.
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
Irene E. Teubner, Matthias Forkel, Benjamin Wild, Leander Mösinger, and Wouter Dorigo
Biogeosciences, 18, 3285–3308, https://doi.org/10.5194/bg-18-3285-2021, https://doi.org/10.5194/bg-18-3285-2021, 2021
Short summary
Short summary
Vegetation optical depth (VOD), which contains information on vegetation water content and biomass, has been previously shown to be related to gross primary production (GPP). In this study, we analyzed the impact of adding temperature as model input and investigated if this can reduce the previously observed overestimation of VOD-derived GPP. In addition, we could show that the relationship between VOD and GPP largely holds true along a gradient of dry or wet conditions.
Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, and Eric F. Wood
Hydrol. Earth Syst. Sci., 25, 17–40, https://doi.org/10.5194/hess-25-17-2021, https://doi.org/10.5194/hess-25-17-2021, 2021
Short summary
Short summary
We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.
Theresa C. van Hateren, Marco Chini, Patrick Matgen, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-583, https://doi.org/10.5194/hess-2020-583, 2020
Manuscript not accepted for further review
Short summary
Short summary
Agricultural droughts occur when the water content of the soil diminishes to such a level that vegetation is negatively impacted. Here we show that, although they are classified as the same type of drought, substantial differences between soil moisture and vegetation droughts exist. This duality is not included in the term agricultural drought, and thus is a potential issue in drought research. We argue that a distinction should be made between soil moisture and vegetation drought events.
Renaud Hostache, Dominik Rains, Kaniska Mallick, Marco Chini, Ramona Pelich, Hans Lievens, Fabrizio Fenicia, Giovanni Corato, Niko E. C. Verhoest, and Patrick Matgen
Hydrol. Earth Syst. Sci., 24, 4793–4812, https://doi.org/10.5194/hess-24-4793-2020, https://doi.org/10.5194/hess-24-4793-2020, 2020
Short summary
Short summary
Our objective is to investigate how satellite microwave sensors, particularly Soil Moisture and Ocean Salinity (SMOS), may help to reduce errors and uncertainties in soil moisture simulations with a large-scale conceptual hydro-meteorological model. We assimilated a long time series of SMOS observations into a hydro-meteorological model and showed that this helps to improve model predictions. This work therefore contributes to the development of faster and more accurate drought prediction tools.
Cited articles
Abatzoglou, J. T.: Development of gridded surface meteorological data for
ecological applications and modelling,
Int. J. Climatol.,
33, 121–131, https://doi.org/10.1002/joc.3413, 2013. a, b
Abatzoglou, J.: Gridded Surface Meteorological Dataset (GRIDMET) PDSI, https://www.climatologylab.org/gridmet.html, last access: 17 May 2021. a
Acreman, M.: Wetlands and water storage: current and future trends and
issues,
available at: https://www.ramsar.org/sites/default/files/documents/library/bn2.pdf (last access: 14 December 2020),
2012. a
Ashman, K. A., Bird, C. M., and Zepf, S. E.: Detecting bimodality in
astronomical datasets, Astron. J., 108, 2348,
https://doi.org/10.1086/117248, 1994. a
Bartsch, A., Trofaier, A. M., Hayman, G., Sabel, D., Schlaffer, S., Clark, D. B., and Blyth, E.: Detection of open water dynamics with ENVISAT ASAR in support of land surface modelling at high latitudes, Biogeosciences, 9, 703–714, https://doi.org/10.5194/bg-9-703-2012, 2012. a, b, c
Bertassello, L. E., Jawitz, J. W., Aubeneau, A. F., Botter, G., and Rao, P.
S. C.: Stochastic dynamics of wetlandscapes: Ecohydrological implications of
shifts in hydro-climatic forcing and landscape configuration, Sci. Total Environ., 694, 133765, https://doi.org/10.1016/j.scitotenv.2019.133765,
2019. a
Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M.: The SWOT Mission and
Its Capabilities for Land Hydrology, Surv. Geophys., 37, 307–337,
https://doi.org/10.1007/s10712-015-9346-y, 2016. a
Brisco, B.: Mapping and Monitoring Surface Water and Wetlands with Synthetic
Aperture Radar, in: Remote Sensing of Wetlands: Applications and Advances,
edited by: Tiner, R. W., Lang, M. W., and Klemas, V., chap. 6, 119–136,
CRC Press, ISBN 978-1-4822-3738-2, 2015. a
Brooks, J. R., Mushet, D. M., Vanderhoof, M. K., Leibowitz, S. G., Christensen,
J. R., Neff, B. P., Rosenberry, D. O., Rugh, W. D., and Alexander, L. C.:
Estimating Wetland Connectivity to Streams in the Prairie Pothole Region: An
Isotopic and Remote Sensing Approach, Water Resour. Res., 54, 1–23,
https://doi.org/10.1002/2017WR021016, 2018. a, b
Cheng, F. Y. and Basu, N. B.: Biogeochemical hotspots: Role of small water
bodies in landscape nutrient processing, Water Resour. Res., 53,
5038–5056, https://doi.org/10.1002/2016WR020102, 2017. a
Chini, M., Hostache, R., Giustarini, L., and Matgen, P.: A Hierarchical
Split-Based Approach for Parametric Thresholding of SAR Images: Flood
Inundation as a Test Case,
IEEE T. Geosci. Remote, 55, 6975–6988, https://doi.org/10.1109/TGRS.2017.2737664, 2017. a, b, c
Cleveland, W. S., Grosse, E., and Shyu, W. M.: Local regression models,
chap. 8, edited by: Chambers, J. M. and Hastie, T. J., Wadsworth & Brooks/Cole, ISBN 978-0534167646, 1992. a
Cohen, M. J., Creed, I. F., Alexander, L., Basu, N. B., Calhoun, A. J., Craft,
C., D'Amico, E., DeKeyser, E., Fowler, L., Golden, H. E., Jawitz, J. W.,
Kalla, P., Kirkman, L. K., Lane, C. R., Lang, M., Leibowitz, S. G., Lewis,
D. B., Marton, J., McLaughlin, D. L., Mushet, D. M., Raanan-Kiperwas, H.,
Rains, M. C., Smith, L., and Walls, S. C.: Do geographically isolated
wetlands influence landscape functions?, P. Natl. Acad. Sci. USA, 113, 1978–1986,
https://doi.org/10.1073/pnas.1512650113, 2016. a, b
Copernicus: Sentinel-1 data, https://scihub.copernicus.eu/, last access: 20 January 2021. a
D'Addabbo, A., Refice, A., Pasquariello, G., Lovergine, F. P., Capolongo, D.,
and Manfreda, S.: A Bayesian Network for Flood Detection Combining SAR
Imagery and Ancillary Data,
IEEE T. Geosci. Remote, 54, 3612–3625, https://doi.org/10.1109/TGRS.2016.2520487, 2016. a, b, c, d
ESA CEOS EO Handbook: Mission Summary – Sentinel-1 C,
available at: http://database.eohandbook.com/database/missionsummary.aspx?missionID=577, last access: 20 April 2021. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.:
The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004,
https://doi.org/10.1029/2005RG000183, 2007. a
Frey, D., Butenuth, M., and Straub, D.: Probabilistic graphical models for
flood state detection of roads combining imagery and DEM,
IEEE Geosci. Remote S., 9, 1051–1055, https://doi.org/10.1109/LGRS.2012.2188881,
2012. a
Giustarini, L., Hostache, R., Kavetski, D., Chini, M., Corato, G., Schlaffer,
S., and Matgen, P.: Probabilistic Flood Mapping Using Synthetic Aperture
Radar Data, IEEE T. Geosci. Remote, 54,
6958–6969, https://doi.org/10.1109/TGRS.2016.2592951, 2016. a, b
Gleason, R. A., Tangen, B. A., Laubhan, M. K., Kermes, K. E., and Euliss Jr.,
N. H.: Estimating Water Storage Capacity of Existing and Potentially
Restorable Wetland Depressions in a Subbasin of the Red River of the North,
Tech. rep., Geological Survey (U.S.), 2007-1159, 37 pp., https://doi.org/10.3133/ofr20071159, 2007. a
GRASS Development Team: Geographic Resources Analysis Support System (GRASS)
Software, Version 7.2, available at: http://grass.osgeo.org/ (last access: 12 November 2021), 2017. a
Henry, J.-B., Chastanet, P., Fellah, K., and Desnos, Y.-L.: Envisat
multi-polarized ASAR data for flood mapping,
Int. J. Remote Sens., 27, 1921–1929, https://doi.org/10.1080/01431160500486724, 2006. a
Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K., and Nowosad, J.:
landscapemetrics: an open-source R tool to calculate landscape metrics,
Ecography, 42, 1648–1657, https://doi.org/10.1111/ecog.04617, 2019. a
Huang, S., Dahal, D., Young, C., Chander, G., and Liu, S.: Integration of
Palmer Drought Severity Index and remote sensing data to simulate wetland
water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota,
Remote Sens. Environ., 115, 3377–3389,
https://doi.org/10.1016/j.rse.2011.08.002, 2011a. a, b
Huang, S., Young, C., Feng, M., Heidemann, K., Cushing, M., Mushet, D. M., and
Liu, S.: Demonstration of a conceptual model for using LiDAR to improve the
estimation of floodwater mitigation potential of Prairie Pothole Region
wetlands, J. Hydrol., 405, 417–426,
https://doi.org/10.1016/j.jhydrol.2011.05.040, 2011b. a, b, c
Krapu, C., Kumar, M., and Borsuk, M.: Identifying Wetland Consolidation Using
Remote Sensing in the North Dakota Prairie Pothole Region,
Water Resour. Res., 54, 7478–7494, https://doi.org/10.1029/2018WR023338, 2018. a, b
Kumar, R., Rosen, P., and Misra, T.: NASA-ISRO synthetic aperture radar:
science and applications, Proc. SPIE, 9881, 988103,
https://doi.org/10.1117/12.2228027, 2016. a
Li, Y., Martinis, S., Wieland, M., Schlaffer, S., and Natsuaki, R.: Urban
Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian
Network Fusion, Remote Sens., 11, 2231, https://doi.org/10.3390/rs11192231, 2019. a, b
Lopes, A., Nezry, E., Touzi, R., and Laur, H.: Structure detection and
statistical adaptive speckle filtering in SAR images, Int. J. Remote Sens., 14, 1735–1758, https://doi.org/10.1080/01431169308953999, 1993. a
Martinis, S., Twele, A., and Voigt, S.: Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data, Nat. Hazards Earth Syst. Sci., 9, 303–314, https://doi.org/10.5194/nhess-9-303-2009, 2009. a, b
Martinis, S., Plank, S., and Ćwik, K.: The Use of Sentinel-1 Time-Series
Data to Improve Flood Monitoring in Arid Areas, Remote Sens., 10, 583,
https://doi.org/10.3390/rs10040583, 2018. a
McIntyre, N. E., Wright, C. K., Swain, S., Hayhoe, K., Liu, G., Schwartz,
F. W., and Henebry, G. M.: Climate forcing of wetland landscape connectivity
in the Great Plains, Front. Ecol. Environ., 12, 59–64,
https://doi.org/10.1890/120369, 2014. a
McKenna, O. P., Kucia, S. R., Mushet, D. M., Anteau, M. J., and Wiltermuth,
M. T.: Synergistic Interaction of Climate and Land-Use Drivers Alter the
Function of North American, Prairie-Pothole Wetlands, Sustainability, 11,
6581, https://doi.org/10.3390/su11236581, 2019. a
Montgomery, J. S., Hopkinson, C., Brisco, B., Patterson, S., and Rood, S. B.:
Wetland hydroperiod classification in the western prairies using
multitemporal synthetic aperture radar, Hydrol. Process., 32,
1476–1490, https://doi.org/10.1002/hyp.11506, 2018. a, b, c
Mushet, D. M., Roth, C., and Scherff, E.: Cottonwood Lake Study Area –
Digital Elevation Model with Topobathy, https://doi.org/10.5066/F7V69GTD, 2017. a
NOAA: Spring flooding summary 2019,
available at: https://www.weather.gov/dvn/summary_SpringFlooding_2019 (last access: 3 May 2021),
2019. a
NOAA NCDC Global Surface Summary of the Day (GSOD): Precipitation data, https://www.ncei.noaa.gov/access/search/data-search/global-summary-of-the-day, last access: 8 February 2022. a
North Dakota State Water Commission: Topography data, https://www.gis.nd.gov/, last access: 8 February 2022. a
Otsu, N.: A Threshold Selection Method from Gray-Level Histograms, IEEE
T. Syst. Man. Cyb., 9, 62–66,
https://doi.org/10.1109/TSMC.1979.4310076, 1979. a
Ozesmi, S. L. and Bauer, M. E.: Satellite remote sensing of wetlands,
Wetl. Ecol. Manag., 10, 381–402,
https://doi.org/10.1023/A:1020908432489, 2002. a
Pekel, J.-F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540,
418–422, https://doi.org/10.1038/nature20584, 2016. a
Proulx, R. A., Knudson, M. D., Kirilenko, A., Vanlooy, J. A., and Zhang, X.:
Significance of surface water in the terrestrial water budget: A case study
in the Prairie Coteau using GRACE, GLDAS, Landsat, and groundwater well
data, Water Resour. Res., 49, 5756–5764, https://doi.org/10.1002/wrcr.20455,
2013. a, b
Rennó, C. D., Nobre, A. D., Cuartas, L. A., Soares, J. V., Hodnett,
M. G., Tomasella, J., and Waterloo, M. J.: HAND, a new terrain descriptor
using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia,
Remote Sens. Environ., 112, 3469–3481,
https://doi.org/10.1016/j.rse.2008.03.018, 2008. a
Reschke, J., Bartsch, A., Schlaffer, S., and Schepaschenko, D.: Capability of
C-Band SAR for Operational Wetland Monitoring at High Latitudes, Remote
Sens., 4, 2923–2943, https://doi.org/10.3390/rs4102923, 2012. a
Richards, J. A.: Remote Sensing with Imaging Radar, Signals and Communication
Technology, Springer, Berlin, Heidelberg, 361 pp., ISBN 978-3-642-02019-3, https://doi.org/10.1007/978-3-642-02020-9,
2009. a, b
Rover, J., Wright, C. K., Euliss, N. H., Mushet, D. M., and Wylie, B. K.:
Classifying the hydrologic function of prairie potholes with remote sensing
and GIS, Wetlands, 31, 319–327, https://doi.org/10.1007/s13157-011-0146-y, 2011. a
Schlaffer, S., Matgen, P., Hollaus, M., and Wagner, W.: Flood detection from
multi-temporal SAR data using harmonic analysis and change detection,
Int. J. Appl. Obs., 38,
15–24, https://doi.org/10.1016/j.jag.2014.12.001, 2015. a, b
Schlaffer, S., Chini, M., Dettmering, D., and Wagner, W.: Mapping Wetlands in
Zambia Using Seasonal Backscatter Signatures Derived from ENVISAT ASAR Time
Series, Remote Sens., 8, 402, https://doi.org/10.3390/rs8050402, 2016. a, b
Schlaffer, S., Chini, M., Giustarini, L., and Matgen, P.: Probabilistic
mapping of flood-induced backscatter changes in SAR time series,
Int. J. Appl. Obs., 56,
77–87, https://doi.org/10.1016/j.jag.2016.12.003, 2017. a
Shaw, D. A., Pietroniro, A., and Martz, L.: Topographic analysis for the
prairie pothole region of Western Canada, Hydrol. Process., 27,
3105–3114, https://doi.org/10.1002/hyp.9409, 2013. a
State Water Commission: LiDAR-Derived Elevation Data,
available at: https://gishubdata.nd.gov/dataset/lidar-derived-elevation-data (last access: 5 October 2020),
2018. a
Torres, R., Snoeij, P., Geudtner, D., Bibby, D., Davidson, M., Attema, E.,
Potin, P., Rommen, B., Floury, N., Brown, M., Traver, I. N., Deghaye, P.,
Duesmann, B., Rosich, B., Miranda, N., Bruno, C., L'Abbate, M., Croci, R.,
Pietropaolo, A., Huchler, M., and Rostan, F.: GMES Sentinel-1 mission,
Remote Sens. Environ., 120, 9–24, https://doi.org/10.1016/j.rse.2011.05.028,
2012. a, b
Tsyganskaya, V., Martinis, S., Marzahn, P., and Ludwig, R.: SAR-based
detection of flooded vegetation – a review of characteristics and
approaches, Int. J. Remote Sens., 39, 2255–2293,
https://doi.org/10.1080/01431161.2017.1420938, 2018. a
Twele, A., Cao, W., Plank, S., and Martinis, S.: Sentinel-1-based flood
mapping: a fully automated processing chain, Int. J. Remote Sens., 37, 2990–3004, https://doi.org/10.1080/01431161.2016.1192304, 2016. a, b
USDA Farm Service Agency: NAIP imagery, https://doi.org/10.5066/F7QN651G, https://earthexplorer.usgs.gov/, last access: 16 October 2020. a
USGS National Water Information System: Discharge data, https://waterdata.usgs.gov/nwis/uv?06469400, last access: 3 April 2021. a
Van Meter, K. J. and Basu, N. B.: Signatures of human impact: size
distributions and spatial organization of wetlands in the Prairie Pothole
landscape, Ecol. Appl., 25, 451–465, https://doi.org/10.1890/14-0662.1,
2015.
a
Vanderhoof, M. K. and Lane, C. R.: The potential role of very high-resolution
imagery to characterise lake, wetland and stream systems across the Prairie
Pothole Region, United States, Int. J. Remote Sens., 40,
5768–5798, https://doi.org/10.1080/01431161.2019.1582112, 2019. a, b, c
Vanderhoof, M. K., Alexander, L. C., and Todd, M. J.: Temporal and spatial
patterns of wetland extent influence variability of surface water
connectivity in the Prairie Pothole Region, United States, Landscape
Ecol., 31, 805–824, https://doi.org/10.1007/s10980-015-0290-5, 2016. a, b, c
Westerhoff, R. S., Kleuskens, M. P. H., Winsemius, H. C., Huizinga, H. J., Brakenridge, G. R., and Bishop, C.: Automated global water mapping based on wide-swath orbital synthetic-aperture radar, Hydrol. Earth Syst. Sci., 17, 651–663, https://doi.org/10.5194/hess-17-651-2013, 2013. a, b
White, L., Brisco, B., Dabboor, M., Schmitt, A., and Pratt, A.: A Collection
of SAR Methodologies for Monitoring Wetlands, Remote Sens., 7, 7615–7645,
https://doi.org/10.3390/rs70607615, 2015. a
Wu, Q. and Lane, C. R.: Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery, Hydrol. Earth Syst. Sci., 21, 3579–3595, https://doi.org/10.5194/hess-21-3579-2017, 2017. a, b, c, d
Wu, Q., Lane, C. R., Li, X., Zhao, K., Zhou, Y., Clinton, N., DeVries, B.,
Golden, H. E., and Lang, M. W.: Integrating LiDAR data and multi-temporal
aerial imagery to map wetland inundation dynamics using Google Earth Engine,
Remote Sens. Environ., 228, 1–13, https://doi.org/10.1016/j.rse.2019.04.015,
2019. a, b
Yin, Y., Byrne, B., Liu, J., Wennberg, P. O., Davis, K. J., Magney, T.,
Köhler, P., He, L., Jeyaram, R., Humphrey, V., Gerken, T., Feng, S.,
Digangi, J. P., and Frankenberg, C.: Cropland Carbon Uptake Delayed and
Reduced by 2019 Midwest Floods, AGU Advances, 1, e2019AV000140,
https://doi.org/10.1029/2019AV000140, 2020. a
Short summary
Prairie wetlands are important for biodiversity and water availability. Knowledge about their variability and spatial distribution is of great use in conservation and water resources management. In this study, we propose a novel approach for the classification of small water bodies from satellite radar images and apply it to our study area over 6 years. The retrieved dynamics show the different responses of small and large wetlands to dry and wet periods.
Prairie wetlands are important for biodiversity and water availability. Knowledge about their...
